4.8 Article

Mapping circuit dynamics during function and dysfunction

Journal

ELIFE
Volume 11, Issue -, Pages -

Publisher

eLIFE SCIENCES PUBL LTD
DOI: 10.7554/eLife.76579

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Funding

  1. NIH [MH060605]
  2. DFG [SCHN 1594/1-1]

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Neural circuits can generate various spike patterns, but only some are functional. However, the study of circuit dynamics and how they generate functional dynamics is limited by a lack of description. By recording the circuit dynamics of the pyloric circuit in Cancer borealis, statistical features of spike times were used to visualize the patterns generated by this circuit. This approach captures the variability of functional rhythms and diversity of atypical dynamics in a single map.
Neural circuits can generate many spike patterns, but only some are functional. The study of how circuits generate and maintain functional dynamics is hindered by a poverty of description of circuit dynamics across functional and dysfunctional states. For example, although the regular oscillation of a central pattern generator is well characterized by its frequency and the phase relationships between its neurons, these metrics are ineffective descriptors of the irregular and aperiodic dynamics that circuits can generate under perturbation or in disease states. By recording the circuit dynamics of the well-studied pyloric circuit in Cancer borealis, we used statistical features of spike times from neurons in the circuit to visualize the spike patterns generated by this circuit under a variety of conditions. This approach captures both the variability of functional rhythms and the diversity of atypical dynamics in a single map. Clusters in the map identify qualitatively different spike patterns hinting at different dynamic states in the circuit. State probability and the statistics of the transitions between states varied with environmental perturbations, removal of descending neuromodulatory inputs, and the addition of exogenous neuromodulators. This analysis reveals strong mechanistically interpretable links between complex changes in the collective behavior of a neural circuit and specific experimental manipulations, and can constrain hypotheses of how circuits generate functional dynamics despite variability in circuit architecture and environmental perturbations.

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