ECHELON Signals Intelligence-Electronic Bioweapon System Revealed (pdf) w/ 8 Referenced Articles and Professional Papers

I. Eschelon Signals Intelligence Systems: Invasive Surveillance, Unauthorized Medical Device, Environmental Hazard, and Biological Weapon (pdf)

ESCHELON Signals Intelligence System Revealed (pdf)

Webmaster Introduction: I received this link from an email list of supposed targeted individuals. It all sounded credible… then I started looking at the videos made by the author of this piece, a young woman who calls herself Alaina Hacker. She is an attractive, 30ish young woman who claims to have cracked many of these scientific mysteries herself and also sat in on high level Presidential meetings with Trump, Biden, Putin, etc. She also claims to be suffering from Havana Syndrome due to her whistle blowing activities. Are we in loony world or is this real? In my day, this attractive young woman would more likely be a secretary. Is she acting? Mixing the real with the unreal, the true with the false, is a classic trick of intelligence agencies. We, the bewildered public, are left to sort out which is which.

One clue that Alaina is acting is that she mispronounces ECHELON (as EKELON rather than ESHELON). Another is that she refers to “Direct Energy Weapons” rather than “Directed Energy Weapons,” the proper term. Another inconsistency is that the author of this piece uses the British spelling of the word organization, organisation, which suggests this piece was written by a British scientist, spy, or whistleblower. Thus, she may be fronting for some real expert(s). Yikes. This reminds me of the old TV quiz show, “Whom Do You Trust?”

Introduction From Article’s Author (

Government Informant Whistleblower

I am a medical device engineer, biologist, and business owner. My specialty is capability analysis of electrical bioweapons. I have been in many meetings with multiple US presidents, top US officials, and foreign leaders, including President Putin. I have been a government informant as a private citizen for over a year. I am now whistleblowing on all of the illegal and unethical technologies in use by the US government and select other governments. An electrical bioweapon has been used upon the citizens of the world. This technology is behind Havana Syndrome, and was used for much more than just what has been reported on the news. This was used to cause large-scale election fraud in the 2020 US election. This electrical bioweapon has been shared with other countries, including Ukraine. It violates many laws, regulations, and international treaties. This technology must be properly investigated and all illegal operations with this technology should stop.

Table of Contents

(1) Legal Disclaimer
(2) Background Information
(3) What is ECHELON Signals Intelligence?
Stochastic Resonance:
(4) Capabilities of ECHELON Sigint Systems
Audio and Visual Signals:
Screen Mirroring:
Brain Waves: ..
(5) Unauthorized Medical Device
Protective Measures:
Health and Environmental Hazards: .
(6) Bioweapon Capabilities of ECHELON Sigint Systems .
Havana Syndrome:…………….

Excerpts From the Report:

(2) Background Information

For roughly a year the government has tried to silence me on this matter. I would like to remain anonymous for now under my website name: I am a biologist and a medical device engineer that works in the private medical device industry. Through my line of work, I had accidentally stumbled upon an unauthorized medical device that the government has operated on the general public for years. The public is not aware of this medical device, that is because it has been masked with an unsuspicious name. Medical devices cannot be used upon one’s body unless that person consents to it. It is impossible for anyone to provide consent if it is deemed highly classified by government. The medical device I speak of has been in use for many decades, and the government has ignored its true nature as a medical device.

It has been hidden under ECHELON Signals Intelligence systems (Sigint), which is intended to be a surveillance system operated by the United States and other governments. In order for these systems to pick up on signals intelligence around the world, they must emit a very powerful inaudible frequency that spans the entirety of the globe. This inaudible frequency penetrates every person’s ears, eyes, and other bodily openings. Anything that penetrates this body in this manner is automatically considered a medical device by health agencies around the world. That officially makes ECHELON Signals Intelligence systems a medical device, even if it is used as a surveillance tool.

I am a civilian engineer who figured this out through my private line of work, outside of any government contracts. I have done nothing illegal in figuring this out, so the government was unable to arrest me to keep me silent about this. Instead, the government mentally abused me, harassed me, interrogated me without a lawyer, and threatened to have me assassinated if I did not stay silent about this. After telling them that I would not stand down in releasing this to the public, they physically tortured my body with Havana Syndrome for months. I received this torture without any medical treatment from the government. Through the months of their torture, I had to pay out of pocket and go through civilian hospital systems and doctors to heal myself. I still am not fully healed, and I continue to get attacked with Havana Syndrome to this day. The more I work on this document the worse my Havana Syndrome attacks get. Fortunately, I have finally finished, and I am ready to release this information.

6) Bioweapon Capabilities of ECHELON Sigint Systems

Havana Syndrome

One of the technologies responsible for Havana Syndrome is the ECHELON Sigint system. How
does ECHELON Sigint systems do this? Just like how these systems can read our brain waves
through the electromagnetic fields emitted from our electronic devices. These systems can also do the reverse action. They can force artificial signals into our brains and nervous systems through the electromagnetic fields generated by our electronic devices.

As stated before, each person’s brain waves have an identifying feature. If someone is a threat, the government can use the ECHELON Sigint systems to tune to that individual’s unique identifier (or channel) to listen in on their decoded brain waves. In the event of that person being a threat, they can directly attack their brain by tracing back the same pathway used to surveillance that person’s mind. Once they have homed in on that person’s unique channel, they are able to send electrical signals to their brain.

They can produce symptoms ranging from mild to severe. These symptoms are including, but not limited to: brain fog, cognition issues, dizziness, distinct voices (as if heard through a microphone), loss of coordination, brain trauma, tunnel vision and hearing, chest pressure, heart attack, lung closure, nasal passage closure, muscular spasms, gag reflex spasms (possible vomiting), incontinence, numbness, tingling, joint pain. The long-term damage varies among victims. Depending on the person, the attack may be intended for assassination, or they may intend to debilitate someone to slow them down, or to stop whistleblowers from leaking important information to the public.

(7) Conclusion

The use of unauthorized medical devices upon the public goes against many national laws and regulations, no matter the country. It also goes against international treaties, like the Geneva Convention.13 Laws, regulations, and treaties exist to prevent any unethical medical practices, unauthorized medical experimentation, and bioweapon usage upon the public. The United States, among other countries involved in ECHELON Sigint systems, have gone against these laws and treaties. They have access to inhumane technologies that can be used upon its citizens, with unknown ethics standards and protocols. The ECHELON Sigint systems never obtained proper approvals as a medical device and (was) never deemed safe for the environment. The ECHELON Sigint systems must be investigated for unethical practices, for medical safety, environmental safety, and their use as a bioweapon against foreign and domestic citizens.

II. Inside ESCHELON: The history, structure and function of the global surveillance system known as Echelon

Duncan Campbell


Since 1998, much has been written and spoken about the so-called Echelon system of international communications surveillance. Most of what has been written has been denied or ignored by US and European authorities. But much of what has been written has also been exaggerated or wrong. Amongst a sea of denials, obfuscations and errors, confusion has reigned. This review by Duncan Campbell, author of the European Parliament’s 1999 “Interception Capabilities 2000” report , is intended to help clear up the confusion, to say what Echelon is (and isn’t), where it came from and what it does. Echelon, or systems like it, will be with us a long time to come.

Menwith Hill (Photo: Duncan Campbell)

Echelon is a system used by the United States National Security Agency (NSA) to intercept and process international communications passing via communications satellites. It is one part of a global surveillance systems that is now over 50 years old. Other parts of the same system intercept messages from the Internet, from undersea cables, from radio transmissions, from secret equipment installed inside embassies, or use orbiting satellites to monitor signals anywhere on the earth’s surface. The system includes stations run by Britain, Canada, Australia and New Zealand, in addition to those operated by the United States. Although some Australian and British stations do the same job as America’s Echelon sites, they are not necessarily called “Echelon” stations. But they all form part of the same integrated global network using the same equipment and methods to extract information and intelligence illicitly from millions of messages every day, all over the world.

The first reports about Echelon in Europe credited it with the capacity to intercept “within Europe, all e-mail, telephone, and fax communications”. This has proven to be erroneous; neither Echelon nor the signals intelligence (“sigint”) system of which it is part can do this. Nor is equipment available with the capacity to process and recognise the content of every speech message or telephone call. But the American and British-run network can, with sister stations, access and process most of the worlds satellite communications, automatically analysing and relaying it to customers who may be continents away.

The world’s most secret electronic surveillance system has its main origin in the conflicts of the Second World War. In a deeper sense, it results from the invention of radio and the fundamental nature of telecommunications. The creation of radio permitted governments and other communicators to pass messages to receivers over transcontinental distances. But there was a penalty – anyone else could listen in. Previously, written messages were physically secure (unless the courier carrying them was ambushed, or a spy compromised communications). The invention of radio thus created a new importance for cryptography, the art and science of making secret codes. It also led to the business of signals intelligence, now an industrial scale activity. Although the largest surveillance network is run by the US NSA, it is far from alone. Russia, China, France and other nations operate worldwide networks. Dozens of advanced nations use sigint as a key source of intelligence. Even smaller European nations such as Denmark, the Netherlands or Switzerland have recently constructed small, Echelon-like stations to obtain and process intelligence by eavesdropping on civil satellite communications.

During the 20th century, governments realised the importance of effective secret codes. But they were often far from successful. During the Second World War, huge allied codebreaking establishments in Britain and America analysed and read hundreds of thousands of German and Japanese signals. What they did and how they did it remained a cloely-guarded secret for decades afterwards. In the intervening period, the US and British sigint agencies, NSA and Government Communications Headquarters (GCHQ) constructed their worldwide listening network.

The system was established under a secret 1947 “UKUSA Agreement,” which brought together the British and American systems, personnel and stations. To this was soon joined the networks of three British commonwealth countries, Canada, Australia and New Zealand. Later, other countries including Norway, Denmark, Germany and Turkey signed secret sigint agreements with the United States and became “third parties” participants in the UKUSA network.

