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Related Experiment Video

Updated: Aug 6, 2025

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

Learning predictive cognitive maps with spiking neurons during behavior and replays.

Jacopo Bono¹, Sara Zannone¹, Victor Pedrosa¹

¹Department of Bioengineering, Imperial College London, London, United Kingdom.

|March 17, 2023

Summary

This study proposes a biologically plausible plasticity rule for the hippocampus to learn environmental predictions, linking it to the TD-lambda reinforcement learning algorithm and explaining neural replays.

Keywords:

computational biology hippocampus modelling mouse predictive successor representation systems biology

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Area of Science:

Neuroscience
Computational Neuroscience
Artificial Intelligence

Background:

The hippocampus is theorized to use successor representations for environmental encoding.
Mechanisms for learning these representations in the hippocampal circuit remain unclear.

Purpose of the Study:

To propose a biologically plausible plasticity rule for learning successor representations in the hippocampus.
To connect this rule to reinforcement learning and explain neural phenomena like replays.

Main Methods:

Development of a spiking neural network model with a novel plasticity rule.
Mathematical and numerical analysis linking the rule to the TD-lambda algorithm.
Investigation of biological parameters and their relation to reinforcement learning concepts.

Main Results:

The proposed plasticity rule implements the TD-lambda algorithm.
The framework explains behavioral activity, neural replays, and the transition from rate to temporal coding.
Biological parameters correlate with the TD algorithm's discount factor, influencing learned representations.
The discount factor was found to decrease hyperbolically with time, aligning with psychological data.

Conclusions:

The model provides a unified framework for understanding hippocampal function in navigation and learning.
It elucidates the role of neural replays in learning and discovering novel strategies.
The findings bridge computational reinforcement learning with biological neural mechanisms.