Hideaki Shimazaki WEBSITE
Consciousness and the thermodynamics of the Bayesian brain
What aspects of neural activity underpin our conscious experiences? While we may need various measures to characterize them, here we propose that seeing the brain as ‘an information-theoretic engine’ might carve out an essential aspect of the consciousness experiences.
Neurophysiological studies on early visual cortices revealed that an initial feedforward-sweep of neural response conveys stimulus features only, whereas modulation of the late component (e.g., ~100 ms after the stimulus onset) presumably mediated by feedback connections from higher brain regions carries perceptual effects such as awareness and attention. 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 we provide a unified computational and statistical view of the modulation of sensory representation by internal dynamics in the brain.
A key computation is the nonlinear integration of multiple signals known as the gain modulation ubiquitously found in nervous systems as a mechanism to adapt neurons’ nonlinear response functions to stimulus distributions. We show that the Bayesian view of the brain provides a statistical paradigm for gain modulation. More precisely, we can model the delayed gain-modulation of the stimulus-response via recurrent feedback connections as dynamics of the Bayesian inference that combines the observation and top-down prior with time delay. Interestingly, this process becomes a mathematical analog of a heat engine in thermodynamics [1,2]. This view, ‘the brain as an information-theoretic engine,’ allows us to quantify the amount of delayed gain modulation in terms of entropy changes in neural activity, which quantifies the perceptual capacity of neural dynamics. We present a data-driven approach to quantify the delayed gain modulation from spike data using the state-space model of neural populations [3,4].
With the theoretical and methodological advances, we share the view that the thermodynamic approaches to neural systems [4-6] will be critical to uncovering neural activity underlying conscious experiences.
 Shimazaki (2015) Neurons as an Information-theoretic Engine. arXiv:1512.07855 (published as a book chapter)
 Shimazaki (2020) arXiv:2006.13158
 Shimazaki, Amari, Brown, Gruen (2012) PLoS Comp Biol 8(3): e1002385
 Donner, Obermeyer, Shimazaki (2017) PLoS Comp Biol 13(1): e1005309
 Aguilera, Moosavi, Shimazaki (2021) Nat Commun 12, 1197
 Aguilera, Igarashi, Shimazaki (2022) arXiv:2205.09886
Hideaki Shimazaki is a specially-appointed associate professor at the Center for Human Nature, Artificial Intelligence, and Neuroscience (CHAIN) of Hokkaido University in Japan. Before that, he took joint positions in industry and academia as a senior scientist at Honda Research Institute Japan Co., Ltd. and an associate professor at the Graduate School of Informatics in Kyoto University. His research focuses on uncovering the intelligence of organisms and machines using physics, statistics, and machine learning methods, emphasizing data analysis on spiking neural activities. He received M.A. in neuroscience at Johns Hopkins University in 2003 and Ph.D. in physics at Kyoto University in 2007. After his Ph.D., he pursued postdoctoral training in computational neuroscience at RIKEN Brain Science Institute and MIT Department of Brain and Cognitive Sciences.