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Shaping dynamical neural computations using spatiotemporal constraints.

Jason Z Kim¹, Bart Larsen², Linden Parkes³

¹Department of Physics, Cornell University, Ithaca, NY 14853, USA.

|December 11, 2023

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Biological and artificial computation rely on dynamics. Leveraging neurobiological constraints can enhance artificial neural networks, inspired by recurrent neural networks (RNNs).

Area of Science:

Computational neuroscience
Artificial intelligence
Network dynamics

Background:

Biological and artificial computation utilize state evolution over time for information processing.
Neurobiology shapes brain network architecture, influencing computational patterns.
Recurrent neural networks (RNNs) serve as a model for neural computation.

Approach:

Discuss how neural systems employ dynamics for computation.
Explore the common computational substrate (connectivity) in brains and RNNs.
Examine biophysical constraints like resource efficiency and spatial embedding.

Key Points:

Brain network architecture, determined by neurobiology, constrains neural activity patterns for computation.
Connectivity is a fundamental computational substrate in both biological and artificial neural networks.

Keywords:

computation dynamics neurodevelopment recurrent neural networks spatial constraints

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Last Updated: Jul 8, 2025

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Biophysical constraints offer unique computational advantages, including efficiency and developmental aspects.

Conclusions:

Insights from biological neural systems can inform the design of more effective artificial neural networks.
Biophysical constraints present opportunities for improving artificial neural network implementation.
Understanding neural dynamics is key to advancing both biological and artificial computation.