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Published on: July 14, 2023
Population Vectors Can Provide Near Optimal Integration of Information
Josue Orellana1, Jordan Rodu2, Robert E Kass3
1Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, and Carnegie Mellon University, Pittsburgh, PA 15213, U.S.A. josue@cmu.edu.
Abstract:
Much attention has been paid to the question of how Bayesian integration of information could be implemented by a simple neural mechanism. We show that population vectors based on point-process inputs combine evidence in a form that closely resembles Bayesian inference, with each input spike carrying information about the tuning of the input neuron. We also show that population vectors can combine information relatively accurately in the presence of noisy synaptic encoding of tuning curves.
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