title: 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.
authors.affiliation: Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, 10 Crichton Street, Edinburgh EH8 9AB, UK.
authors.forename: Panagiotis
authors.initials: P
authors.lastname: Nezis
authors.forename: Mark C W
authors.initials: MC
authors.lastname: van Rossum
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D039301:Computers, Molecular; D008432:Mathematical Computing; D008433:Mathematics; D008959:Models, Neurological; D009415:Nerve Net; D009474:Neurons / Q000502:physiology*