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Related Experiment Videos

Mean-driven and fluctuation-driven persistent activity in recurrent networks.

Alfonso Renart1, Rubén Moreno-Bote, Xiao-Jing Wang

  • 1Departamento de Físca Teórica, Universidad Autónoma de Madrid, Cantoblanco 28049, Madrid, Spain. arenart@andromeda.rutgers.edu

Neural Computation
|December 1, 2006
PubMed
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Cortical neurons exhibit irregular firing, potentially reflecting balanced excitation and inhibition. This study explores if neural networks can maintain multiple balanced states, finding fluctuation-driven activity is not robust in simplified models.

Area of Science:

  • Computational Neuroscience
  • Neural Dynamics
  • Network Theory

Background:

  • Cortical neurons display highly irregular spike trains, with coefficients of variation (CV) near or exceeding one.
  • This irregularity is hypothesized to arise from balanced excitation and inhibition within local cortical circuits.
  • Balanced states involve subthreshold mean currents, with firing driven by fluctuations, leading to Poisson-like spike trains.

Purpose of the Study:

  • To investigate the theoretical possibility of coexisting balanced network states at different firing rates.
  • To extend mean-field theories to self-consistently determine both firing rate and spike train irregularity (CV).
  • To analyze the robustness of fluctuation-driven persistent activity in recurrent neural networks.

Main Methods:

Related Experiment Videos

  • Utilized mean-field techniques to model recurrent networks of current-based leaky integrate-and-fire (LIF) neurons.
  • Developed an extended mean-field theory to self-consistently calculate the coefficient of variation (CV) of interspike intervals.
  • Investigated network dynamics under varying connectivity parameters (excitatory vs. inhibitory balance).

    • Main Results:

    • Identified bistable solutions (coexisting steady states) in recurrent LIF networks, dependent on connectivity.
    • Found that excitatory-dominated networks exhibit states differing in mean current, with irregular firing in low-activity states.
    • Demonstrated that balanced or inhibition-dominated networks can have two stable, subthreshold states with high spiking variability, but require fine-tuning.

    Conclusions:

    • Fluctuation-driven persistent activity is not a robust phenomenon in the analyzed simplified network models.
    • The robustness of balanced states depends on network parameters and size, with simplified models showing limitations.
    • Further investigation with more biologically realistic neuron and network models is warranted to understand persistent activity.