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DeltaQ: Value-Guided Hebbian Learning in Spiking Neuronal Networks for Multi-Goal Navigation.

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|June 29, 2026

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We developed a spiking neuronal network (SNN) model using biologically inspired spatial representations and value-guided plasticity. This model successfully learns efficient navigation policies in complex environments with sparse rewards.

Area of Science:

Computational neuroscience
Neural networks
Reinforcement learning

Background:

Animals navigate complex environments using spatial memory and internal representations.
The hippocampal-entorhinal system is crucial for spatial navigation.
Existing models often focus on neural dynamics, not learning in navigation tasks.

Purpose of the Study:

To present a biologically inspired spiking neuronal network (SNN) model for navigation.
To demonstrate how spatial representations and plasticity support learning under sparse reward conditions.
To investigate the role of contextual modulation in supporting multiple navigation objectives.

Main Methods:

Developed a spiking neuronal network (SNN) model integrating grid-cell-derived spatial codes, association cells, and context cells.

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Last Updated: Jun 30, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain

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Published on: October 13, 2023

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Incorporated ΔQ-modulated Hebbian plasticity for learning from sparse and delayed rewards.

Utilized a goal-conditioned Q-table to compute value changes (ΔQ) for synaptic plasticity.

Main Results:

The model successfully generated distinct spatial representations.
It learned efficient navigation policies in maze environments with sparse and delayed rewards.
Contextual modulation enabled a shared network to support multiple, distinct navigation policies.

Conclusions:

Biologically inspired spatial representations, value-guided plasticity, and contextual modulation jointly enable flexible navigation in SNNs.
The model bridges mechanistic neural circuit models and functional reinforcement learning.
Contextual modulation allows for task-dependent variations in navigation behavior at shared spatial locations.