Besides integrating their stations, each country appoints senior officials to work as liaison staff at the others’ headquarters. The United States operates a Special US Liaison Office (SUSLO) in London and Cheltenham, while a SUKLO official from GCHQ has his own suite of offices inside NSA headquarters at Fort Meade, between Washington and Baltimore.

Danish Interception Station (Photo: Ekstrabladet)

Under the UKUSA agreement, the five main English-speaking countries took responsibility for overseeing surveillance in different parts of the globe . Britain’s zone included Africa and Europe, east to the Ural Mountains of the former USSR; Canada covered northern latitudes and polar regions; Australia covered Oceania. The agreement prescribed common procedures, targets, equipment and methods that the sigint agencies would use. Among them were international regulations for sigint security , which required that before anyone was admitted to knowledge of the arrangements for obtaining and handling sigint, they must first undertake a lifelong commitment to secrecy. Every individual joining a UKUSA sigint organisation must be “indoctrinated” and, often “re-indoctrinated” each time they are admitted to knowledge of a specific project. They are told only what they “need to know”, and that the need for total secrecy about their work “never ceases”.

Everything produced in the sigint organisations is marked by hundreds of special codewords that “compartmentalise” knowledge of intercepted communications and the systems used to intercept them. The basic level, which is effectively a higher classification than “Top Secret” is “Top Secret Umbra”. More highly classified documents are identified as “Umbra Gamma”; other codewords can be added to restrict circulation still further. Less sensitive information, such as analyses of telecommunications traffic, may be classified “Secret Spoke”.

The scale and significance of the global surveillance system has been transformed since 1980. The arrival of low cost wideband international communications has created a wired world. But few people are aware that the first global wide area network (WAN) was not the internet, but the international network connecting sigint stations and processing centres. The network is connected over transoceanic cables and space links. Most of the capacity of the American and British military communications satellites, Milstar and Skynet, is devoted to relaying intelligence information. It was not until the mid 1990s that the public internet became larger than the secret internet that connects surveillance stations. Britain’s sigint agency GCHQ now openly boasts on its web site that it helps operate “one of the largest WANs [Wide Area Networks} in the world” and that “all GCHQ systems are linked together on the largest LAN in Europe … connected to other sites around the world”. The same pages also claim that “the immense size and sheer power of GCHQ’s supercomputing architecture is difficult to imagine”.

The UKUSA alliance’s wide area network is engineered according to the same principles as the internet , and provides access from all field interception stations to and from NSA’s central computer system, known as Platform. Other parts of the system are known as Embroidery, Tideway and Oceanfront. The intelligence news network is Newsdealer. A TV conference system, highly encrypted like every other part of the network, is called Gigster. They are supported by applications known as Preppy and Droopy. NSA’s e-mail system looks and feels like everybody else’s e-mail, but is completely separate from the public network. Messages addressed to its secret internal internet address, which is simply “nsa”, will not get through.

The delivery of NSA intelligence also now looks and feels like using the internet. Authorised users with appropriate permissions to access “Special Compartmented Intelligence”6 use standard web browsers to look at the output of NSA’s Operations Department from afar. The system, known as “Intelink”, is run from the NSA’s Fort Meade HQ. Completed in 1996, Intelink connects 13 different US intelligence agencies and some allied agencies with the aim of providing instant access to all types of intelligence information. Just like logging onto the world wide web, intelligence analysts and military personnel can view an atlas on Intelink’s home page, and then click on any country they choose in order to access intelligence reports, video clips, satellite photos, databases and status reports.

In the early post war years, and for the next quarter century, there was little sign of this automation or sophistication. In those years, most of the world’s long distance communications – civil, military or diplomatic – passed by high frequency radio. NSA and its collaborators operated hundreds of remote interception sites, both surrounding the Soviet Union and China and scattered around the world. Inside windowless buildings, teams of intercept operators passed long shifts listening into silence, interspersed with sudden periods of frenetic activity. For the listening bases on the front line of the cold war, monitoring military radio messages during the cold war brought considerable stress. Operators at such bases often recall colleagues breaking down under the tension, perhaps fleeing into closets after believing that they had just intercepted a message marking the beginning of global thermonuclear war.

Dutch Interception Station (Photo: Netherlands Military Intelligence Service)

The Second World War left Britain’s agency GCHQ with an extensive network of sigint outposts. Many were fixed in Britain, while others were scattered around the then Empire. From stations including Bermuda, Ascension, Cyprus, Gibraltar, Iraq, Singapore, and Hong Kong, radio operators tracked Soviet and, soon, Chinese political and military developments. These stations complemented a US network which by 1960 included thousands of continuously operated interception positions. The other members of the UKUSA alliance, Australia, Canada and New Zealand contributed stations in the South Pacific and arctic regions.

After the signing of the UKUSA pact, a new chain of stations began operating along the boundaries of the western sphere of influence, monitoring the signals of Soviet ground and air forces. British sigint outposts were established in Germany and, secretly in Austria and Iran. US listening posts were set up in central and southern Germany and later in Turkey, Italy and Spain. One major US sigint base – Kagnew Station at Asmara in Eritrea – was taken over from the British in 1941 and grew to become, until its closure in 1970, one of the largest intercept stations in the world. One of its more spectacular features was a tracking dish used to pass messages to the United States by reflecting them off the surface of the moon.

By the mid 1960s, many of these bases featured gigantic antenna systems that could monitor every HF (High Frequency) radio message, from all angles, while simultaneously obtaining bearings that could enable the position of a transmitter to be located. Both the US Navy and the US Air Force employed global networks of this kind. The US Air Force installed 500 metre wide arrays known as FLR-9 at sites including Chicksands, England, San Vito dei Normanni in Italy, Karamursel in Turkey, the Philippines, and at Misawa, Japan. Codenamed “Iron Horse”, the first FLR-9 stations came into operation in 1964. The US Navy established similar bases in the US and at Rota, Spain, Bremerhaven, Germany, Edzell, Scotland, Guam, and later in Puerto Rico, targetted on Cuba.

When the United States went to war in Vietnam, Australian and New Zealand operators in Singapore, Australia and elsewhere worked directly in support of the war. Britain; as a neutral country was not supposed to be involved. In practice, however British operators at the GCHQ intercept station no UKC201 at Little Sai Wan, Hong Kong monitored and reported on the North Vietnamese air defence networks while US B52 bombers attacked Hanoi and other North Vietnamese targets.

Since the end of the cold war, the history of some cold war signals intelligence operations have been declassified. At the US National Cryptologic Museum, run by NSA at its headquarters, the agency now openly acknowledges many of its cold war listening operations. It also describes the controversial use of ships and aircraft to penetrate or provoke military defences in operations that cost the lives of more than 100 of its staff. But another longstanding aspect of sigint operations remain unacknowledged. During the second world war as well as in the cold war and since, British and US intelligence agencies monitored the signals and broke the codes of allies and friends, as well as of civilians and commercial communications around the world. The diplomatic communications of every country were and are attacked.

The stations and methods were the same as for military targets. Within the intelligence agencies, the civilian target was known as “ILC”. ILC stood for “International Leased Carrier”, and referred to the private companies or telecommunications administrations operating or administrating long range undersea cables or radio stations. Some ILC circuits were rented to governments or large companies as permanent links. The majority were used for public telegraph, telex or telephone services.

Many details of the operation of the English-speaking sigint axis were revealed by two NSA defectors at a press conference held in Moscow on 6 September 1960. There, two NSA analysts, Bernon Mitchell and William Martin, told the world what NSA was doing:

We know from working at NSA [that] the United States reads the secret communications of more than forty nations, including its own allies … NSA keeps in operation more than 2000 manual intercept positions … Both enciphered and plain text communications are monitored from almost every nation in the world, including the nations on whose soil the intercept bases are located. (New York Times, 7 September 1960 )

The revelations were reported in full in the US, but their impact was soon buried by security recriminations and accusations. Martin and Mitchell revealed that NSA’s operations division included two key groups. One group covered the Soviet Union and its allies. The second analysis division was known as ALLO, standing for “all other [countries]”. This part of NSA’s production organisation was later renamed ROW, starting for “Rest of the World”.

Thus, in 1965, while intercept operators at the NSA’s Chicksands station in England focussed on the radio messages of Warsaw Pact air forces, their colleagues 200 kilometres north at Kirknewton, Scotland were covering “ILC” traffic, including commercially run radio links between major European cities. These networks could carry anything from birthday telegrams to detailed economic or commercial information exchanged by companies, to encrypted diplomatic messages. In the intercept rooms, machines tuned to transmission channels continuously spewed out 8-ply paper to be read and marked up by intelligence analysts. Around the world, thousands of analysts worked on these mostly unencrypted communications using NSA ‘watch lists’ – weekly key word lists of people, companies, commodities of interest for the NSA watchers to single out from ‘clear’ traffic. Coded messages were passed on immediately. Among the regular names on the watch lists were the leaders of African guerrilla movements who were later to become their countries’ leaders. In time, many prominent Americans were added to the list. The international communications of the actress Jane Fonda, Dr Benjamin Spock and hundreds of others were put under surveillance because of their opposition to the war in Vietnam. Back power leader Eldridge Cleaver and his colleagues were included because of their civil rights activities in the US.

A short distance to the north at Cupar, Scotland, another intercept station was operated by the British Post Office, and masqueraded as a long distance radio station. In fact, it was another GCHQ interception site, which collected European countries’ communications, instead of sending them.

In time, these operations were integrated. In 1976, NSA set up a special new civilian unit at its Chicksands base to carry out diplomatic and civilian interception. The unit, called “DODJOCC” (Department of Defense Joint Operations Centre Chicksands) was targeted on non-US Diplomatic Communications, known as NDC. One specific target, known as FRD, stood for French diplomatic traffic. Italian diplomatic signals, known similarly as ITD, were collected and broken by NSA’s counterpart agency GCHQ, at its Cheltenham centre.

Entering Chicksands’ Building 600 through double security fences and a turnstile where green and purple clearance badges were checked, the visitor would first encounter a sigint in-joke – a copy of the International Telecommunications Convention pasted up on the wall. Article 22 of the Convention, which both the United Kingdom and the United States have ratified, promises that member states “agree to take all possible measures, compatible with the system of telecommunication used, with a view to ensuring the secrecy of international correspondence”.

