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Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

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Published on: May 8, 2021

An imperfect dopaminergic error signal can drive temporal-difference learning.

Wiebke Potjans¹, Markus Diesmann, Abigail Morrison

¹Institute of Neuroscience and Medicine (INM-6), Computational and Systems Neuroscience, Research Center Jülich, Jülich, Germany. w.potjans@fz-juelich.de

Plos Computational Biology

|May 19, 2011

Summary

This study presents a spiking temporal-difference (TD) learning model linking brain plasticity to learning. The model shows a realistic dopamine signal enables learning with positive rewards but not negative ones.

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Area of Science:

Computational Neuroscience
Machine Learning
Neurobiology

Background:

Linking synaptic plasticity to system-level learning is a key challenge in computational neuroscience.
Temporal-difference (TD) learning is a promising framework, supported by evidence linking dopamine signaling to TD errors and cortico-striatal plasticity.

Purpose of the Study:

To develop a spiking temporal-difference learning model that dynamically generates a realistic dopaminergic signal.
To investigate how this biologically constrained signal modulates synaptic plasticity and influences learning in complex tasks.

Main Methods:

Developed an actor-critic based spiking temporal-difference learning model.
Dynamically generated a realistic dopaminergic signal to modulate synaptic plasticity as a third factor.
Analyzed the model's predictions against experimental results and mapped its parameters to classical TD algorithms.

Main Results:

The model's plasticity dynamics align with experimental findings on dopamine, pre-, and post-synaptic activity.
A mapping revealed a modified TD algorithm with self-adapting parameters and offset due to biological constraints.
The model learned tasks with sparse positive rewards effectively but struggled with negative rewards.

Conclusions:

The asymmetry of realistic dopaminergic signals enables TD learning for positive rewards but hinders it for negative rewards.
This biologically constrained model offers insights into the brain's learning mechanisms and limitations.