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Found 5 papers, 1 papers with code
Information theoretic analysis of computational models as a tool to understand the neural basis of behaviors
In this work, I provide an introduction, a review and discussion to make a case for how information theoretic analysis of computational models is a potent research methodology to help us better understand the neural basis of behavior.
infotheory: A C++/Python package for multivariate information theoretic analysis
This paper introduces \texttt{infotheory}: a package that implements multivariate information theoretic measures for discrete and continuous data.
Information Theory Information Theory Neurons and Cognition
On Training Flexible Robots using Deep Reinforcement Learning
The use of robotics in controlled environments has flourished over the last several decades and training robots to perform tasks using control strategies developed from dynamical models of their hardware have proven very effective.
Hierarchical Policy Learning is Sensitive to Goal Space Design
Hierarchy in reinforcement learning agents allows for control at multiple time scales yielding improved sample efficiency, the ability to deal with long time horizons and transferability of sub-policies to tasks outside the training distribution.
Multifunctionality in embodied agents: Three levels of neural reuse
Further, we lay out the different levels at which neural reuse can occur through a dynamical filtering of the brain-body-environment system's operation: structural network, autonomous dynamics, and transient dynamics.
