Commissariat &#xc0; L'&#xc9;nergie Atomique Et Aux &#xc9;nergies Alternatives

Centre National De La Recherche Scientifique

Christian Gamrat

Olivier Bichler

Manan Suri

Damien Querlioz

Barbara De Salvo

Barbara Desalvo

A neural network comprises a plurality of artificial neurons and a plurality of artificial synapses each input neuron being connected to each output neuron by way of an artificial synapse, the network being characterized in that each synapse consists of a first memristive device connected to a first input of an output neuron and of a second memristive device, mounted in opposition to said first device and connected to a second, complemented, input of said output neuron so that said output neuron integrates the difference between the currents originating from the first and second devices.

Network of artificial neurons based on complementary memristive devices

A neuromorphic system comprises a set of at least one input neuron, a set of at least one output neuron and a synaptic network formed from a set of at least one variable-resistance memristive component, said synaptic network connecting at least one input neuron to at least one output neuron, the resistance of the at least one memristive component being adjusted by delivering to the synaptic network write pulses generated by the at least one input neuron, and return pulses generated by the at least one output neuron, the characteristics of the write and return pulses being deduced from the intrinsic characteristics of the at least one memristive component so that the combination of a write pulse and a return pulse in the at least one memristive component results in a modification of its resistance according to a learning rule chosen beforehand.

Neuromorphic system exploiting the intrinsic characteristics of memory cells

An unsupervised learning method is provided implemented in an artificial neural network based on memristive devices. It consists notably in producing an increase in the conductance of a synapse when there is temporal overlap between a pre-synaptic pulse and a post-synaptic pulse and in decreasing its conductance on receipt of a post-synaptic pulse when there is no temporal overlap with a pre-synaptic pulse.

Method for non-supervised learning in an artificial neural network based on memristive nanodevices, and artificial neural network implementing said method

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Les Ulis, France

Christian Gamrat