Besides intercepting ILC communications at radio stations, NSA, GCHQ and their counterparts also collected printed copies of all international telegrams from public and commercial operators in London, New York and other centres. They were then taken to sigint analysts and processed in the same way as foreign telegrams snatched from the air at sites like Chicksands and Kirknewton. Britain had done this since 1920, and the United States since 1945. The joint programme was known as Operation Shamrock, and continued until it was exposed by US Congressional intelligence investigations in the wake of the Watergate affair.

On 8 August 1975, NSA Director Lt General Lew Allen admitted to the Pike Committee of the US House of Representatives that : “NSA systematically intercepts international communications, both voice and cable” He also admitted that “messages to and from American citizens have been picked up in the course of gathering foreign intelligence”. At a later hearing, he described how NSA used “‘watch lists” an aid to watch for foreign activity of reportable intelligence interest”.

US legislators considered that these operations might have been unconstitutional. During 1976, a Department of Justice team investigated possible criminal offences by NSA. Part of their report was released in 1980 It described how intelligence on US citizens, known as MINARET “was obtained incidentally in the course of NSA’s interception of aural and non-aural (e.g, telex) international communications and the receipt of GCHQ-acquired telex and ILC (International Leased Carrier) cable traffic (SHAMROCK)” (emphasis in original).

As in the United Kingdom, from 1945 onwards NSA and its predecessors had systematically obtained cable traffic from the offices of major cable companies – RCA Global, ITT World Communications and Western Union. Over time, the collection of copies of telegrams on paper was replaced by the delivery of magnetic tapes and eventually by direct connection of the monitoring centres to international communications circuits. In Britain, all international telex links and telegram circuits passing in, out or through the country were and are connected to a GCHQ monitoring site in central London, known as UKC1000.

By the early 1970s, the laborious process of scanning paper printouts for names or terms appearing on the “watch lists” had begun to be replaced by automated computer systems. These computers performed a task essentially similar to the search engines of the internet. Prompted with a word, phrase or combination of words, they will identify all messages containing the desired words or phrases. Their job, now performed on a huge scale, is to match the “key words” or phrases of interest to intelligence agencies to the huge volume of international communications, to extract them and pass them to where they are wanted. During the 1980s, the NSA developed a “fast data finder” microprocessor that was optimally designed for this purpose. It was later commercially marketed, with claims that it “the most comprehensive character-string comparison functions of any text retrieval system in the world”. A single unit could work with:

“trillions of bytes of textual archive and thousands of online users, or gigabytes of live data stream per day that are filtered against tens of thousands of complex interest profiles”

Although different systems are in use, the key computer system at the heart of a modern sigint station’s processing operations is the “Dictionary”. Every Echelon or Echelon-like station contains a Dictionary. Portable versions are even available, and can be loaded into briefcase-sized units known as “Oratory”. The Dictionary computers scan communications input to them, and extract for reporting and further analysis those that match the profiles of interest. In one sense, the main function of Dictionary computers are to throw most intercepted information away.

In a 1992 speech on information management, former NSA Director William Studeman described the type of filtering involved in systems like ECHELON:

“One [unidentified] intelligence collection system alone can generate a million inputs per half hour; filters throw away all but 6500 inputs; only 1,000 inputs meet forwarding criteria; 10 inputs are normally selected by analysts and only one report is produced. These are routine statistics for a number of intelligence collection and analysis systems which collect technical intelligence”.

In other words, for every million communications intercepted only one might result in action by an intelligence agency. Only one in a thousand would ever be seen by human eyes.

Supporting the operations of each Dictionary are gigantic intelligence databases which contain tables of information related to each target. At their simplest, these can be a list of telephone, mobile phone, fax or pager numbers which associated with targets in each group. They can include physical or e-mail addresses, names, or any type of phrase or concept that can be formulated under normal information retrieval rules.

Powerful though Dictionary methods and keyword search engines may be, however, they and their giant associated intelligence databases may soon be replaced by “topic analysis”, a more powerful and intuitive technique, and one that NSA is developing strongly. Topic analysis enables Comint customers to ask their computers to “find me documents about subject X”. X might be “Shakespeare in love” or “Arms to Iran”.

In a standard US test used to evaluate topic analysis systems, one task the analysis program is given is to find information about “Airbus subsidies”. The traditional approach involves supplying the computer with the key terms, other relevant data, and synonyms. In this example, the designations A-300 or A-320 might be synonymous with “Airbus”. The disadvantage of this approach is that it may find irrelevant intelligence (for example, reports about export subsidies to goods flown on an Airbus) and miss relevant material (for example a financial analysis of a company in the consortium which does not mention the Airbus product by name). Topic analysis overcomes this and is better matched to human intelligence.

In 1991, a British television programme reported on the operations of one Dictionary computer at GCHQ’s London station in Palmer Street, Westminster (station UKC1000). The programme quoted GCHQ employees, who spoke off the record:

“Up on the fourth floor there, [GCHQ] has hired a group of carefully vetted British Telecom people. [Quoting the ex-GCHQ official:] It’s nothing to do with national security. It’s because it’s not legal to take every single telex. And they take everything: the embassies, all the business deals, even the birthday greetings, they take everything. They feed it into the Dictionary.”

Among the targets of this station were politicians, diplomats, businessmen, trades union leaders, non- government organisations like Amnesty International, and even the hierarchy of the Catholic church.

The Echelon system appears to have been in existence since the early 1970s, and to have gone through extensive evolution and development. The need for efficient processing systems to replace the human operators who performed watch list scans was first foreseen in the late 1960s, when NSA and GCHQ were planning the first large satellite interception sites. The first such station was built at Morwenstow, Cornwall, and utilised two large dish antennae to intercept communications crossing the Atlantic and Indian Oceans. The second was built at Yakima, in the northwestern US state of Washington. Yakima intercepted satellite communications over the Pacific Ocean.

Also in the early 1970s, NSA and CIA discovered that sigint collection from space was far more effective and productive than had been foreseen, resulting in vast accumulations of magnetic tapes that quickly outstripped the available supply of Soviet linguists and analysts. By the end of the 1970s, one of the main sites processing communications intercepted from space was Menwith Hill, in central England. A document prepared there in 1981 identifies intelligence databases used at Menwith Hill as “Echelon 2”. This suggests that the Echelon network was already into its second generation by 1981.

By the mid 1980s, communications handled by Dictionary computers around the world were heavily sifted, with a wide variety of specifications available for non-verbal traffic. Extensive further automation was planned in the mid 1980s under two top secret NSA Projects, P-377 and P-415. The implementation of these projects completed the automation of the “watch list” activity of pevious decades. Computers replaced the analysts who compared reams of paper intercepts to names and topics on the watch list. In the late 1980s, staff from sigint agencies from countries including the UK, New Zealand and China attended training courses on the new Echelon computer systems.

Project P-415 made heavy use of NSA and GCHQ’s global internet to enable remote intelligence customers to task computers at each collection site, and then receive the results automatically. Selected incoming messages were compared to forwarding criteria held on the Dictionary. If a match was found, the raw intelligence was forwarded automatically to the designated recipients. According to New Zealand author Nicky Hager, Dictionary computers are tasked with many thousands of different collection requirements, described as “numbers” (four digit codes).

Details of project P-415 and the plans for the massive global expansion of the Echelon system were revealed in 1988 by Margaret “Peg” Newsham. Ms Newsham a former computer systems manager, worked on classified projects for NSA contractors until the mid 1980s. From August 1978 onwards, she worked at the NSA’s Menwith Hill Station as a software co-ordinator. In this capacity, she helped managed a number of Sigint computer databases, including “Echelon 2”. She and others also helped establish “Silkworth”, a system for processing information relayed from signals intelligence satellites called Chalet, Vortex and Mercury. Her revelations led to the first ever report about Echelon, published in 1988.

In Sunnyvale, California, Peg Newsham worked for Lockheed Space and Missiles Corporation. In that capacity, she worked on plans for the massive expansion of the Echelon network, a project identified internally as P-415. During her employment by Lockheed, she also become concerned about corruption, fraud and abuse within the organisations planning and operating electronic surveillance systems. She reported her concerns to the US Congress House Permanent Select Committee on Intelligence early in 1988. She also told them how she had witnessed the interception of a telephone call made by a US Senator, Strom Thurmond, while working at Menwith Hill.

The full details of Echelon would probably never have come to serious public attention but for 6 further years of research by New Zealand writer Nicky Hager, who assiduously investigated the new Echelon station that started operating at Waihopai on the South Island of New Zealand in 1989. His 1996 book Secret Power is based on extensive interviews with and help from members of the New Zealand signals intelligence organisation. It remains the best informed and most detailed account of how Echelon works.

Early in 2000, information and documents leaked to a US researcher provided many details of how Echelon was developed for use worldwide. Under a 1982 NSA plan assigned to Lockheed Space and Missiles Systems, engineers and scientists worked on Project P-377 – also known as CARBOY II. This project called for the development of a standard kit of “ADPE” (automated data processing equipment) parts for equipping Echelon sites. The “commonality of automated data processing equipment (ADPE) in the Echelon system” included the following elements:

Local management subsystem
Remote management subsystem
Radio frequency distribution
Communications handling subsystem
Telegraphy message processing subsystem
Frequency division multiplex telegraphy processing subsystem
Time division multiplex telegraphy processing subsystem
Voice processing subsystem
Voice collection module
Facsimile processing subsystem
[Voice] Tape Production Facility

The CARBOY II project also called for software systems to load and update the Dictionary databases. At this time, the hardware for the Dictionary processing subsystem was based on a cluster of DEC VAX mini-computers, together with special purpose units for processing and separating different types of satellite communications.

