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A dynamic neural field model of continuous input integration.

Weronika Wojtak^1,2, Stephen Coombes³, Daniele Avitabile^4,5

¹Research Centre of Mathematics, University of Minho, Guimarães, Portugal. w.wojtak@dei.uminho.pt.

Biological Cybernetics

|August 21, 2021

Summary

This summary is machine-generated.

This study introduces a new neural network model for working memory. The model enhances memory representation by allowing bump width and amplitude to reflect input characteristics and integrate evidence over time.

Keywords:

conservation law dynamic neural field input integration localized states stability

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Area of Science:

Computational neuroscience
Cognitive modeling

Background:

Persistent neural activity is crucial for cognitive functions like working memory.
Continuous attractor networks often exhibit stereotyped
bump
activity patterns.
Existing models may not fully capture the integration of evidence over extended periods.

Purpose of the Study:

To investigate a novel bump attractor neural network model.
To explore how bump width and amplitude encode input characteristics and temporal integration.
To enhance the modeling of working memory and decision-making tasks.

Main Methods:

Formalized a new model using two coupled dynamic field equations of Amari-type.
Incorporated Mexican-hat connectivity and local feedback mechanisms.
Analyzed stability, bifurcation, and pattern formation of single and multi-bump solutions.

Main Results:

The new model's bump width and amplitude reflect input characteristics and evidence integration.
Balanced local feedback mechanisms improve multi-item memory encoding and maintenance.
Stable subthreshold bumps prevent complete information loss from suppression effects.

Conclusions:

The proposed model offers a more nuanced representation of working memory compared to classical models.
Enhanced fidelity in memory representation is achieved through amplitude and reduced vulnerability to noise.
This model advances understanding of neural mechanisms underlying cognitive functions.