Signatures of criticality in efficient coding networks

The critical brain hypothesis states that the brain can benefit from operating close to a second-order phase transition. While it has been shown that several computational aspects of sensory processing (e.g., sensitivity to input) can be optimal in this regime, it is still unclear whether these computational benefits of criticality can be leveraged by neural systems performing behaviorally relevant computations. To address this question, we investigate signatures of criticality in networks of leaky integrate-and-fire neurons optimized to perform efficient coding. Previously, it was shown that the performance of such networks varies non-monotonically with the noise amplitude. Interestingly, we find that in the vicinity of the optimal noise level for efficient coding, the network dynamics exhibit some signatures of criticality, namely, scale-free dynamics of the spiking and the presence of crackling noise relation. Our work suggests that two influential, and previously disparate theories of neural processing optimization (efficient coding and criticality) may be intimately related.

Shervin started his scientific journey in Physics, and after his undergrad, he switched to neuroscience. He did his PhD and postdoc at Max Planck Institute for Biological Cybernetics and Tübingen AI Center, in the lab of Nikos Logothetis (for PhD) and Peter Dayan (for postdoc). He started his lab, Computational Machinery of Cognition (CMC) lab, in October 2023.

Topic: KCNHub Dec Talk - Dr. Shervin Safavi (Host: Dr. Milad Lankarany)

Time: Dec 10, 2024 09:30 AM Eastern Time (US and Canada)

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