In 1998 and 1999, the intelligence specialist Dr Jeff Richelson of the National Security Archive Washington, DC used the Freedom of Information Act to obtain a series of modern official US Navy and Air Force documents which have confirmed the continued existence, scale and expansion of the Echelon system. The documents from the US Air Force and US Navy identify Echelon units at four sites and suggest that a fifth site also collects information from communications satellites as part of the Echelon system.

One of the sites is Sugar Grove, West Virgina US, about 250 miles south-west of Washington in a remote area of the Shenandoah Mountains. It is operated by the US Naval Security Group and the US Air Force Intelligence Agency. An upgraded sigint system called Timberline II was installed at Sugar Grove in the summer of 1990. At the same time, according to official US documents, an “Echelon training department” was established. With training complete, the task of the station in 1991 became “to maintain and operate an ECHELON site”.

The US Air Force has publicly identified the intelligence activity at Sugar Grove as “to direct satellite communications equipment [in support of] consumers of COMSAT information … this is achieved by providing a trained cadre of collection system operators, analysts and managers”. The 1998-99 USAF Air Intelligence Agency Almanac described the mission of the Sugar Grove unit as providing “enhanced intelligence support to air force operational commanders and other consumers of COMSAT information.” In 1990, satellite photographs showed that there were 4 satellite antennae at Sugar Grove. By November 1998, ground inspection revealed that this had expanded to nine.

Further information published by the US Air Force identifies the US Naval Security Group Station at Sabana Seca, Puerto Rico as a COMSAT interception site. Its mission is “to become the premier satellite communications processing and analysis field station”. These and further documents concerning Echelon and COMSAT interception stations at Yakima, Sabana Seco (Puerto Rico), Misawa (Japan) and Guam have been published on the web.

From 1984 onwards, Australia, Canada and New Zealand joined the US and the UK in operating Comsat (communications satellite) interception stations. Australia’s site at Kojarena, Geraldton near Perth in western Australia includes four interception dishes. The station’s top targets include Japanese diplomatic and commercial messages, communications of all types from and within North Korea, and data on Indian and Pakistani nuclear weapons developments. A second Australian satcom intercept site, at Shoal Bay in the Northern Territories, mainly targets Australia’s northern neighbour, Indonesia. Australian sources say however that Shoal Bay is not part of the Echelon system, as Australia is unwilling to allow the US and Britain to obtain raw intercepts directly.

The New Zealand site, Waihopai now has two dishes targeted on Intelsat satellites covering the south Pacific. In 1996, shortly after “Secret Power” was published, a New Zealand TV station obtained images of the inside of the station’s operations centre. The pictures were obtained clandestinely by filming through partially curtained windows at night. The TV reporter was able to film close-ups of technical manuals held in the control centre. These were Intelsat technical manuals, providing confirmation that the station targeted these satellites. Strikingly, the station was seen to be virtually empty, operating fully automatically.

Before the introduction of Echelon, different countries and different stations knew what was being intercepted and to whom it was being sent. Now, all but a fraction of the messages selected by Dictionary computers at remote sites may be forwarded to overseas customers, normally NSA, without any local knowledge of the intelligence obtained.

Information from the Echelon network and other parts of the global surveillance system is used by the US and its allies for diplomatic, military and commercial purposes. In the post cold war years, the staff levels at both NSA and GCHQ have contracted, and many overseas listening posts have been closed or replaced by Remote Operations Facilities, controlled from a handful of major field stations. Although routinely denied, commercial and economic intelligence is now a major target of international sigint activity. Under a 1993 policy colloquially known as “levelling the playing field”, the United States government under President Clinton established new trade and economic committees and told the NSA and CIA to act in support of US businesses in seeking contracts abroad. In the UK, GCHQ’s enabling legislation from 1994 openly identifies one of its purposes as to promote “the economic well-being of the United Kingdom in relation to the actions or intentions of persons outside the British Islands”.

Massive new storage and processing systems are being constructed to provide on-line processing of the internet and new international communications networks. By the early 1990s, both GCHQ and NSA employed “near line” storage systems capable of holding more than a terabyte of data. In the near future, they are likely to deploy systems one thousand times larger. Key word spotting in the vast volumes of intercepted daily written communications – telex, e-mail, and data – is a routine task. “Word spotting” in spoken communications is not an effective tool, but individual speaker recognition techniques have been in use for up to 10 years. New methods which have been developed during the 1990s will become available to recognise the “topics” of phone calls, and may allow NSA and its collaborators to automate the processing of the content of telephone messages – a goal that has eluded them for 30 years.

Under the rubric of “information warfare”, the sigint agencies also hope to overcome the ever more extensive use of encryption by direct interference with and attacks on targeted computers. These methods remain controversial, but include information stealing viruses, software audio, video, and data bugs, and pre-emptive tampering with software or hardware (“trapdoors”).

In the information age, we need to re-learn a lesson now a century old. Despite the sophistication of 21st century technology, today’s e-mails are as open to the eyes of snoopers and intruders as were the first crude radio telegraph messages. Part of the reason for this is that, over many decades, NSA and its allies worked determinedly to limit and prevent the privacy of international telecommunications. Their goal was to keep communications unencrypted and, thus, open to easy access and processing by systems like Echelon. They knew that privacy and security, then as a century ago, lay in secret codes or encryption. Until such protections become effective and ubiquitous, Echelon or systems like it, will remain with us.

III. Modelling non-invasive brain stimulation in cognitive neuroscience☆

Author links open overlay panelCarloMiniussiab

Justin A.Harrisc Manuela Ruzzolid

• We describe the mutual interactions between NIBS and brain activity.
• NIBS impact on a neural system can be easily understood as introducing noise.
• We provide an updated perspective on the theoretical frameworks of NIBS in cognitive neuroscience.
• A sigmoid input-response function can explain the relation between signal and noise induced by NIBS.


Non-invasive brain stimulation (NIBS) is a method for the study of cognitive function that is quickly gaining popularity. It bypasses the correlative approaches of other imaging techniques, making it possible to establish a causal relationship between cognitive processes and the functioning of specific brain areas. Like lesion studies, NIBS can provide information about where a particular process occurs. However, NIBS offers the opportunity to study brain mechanisms beyond process localisation, providing information about when activity in a given brain region is involved in a cognitive process, and even how it is involved. When using NIBS to explore cognitive processes, it is important to understand not only how NIBS functions but also the functioning of the neural structures themselves. We know that NIBS techniques have the potential to transiently influence behaviour by altering neuronal activity, which may have facilitatory or inhibitory behavioural effects, and these alterations can be used to understand how the brain works. Given that NIBS necessarily involves the relatively indiscriminate activation of large numbers of neurons, its impact on a neural system can be easily understood as modulation of neural activity that changes the relation between noise and signal. In this review, we describe the mutual interactions between NIBS and brain activity and provide an updated and precise perspective on the theoretical frameworks of NIBS and their impact on cognitive neuroscience. By transitioning our discussion from one aspect (NIBS) to the other (cognition), we aim to provide insights to guide future research.

Stochastic resonance

1. Introduction

Non-invasive brain stimulation (NIBS) methods, which include transcranial magnetic stimulation (TMS) and transcranial electric stimulation (tES), are used in cognitive neuroscience to induce transient changes in brain activity and thereby alter the behaviour of the subject. The application of NIBS aims at establishing the role of a given cortical area in an ongoing specific motor, perceptual or cognitive process (Hallett, 2000, Walsh and Cowey, 2000).

Physically, NIBS techniques affect neuronal states through different mechanisms. In TMS, a solenoid (coil) is used to deliver a strong and transient magnetic field, or “pulse”, to induce a transitory electric current at the cortical surface beneath the coil. The pulse causes the rapid and above-threshold depolarisation of cell membranes affected by the current (Barker et al., 1985, Barker et al., 1987), followed by the transynaptic depolarisation or hyperpolarisation of inter-connected neurons. Therefore, TMS induces a current that elicits action potentials in neurons.

By contrast, in tES techniques, the stimulation involves the application of weak electrical currents directly to the scalp through a pair of electrodes (Nitsche and Paulus, 2000, Priori et al., 1998). As a result, tES induces a subthreshold polarisation of cortical neurons that is too weak to generate an action potential. However, by changing the intrinsic neuronal excitability, tES can induce changes in the resting membrane potential and the postsynaptic activity of cortical neurons. This, in turn, can alter the spontaneous firing rate of neurons and modulate their response to afferent signals (Bindman et al., 1962, Bindman et al., 1964, Bindman et al., 1979, Creutzfeldt et al., 1962), leading to changes in synaptic efficacy.

The application of NIBS involves different types of protocols: TMS can be delivered as a single pulse (spTMS) at a precise time, as pairs of pulses separated by a variable interval, or as a series of stimuli in conventional or patterned protocols of repetitive TMS (rTMS) (for a complete classification see Rossi et al., 2009). In tES, different protocols are established by the electrical current used and by its polarity, which can be direct (anodal or cathodal transcranial direct current stimulation: tDCS), alternating at a fix frequency (transcranial alternating current stimulation: tACS) or at random frequencies (transcranial random noise stimulation: tRNS) (Nitsche et al., 2008, Paulus, 2011).

In general, the final effects of NIBS on the central nervous system depend on a lengthy list of parameters (e.g., frequency, temporal characteristics, intensity, geometric configuration of the coil/electrode, current direction), when it is delivered before (off-line) or during (on-line) the task as part of the experimental procedure (e.g., Jacobson et al., 2011, Nitsche and Paulus, 2011, Sandrini et al., 2011). In addition, these factors interact with several variables related to the anatomy (e.g., properties of the brain tissue and its location, Radman et al., 2007), as well as physiological (e.g., gender and age, Landi and Rossini, 2010, Lang et al., 2011, Ridding and Ziemann, 2010) and cognitive (e.g., Miniussi et al., 2010, Silvanto et al., 2008, Walsh et al., 1998) states of the stimulated area/subject.

