An organic transistor paves the way for new generations of neuro-inspired computers

Jan 22, 2010

For the first time, French researchers at CNRS and CEA have developed a transistor that can mimic the main functionalities of a synapse.

This organic transistor, based on pentacene and nanoparticles and known as a NOMFET (Nanoparticle Organic Memory Field-Effect Transistor), has opened the way to new generations of neuro-inspired computers, capable of responding in a manner similar to the nervous system. The study is published in the 22 January 2010 issue of the journal Advanced Functional Materials.

In the development of new information processing strategies, one approach consists in mimicking the way biological systems such as neuron networks operate to produce electronic circuits with new features. In the nervous system, a synapse is the junction between two neurons, enabling the transmission of electric messages from one neuron to another and the adaptation of the message as a function of the nature of the incoming signal (plasticity). For example, if the synapse receives very closely packed pulses of incoming signals, it will transmit a more intense action potential. Conversely, if the pulses are spaced farther apart, the action potential will be weaker.

It is this plasticity that the researchers have succeeding in mimicking with the NOMFET.

A transistor, the basic building block of an , can be used as a simple switch - it can then transmit, or not, a signal - or instead offer numerous functionalities (amplification, modulation, encoding, etc.).

The innovation of the NOMFET resides in the original combination of an organic transistor and gold nanoparticles. These encapsulated , fixed in the channel of the transistor and coated with pentacene, have a that allows them to mimic the way a synapse works during the transmission of action potentials between two neurons. This property therefore makes the electronic component capable of evolving as a function of the system in which it is placed. Its performance is comparable to the seven CMOS (at least) that have been needed until now to mimic this plasticity.

The devices produced have been optimized to nanometric sizes in order to be able to integrate them on a large scale. Neuro-inspired computers produced using this technology are capable of functions comparable to those of the human brain.

Unlike silicon computers, widely used in high performance computing, neuro-inspired computers can resolve much more complex problems, such as visual recognition.

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More information: An Organic Nanoparticle Transistor Behaving as a Biological Spiking Synapse. Fabien Alibart, Stéphane Pleutin, David Guérin, Christophe Novembre, Stéphane Lenfant, Kamal Lmimouni, Christian Gamrat and Dominique Vuillaume. Advanced Functional Materials. 22 January 2010

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not rated yet Jan 22, 2010
For example, if the synapse receives very closely packed pulses of incoming signals, it will transmit a more intense action potential. Conversely, if the pulses are spaced farther apart, the action potential will be weaker.

That's just plain WRONG: action potentials (a.k.a. "pulses") always have the SAME magnitude. They're transmitted by AXONS, not by synapses.

It is this plasticity that the researchers have succeeding in mimicking with the NOMFET.

In neuroscience, neural plasticity refers to the ability of synapses to (semi)permanently adjust their strength based on correlative trends in activity between sending and receiving neuron. Such adjustments are thought to be the fundamental mechanism behind memory formation, as well as skill learning and general brain development/reconfiguration during a person's life.

That is NOT, even remotely, the same type of "plasticity" that the article above refers to.

This is a poorly written, and misleading article.
not rated yet Jan 22, 2010
It seems what they really are trying to refer to, in terms of neurobiology, is the integration function performed by neuronal cell bodies.

If that were the case, then these NOMFETs would behave a bit like a sensory neuron: essentially counting up the number of incoming spikes over time, and if over a given interval the number of inputs exceeds a certain threshold, firing a spike of their own (following which, the counter is reset back to 0.)

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