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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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Balanced input allows optimal encoding in a stochastic binary neural network model: an analytical study.

Gustavo Deco1, Etienne Hugues

  • 1Center of Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain. gustavo.deco@upf.edu

Plos One
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PubMed
Summary
This summary is machine-generated.

Attention actively influences neural information encoding by reducing neural variability and correlations. This balance enhances information processing, as demonstrated in neural network models.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience

Background:

  • Recent neurophysiological experiments reveal attention's active role in modulating neural activity.
  • Attention reduces neural variability and correlations, enhancing stimulus information encoding.

Purpose of the Study:

  • To investigate how neural circuit activity sensitivity emerges from modulations.
  • To demonstrate how balanced synaptic currents enhance information encoding in neural networks.

Main Methods:

  • Utilized an analytically tractable neural network model.
  • Employed a more realistic spiking neural network model.
  • Measured network encoding sensitivity using Fisher information.

Main Results:

  • Information enhancement in neural networks emerges when excitatory and inhibitory synaptic currents are balanced.
  • Network encoding sensitivity is maximized at the exact balance of synaptic currents.
  • A similar result was observed in both model types.

Conclusions:

  • Balanced synaptic input regimes are functionally crucial for optimal information encoding.
  • The findings suggest a mechanism for how attention enhances neural information processing.
  • Balanced activity may be a general principle for sensitive neural computation.