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In sparse neuronal networks, inhibition does not control absorbing transitions. This finding allows networks to leverage criticality for computation and memory while adjusting inhibition for learning.

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Area of Science:

  • Computational Neuroscience
  • Complex Systems
  • Network Dynamics

Background:

  • Inhibition is crucial for stabilizing neuronal dynamics and pattern formation in neural systems.
  • In complete graph networks, inhibition is typically considered a control parameter for absorbing phase transitions.

Purpose of the Study:

  • To investigate the role of inhibition weight in the absorbing phase transition of low-connectivity sparse networks.
  • To determine if inhibition remains a control parameter in sparse networks, unlike in dense networks.

Main Methods:

  • Developed mean-field theories for neuronal models.
  • Utilized analytical and simulation results from generic stochastic integrate-and-fire neurons.
  • Demonstrated validity for simpler stochastic neuron models under specific conditions.

Main Results:

  • Inhibition weight is not a control parameter for the absorbing transition in low-connectivity sparse networks.
  • The role of inhibition in phase transitions is dependent on network topology, even for high-dimensional topologies.
  • Results are robust across different stochastic neuron models.

Conclusions:

  • The independence of the absorbing transition from inhibitory weight in sparse networks is a key feature.
  • This independence enables sparse networks to maintain a near-critical regime for self-tuning excitation.
  • It also allows flexible adjustment of inhibitory weights for computation, learning, and memory, utilizing criticality benefits.