Burst firing optimizes invariant coding of natural communication signals by electrosensory neural populations

Accurate perception of objects within the environment independent of context is essential for the survival of an organism. While neurons that respond in an invariant manner to different stimulus waveforms resulting from identitypreserving transformations of objects are thought to provide a neural correlate of context-independent perception, how such responses emerge in the brain remains poorly understood. Here, we demonstrate that burst firing in neural populations can give rise to an invariant representation of highly heterogeneous natural communication stimuli. Multi-unit recordings from central sensory neural populations showed that considering burst spike trains led to invariant representations at the population but not the single neuron level. Computational modeling further revealed that optimal invariance is achieved at burst firing levels seen experimentally. Taken together, our results demonstrate an important function for burst firing toward establishing invariant representations of sensory input in neural populations.

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Publication Details

Journal
iScience
Published
2025-04-09
DOI
https://doi.org/10.1016/j.isci.2025.112399
Citations
4
Primary Topic
Neural Networks and Applications
Type
article
Field-Weighted Citation Impact
9.92

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article

Burst firing optimizes invariant coding of natural communication signals by electrosensory neural populations

Michael G. Metzen, Pouya Bashivan, Maurice J. Chacron, Amin Akhshi et al.
4 citations
iScience
Neural Networks and Applications
9.92
article

Burst firing optimizes invariant coding of natural communication signals by electrosensory neural populations

Michael G. Metzen, Pouya Bashivan, Maurice J. Chacron, Amin Akhshi, Anmar Khadra
article en
4 citations

Abstract

Accurate perception of objects within the environment independent of context is essential for the survival of an organism. While neurons that respond in an invariant manner to different stimulus waveforms resulting from identitypreserving transformations of objects are thought to provide a neural correlate of context-independent perception, how such responses emerge in the brain remains poorly understood. Here, we demonstrate that burst firing in neural populations can give rise to an invariant representation of highly heterogeneous natural communication stimuli. Multi-unit recordings from central sensory neural populations showed that considering burst spike trains led to invariant representations at the population but not the single neuron level. Computational modeling further revealed that optimal invariance is achieved at burst firing levels seen experimentally. Taken together, our results demonstrate an important function for burst firing toward establishing invariant representations of sensory input in neural populations.

iScienceVol. 28(5)
Mila - Quebec Artificial Intelligence Institute (CA), McGill University (CA)
Canada First Research Excellence Fund, Canadian Institutes of Health Research, Natural Sciences and Engineering Research Council of Canada, Fonds de recherche du Québec – Nature et technologies
Openalex Percentile: Top 2%
Neural Networks and Applications
9.92
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