This review will focus only on the so-called “on-line” procedures. It will not include consideration of “off-line” protocols with TMS and tES, in which task performance is compared before versus after stimulation, but is not assessed during stimulation. Off-line stimulation involves neuronal activity changes that last beyond stimulation (i.e., short- and long-term potentiation or depression, homeostasis of the system, metaplasticity) and to a certain extent are different from the basic mechanisms of action by which NIBS directly modulates ongoing brain function in on-line protocols. Off-line protocols with TMS and tES induce a change in the state of stimulated area and therefore they can be compare to the concept of state dependency. In this case the change in the state is not induced by a task or subministration of a substance but by NIBS after effects.

We aim to present a unified framework for NIBS approaches. We will lay the groundwork by focusing on the theoretical and physiological mechanisms of action that historically have been applied to TMS and tES, building up a coherent view for explaining NIBS effects in cognitive neuroscience. The unifying theme of our perspective is the induction of neural noise, whose interactions with the task-induced state of the stimulated area will determine the final behavioural outcome. We believe that our perspective offers increased explanatory power for NIBS-induced effects, and will therefore provide added impetus for future applications.

2. Transcranial magnetic stimulation

2.1. The virtual lesion metaphor

The first idea concerning the effects of TMS was that of the “virtual lesion” approach (Pascual-Leone et al., 2000, Walsh et al., 1998, Walsh and Rushworth, 1999). By analogy with neuropsychological studies, but without many of the confounding factors that trouble patient studies (such as compensation mechanisms, diaschisis, dimension of the lesion and single-subject samples). The application of TMS could hinder the functioning of a given area for several milliseconds, and thereby establish a causal nexus between the stimulated brain region and a particular function. The idea follows the standard logic of inference. If cortical area A is involved in cognitive process X and is not involved in process Y, the alteration of the activity of area A will result in altered performance in X (and not Y); thus, area A plays a causal role in the performance of X (and not Y). In this sense, TMS describes a process in which theory is extracted from direct interventions and overcomes the fundamental limits of the correlative approaches of imaging techniques [e.g., functional magnetic resonance, positron emission tomography, electroencephalography (EEG)], providing an opportunity to test directly and non-invasively causal relationships between the brain and cognition. The first experiment that applied this logic in cognitive neuroscience was performed by Amassian et al. (1989). They stimulated the occipital cortex by spTMS, which was time-locked to the presentation of a visual stimulus, while the participants tried to detect the visual stimulus. The participants’ error rate increased when TMS was applied between ∼80 and 120 ms following the presentation of the visual stimulus. The authors concluded that the occipital cortex (i.e., where) makes a critical contribution to stimulus recognition only at that precise time window (i.e., when) (Amassian et al., 1989).

The virtual lesion approach refers to the possibility of causally ascertaining where cognition occurs in the brain. In this sense, TMS has borrowed experimental hypotheses from neuropsychology and, after extensive testing, has confirmed most of them (see Miniussi et al., 2012b, Walsh and Pascual-Leone, 2003, Wassermann et al., 2008). Due to its high temporal specificity, TMS has also been employed to study the time point at which a cognitive event occurs in the brain. For this purpose, spTMS is superior to rTMS because it confines the impact of stimulation to a small fraction of a second. Mental chronometry has been extensively applied to perceptual (e.g., Amassian et al., 1989, Corthout et al., 1999, Laycock et al., 2007, Marzi et al., 1998, Seyal et al., 1992) or higher-order cognitive processes (e.g., Ashbridge et al., 1997, Chambers et al., 2004, Harris and Miniussi, 2003, Kahn et al., 2005, Mottaghy et al., 2003) and has been useful in defining the temporal activation of single brain areas as well as ascertaining the relative temporal roles of different areas in the same cognitive process, along the continuum of information processing.

Although the ‘virtual lesion assumption’ is a very useful heuristic when interpreting the behavioural effects of NIBS, we need to develop a more sophisticated explanatory framework if we wish to use NIBS to develop and test more complex theoretical models. The virtual lesion approach attributes an impairment of performance to a lesion, yet there is no actual evidence to support this assumption and it was not originally intended in that manner. Indeed, in its original definition, the concept was expressed as ‘In the context of a task, the induced current operates as “neural noise”; that is, the pulse adds random activity in the midst of organised activity in the cortical region. This neural noise serves to delay or disrupt performance, and it is in this sense that TMS operates as a lesion’ (Walsh and Rushworth, 1999, p. 127). As is frequently the case, taking an analogy too literally and transforming it into a mechanism of action is unproductive in science. We should consider the term lesion to be equivalent to a lack of neural activity as a whole and, consequently, a missed opportunity to process information. By contrast, the acute impact of stimulation can be positive in the sense of induced neural activity in pools of cortical neurons underneath the coil, even if that neural activity might interfere with the opportunity to process specific information because it competes with the neural activity that represents the stimulus. Thus the useful heuristic used to describe the final results cannot be used to interpret the functional mechanisms of the effects induced; as such the effects that are highlighted in TMS studies cannot be directly compared with those of lesion studies. Furthermore, the use of TMS as a disturbance apparatus has never produced a categorical failure in the subject’s performance similar to the effects observed in neuropsychological patients. The type of effect obtained is often related to an increase in the length of time required for information processing (e.g., increased reaction time), and if a reduction in the subject’s performance is observed, it is most likely explained by the complexity of the processing that is needed to solve the task (Manenti et al., 2008). In this context, we should consider TMS to be a tool that injects activity that competes or interacts with resources to solve the task, thus slowing or hindering task execution. Moreover, TMS has also been shown to enhance performance on many perceptual and cognitive tasks (for a review see Vallar and Bolognini, 2011), often to the surprise of the researchers involved, and leading to contradictory explanations in the virtual lesion framework. Indeed, one of the limits of the virtual lesion hypothesis is that it only postulates an impairment of performance, while any positive results have been addressed as a paradoxical effect. Another shortcoming with the “virtual lesion” framework is that its meaning is unclear – what form does a “virtual” lesion take and how is it generated? TMS may interrupt the relevant signal by terminating neuronal activity or it might induce interfering activity (neural noise) in the stimulated area; both would modify performance but through completely different mechanisms of action (Ruzzoli et al., 2010).

At this point, based on the literature, we can trade some of the simplicity of the virtual lesion approach for increased explanatory power by examining possible alternative hypotheses. Clearly, this step forward will not invalidate the standard logic of inference (area A plays a causal role in the performance of process X but not Y). The logic will be the same, but it will allow us to draw the conclusions from a more informed perspective, above all taking into account that we are stimulating a complex adaptive system.
2.2. Signal reduction versus noise generation

TMS introduces activity by depolarising neurons (Ruohonen, 2003). For example, a study that aimed to directly measure the effects induced by TMS at the cellular level (Moliadze et al., 2003) demonstrated that spTMS induces a neuronal facilitation effect, enhancing evoked activity during the first ∼500 ms, and thereafter decreasing this activity for up to a few seconds. The duration of these effects was modulated by the intensity of stimulation, and increasing stimulus intensity led to an early partial suppression of activity approximately 100–150 ms, followed by stronger facilitation. How can depolarisation of neurons cause interference?

Neural coding is concerned with the way in which sensory information is represented in the brain by neurons. One of the ways to code the signal intensity/strength is related to neuron firing frequency or rate coding, where significant events are encoded by the average activity of a pool of neurons (e.g., Adrian, 1928, Bialek and Rieke, 1992) (consider that this is a simplification, and other types of temporal coding exists as well). Neurons respond to the increased strength of a stimulus by increasing their firing frequency and by increasing the number of firing neurons (population coding). In behavioural terms, we can say that the activation of a small number of neurons and/or a low firing rate (i.e., a weak signal) will produce a slow reaction time (RT) and a low level of accuracy in detecting the stimulus target. If the number of neurons and the frequency rate increase, the RT will likely be faster, and the accuracy may be higher.

Nevertheless, the final response given by the system will be based not solely on the strength of the signal that codes for the target but on the ratio between that signal and other irrelevant activity that we can define as noise (see Fig. 1a and b no NIBS conditions). Thus, the accuracy in detecting the target will be based on the relation between signal and noise (i.e., the signal-to-noise ratio). If TMS increases the neural noise, it will change the ratio between the activity of neurons that code for the target and the activity of other neurons (non-specific activity for the task), decreasing the final performance. This effect could be interpreted as the generation (increase) of background neural activity (noise) by TMS, activity that is unrelated with respect to the relevant information/signal carried by the stimulated area (Fig. 1b NIBS high coherence). Nevertheless the TMS-induced activity is not totally random; that is, the activity induced by TMS may not be independent of the stimulus-induced neural activity or what we refer to as the ‘state of the area’ (see the state-dependency described below) (Pasley et al., 2009), in which case the effect of TMS is not statistically pure noise (Harris et al., 2008, Ruzzoli et al., 2011) (see Fig. 1b NIBS conditions). The probability that a neuron will be activated by a magnetic pulse depends on its neurophysiological state and on its spatial and anatomical characteristics, in relation to the induced electric field (Amassian et al., 1992, Roth, 1994) as illustrated in Fig. 2. Moreover, because the signal can only be defined in conjunction with a well-defined state of the system, defining the assumed nature of the system and the task demand helps to define signals.

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Fig. 1. (A) This figure illustrates the relation between target signal and other non-target signals. Those neurons that respond according to the task-goal are displayed as target signal (yellow), all other sources of activity that are not associated with the final task-goal are defined as neuronal noise (purple). The threshold represents the minimum intensity of a signal to reach the level to be included in the final subjective judgement. The vertical bar indicates the signal strength for the judgement, its dimension represents the features of the final behavioural outcome of a system e.g., the speed of reaction times or degree of accuracy. The larger the difference, the faster/better the behavioural performance. (B) The no NIBS plots illustrate the interaction between target signal and non-target activity (noise) when an observer tries to identify the direction of motion of a moving stimulus (upper part of the figure), depending on the difficulty of the task (low vs. high motion coherence). The NIBS plots represent possible effects of NIBS on the neural population whose activity is based on the task demands. The final behavioural outcome will depend on the final neuronal patterns. This pattern will be given by the interaction between the present state and NIBS induced activity. NIBS = non-invasive brain stimulation. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)

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Fig. 2. The figure depicts the situation where the random neural noise induced by transcranial magnetic stimulation (TMS) will interact with the system state. Circles represent the hypothetical state of neurons. The final pattern will depend on the relation between activated and non-activated neurons and the location of the induced electric field. The final behavioural outcome will likely be an improvement in performance in (A) or a worsening of performance in (B).

