Accurate multiplication with noisy spiking neurons.

Abstract

Multiplication is an operation which is fundamental in mathematics, but it is also relevant for many sensory computations in the nervous system. Nevertheless, despite a number of suggestions in the literature, it is not known how multiplication is implemented in neural circuitry. We propose a simple feedforward circuit that combines a rate model of neural activity and a realistic neural input-output relation to accurately and efficiently implement multiplication of two rate-coded quantities. By simulating a network of integrate and fire neurons, we demonstrate the functional efficiency of the circuit. Finally we discuss how the model can be tested experimentally.