University of Cambridge > Talks.cam > Computational Neuroscience > Computational Neuroscience Journal Club

  Browse A-Z

Log in

Cambridge users (raven)details Other usersdetails No account?details

Information on

Subscribing to talksdetails Finding a talkdetails Adding a talkdetails Disseminating talksdetails Help and Documentationdetails

Computational Neuroscience Journal Club

Add to your list(s) Download to your calendar using vCal

If you have a question about this talk, please contact Rodrigo Echeveste.

Greg Sotiropoulos will cover:

• Toward a unified theory of efficient, predictive, and sparse coding

• Matthew Chalk, Olivier Marre, and Gašper Tkačik

• PNAS (2017)

• http://www.pnas.org/content/115/1/186.abstract

Abstract: A central goal in theoretical neuroscience is to predict the response properties of sensory neurons from first principles. To this end, “efficient coding” posits that sensory neurons encode maximal information about their inputs given internal constraints. There exist, however, many variants of efficient coding (e.g., redundancy reduction, different formulations of predictive coding, robust coding, sparse coding, etc.), differing in their regimes of applicability, in the relevance of signals to be encoded, and in the choice of constraints. It is unclear how these types of efficient coding relate or what is expected when different coding objectives are combined. Here we present a unified framework that encompasses previously proposed efficient coding models and extends to unique regimes. We show that optimizing neural responses to encode predictive information can lead them to either correlate or decorrelate their inputs, depending on the stimulus statistics; in contrast, at low noise, efficiently encoding the past always predicts decorrelation. Later, we investigate coding of naturalistic movies and show that qualitatively different types of visual motion tuning and levels of response sparsity are predicted, depending on whether the objective is to recover the past or predict the future. Our approach promises a way to explain the observed diversity of sensory neural responses, as due to multiple functional goals and constraints fulfilled by different cell types and/or circuits.

This talk is part of the Computational Neuroscience series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.

Other lists

Sedgwick Museum of Earth Sciences Phonetics & Phonology Research Cluster CMS Special Lectures

Other talks

Preparing Your Research for Publication Numerical solution of the radiative transfer equation with a posteriori error bounds Parkinson's Rehabilitation using interactive Dance Technology Beyond truth-as-correspondence: realism for realistic people Panel comparisons: Challenor, Ginsbourger, Nobile, Teckentrup and Beck Surrogate models in Bayesian Inverse Problems
 

© 2006-2025 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity