Spatial-Pooling Memristor Crossbar Converting Sensory Information to Sparse Distributed Representation of Cortical Neurons

This is the authors' abstract. We don't add key points for this paper.

The spatial pooler in brain's neocortex models how the cortical neurons learn the feedforward connections and forms the cortical representation from various sensory information. To realize the spatial pooler's operation in hardware, we propose a new spatial-pooling memristor crossbar that converts the inputs from sensory organ into the sparse distributed representation (SDR) of cortical neurons, in this paper. The spatial pooling is composed of overlapping, inhibition, and learning steps. The proposed memristor crossbar can perform exactly the same functions of overlapping, inhibition, and learning with the spatial-pooling software algorithm. By converting the sensory information to SDR using the spatial-pooling memristor crossbar, we can have the following advantages for the cortical information processing: first, preserving the semantic similarity through the spatial pooling; second, changing synaptic weights continuously; third, keeping the sparsity of SDR as low as 2% like brain's neocortex; and fourth, improving the noise robustness. In this paper, these four properties of the spatial pooling in brain's neocortex have been simulated and verified in the memristor crossbar circuit. For verifying the spatial-pooling operation, the memristor crossbar has been tested for MNIST image recognition in this paper. The recognition rate is as high as 95% for the memristor crossbar with 4096 columns.