P-1A.40

Optimising Recurrent Neural Networks for System-Level Communication Results in Low-Entropy Structural Robustness

Cornelia Sheeran, Duncan Astle, Jascha Achterberg, Danyal Akarca, University of Cambridge, United Kingdom

Session:
Posters 1A Poster

Track:
Cognitive science

Location:
North Schools

Presentation Time:
Thu, 24 Aug, 17:00 - 19:00 United Kingdom Time (UTC +1)

Abstract:
Brain networks share many functional and topological features as a result of exposure to similar underlying biophysical forces. To understand how these forces lead to conserved structural network characteristics, a new type of RNN termed the spatially-embedded RNN (seRNN) has recently been developed. These rate-based seRNNs exhibit numerous structural and functional properties which align with empirical findings found in cortical networks. This includes modularity, a spatially-organized code, mixed selectivity and energetic efficiency. However, so far, structural analyses of seRNNs have focused only on characterising the topology of their hidden layers using graph theory, and it is still unclear as to why these structural features develop and what their relevance is for the network’s functioning. Here we use information theoretic measures to help elucidate these findings and highlight how the system-level optimisation of communication seRNNs results in unique patterns of network complexity and robustness.

Manuscript:
License:
Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
DOI:
10.32470/CCN.2023.1318-0
Publication:
2023 Conference on Cognitive Computational Neuroscience
Presentation
Discussion
Resources
No resources available.
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