Information theory in neuroscience
Information Theory in Neuroscience Page 1 entropy Editorial Information Theory in Neuroscience
Eugenio Piasini 1,* and Stefano Panzeri 2,* 1 Computational Neuroscience Initiative and …
Eugenio Piasini 1,* and Stefano Panzeri 2,* 1 Computational Neuroscience Initiative and …
[HTML][HTML] Structure and dynamics analysis of brain functional hypernetworks based on the null models
C Cheng, Y Li, C Wang, Y Yang, H Guo - Brain Research Bulletin, 2025 - Elsevier
Brain functional hypernetworks that can characterize the complex and multivariate
interactions among multiple brain regions have been widely used in the diagnosis and …
interactions among multiple brain regions have been widely used in the diagnosis and …
Small-correlation expansion to quantify information in noisy sensory systems
Neural networks encode information through their collective spiking activity in response to
external stimuli. This population response is noisy and strongly correlated, with a complex …
external stimuli. This population response is noisy and strongly correlated, with a complex …
Maximum entropy principle analysis in network systems with short-time recordings
In many realistic systems, maximum entropy principle (MEP) analysis provides an effective
characterization of the probability distribution of network states. However, to implement the …
characterization of the probability distribution of network states. However, to implement the …
Higher-order cumulants drive neuronal activity patterns, inducing up-down states in neural populations
R Baravalle, F Montani - Entropy, 2020 - mdpi.com
A major challenge in neuroscience is to understand the role of the higher-order correlations
structure of neuronal populations. The dichotomized Gaussian model (DG) generates spike …
structure of neuronal populations. The dichotomized Gaussian model (DG) generates spike …
AN INFORMATION THEORETIC REPRESENTATION OF HUMAN BRAIN FOR DECODING MENTAL STATES OF COMPLEX PROBLEM SOLVING
G Gunal Degirmendereli - 2022 - open.metu.edu.tr
In this thesis, we propose an information theoretic method for the representation of human
brain activity to decode mental states of a high-order cognitive process, complex problem …
brain activity to decode mental states of a high-order cognitive process, complex problem …
Emotion-Driven Decision Making: When Rationality Meets Occasional Sub-Optimality
S Das, P Mondal - Authorea Preprints - techrxiv.org
This paper introduces a mathematical and computational model elucidating human decision-
making processes within complex action spaces. It explores how individuals navigate such …
making processes within complex action spaces. It explores how individuals navigate such …
A cautionary tale of entropic criteria in assessing the validity of the maximum entropy principle
The maximum entropy principle (MEP) has been applied to study various problems in
equilibrium and nonequilibrium systems in physics and other disciplines. Through analyses …
equilibrium and nonequilibrium systems in physics and other disciplines. Through analyses …
An Information Theoretic Representation of Human Brain for Decoding Mental States of Complex Problem Solving
GG Degirmendereli - 2022 - search.proquest.com
AN INFORMATION THEORETIC REPRESENTATION OF HUMAN BRAIN FOR DECODING
MENTAL STATES OF COMPLEX PROBLEM SOLVING A THESIS SUBMITTED Page 1 AN …
MENTAL STATES OF COMPLEX PROBLEM SOLVING A THESIS SUBMITTED Page 1 AN …