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Related Experiment Video

Updated: Jan 20, 2026

Vibrodissociation of Neurons from Rodent Brain Slices to Study Synaptic Transmission and Image Presynaptic Terminals
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Published on: May 25, 2011

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Phase-locking and bistability in neuronal networks with synaptic depression.

Zeynep Akcay1, Xinxian Huang2, Farzan Nadim2,3

  • 1Department of Mathematics and Computer Science, Queensborough Community College, Bayside, NY 11364, USA.

Physica D. Nonlinear Phenomena
|August 30, 2019
PubMed
Summary

Short-term synaptic depression in inhibitory neural networks can create bistable states. This occurs when neuron properties or synaptic depression change rapidly, leading to multiple stable firing patterns in oscillatory networks.

Keywords:
BistabilityCoupled OscillatorsPhase Response CurveShort-Term Synaptic DepressionTwo-dimensional Poincarè Map

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Area of Science:

  • Computational Neuroscience
  • Systems Neuroscience
  • Theoretical Neuroscience

Background:

  • Recurrent neural networks with inhibitory synapses are fundamental to brain function.
  • Understanding the dynamics of coupled oscillatory neurons is crucial for neuroscience.
  • Short-term synaptic plasticity significantly impacts neural network activity.

Purpose of the Study:

  • To investigate how short-term synaptic depression influences the dynamics of a two-neuron recurrent network.
  • To identify the conditions under which bistable phase-locked solutions emerge in such networks.
  • To analyze the role of phase response curves and synaptic depression profiles in network bistability.

Main Methods:

  • Utilized phase response curves of individual neurons.
  • Incorporated properties of short-term synaptic depression.
  • Defined Poincaré maps to analyze network activity.
  • Identified fixed points of Poincaré maps corresponding to phase-locked modes.

Main Results:

  • Short-term synaptic depression can lead to bistable phase-locked periodic solutions.
  • Bistability arises when the neuron's phase response curve or the synaptic depression profile exhibits steep changes.
  • Demonstrated the emergence of bistability under specific conditions of network parameters.

Conclusions:

  • Short-term synaptic depression is a key mechanism for generating complex dynamics, including bistability, in neural networks.
  • The findings are applicable to Type I oscillators, including the Quadratic Integrate-and-Fire and Morris-Lecar models.
  • Provides insights into how synaptic properties shape network behavior and potential for multiple stable states.