An electronic synapse

It is a truism that simulation of the brain with ‘electronic neurons’ has been very approximate at best. One problem was simulating the behavior of synapses in software – and synapses are key to ‘biological neuron’ communication. But soon there may be a simulation of the synapse in hardware. This does not solve all the problems with simulating the brain but it will be a large step in that direction. This is somewhat like the brain’s architecture which is more physically based than algorithmically based. Of course, miniaturization is necessary as the number of synapses in any smallish part of the brain is astronomical.


The idea is to use a very thin sheet of samarium nickelate between two platinum terminals. The sheet can be changed from isolating to conducting by the concentration of oxygen ions in the sheet. The oxygen ions can be made to leak out or in - from a small reservoir of ionic liquid by applied voltages. The voltage is controlled by the strength and timing of spikes on the ‘dentrite’ and ‘axon’ terminals. The changes in conductivity are stable until forced to change by another voltage signal. The devices can therefore ‘learn’/’remember’.


They have the advantages that they: can be integrated into silicon-based circuits, are fast, can work at room temperature, are energy efficient, do not require continuous power to maintain their ‘learning’.


Here is the citation and abstract:


Jian Shi, Sieu D. Ha, You Zhou, Frank Schoofs, Shriram Ramanathan. A correlated nickelate synaptic transistor. Nature Communications, 2013


Inspired by biological neural systems, neuromorphic devices may open up new computing paradigms to explore cognition, learning and limits of parallel computation. Here we report the demonstration of a synaptic transistor with SmNiO3, a correlated electron system with insulator–metal transition temperature at 130°C in bulk form. Non-volatile resistance and synaptic multilevel analogue states are demonstrated by control over composition in ionic liquid-gated devices on silicon platforms. The extent of the resistance modulation can be dramatically controlled by the film microstructure. By simulating the time difference between postneuron and preneuron spikes as the input parameter of a gate bias voltage pulse, synaptic spike-timing-dependent plasticity learning behaviour is realized. The extreme sensitivity of electrical properties to defects in correlated oxides may make them a particularly suitable class of materials to realize artificial biological circuits that can be operated at and above room temperature and seamlessly integrated into conventional electronic circuits.



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