Brain-inspired recurrent neural network with plastic RRAM synapses

Abstract

The development of neuromorphic systems capable of mimicking the behavior of the human brain has recently received an increasing deal of interest. However, the building of such artificial systems has been hindered by the lack of commercial technologies with nanoscale integration of synaptic devices as well as the complexity of the biological neural architecture in terms of connectivity, parallelism, and plasticity behavior. In particular, there is a wide consensus on the relevance of recurrent connections in the human brain, and their key role for associative learning and pattern classification. Fundamental primitives of cognitive computing can therefore be demonstrated by means of Recurrent Neural Networks (RNNs). In this work, we design and simulate a Hopfield-type RNN with HfO2 RRAM devices capable of learning via spike-timing dependent plasticity (STDP). We first demonstrate learning and recall of a single attractor state in a 4-neuron RNN. Based on this result, we then simulate signal restoration of two orthogonal patterns in a 64-neuron RNN, thus supporting RRAM-based RNN with cognitive computing functionalities.