Jeffery R. Wickens

Bio: Jeffery R. Wickens is an academic researcher from Okinawa Institute of Science and Technology. The author has contributed to research in topics: Striatum & Medium spiny neuron. The author has an hindex of 34, co-authored 93 publications receiving 6298 citations. Previous affiliations of Jeffery R. Wickens include University of Otago.

A cellular mechanism of reward-related learning

Abstract: Positive reinforcement helps to control the acquisition of learned behaviours. Here we report a cellular mechanism in the brain that may underlie the behavioural effects of positive reinforcement. We used intracranial self-stimulation (ICSS) as a model of reinforcement learning, in which each rat learns to press a lever that applies reinforcing electrical stimulation to its own substantia nigra. The outputs from neurons of the substantia nigra terminate on neurons in the striatum in close proximity to inputs from the cerebral cortex on the same striatal neurons. We measured the effect of substantia nigra stimulation on these inputs from the cortex to striatal neurons and also on how quickly the rats learned to press the lever. We found that stimulation of the substantia nigra (with the optimal parameters for lever-pressing behaviour) induced potentiation of synapses between the cortex and the striatum, which required activation of dopamine receptors. The degree of potentiation within ten minutes of the ICSS trains was correlated with the time taken by the rats to learn ICSS behaviour. We propose that stimulation of the substantia nigra when the lever is pressed induces a similar potentiation of cortical inputs to the striatum, positively reinforcing the learning of the behaviour by the rats.

Dopamine Cells Respond to Predicted Events during Classical Conditioning: Evidence for Eligibility Traces in the Reward-Learning Network

Abstract: Behavioral conditioning of cue-reward pairing results in a shift of midbrain dopamine (DA) cell activity from responding to the reward to responding to the predictive cue. However, the precise time course and mechanism underlying this shift remain unclear. Here, we report a combined single-unit recording and temporal difference (TD) modeling approach to this question. The data from recordings in conscious rats showed that DA cells retain responses to predicted reward after responses to conditioned cues have developed, at least early in training. This contrasts with previous TD models that predict a gradual stepwise shift in latency with responses to rewards lost before responses develop to the conditioned cue. By exploring the TD parameter space, we demonstrate that the persistent reward responses of DA cells during conditioning are only accurately replicated by a TD model with long-lasting eligibility traces (nonzero values for the parameter λ) and low learning rate (α). These physiological constraints for TD parameters suggest that eligibility traces and low per-trial rates of plastic modification may be essential features of neural circuits for reward learning in the brain. Such properties enable rapid but stable initiation of learning when the number of stimulus-reward pairings is limited, conferring significant adaptive advantages in real-world environments.

Dopamine D-1/D-5 Receptor Activation Is Required for Long-Term Potentiation in the Rat Neostriatum In Vitro

Abstract: Dopamine and glutamate are key neurotransmitters involved in learning and memory mechanisms of the brain. These two neurotransmitter systems converge on nerve cells in the neostriatum. Dopamine mod...

Reinforcement Learning: An Introduction

Abstract: Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability. The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.

A Neural Substrate of Prediction and Reward

Abstract: The capacity to predict future events permits a creature to detect, model, and manipulate the causal structure of its interactions with its environment. Behavioral experiments suggest that learning is driven by changes in the expectations about future salient events such as rewards and punishments. Physiological work has recently complemented these studies by identifying dopaminergic neurons in the primate whose fluctuating output apparently signals changes or errors in the predictions of future salient and rewarding events. Taken together, these findings can be understood through quantitative theories of adaptive optimizing control.

Predictive Reward Signal of Dopamine Neurons

Abstract: Schultz, Wolfram. Predictive reward signal of dopamine neurons. J. Neurophysiol. 80: 1–27, 1998. The effects of lesions, receptor blocking, electrical self-stimulation, and drugs of abuse suggest t...

Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems

Abstract: Theoretical neuroscience provides a quantitative basis for describing what nervous systems do, determining how they function, and uncovering the general principles by which they operate This text introduces the basic mathematical and computational methods of theoretical neuroscience and presents applications in a variety of areas including vision, sensory-motor integration, development, learning, and memory The book is divided into three parts Part I discusses the relationship between sensory stimuli and neural responses, focusing on the representation of information by the spiking activity of neurons Part II discusses the modeling of neurons and neural circuits on the basis of cellular and synaptic biophysics Part III analyzes the role of plasticity in development and learning An appendix covers the mathematical methods used, and exercises are available on the book's Web site

The neural basis of human error processing: Reinforcement learning, dopamine, and the error-related negativity.

Abstract: The authors present a unified account of 2 neural systems concerned with the development and expression of adaptive behaviors: a mesencephalic dopamine system for reinforcement learning and a “generic” error-processing system associated with the anterior cingulate cortex The existence of the error-processing system has been inferred from the error-related negativity (ERN), a component of the event-related brain potential elicited when human participants commit errors in reaction-time tasks The authors propose that the ERN is generated when a negative reinforcement learning signal is conveyed to the anterior cingulate cortex via the mesencephalic dopamine system and that this signal is used by the anterior cingulate cortex to modify performance on the task at hand They provide support for this proposal using both computational modeling and psychophysiological experimentation