Neural Variability and Sampling-Based Probabilistic Representations in the Visual Cortex

Abstract

SummaryNeural responses in the visual cortex are variable, and there is now an abundance of data characterizing how the magnitude and structure of this variability depends on the stimulus. Current theories of cortical computation fail to account for these data; they either ignore variability altogether or only model its unstructured Poisson-like aspects. We develop a theory in which the cortex performs probabilistic inference such that population activity patterns represent statistical samples from the inferred probability distribution. Our main prediction is that perceptual uncertainty is directly encoded by the variability, rather than the average, of cortical responses. Through direct comparisons to previously published data as well as original data analyses, we show that a sampling-based probabilistic representation accounts for the structure of noise, signal, and spontaneous response variability and correlations in the primary visual cortex. These results suggest a novel role for neural variability in cortical dynamics and computations.

Critical dynamics on a large human Open Connectome network

“…Neural variability makes the brain more efficient [44], therefore one must consider its effect in modeling. To study this, large-scale dynamical simulations have been performed on a human connectome model.…”

Inference in the Brain: Statistics Flowing in Redundant Population Codes

“…In temporal representations of uncertainty, like the ‘sampling hypothesis,’ instantaneous neural activity represents a single interpretation, without uncertainty, and probabilities are reflected by the set of interpretations over time (Hoyer and Hyvärinen 2003; Berkes et al 2011; Moreno-Bote et al 2011; Buesing et al 2011; Haefner et al 2016; Orbán et al 2016). …”

Higher performers upregulate brain signal variability in response to more feature-rich visual input

“…We have hypothesized for some time that the ability to modulate variability across distinct cognitive states should typify organisms able to flexibly and optimally adapt to a host of environmental challenges (Garrett, Samanez-Larkin, et al 2013). Specifically, we and others have postulated that modulation of brain signal variability could reflect differences in stimulus input (Knill and Pouget 2004;Ma et al 2006;Beck et al 2008;Garrett, Samanez-Larkin, et al 2013;Orban et al 2016). For example, early visual regions may be actively suppressed in response to more common stimuli, yet may exhibit a more dynamic response to more differentiated stimuli (Hamm and Yuste 2016;Homann et al 2017;Vinken et al 2017).…”