2018 Conference on Cognitive Computational Neuroscience

5-8 September 2018    Philadelphia, Pennsylvania

Technical Program

Paper Detail

Paper: PS-2B.8
Session: Poster Session 2B
Location: Symphony/Overture
Session Time: Friday, September 7, 19:30 - 21:30
Presentation Time:Friday, September 7, 19:30 - 21:30
Presentation: Poster
Publication: 2018 Conference on Cognitive Computational Neuroscience, 5-8 September 2018, Philadelphia, Pennsylvania
Paper Title: The contribution of response correlations to the neural code of V1
Manuscript:  Click here to view manuscript
DOI: https://doi.org/10.32470/CCN.2018.1061-0
Authors: Mihály Bányai, Marcell Stippinger, Dávid Szalai, Gergő Orbán, MTA Wigner Research Centre for Physics, Hungary; Andreea Lazar, Liane Klein, Johanna Klon-Lipok, Wolf Singer, Ernst Strüngmann Institute for Neuroscience in Cooperation with Max Planck Society, Germany
Abstract: Contribution of joint statistics of neuron populations to stimulus encoding can distinguish theories of neural computation. Specifically, probabilistic inference in a hierarchical model of perception predicts the emergence of content-specific modulations in the fine structure of spike count correlations. By recording spiking activity from the V1 of behaving macaques viewing naturalistic and synthetic stimuli we demonstrate that compositional objects elicit correlational structures that are more specific to the identity of the stimulus than stimuli without structured content. Further, we demonstrate that decoding schemes exploiting stimulus-specific pairwise response statistics outperform those relying on marginal statistics, thus showing that joint statistics carry information about the stimulus independently from marginal statistics. To rule out possible simpler explanations of the observed patterns in the correlation structure, we introduce an array of controls. We develop Contrastive Rate Matching to control for firing rate-related changes in correlation magnitudes. Further, we analyze phenomenological models of noise correlations, the raster marginal model and a family of models featuring collective additive and/or multiplicative noise sources. Our results show that stimulus-dependence of noise correlations at the level of V1 reflect high-order structure in the stimulus, is independent of changes in firing rates and cannot be explained by phenomenological accounts.