Dopamine-dependent plasticity of corticostriatal synapses

Authors:
John N. J. Reynolds;Jeffery R. Wickens
Affiliations:
The Neuroscience Research Centre and Department of Anatomy and Structural Biology, School of Medical Sciences, University of Otago, P.O. Box 913, Dunedin, New Zealand;The Neuroscience Research Centre and Department of Anatomy and Structural Biology, School of Medical Sciences, University of Otago, P.O. Box 913, Dunedin, New Zealand
Venue:
Neural Networks - Computational models of neuromodulation
Year:
2002

Citing 1
Cited 18

A Theory of the Striatum

A Theory of the Striatum

Metalearning and neuromodulation

Neural Networks - Computational models of neuromodulation
Temporal Sequence Learning, Prediction, and Control: A Review of Different Models and Their Relation to Biological Mechanisms

Neural Computation
Neural mechanism for stochastic behaviour during a competitive game

Neural Networks - 2006 Special issue: Neurobiology of decision making
Dissociation between Striatal Regions while Learning to Categorize via Feedback and via Observation

Journal of Cognitive Neuroscience
Learning with “Relevance”: Using a Third Factor to Stabilize Hebbian Learning

Neural Computation
Differences in prefrontal and motor structures learning dynamics depend on task complexity: A neural network model

Neurocomputing
Letters: Synaptic plasticity model of a spiking neural network for reinforcement learning

Neurocomputing
A spiking neural network model of an actor-critic learning agent

Neural Computation
Calcium Responses Model in Striatum Dependent on Timed Input Sources

ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Learning and Reversal Learning in the Subcortical Limbic System: A Computational Model

Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
A model study on the circuit mechanism underlying decision-making in Drosophila

Neural Networks
A computational model of how cholinergic interneurons protect striatal-dependent learning

Journal of Cognitive Neuroscience
Working memory and response selection: A computational account of interactions among cortico-basalganglio-thalamic loops

Neural Networks
Stabilising hebbian learning with a third factor in a food retrieval task

SAB'06 Proceedings of the 9th international conference on From Animals to Animats: simulation of Adaptive Behavior
Using TD learning to simulate working memory performance in a model of the prefrontal cortex and basal ganglia

Cognitive Systems Research
Phasic dopamine as a prediction error of intrinsic and extrinsic reinforcements driving both action acquisition and reward maximization: A simulated robotic study

Neural Networks
2013 Special Issue: Intrinsically motivated action-outcome learning and goal-based action recall: A system-level bio-constrained computational model

Neural Networks
Reinforcement learning of two-joint virtual arm reaching in a computer model of sensorimotor cortex

Neural Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

Knowledge of the effect of dopamine on corticostriatal synaptic plasticity has advanced rapidly over the last 5 years. We consider this new knowledge in relation to three factors proposed earlier to describe the rules for synaptic plasticity in the corticostriatal pathway. These factors are a phasic increase in dopamine release, presynaptic activity and postsynaptic depolarisation. A function is proposed which relates the amount of dopamine release in the striatum to the modulation of corticostriatal synaptic efficacy. It is argued that this function, and the experimental data from which it arises, are compatible with existing models which associate the reward-related firing of dopamine neurons with changes in corticostriatal synaptic efficacy.