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An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids
Published on: December 4, 2017
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Continuous Quasi-Attractors dissolve with too much - or too little - variability
Francesca Schönsberg1,2, Rémi Monasson1, Alessandro Treves2,3
1Laboratory of Physics of the Ecole Normale Supérieure, PSL and CNRS UMR8023, Sorbonne Université, Paris 75005, France.
PNAS Nexus
|December 13, 2024
Summary
Continuous attractor models for hippocampal spatial memory remain relevant despite neural variability. Simulations reveal noise limits, showing that too much or too little irregularity disrupts continuous attractors, impacting memory capacity.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Cognitive Science
Background:
- Hippocampal place cells exhibit variability in activity, challenging traditional continuous attractor network models.
- Understanding spatial memory requires reconciling neural irregularity with network dynamics.
Purpose of the Study:
- To determine the noise limits for continuous attractor models of spatial memory.
- To investigate how variability in hippocampal place cell activity affects network function.
Main Methods:
- Numerical simulations of self-organizing recurrent networks.
- Analysis of synaptic weight inhomogeneity and fixed point dynamics.
- Comparison of simulation results with analytical estimates.
Main Results:
- Continuous attractors can be approximated with limited fixed points, supporting localized activity.
- Excessive or insufficient place field irregularity disrupts the continuous manifold.
- Parameter space boundaries were identified, delineating different network regimes.
Conclusions:
- Continuous attractor models are robust within specific noise limits.
- Neural variability influences the continuity of spatial maps and memory capacity.
- Predicts limits on 1D environment memory and spatial replay duration.
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