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Optogenetic Entrainment of Hippocampal Theta Oscillations in Behaving Mice
07:33

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Published on: June 29, 2018

Inhibitory feedback promotes stability in an oscillatory network.

F Nadim1, S Zhao, L Zhou

  • 1Department of Mathematical Sciences, New Jersey Institute of Technology, Newark, NJ 07102, USA. farzan@njit.edu

Journal of Neural Engineering
|November 8, 2011
PubMed
Summary

The lateral pyloric (LP) neuron synapse stabilizes crustacean pyloric rhythm by reducing neuronal activity variability. Removing this synapse increases period variability, highlighting its crucial role in network function.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Systems Neuroscience

Background:

  • Neuronal network function relies on both reliability and variability.
  • The crustacean pyloric rhythm is a model system for studying neuronal network oscillations.
  • The role of synaptic feedback in pacemaker neuron activity remains largely unknown.

Purpose of the Study:

  • To investigate the hypothesis that the lateral pyloric (LP) neuron synapse stabilizes the pyloric rhythm.
  • To determine if this synapse reduces cycle-by-cycle variability in neuronal activity.

Main Methods:

  • Experimental manipulation of the LP-pyloric dilator (PD) synapse by hyperpolarizing the LP neuron.
  • Analysis of pyloric period variability and phase response curves.
  • Development and analysis of a reduced mathematical model of the pyloric network.

Main Results:

  • Functionally removing the LP-PD synapse significantly increased pyloric period variability.
  • The presence of the LP-PD synapse decreased the amplitude of the PD neuron's phase response curve, indicating increased rhythm stability.
  • Mathematical modeling confirmed that periodic inhibition enhances oscillation stability and reduces cycle period variability.

Conclusions:

  • The LP-PD synapse plays a critical role in stabilizing the crustacean pyloric rhythm.
  • Synaptic feedback mechanisms are essential for regulating neuronal network reliability.
  • Mathematical models can effectively explain experimental observations in neuronal network dynamics.