Accurate multiplication with noisy spiking neurons

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.