Frontiers in Behavioral Neuroscience (Mar 2022)

The Consistency of Gastropod Identified Neurons Distinguishes Intra-Individual Plasticity From Inter-Individual Variability in Neural Circuits

  • Arianna N. Tamvacakis,
  • Joshua L. Lillvis,
  • Akira Sakurai,
  • Paul S. Katz

DOI
https://doi.org/10.3389/fnbeh.2022.855235
Journal volume & issue
Vol. 16

Abstract

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Gastropod mollusks are known for their large, individually identifiable neurons, which are amenable to long-term intracellular recordings that can be repeated from animal to animal. The constancy of individual neurons can help distinguish state-dependent or temporal variation within an individual from actual variability between individual animals. Investigations into the circuitry underlying rhythmic swimming movements of the gastropod species, Tritonia exsulans and Pleurobranchaea californica have uncovered intra- and inter-individual variability in synaptic connectivity and serotonergic neuromodulation. Tritonia has a reliably evoked escape swim behavior that is produced by a central pattern generator (CPG) composed of a small number of identifiable neurons. There is apparent individual variability in some of the connections between neurons that is inconsequential for the production of the swim behavior under normal conditions, but determines whether that individual can swim following a neural lesion. Serotonergic neuromodulation of synaptic strength intrinsic to the CPG creates neural circuit plasticity within an individual and contributes to reorganization of the network during recovery from injury and during learning. In Pleurobranchaea, variability over time in the modulatory actions of serotonin and in expression of serotonin receptor genes in an identified neuron directly reflects variation in swimming behavior. Tracking behavior and electrophysiology over hours to days was necessary to identify the functional consequences of these intra-individual, time-dependent variations. This work demonstrates the importance of unambiguous neuron identification, properly assessing the animal and network states, and tracking behavior and physiology over time to distinguish plasticity within the same animal at different times from variability across individual animals.

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