Communication in the nervous system also relies on the temporal component of the firing rate of a neural population. The precise timing of action potentials, and in particular the temporal relationship of action potential generation between neurons, is a significant element in neural communication (temporal coding, Bialek and Rieke, 1992). In a given area at a given time, many signals converge, but only those that will be associated in time (have a similar distribution, see Wu et al., 2002) will give rise to effective communication mechanisms (Bi and Poo, 2001). TMS-induced noise can interfere with performance because it can increase the number of neurons that fire without temporal synchronisation, thus obstructing the synchronised conversation between neurons that code for the goal. Therefore, neuron responses will not vary linearly with the characteristics of the stimulus, and the variance of the internal stimulus distribution will increase, so that the temporal coding of discharge by a given population will not be ensured. Consequently, communication at a higher hierarchical level that relies on the timing of the spiking of converging information from different areas will not be possible (Guyonneau et al., 2004, Masquelier and Thorpe, 2007). This is just a different way to see the action of NIBS on neurons, while the effects on the systems will be the same regardless of the fact that the information is carried by rate or temporal coding.

As stated previously, the virtual lesion hypothesis can only predict impairments of performance; any positive results are considered paradoxical. But, based on the neural noise generation hypothesis, it is easy to explain either outcome. Noise is the major source of variability because it is random activity that is uncorrelated within itself and with the goal of the task and will result in the impairment of performance. Nevertheless we should consider that noise pervades every level of information processing in the nervous system, from receptor signal transduction to the final behavioural response (Faisal et al., 2008). Moreover, in non-linear systems, such as the brain, information at the threshold level can be better processed within an optimum level of noise (compared to without noise), as suggested by the concept of stochastic resonance. This can be considered a potential benefit of noise (Kitajo et al., 2003, Kitajo et al., 2007, Miniussi et al., 2010, Moss et al., 2004, Ruzzoli et al., 2010, Schwarzkopf et al., 2011) because the induced noisy activity may be synchronised with the ongoing relevant signal (Ermentrout et al., 2008, Stein et al., 2005). In this context the presence of neuronal noise might confer to neurons more sensitivity to a given range of weak inputs (Kitajo et al., 2003, Kitajo et al., 2007), thereby rendering the signal “stronger” or even “synchronised” (see Fig. 3). TMS may induce neuronal activity that adds to the ongoing neural activity, which can be considered to be part of the signal and not random noise depending on the neurons that will be activated by the task and the stimulus.

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Fig. 3. Stochastic resonance. The amount of noise introduced in the subthreshold sinusoidal signal can change the final output. The final signal results in: (A) No output when no noise is introduce. (B) Very little output when presented in the presence of weak noise. (C) The best signal representation when combined with optimum level of noise. (D) Random and indistinguishable from noise alone, when high noise is introduced (from Ward et al., 2006).

The noise generation hypothesis in NIBS can be understood within a slightly different framework based in psychophysics (Solomon, 2009). This perspective was tested experimentally by Abrahamyan et al. (2011), who applied spTMS at different intensities over V1 and concurrently measured the threshold for detection of a visual stimulus. They found that, at weak intensities below the phosphene threshold, TMS significantly improved performance. The study also confirmed the well-established effect that high TMS intensities (above the phosphene threshold) decreases subjects’ visual sensitivity (Amassian et al., 1989). Abrahamyan and colleagues argued that TMS acts as a “pedestal” (Nachmias and Sansbury, 1974) to increase the response of the visual system, and this increase could result in an improvement or a decrement in sensitivity depending on the scale of the sensory and TMS-induced inputs. We return to this description in more detail later, when describing the biphasic input-response function that characterises the behaviour of neural systems.

Experimental evidence supporting the noise generation hypothesis has been provided independently by different TMS studies (Rahnev et al., 2012, Ruzzoli et al., 2010, Schwarzkopf et al., 2011, Waterston and Pack, 2010), and the final result is the physiological sum of the underlined complex activity of subpopulations of neurons that coexist in the stimulated area (Rahnev et al., 2012). Thus, abandoning the virtual lesion approach in favour of the definition of the precise mechanisms of action makes it possible to test new hypotheses and to expand the prospective applications of TMS.
2.3. State dependency

As described above, we cannot deduce pure TMS-induced effects because the effects of TMS are proportional to the level of neuronal activation during the application of the pulses (Epstein and Rothwell, 2003). In the motor system, for example, the amplitude of the motor-evoked potential can be increased by the voluntary contraction of the target muscle (Rothwell et al., 1987) and cortical connections can also be ‘modulated’ by the system state (Ferbert et al., 1992). This dependence on state was first articulated, in the TMS field, by Silvanto (see also Moliadze et al., 2003, Sack and Linden, 2003, Silvanto et al., 2008). According to state-dependency, TMS will affect the “less-active neurons within the stimulated area”. In a well-designed experiment, Silvanto et al. (2007) adapted subjects to a red/green screen. After colour adaptation, delivery of spTMS over the occipital cortex elicited phosphenes that took on the same colour as the adapting stimulus. Similarly, adaptation to a motion stimulus allowed TMS to facilitate the detection of motion in the adapted direction, while impairing the detection of motion in the opposite direction (Silvanto et al., 2007). State-dependency has been tested and validated under different experimental protocols (i.e., priming or adaptation) and for different brain areas (Cattaneo et al., 2008, Cattaneo et al., 2010). Pasley et al. (2009) attempted to provide physiological support for this hypothesis. They applied rTMS to the visual cortex of anaesthetised cats and observed spontaneous and visually evoked neural activity in terms of variability. They found that the higher the pre-TMS level of activity, the greater the impact of TMS during spontaneous activity. By contrast, for evoked activity (evoked by a visual stimulus), the greater the baseline activity, the lower the power of the effect induced by TMS (Pasley et al., 2009). Clearly, state dependency again does not provide an explicit mechanism of how TMS affects cognition; however, state dependency is an approach that does allow neuroscientists to reconsider the importance of the stimulated area based on its functional activation during a particular task. Using this practical approach we have the opportunity to disentangle the role of different neural populations within the stimulated area. In this context, we could consider state dependency a form of metaplasticity that describes the activity-dependent modification of the system (Abraham, 2008, Bienenstock et al., 1982).

2.4. Entrainment

A more recent application of TMS is based on what is known as the entrainment hypothesis (Thut and Miniussi, 2009, Thut et al., 2011a, Thut et al., 2012), that is, the possibility of inducing a particular oscillation frequency in the brain by means of an external oscillatory force (e.g., rTMS, but also tACS). The physiological basis of oscillatory cortical activity lies in the timing of the interacting neurons; when groups of neurons synchronise their firing activities, brain rhythms emerge, network oscillations are generated, and the basis for interactions between brain areas may develop (Buzsàki, 2006). Different cognitive states are associated with different oscillatory patterns in the brain (Buzsàki, 2006, Canolty and Knight, 2010, Varela et al., 2001).

Recently, Thut et al. (2011b) directly tested the entrainment hypothesis by means of a concurrent EEG–TMS experiment. They first determined the individual source of the parietal–occipital alpha modulation and the individual alpha frequency (magnetoencephalography study). They then applied rTMS at the individual alpha power while recording the EEG activity at rest. The results confirmed the three predictions of the entrainment hypothesis: the induction of a specific frequency after TMS, the enhancement of oscillation during TMS stimulation due to synchronisation, and a phase alignment of the induced frequency and the ongoing activity (Thut et al., 2011b). If associative stimulation is a general principle for human neural plasticity in which the timing and strength of activation are critical factors, it is possible that synchronisation within or between areas using an external force to phase/align oscillations can also favour efficient communication and associative plasticity (or alter communication). In this respect associative, cortico-cortical stimulation has been shown to enhance coherence of oscillatory activity between the stimulated areas (Plewnia et al., 2008). Here, another form of stochastic resonance, the coherence resonance (Longtin, 1997), can be introduced. In coherence resonance, the addition of a certain amount of noise in an excitable system results in the most coherent and proficient oscillatory responses. The brain’s response to external timing-embedded stimulation can result in a decrease in phase variance and an enhanced alignment (clustering) of the phase components of the ongoing EEG activity (entraining, phase resetting) that can change the signal-to-noise ratio and increase (or decrease) signal efficacy. In this context, phase resetting or shifting can synchronise inputs and favour communication and, eventually, Hebbian plasticity (Hebb, 1949). Thus, rhythmic stimulation may induce a statistically higher degree of coherence in spiking neurons, which facilitates the induction of a specific cognitive process (or hinders that process). Here, the perspective is slightly different (coherence resonance), but the underlining mechanisms are similar to the ones described so far (stochastic resonance) and the additional key factor is the repetition at a specific rhythm of the stimulation.

There are indications in TMS–EEG research that entrainment is plausible because of the characteristics of the EEG responses to a single TMS pulse. The spectral compositions of the EEG responses resemble the spontaneous oscillations of the stimulated cortex. For example, TMS of the “resting” visual (Rosanova et al., 2009) or motor cortices (Veniero et al., 2011) triggers alpha-waves, the natural frequency at the resting state of both types of cortices.

With the entrainment hypothesis, the noise generation framework moves to a more complex and extended level in which noise is synchronised with on-going activity. Nevertheless the model to explain the final outcome will not change, stimulation will interact with the system and the final result will depend on introducing or modifying the noise level.

