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Found 7 papers, 1 papers with code
A projected nonlinear state-space model for forecasting time series signals
Learning and forecasting stochastic time series is essential in various scientific fields.
Alternating Shrinking Higher-order Interactions for Sparse Neural Population Activity
Neurons in living things work cooperatively and efficiently to process incoming sensory information, often exhibiting sparse and widespread population activity involving structured higher-order interactions.
The principles of adaptation in organisms and machines II: Thermodynamics of the Bayesian brain
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 such dynamics is described by the laws for the entropy of neural activity, a paradigm that we call thermodynamics of the Bayesian brain.
The principles of adaptation in organisms and machines I: machine learning, information theory, and thermodynamics
We start with constructing a hierarchical model of the world as an internal model in the brain, and review standard machine learning methods to infer causes by approximately learning the model under the maximum likelihood principle.
Online Estimation of Multiple Dynamic Graphs in Pattern Sequences
Sequences of correlated binary patterns can represent many time-series data including text, movies, and biological signals.
State-space analysis of an Ising model reveals contributions of pairwise interactions to sparseness, fluctuation, and stimulus coding of monkey V1 neurons
Here we report that, in all examined stimulus conditions, pairwise interactions contribute to increasing sparseness and fluctuation.
Single-trial estimation of stimulus and spike-history effects on time-varying ensemble spiking activity of multiple neurons: a simulation study
In this study, we develop a parametric method for simultaneously estimating the stimulus and spike-history effects on the ensemble activity from single-trial data even if the neurons exhibit dynamics that is largely unrelated to these effects.
