DOI: 1906.00092Jul 30, 2019Paper

Signal Coding and Perfect Reconstruction using Spike Trains

Anik Chattopadhyay, Arunava Banerjee


In many animal sensory pathways, the transformation from external stimuli to spike trains is essentially deterministic. In this context, a new mathematical framework for coding and reconstruction, based on a biologically plausible model of the spiking neuron, is presented. The framework considers encoding of a signal through spike trains generated by an ensemble of neurons via a standard convolve-then-threshold mechanism. Neurons are distinguished by their convolution kernels and threshold values. Reconstruction is posited as a convex optimization minimizing energy. Formal conditions under which perfect reconstruction of the signal from the spike trains is possible are then identified in this setup. Finally, a stochastic gradient descent mechanism is proposed to achieve these conditions. Simulation experiments are presented to demonstrate the strength and efficacy of the framework

Related Concepts

Related Feeds

Basal Forebrain & Food Avoidance

Neurons in the basal forebrain play specific roles in regulating feeding. Here are the latest discoveries pertaining to the basal forebrain and food avoidance.

Related Papers

M J Wayner, Y Oomura
Computational Intelligence and Neuroscience
Theodore W BergerNitish V Thakor
Journal of Mathematical Neuroscience
Hannah Julienne, Conor Houghton
© 2021 Meta ULC. All rights reserved