1/28 The brain as an information-theoretic engine by Hideaki Shimazaki

【CIPA】Combining Information-theoretic Perspectives on Agency
https://cipa-workshop.weebly.com/

【Speaker】Hideaki Shimazaki​, Kyoto University
【Title】
The brain as an information-theoretic engine: A new paradigm for quantifying perceptual capacity of neural dynamic​s
【Abstract】
Neurophysiological studies on early visual cortices revealed that an initial feedforward-sweep of neural response depends only on stimulus features whereas perceptual effect such as awareness and attention is represented as modulation of the late component (e.g., ~100 ms after the stimulus onset). The delayed modulation is presumably mediated by feedback connections from higher brain regions. Psychophysical experiments on humans using visual masking or transcranial magnetic stimulation showed that selective disruption of the late component vanishes conscious experiences of the stimulus. Here I provide a unified computational and statistical view on the modulation of sensory representation by internal dynamics in the brain, which provides a way to quantify the perceptual capacity of neural dynamics.

A key computation is the gain modulation that represents integration of multiples signals by nonlinear devices (neurons). The gain modulation is ubiquitously observed in nervous systems as a mechanism to adapt nonlinear response functions to stimulus distributions. It will be shown that the Bayesian view of the brain provides a statistical paradigm for the gain modulation as a way to integrate an observed stimulus with prior knowledge. Furthermore, the delayed gain-modulation of the stimulus response via recurrent connections is modeled as a dynamic process of the Bayesian inference that combines the observation and prior with time-delay. Interestingly, it will be shown that this process is mathematically equivalent to a heat engine in thermodynamics. This view provides us to quantify the amount of the delayed gain modulation and its efficiency in terms of the entropy of neural activity. I will show how we can quantify the perceptual capacity from spike data using the state-space Ising model of neural populations, which we have been developing in the past 10 years.

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