Movement: how the brain communicates with the world
AB Schwartz - Cell, 2016 - cell.com
Voluntary movement is a result of signals transmitted through a communication channel that
links the internal world in our minds to the physical world around us. Intention can be …
links the internal world in our minds to the physical world around us. Intention can be …
Trustworthy reinforcement learning against intrinsic vulnerabilities: Robustness, safety, and generalizability
A trustworthy reinforcement learning algorithm should be competent in solving challenging
real-world problems, including {robustly} handling uncertainties, satisfying {safety} …
real-world problems, including {robustly} handling uncertainties, satisfying {safety} …
Inverse reinforcement learning as the algorithmic basis for theory of mind: current methods and open problems
J Ruiz-Serra, MS Harré - Algorithms, 2023 - mdpi.com
Theory of mind (ToM) is the psychological construct by which we model another's internal
mental states. Through ToM, we adjust our own behaviour to best suit a social context, and …
mental states. Through ToM, we adjust our own behaviour to best suit a social context, and …
Where do you think you're going?: Inferring beliefs about dynamics from behavior
Inferring intent from observed behavior has been studied extensively within the frameworks
of Bayesian inverse planning and inverse reinforcement learning. These methods infer a …
of Bayesian inverse planning and inverse reinforcement learning. These methods infer a …
Inverse decision modeling: Learning interpretable representations of behavior
Decision analysis deals with modeling and enhancing decision processes. A principal
challenge in improving behavior is in obtaining a transparent* description* of existing …
challenge in improving behavior is in obtaining a transparent* description* of existing …
Inverse reinforcement learning with simultaneous estimation of rewards and dynamics
M Herman, T Gindele, J Wagner… - Artificial intelligence …, 2016 - proceedings.mlr.press
Abstract Inverse Reinforcement Learning (IRL) describes the problem of learning an
unknown reward function of a Markov Decision Process (MDP) from observed behavior of …
unknown reward function of a Markov Decision Process (MDP) from observed behavior of …
Reinforcement learning with non-exponential discounting
M Schultheis, CA Rothkopf… - Advances in neural …, 2022 - proceedings.neurips.cc
Commonly in reinforcement learning (RL), rewards are discounted over time using an
exponential function to model time preference, thereby bounding the expected long-term …
exponential function to model time preference, thereby bounding the expected long-term …
Inverse optimal control adapted to the noise characteristics of the human sensorimotor system
M Schultheis, D Straub… - Advances in Neural …, 2021 - proceedings.neurips.cc
Computational level explanations based on optimal feedback control with signal-dependent
noise have been able to account for a vast array of phenomena in human sensorimotor …
noise have been able to account for a vast array of phenomena in human sensorimotor …
Internal models for interpreting neural population activity during sensorimotor control
To successfully guide limb movements, the brain takes in sensory information about the
limb, internally tracks the state of the limb, and produces appropriate motor commands. It is …
limb, internally tracks the state of the limb, and produces appropriate motor commands. It is …
Principled BCI decoder design and parameter selection using a feedback control model
Decoders optimized offline to reconstruct intended movements from neural recordings
sometimes fail to achieve optimal performance online when they are used in closed-loop as …
sometimes fail to achieve optimal performance online when they are used in closed-loop as …