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ATTRACTOR-BASED MODELS FOR SEQUENCES AND PATTERN GENERATION IN NEURAL CIRCUITS.

Juliana Londono Alvarez¹

¹The Pennsylvania State University, The Graduate School.

|November 1, 2024

Summary

This summary is machine-generated.

This research introduces novel threshold-linear network models for neural circuits, unifying rhythmic activity and sequence encoding. The models successfully replicate complex functions like locomotion and input counting.

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Area of Science:

Computational Neuroscience
Neural Network Modeling

Background:

Neural circuits generate rhythms (e.g., breathing) and process information via activity sequences.
Central pattern generators (CPGs) traditionally use coupled oscillators for rhythmic activity.
Attractor-based models, like Hopfield networks, encode patterns as network attractors.

Purpose of the Study:

To develop unified models for neural circuit functions including rhythmic activity and sequence encoding.
To explore threshold-linear networks as an alternative to coupled oscillators for CPGs.
To unify attractor-based models for static and dynamic pattern encoding.

Main Methods:

Development of threshold-linear network models.
Application of attractor-based modeling principles.
Analysis of network architectures for sequential and fusion attractors.

Main Results:

A network model capable of counting external inputs.
Models for locomotion, including quadruped gaits and mollusk orientation.
A network generating sequences of dynamic attractors by linking fixed points and locomotion attractors.
Theoretical results on conditions for sequential attractors and properties of composite networks.

Conclusions:

Threshold-linear networks offer a unified approach to modeling diverse neural functions.
The developed models demonstrate capabilities in rhythmic activity, sequence generation, and pattern completion.
New theoretical insights advance the understanding of network architecture and attractor dynamics.