Adaptive filtering enhances information transmission in visual cortex

By Tatyana O. Sharpee, H. Sugihara, A. Kurgansky, S. Rebrik, Michael P. Stryker, Kenneth D. Miller. Nature, 2006

343 citations recorded by the source corpus.

Sensory neuroscience seeks to understand how the brain encodes natural environments. However, neural coding has largely been studied using simplified stimuli. In order to assess whether the brain's coding strategy depends on the stimulus ensemble, we apply a new information-theoretic method that allows unbiased calculation of neural filters (receptive fields) from responses to natural scenes or other complex signals with strong multipoint correlations. In the cat primary visual cortex we compare responses to natural inputs with those to noise inputs matched for luminance and contrast. We find that neural filters adaptively change with the input ensemble so as to increase the information carried by the neural response about the filtered stimulus. Adaptation affects the spatial frequency composition of the filter, enhancing sensitivity to under-represented frequencies in agreement with optimal encoding arguments. Adaptation occurs over 40 s to many minutes, longer than most previously reported forms of adaptation.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/54367d8f6a08dac9023a6ccf67890ef6319ec6de

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Citations in the corpus

Functional mechanisms shaping lateral geniculate responses to artificial and natural stimuli. (2008, result): “The RF of V1 neurons was found to vary with the type of stimulus used (David et al., 2004; Sharpee et al., 2006), and similar results were seen in the neurons' responsiveness (Felsen et al., 2005; Smyth et al., 2003).”
Space and time in visual context (2007, methodology): “These techniques could be used to investigate spatial and/or temporal contex”
Equitability, mutual information, and the maximal information coefficient (2013, background): “Although substantial difficulties with estimating mutual information on continuous data remain, such estimates have proved useful in a variety of realworld problems in neuroscience (14, 15, 25), molecular biology (16, 17, 26–28), medical imaging (29), and signal processing (13).”
Visual adaptation: neural, psychological and computational aspects. (2007, methodology): “Neural ﬁlters were computed for 40 simple cells, using the method of maximally informative dimensions in order to compensate for eﬀects of non-Gaussian correlations in the input statistics (Sharpee et al., 2006).”
The Operating Point of the Cortex: Neurons as Large Deviation Detectors (2007, result): “The shape of our reconstructed nonlinearities, including the lack of significant saturation at high values of the generator signal, is in general agreement with similar estimates in the retina, LGN, and V1 (Chander and Chichilnisky, 2001; Kim and Rieke, 2001; Baccus and Meister, 2004; Carandini, 200”
Bayesian Inference Explains Perception of Unity and Ventriloquism Aftereffect: Identification of Common Sources of Audiovisual Stimuli (2007, background): “The other possibility is to change the prior distribution P(xVp, xAp, ξ ) in order to adapt to the changes in the stimulus properties of the external world (Sharpee et al., 2006; Smirnakis, Berry, Warland, Bialek, & Meister, 1997).”
Adaptation and visual coding (2011, background): “…contrast sensitivity function (CSF; Bex, Solomon, & Dakin, 2009; Webster & Miyahara, 1997) or in the tuning functions of individual cortical cells (Sharpee et al., 2006), though notably, the same adaptation does not bias the suprathreshold perception of image focus in space or time (Bilson,…”