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Area of Science:

  • Computational Neuroscience
  • Systems Neuroscience
  • Neural Dynamics

Background:

  • Cortical neurons are hypothesized to operate near critical states, optimizing information processing.
  • Critical dynamics have been observed in spontaneous neuronal activity, but their role during stimulus processing is less understood.

Purpose of the Study:

  • To investigate how task-dependent neuronal activity, specifically selective visual attention, affects critical dynamics in cortical information processing.
  • To explore the relationship between attention-modulated gamma-band synchrony, object representation, and network dynamics in macaque area V4.

Main Methods:

  • Utilized a computational model of integrate-and-fire neurons to simulate cortical networks.
  • Manipulated excitatory and inhibitory coupling strengths to explore the network's phase space.
  • Quantified stimulus discriminability and information encoding capacity using information entropy.

Main Results:

  • Attention-induced synchrony was reproduced by enhancing recurrent interactions in the model.
  • A narrow critical region, at the transition from subcritical to supercritical dynamics, was identified as crucial for enhanced stimulus discriminability.
  • Information entropy showed a drastic decrease at the supercritical border with synchrony, while maximizing at the subcritical border under coarse observation.

Conclusions:

  • Cortical networks likely operate near critical states, enabling minimal attentional modulations to significantly improve stimulus representation.
  • The findings suggest that selective attention fine-tunes network excitability to leverage near-critical dynamics for enhanced information processing.