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Encoding certainty in bump attractors.

Sam Carroll1, Krešimir Josić, Zachary P Kilpatrick

  • 1Department of Mathematics, University of Houston, Houston, TX, 77204, USA, srcarroll314@gmail.com.

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This study introduces novel neural networks that model stimulus memory by encoding certainty. These networks feature stable activity bumps, representing both position and confidence, enhancing memory robustness.

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Area of Science:

  • Computational neuroscience
  • Neural network modeling

Background:

  • Persistent neuronal activity is crucial for representing spatial memory.
  • Bump attractor networks are common models for persistent activity but typically lack amplitude stability.

Purpose of the Study:

  • To develop neural network models that encode stimulus certainty or salience.
  • To extend bump attractor models to include amplitude stability.

Main Methods:

  • Constructed model networks with balanced recurrent synaptic connections.
  • Investigated networks with separate excitatory and inhibitory populations.
  • Tuned inhibition to precisely cancel background excitation.

Main Results:

  • Developed networks supporting activity bumps stable to both position and amplitude perturbations.
  • Demonstrated a two-dimensional attractor defined by a continuum of positions and amplitudes.
  • Showed that bump amplitude, representing certainty, is determined by initial input and enhances noise robustness.

Conclusions:

  • Novel bump attractor networks can encode stimulus certainty alongside position.
  • Amplitude stability enhances the fidelity and robustness of stored memory representations.
  • Precise balancing of synaptic and network parameters is key to achieving amplitude control.