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Towards a unified theory of efficient, predictive and sparse coding

View ORCID ProfileMatthew Chalk, View ORCID ProfileOlivier Marre, View ORCID ProfileGašper Tkačik
doi: https://doi.org/10.1101/152660
Matthew Chalk
*Institute of Science and Technology Austria, Am Campus 1, A-3400 Klosterneuburg, Austria
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Olivier Marre
†Institut de la Vision, INSERM UMRS 968, UPMC UM 80, CNRS UMR 7210, Paris, France
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Gašper Tkačik
*Institute of Science and Technology Austria, Am Campus 1, A-3400 Klosterneuburg, Austria
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Abstract

A central goal in theoretical neuroscience is to predict the response properties of sensory neurons from first principles. Several theories have been proposed to this end. “Efficient coding” posits that neural circuits maximise information encoded about their inputs. “Sparse coding” posits that individual neurons respond selectively to specific, rarely occurring, features. Finally, “predictive coding” posits that neurons preferentially encode stimuli that are useful for making predictions. Except in special cases, it is unclear how these theories relate to each other, or what is expected if different coding objectives are combined. To address this question, we developed a unified framework that encompasses these previous theories and extends to new regimes, such as sparse predictive coding. We explore cases when different coding objectives exert conflicting or synergistic effects on neural response properties. We show that predictive coding can lead neurons to either correlate or decorrelate their inputs, depending on presented stimuli, while (at low-noise) efficient coding always predicts decorrelation. We compare predictive versus sparse coding of natural movies, showing that the two theories predict qualitatively different neural responses to visual motion. Our approach promises a way to explain the observed diversity of sensory neural responses, as due to a multiplicity of functional goals performed by different cell types and/or circuits.

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Posted June 20, 2017.
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Towards a unified theory of efficient, predictive and sparse coding
Matthew Chalk, Olivier Marre, Gašper Tkačik
bioRxiv 152660; doi: https://doi.org/10.1101/152660
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Towards a unified theory of efficient, predictive and sparse coding
Matthew Chalk, Olivier Marre, Gašper Tkačik
bioRxiv 152660; doi: https://doi.org/10.1101/152660

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