The entrainment hypothesis makes clear predictions with respect to on-line repetitive TMS paradigms’ frequency engagement as well as the possibility of inducing phase alignment, i.e., a reset of ongoing brain oscillations via external spTMS (Thut et al., 2011a, Thut et al., 2012, Veniero et al., 2011). The entrainment hypothesis is superior to the localisation approach in gaining knowledge about how the brain works, rather than where or when a single process occurs. In this sense, TMS is likely the best available method to test a renewed topic in neuroscience: the role of brain oscillations. In fact, it is tempting to speculate that one TMS pulse will phase-align the natural, ongoing oscillation of the target cortex. When additional TMS pulses are delivered in synchrony with the phase-aligned oscillation (i.e., at the same frequency), further synchronised phase-alignment will occur, which will bring the oscillation of the target area in resonance with the TMS train. Hence, we expect entrainment in cases of frequency-tuning of TMS to the underlying brain oscillations (Veniero et al., 2011).
3. Transcranial electric stimulation

As previously reported, tES (tDCS, tACS, and tRNS) is a non-invasive method of cortical stimulation in which weak direct currents are used to polarise target brain regions. The most used and best known method is tDCS, as all considerations for the use of tDCS have been extended to the other tES methods. The hypotheses concerning the application of tDCS in cognition are very similar to those of TMS, with the exception that tDCS was never considered a virtual lesion method. It has been suggested that, depending on the polarity of the stimulation, tDCS can increase or decrease cortical excitability in the stimulated brain regions and facilitate or inhibit behaviour accordingly, thereby enabling the investigation of the causal relationships between brain activity and behaviour by means of neural modulation. As previously mentioned tES does not induce action potentials but instead modulates the neuronal response threshold so that it can be defined as subthreshold stimulation. Changes in the neuronal threshold result from changes in membrane permeability (Liebetanz et al., 2002), which influence the response of the task-related network. It is possible to hypothesise the same mechanism of action for tES methods as for TMS, i.e., the induction of noise in the system. However, the neural activity induced by tES will be highly influenced by the state of the system because it is a neuromodulatory method (Paulus, 2011) and its effect will depend on the activity of the stimulated area. Therefore, the final result will depend strongly on the task characteristics, the system state and the way in which tES will interact with such a state.

3.1. Transcranial direct current stimulation

tDCS induces membrane depolarisation (anodal stimulation) and hyperpolarisation (cathodal stimulation) (Liebetanz et al., 2002, Nitsche et al., 2003a, Nitsche et al., 2003b, Nitsche et al., 2004, Nitsche et al., 2005). From a methodological perspective, most of the general concerns for TMS are valid for tDCS, with some exceptions: tDCS does not induce depolarisation and therefore will only induce the firing of neurons that are near threshold, which means that neurons not influenced by the task are less likely to discharge. From a cognitive neuroscience standpoint, the effect of applying anodal tDCS during task execution is considered to induce facilitation, while cathodal tDCS should induce inhibition of task performance. In this sense, it is believed that tDCS primes the behavioural system by increasing/decreasing cortical excitability and producing corresponding effects in the cognitive system. Therefore, tDCS-induced effects are more likely to be sensitive to the state of the network that is active at that moment. Thus, the polarisation of neurons in combination with ongoing synaptic input can be contextualised in a framework of synaptic co-activation. This is evocative of Hebbian-like plasticity mechanisms as the combination of tDCS with task execution is like the co-activation of a specific network. The spatial and temporal resolution of the tDCS effects are somewhat reduced compared with those of TMS, but this drawback may be overcome by considering the state of the system, as previously described.

While the main framework of a “facilitatory” anodal stimulation and a “worsening” cathodal stimulation is well-grounded, it is only valid for the use of tDCS on the motor system (Nitsche et al., 2008). Anodal tDCS facilitative behavioural effects have been identified for several functions, but the relation between facilitation and inhibition is often quite complex (Jacobson et al., 2011). In many cognitive neuroscience experiments, the stimulation of non-motor areas has led to the observation that behavioural effects are often not unequivocal, with anodal stimulation usually inducing facilitation and cathodal stimulation inducing a range of effects (Jacobson et al., 2011). Here, the point is that the neurophysiological dimension cannot be used as a simple mechanistic approach for mapping onto behavioural effects (Miniussi et al., 2010). It can be suggested that anodal tDCS may induce facilitation when the task is well-trained or familiar, but such facilitation is not present during the performance of a novel task (Dockery et al., 2009). For example in a well-established skilled task, such as naming, the noise is reduced, so that the signal emerges clearly from the noise, and anodal stimulation can facilitate faster processing. In the same task, cathodal tDCS would reduce the possibility of firing in response to a stimulus, but because the signal is strong enough to elicit a response, the probability that cathodal stimulation can interfere with task execution is quite low. In a novel task, the context is different: there is more background noise because the neural networks are not consolidated, and many signals close to the target signal will be present. In this case, an increase in noise by anodal tDCS will not help task execution as it will increase the signal but also the noise, which is close to the threshold. Nevertheless, in such situations, cathodal tDCS can induce facilitation by reducing the general noise and helping the signal emerge (Antal et al., 2004, Dockery et al., 2009). Antal et al. (2004) found that cathodal tDCS applied to the left visual middle temporal area (MT-V5) improved performance in a visuomotor coordination task when a large amount of visual noise was present in the visual stimulus. Therefore, cathodal tDCS appears to act as a neuronal filter that reduces noise. The idea is the same as that of the neurophysiological mechanism called ‘lateral inhibition’ – a mechanism that can reduce the neural activity due to a non-relevant signal (noise) together with that due to the relevant signal, sharpening the profile of the excitatory response and improving the final performance. Therefore, we cannot consider tDCS to be a simple neuromodulatory method in which anodal-tDCS increases excitation to induce behavioural facilitation and cathodal tDCS yields the opposite effect via inhibition. The neural noise induced by the stimulation will affect the performance depending on the state of the system, which is mainly determined by the task input. In this sense, tDCS neuromodulation will interact with the level of excitation of the system, driven by the task to shape the final result (Bienenstock et al., 1982). Once again, the level of noise introduced in the system will be the key factor in shaping the final result.

In contrast to TMS, tDCS is a continuous stimulation procedure. Continuous stimulation can engage neurophysiological homeostasis mechanisms, which serve to maintain neural activity within a normal functional range (Siebner et al., 2004). In this context, it could be suggested that neurons can adjust the threshold of the system based on the constant input. This type of mechanism could therefore alter the final effect of the stimulation in terms of excitatory or inhibitory responses of the stimulated area, particularly in the context of a complex framework.

3.2. Transcranial alternating current stimulation

tACS allows the brain to be stimulated at specific frequencies: like rTMS, it has been suggested that tACS can modulate ongoing neuronal activity (Zaehle et al., 2010) and related behaviour (Kanai et al., 2008) by inducing specific brain oscillations. We can theoretically predict that this mechanism will produce a frequency ‘entrainment’ in the stimulated cortical region or in the connected areas during a prolonged stimulation. Using tACS (as for rTMS), an oscillatory current can be delivered to the cortex to induce it to oscillate at that particular frequency, which is area-dependent (Kanai et al., 2008). An advantage of tACS is that there are fewer safety concerns for this method than for rTMS (Rossi et al., 2009), and therefore there are no restrictions on the frequency that can be used. The idea is that, like for TMS, the so-called ‘rhythmic approach’ (Miniussi et al., 2012a, Thut and Miniussi, 2009) refers to the possibility of investigating how tACS interacts with oscillatory brain activity in order to establish a causal relationship between brain oscillations and cognition.

Several authors applied tACS over the primary motor area with the aim of specifically influencing brain oscillations. Stimulation was applied at different frequencies during motor tasks, and a significant improvement in performance was observed at the alpha frequency stimulation (Antal et al., 2008, Feurra et al., 2012, Joundi et al., 2012, Pogosyan et al., 2009). It has been also shown that changing the local activity with tACS may affect the functional networks that are responsible for motor performance and improved task execution (Joundi et al., 2012). In vision, Zaehle et al. (2010) demonstrated that tACS was able to modulate EEG oscillations, in particular at alpha frequency when subjects were at rest (no task was involved). In contrast, Kanai et al. (2008) reported that occipital stimulation most effectively induced phosphenes when applied at the alpha frequency in darkness; whereas, the beta frequency was more effective in the light (but see Schutter and Hortensius, 2010, Schwiedrzik, 2009).

The effect of tACS may rely on the intrinsic resonance of the system. Resonance is the tendency of a system to oscillate with greater amplitude at specific frequencies than at others; these frequencies are related to the specific structure of a given system. At these specific frequencies, even small alternating currents can produce larger amplitude ringing than the input because the system stores vibrational energy. An easily recognised example is given by the wind-induced collapse of the Tacoma Narrows Bridge ( The wind provided a weak external periodic frequency that matched the bridge’s natural structural frequency, inducing large oscillations that destroyed the bridge. The same may occur in the cortex, which produces frequencies in a range of 0.01 up to 600 Hz. Applying a weak alternating current at a suitable frequency is a cooperative effect that can produce larger amplitude ringing, increasing synchronisation. Resonances have now been described in various central neurons (Hutcheon and Yarom, 2000). Furthermore, in a recent in vitro study (Deans et al., 2007), it was shown that very weak extracellular alternating electric fields have the ability to entrain an oscillating network (Deans et al., 2007, Radman et al., 2007, Reato et al., 2010). Thus, if a given network is carried near the threshold level (prone to activation), a small polarisation may drive the neuronal discharge that will induce phosphene perception.

It has been suggested that the cortex may actually respond to external stimulation (i.e., TMS), producing natural local frequencies (Rosanova et al., 2009, Veniero et al., 2011) depending on the ongoing activity. Given the neuromodulatory characteristics of tACS and previous TMS results, we may be able to modulate cortical oscillations with tACS but are likely unable to superimpose an “out of condition/unnatural” frequency on the system. As with TMS, coherence resonance can be the key mechanism for the addition of certain amounts of noise that make system oscillatory responses more coherent and proficient. In other words, tACS produces a small amount of activity (noise) that is close to the system oscillatory phase (synchronised), and this small amount of activity will sum with the system’s response in coherence resonance (Fig. 3), increasing the signal-to-noise ratio and improving performance (or decreasing it). Once again, the concept of stochastic resonance can be used in this framework: a weak periodic stimulation entrains the system fluctuation, enhancing the biological signal. Although very suggestive, these considerations are only speculations, and even if tACS may be considered an important device for manipulating cortical oscillatory activity, adequate support is lacking (Brignani et al., 2013, Schwiedrzik, 2009).
3.3. Transcranial random noise stimulation

tRNS involves the application of a random electrical oscillation spectrum over the cortex. At present, tRNS can be applied in three frequency ranges: the entire spectrum (from 0.1 to 640 Hz), in the low band (0.1–100 Hz) or in the high band (101–640 Hz) (Terney et al., 2008). This technique is newer than other tES applications; therefore, exploration of its possible mechanisms of action in cognition has been limited.

Terney et al. (2008) recently showed that ten minutes of tRNS on the motor cortex at high frequency bands is able to positively modulate cortical excitability (i.e., increase the amplitude of motor-evoked potentials). Behavioural improvement in a motor learning task also resulted from the application of the entire frequency spectrum (Terney et al., 2008). In a recent study, Fertonani et al. (2011) applied tRNS to the visual system and compared the high/low frequency bands to other tES techniques (anodal/cathodal tDCS). High-frequency tRNS on the visual cortex of healthy subjects during a visual perceptual learning task was found to significantly improve performance more than anodal tDCS, which was previously thought to be the best method to positively modulate behaviour. The authors suggested that the mechanism of action of tRNS might be based on the repeated subthreshold stimulations that prevent homeostasis of the system (Fertonani et al., 2011). This effect might potentiate the activity of the neural populations involved in a task and, in turn, facilitate transmission between neurons.

Also the effects of tRNS may be explained in the context of the stochastic resonance phenomenon; tRNS is a random-frequency stimulation that might induce random activity in the system (i.e., noise). The presence of neuronal noise might serve as a pedestal to boost the sensitivity of the neurons to a given range of weak inputs (i.e., the neurons with the same directionality as the signal), thereby increasing the signal-to-noise ratio. Therefore, as described for TMS, tDCS and tACS, the effect of tRNS on neuronal activity may not just be the random addition of noise but may be related to the functional activation induced by the task.

In conclusion, even if the mechanism of action of tES is different than that of TMS (neuromodulation vs. depolarisation), we can assume that, like TMS, tES induces neural activity in the stimulated area, which can theoretically be defined as noise. Nevertheless, when compared to TMS, the noise induced by tES will never be random but will depend on stimulation parameters, specifically, the system state and input. This is because tES cannot induce a direct over-threshold depolarisation but can modulate the firing rate of the stimulated area. Such induced activity will consequently shape behavioural measurements.

4. A unified hypothesis of the functional effects of NIBS: noise generation in a non-linear system

In TMS, the magnetic pulse causes the rapid and above-threshold depolarisation of cell membranes affected by the current, leading to the transynaptic depolarisation or hyperpolarisation of connected cortical neurons. Therefore, TMS activates a neural population that, depending on several factors, can be congruent (facilitate) or incongruent (inhibit) with task execution. tES induces a polarisation of cortical neurons at a subthreshold level that is too weak to evoke an action potential. However, by inducing a polarity shift in the intrinsic neuronal excitability, tES can alter the spontaneous firing rate of neurons and modulate the response to afferent signals. In this sense, tES-induced effects are even more bound to the state of the stimulated area that is determined by the task conditions. In short, NIBS leads to a stimulation-induced modulation of activity that can be substantially defined as noise induction. Nevertheless, such induced noise will not be just random activity but will depend on the interaction of many parameters, from the characteristics of the stimulation to the task performed. In other words, the noise induced by NIBS will be influenced by the state of the neural population of the stimulated area (Fig. 2).

The relation between signal and noise can be understood within a simple and precise framework based on a sigmoid input-response function. In biological systems, the strength of the response to a given input is rarely a linear function of the strength of the input. In neuroscience, there is ample evidence that the response (typically the firing rate) of individual neurons to varying levels of input intensity is described by a sigmoid (S-shaped) function, of the sort shown in Fig. 4A (Carandini and Ferster, 1997, Sclar et al., 1989). Assuming the strength of a stimulus is fixed (at s), varying only the strength of the noise (n) will change the overall input strength (horizontal axis). Neurons show very little change in their response (vertical axis) to very weak input strength, but as the strength of stimulation passes a “threshold” the response strength rapidly increases, marked by the upward inflexion of the input-response curve. Thereafter, as input strength increases further, the neuronal response begins to “saturate”, where the function begins to flatten. Thus, the responsiveness of a neuron to variation in input strength – its discrimination sensitivity – is reflected in the slope of the sigmoid input-response function: discrimination sensitivity is low with very weak input, increases for input at an intermediate range of intensity, and then decreases again as input approaches the saturation point.

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Fig. 4. (A) A sigmoid input-response function. A fixed signal, s, is added to differing levels of noise, n. The differential response, d, to s varies as a function of the size of n: when n is low (n1) or high (n3), d is smaller than when n is at an intermediate level (n2). The function shown here is based on a cumulative gamma function. (B) The first derivative of the function in panel A, corresponding to the slope of that function. It shows how, d, the sensitivity of the response to s, changes across all values of n.

A similar sigmoid-like function is known to underlie behavioural responses to stimulation in many sensory systems, although in this case the shape of the input-response function is derived by reverse inference from changes in discrimination sensitivity. For example, human participants are relatively poor at detecting a very weak sensory stimulus, and can only discriminate between the presence versus absence of the stimulus when its strength exceeds a threshold. However, they are often much better at discriminating between a stimulus that is at this threshold versus one that is just above the threshold. This increase in discrimination sensitivity, usually measured as a decrease in discrimination thresholds, is known as the “pedestal effect”, and has been shown to operate at low sensory inputs in visual, auditory and tactile domains (for recent review see Solomon, 2009). As stimulation intensity increases further, discrimination sensitivity declines according to “Weber’s law”, in which the size of the “just-noticeable difference” is a fixed ratio of the average intensity of the stimuli being discriminated. The overall pattern of initial improvement and then decline in discrimination sensitivity, described as a “dipper function” when plotting discrimination threshold against stimulus intensity, speaks to an underlying sigmoid function relating stimulus strength to perceptual response: the function is flat for very weak stimuli, becomes steeper for stimuli at low to intermediate intensities, before progressively flattening again at higher intensities (Fig. 4A).

Performance in any situation depends on accurate detection of signal above noise. For example, if an observer tries to identify the direction of motion of a moving stimulus, his or her ability will depend on the strength of the coherent motion signal above all background motion signals. In neurophysiological terms, correctly identifying direction of motion from the response of a population of motion-sensitive neurons (e.g., in area MT) will depend on the difference between the baseline response rate among all neurons in the entire population, which constitutes the level of noise, and the response of those specific neurons that code for the stimulus’ motion (see Fig. 1A). Thus, to identify the signal, the observer must compare the response to noise with the response to signal plus noise. A key property of the input-response function described in the preceding paragraph is that the observed difference in response between two levels of input will depend on the absolute magnitudes of those inputs. Consider, for example, three different levels of noise input, n1, n2, and n3, as depicted in Panel A of Fig. 4. To each level of noise, a stimulus of fixed strength, s, is added. Even though the size of s is the same in each case, the response to s differs depending on the level of noise as it is transduced through the sigmoid input-response function. In the example shown, the difference, d, in response to s + n versus n is larger for s + n2 than for either s + n1 or s + n3. The improvement in detection of s when noise is increased from a very low level, at n1, to an intermediate level, at n2, explains the stochastic resonance effect that is sometimes observed when uncorrelated input (noise) is introduced into a system. The frequent observation that large amounts of noise, such as at n3, impair performance is explained by the decrease in the difference in response to s + n versus n. The complete function relating the detectability of s to n is shown in Panel B of Fig. 4. Clearly as described before, the effects of brain stimulation will be proportional to the level of neuronal activation during the application of the pulses, the so-called state dependency as represented in Fig. 5. It should be noted that a shift in the sigmoid input-response function can be induced also by off-line NIBS protocols.

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Fig. 5. How a sigmoid input-response function can be modified by adaptation. Adaptation can also be induced with an off-line NIBS protocol, changing the state of the subjects and therefore the final relation between input strength and sensitivity (i.e., state dependency).

We propose that the response properties described here present a very useful way to understand the impact of NIBS on brain function and behavioural performance. Given that NIBS necessarily involves the relatively indiscriminate activation of large numbers of neurons, its impact on a neural system can be easily understood as introducing or amplifying noise (or a possible reduction of noise in the case of cathodal tDCS). The framework proposed here offers the opportunity to understand how NIBS, by altering levels of noise, could usually impair, but sometimes improve performance on a task, depending on the amount of noise introduced, the existing level of noise in the system or in the task, and the size of the signal. Another important advantage to this approach is that this single framework can be applied readily across the relevant domains. As described here, it can be applied equally to consideration of responses of individual neurons, population responses of neurons, or the behaviour of a subject performing a task. Thus it provides a theoretical basis for translating explanatory concepts and interpretation of findings across different levels of the system.
5. Conclusions

In sum, although the types and number of neurons “triggered” by NIBS are theoretically random, the induced change in neuronal activity is likely to be correlated with ongoing task-relevant activity, yet even if we are referring to a non-deterministic process, the noise introduced will not be a totally random element. Because it will be partially determined by the experimental variables, we could estimate the level of noise that will be introduced by the stimulation and by the task and potentially determine the interaction between the two levels of noise (stimulation and task). Clearly, with transcranial stimulation, we will never be able to induce stimulation with a focused and highly targeted signal to a clearly defined area of the brain to establish a unique brain-behaviour relationship; therefore, the only definition that we can apply to the introduced stimulus activity in the brain stimulation is ‘noise.’ The neural effects of NIBS protocols have the potential to offer important insights into the mechanisms that underlie the capacities of the central nervous system and will aid the evaluation of neurocognitive theories of the behaviour-brain relationship. The opportunity to directly influence brain activity in a clear theoretical framework raises even more exciting possibilities for future basic and clinical neuroscience studies involving NIBS.

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