Table of Contents
Advances in Neuroscience
Volume 2014, Article ID 907851, 14 pages
http://dx.doi.org/10.1155/2014/907851
Review Article

Understanding Neural Population Coding: Information Theoretic Insights from the Auditory System

1Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di Tecnologia, Via Bettini 31, 38068 Rovereto, Italy
2Doctoral School in Cognitive and Brain Sciences, University of Trento, Via Bettini 31, 38068 Rovereto, Italy
3Institute of Neuroscience and Psychology, University of Glasgow, Glasgow G12 8QB, UK
4Max Planck Institute for Biological Cybernetics, Spemannstraße 38, 72076 Tübingen, Germany
5Bernstein Centre for Computational Neuroscience, 72076 Tübingen, Germany

Received 30 April 2014; Accepted 31 July 2014; Published 19 October 2014

Academic Editor: Xiang-Ping Chu

Copyright © 2014 Arno Onken et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

In recent years, our research in computational neuroscience has focused on understanding how populations of neurons encode naturalistic stimuli. In particular, we focused on how populations of neurons use the time domain to encode sensory information. In this focused review, we summarize this recent work from our laboratory. We focus in particular on the mathematical methods that we developed for the quantification of how information is encoded by populations of neurons and on how we used these methods to investigate the encoding of complex naturalistic sounds in auditory cortex. We review how these methods revealed a complementary role of low frequency oscillations and millisecond precise spike patterns in encoding complex sounds and in making these representations robust to imprecise knowledge about the timing of the external stimulus. Further, we discuss challenges in extending this work to understand how large populations of neurons encode sensory information. Overall, this previous work provides analytical tools and conceptual understanding necessary to study the principles of how neural populations reflect sensory inputs and achieve a stable representation despite many uncertainties in the environment.