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A stochastic model for interconnected neurons

M Cottrell1, F Piat, J P Rospars

  • 1SAMOS, Université Paris 1, France.

Bio Systems
|January 1, 1997
PubMed
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This study introduces a neural network model where neurons use an hourglass metaphor for spiking dynamics. The model exhibits convergent or divergent states, with potential applications in olfactory coding and odor discrimination.

Area of Science:

  • Computational Neuroscience
  • Biophysics
  • Neural Networks

Background:

  • Biologically plausible neural networks with spiking neurons are fundamental to understanding brain function.
  • Previous work explored inhibitory-only networks (Cottrell, 1988, 1992).
  • The hourglass metaphor provides a novel representation for individual neuron spiking dynamics.

Purpose of the Study:

  • To investigate the collective behavior of interconnected spiking neurons with both excitatory and inhibitory connections.
  • To analyze network dynamics as a function of connection strengths.
  • To explore the application of emergent network patterns for neural coding, specifically in the olfactory system.

Main Methods:

  • Development of a computational model for a network of spiking neurons.

Related Experiment Videos

  • Analysis of network dynamics under varying inhibitory and excitatory connection strengths.
  • Examination of limit states: convergent (ergodic, all active) and divergent (some inactive, active sub-network ergodic).

    • Main Results:

    • The model demonstrates two distinct limit states: convergent and divergent.
    • In convergent states, the network is ergodic with all neurons maintaining a positive mean firing rate.
    • Divergent states feature inactive neurons, with the active sub-network remaining ergodic, forming a neural code for external stimuli.

    Conclusions:

    • The proposed neural network model exhibits complex dynamics based on connection strengths.
    • Divergent states offer a mechanism for neural coding of external stimuli, applicable to systems like olfaction.
    • Inhibitory connections play a crucial role in odor discrimination within this model.