Device mismatch in a neuromorphic system implements random features for regression

We use a large-scale analog neuromorphic system to encode the hidden-layer activations of a single-layer feed forward network with random weights. The random activations of the network are implemented using the device mismatch inherent to analog circuits. We show that these activations produced by analog VLSI implementations of integrate and fire neurons are suited to solve multi dimensional, non linear regression tasks. Exploitation of the device mismatch eliminates the storage requirements for the random network weights.