AIMC Topic: Reinforcement, Psychology

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Indirect and direct training of spiking neural networks for end-to-end control of a lane-keeping vehicle.

Neural networks : the official journal of the International Neural Network Society
Building spiking neural networks (SNNs) based on biological synaptic plasticities holds a promising potential for accomplishing fast and energy-efficient computing, which is beneficial to mobile robotic applications. However, the implementations of S...

Deep multiphysics: Coupling discrete multiphysics with machine learning to attain self-learning in-silico models replicating human physiology.

Artificial intelligence in medicine
OBJECTIVES: The objective of this study is to devise a modelling strategy for attaining in-silico models replicating human physiology and, in particular, the activity of the autonomic nervous system.

Hierarchical human-like strategy for aspect-level sentiment classification with sentiment linguistic knowledge and reinforcement learning.

Neural networks : the official journal of the International Neural Network Society
Aspect-level sentiment analysis is a crucial problem in fine-grained sentiment analysis, which aims to automatically predict the sentiment polarity of the specific aspect in its context. Although remarkable progress has been made by deep learning bas...

A Reservoir Computing Model of Reward-Modulated Motor Learning and Automaticity.

Neural computation
Reservoir computing is a biologically inspired class of learning algorithms in which the intrinsic dynamics of a recurrent neural network are mined to produce target time series. Most existing reservoir computing algorithms rely on fully supervised l...

Multiple tracking and machine learning reveal dopamine modulation for area-restricted foraging behaviors via velocity change in Caenorhabditis elegans.

Neuroscience letters
Food exploration is an essential survival behavior in organisms. To find food efficiently, many organisms use a foraging strategy called area-restricted search (ARS) wherein individuals first turn more frequently, restricting their search to one area...

Incorporating causal factors into reinforcement learning for dynamic treatment regimes in HIV.

BMC medical informatics and decision making
BACKGROUND: Reinforcement learning (RL) provides a promising technique to solve complex sequential decision making problems in health care domains. However, existing studies simply apply naive RL algorithms in discovering optimal treatment strategies...

Inverse reinforcement learning for intelligent mechanical ventilation and sedative dosing in intensive care units.

BMC medical informatics and decision making
BACKGROUND: Reinforcement learning (RL) provides a promising technique to solve complex sequential decision making problems in health care domains. To ensure such applications, an explicit reward function encoding domain knowledge should be specified...

Learning mechanisms in cue reweighting.

Cognition
Feedback has been shown to be effective in shifting attention across perceptual cues to a phonological contrast in speech perception (Francis, Baldwin & Nusbaum, 2000). However, the learning mechanisms behind this process remain obscure. We compare t...

Feature Aggregation With Reinforcement Learning for Video-Based Person Re-Identification.

IEEE transactions on neural networks and learning systems
Video-based person re-identification (re-id) matches two tracks of persons from different cameras. Features are extracted from the images of a sequence and then aggregated as a track feature. Compared to existing works that aggregate frame features b...

Estimating Scale-Invariant Future in Continuous Time.

Neural computation
Natural learners must compute an estimate of future outcomes that follow from a stimulus in continuous time. Widely used reinforcement learning algorithms discretize continuous time and estimate either transition functions from one step to the next (...