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Short-term postsynaptic plasticity facilitates predictive tracking in continuous attractors.

Huilin Zhao1, Sungchil Yang1, Chi Chung Alan Fung1

  • 1Department of Neuroscience, City University of Hong Kong, Kowloon, Hong Kong SAR, China.

Frontiers in Computational Neuroscience
|November 29, 2023
PubMed
Summary
This summary is machine-generated.

Short-term postsynaptic plasticity (STPP) enhances neural network mobility, enabling predictive responses to stimuli. This NMDAR-based mechanism aids in sensory prediction and brain-inspired computing.

Keywords:
NMDA receptorsattractor modelscomputational modelspredictive codingsynaptic plasticity

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Systems Neuroscience

Background:

  • N-methyl-D-aspartate receptors (NMDARs) are crucial for synaptic transmission and implicated in neurological disorders.
  • NMDAR-based short-term postsynaptic plasticity (STPP) involves prolonged glutamate binding, retaining input information for up to 500 ms.
  • The impact of STPP on neuronal population dynamics was previously unexplored.

Purpose of the Study:

  • To investigate the effects of STPP on neural information encoding in neuronal populations.
  • To explore how STPP influences the dynamics of neural networks.

Main Methods:

  • Incorporated STPP into a continuous attractor neural network (CANN) model.
  • Analyzed the impact of temporally enhanced synaptic efficacy on network state mobility and stability.

Main Results:

  • STPP destabilizes the CANN network state by increasing its mobility.
  • The CANN model with STPP demonstrated predictive responses to moving stimuli.
  • Enhanced synaptic efficacy due to STPP facilitated stimulus tracking.

Conclusions:

  • STPP enhances neural network mobility and predictive capabilities.
  • This study reveals an STPP-based mechanism for sensory prediction.
  • Findings offer insights for developing brain-inspired computational algorithms for prediction.