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Reinforcement, Psychology

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Reinforcement Learning in Spiking Neural Networks with Stochastic and Deterministic Synapses.

Neural computation
Though succeeding in solving various learning tasks, most existing reinforcement learning (RL) models have failed to take into account the complexity of synaptic plasticity in the neural system. Models implementing reinforcement learning with spiking...

Gated spiking neural network using Iterative Free-Energy Optimization and rank-order coding for structure learning in memory sequences (INFERNO GATE).

Neural networks : the official journal of the International Neural Network Society
We present a framework based on iterative free-energy optimization with spiking neural networks for modeling the fronto-striatal system (PFC-BG) for the generation and recall of audio memory sequences. In line with neuroimaging studies carried out in...

Assisted therapeutic system based on reinforcement learning for children with autism.

Computer assisted surgery (Abingdon, England)
Assisted therapy is increasingly used in autism spectrum disorders (ASD) for improving social interaction and communication skills in recent years. A lot of studies have proven that the form of interactive games for therapy has a good effect on child...

Clustering Neural Patterns in Kernel Reinforcement Learning Assists Fast Brain Control in Brain-Machine Interfaces.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Neuroprosthesis enables the brain control on the external devices purely using neural activity for paralyzed people. Supervised learning decoders recalibrate or re-fit the discrepancy between the desired target and decoder's output, where the correct...

Convergent Temperature Representations in Artificial and Biological Neural Networks.

Neuron
Discoveries in biological neural networks (BNNs) shaped artificial neural networks (ANNs) and computational parallels between ANNs and BNNs have recently been discovered. However, it is unclear to what extent discoveries in ANNs can give insight into...

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...