37 citations · 116 across the 12 of their papers we have counts for
12 papers
Modeling Higher-Order Interactions in Sparse and Heavy-Tailed Neural Population Activity
Ulises Rodríguez-Domínguez, Hideaki Shimazaki
Neurons process sensory stimuli efficiently, showing sparse yet highly variable ensemble spiking activity involving structured higher-order interactions. Notably, while neural popu…
Nonequilibrium thermodynamics of the asymmetric Sherrington-Kirkpatrick model
Miguel Aguilera, Masanao Igarashi, Hideaki Shimazaki
Most natural systems operate far from equilibrium, displaying time-asymmetric, irreversible dynamics characterized by a positive entropy production while exchanging energy and matt…
The principles of adaptation in organisms and machines II: Thermodynamics of the Bayesian brain
Hideaki Shimazaki
This article reviews how organisms learn and recognize the world through the dynamics of neural networks from the perspective of Bayesian inference, and introduces a view on how su…
A unifying framework for mean-field theories of asymmetric kinetic Ising systems
Miguel Aguilera, S. Amin Moosavi, Hideaki Shimazaki
Kinetic Ising models are powerful tools for studying the non-equilibrium dynamics of complex systems. As their behavior is not tractable for large networks, many mean-field methods…
Structured Mean-field Variational Inference and Learning in Winner-take-all Spiking Neural Networks
Shashwat Shukla, Hideaki Shimazaki, Udayan Ganguly
The Bayesian view of the brain hypothesizes that the brain constructs a generative model of the world, and uses it to make inferences via Bayes' rule. Although many types of approx…
The principles of adaptation in organisms and machines I: machine learning, information theory, and thermodynamics
Hideaki Shimazaki
How do organisms recognize their environment by acquiring knowledge about the world, and what actions do they take based on this knowledge? This article examines hypotheses about o…
Online Estimation of Multiple Dynamic Graphs in Pattern Sequences
Jimmy Gaudreault, Arunabh Saxena, Hideaki Shimazaki
Sequences of correlated binary patterns can represent many time-series data including text, movies, and biological signals. These patterns may be described by weighted combinations…
State-space analysis of an Ising model reveals contributions of pairwise interactions to sparseness, fluctuation, and stimulus coding of monkey V1 neurons
Jimmy Gaudreault, Hideaki Shimazaki
In this study, we analyzed the activity of monkey V1 neurons responding to grating stimuli of different orientations using inference methods for a time-dependent Ising model. The m…
Approximate Inference for Time-varying Interactions and Macroscopic Dynamics of Neural Populations
Christian Donner, Klaus Obermayer, Hideaki Shimazaki
The models in statistical physics such as an Ising model offer a convenient way to characterize stationary activity of neural populations. Such stationary activity of neurons may b…
Neurons as an Information-theoretic Engine
Hideaki Shimazaki
We show that dynamical gain modulation of neurons' stimulus response is described as an information-theoretic cycle that generates entropy associated with the stimulus-related acti…
Single-trial estimation of stimulus and spike-history effects on time-varying ensemble spiking activity of multiple neurons: a simulation study
Hideaki Shimazaki
Neurons in cortical circuits exhibit coordinated spiking activity, and can produce correlated synchronous spikes during behavior and cognition. We recently developed a method for e…
Bose-Einstein Condensation in Competitive Processes
Hideaki Shimazaki, Ernst Niebur
We introduce an irreversible discrete multiplicative process that undergoes Bose-Einstein condensation as a generic model of competition. New players with different abilities succe…