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Theoretical framework for learning through structural plasticity
Gianmarco Tiddia1, Luca Sergi1, Bruno Golosio1
1Department of Physics, <a href="https://ror.org/003109y17">University of Cagliari</a>, 09042 Monserrato, Italy and <a href="https://ror.org/03paz5966">Istituto Nazionale di Fisica Nucleare (INFN)</a>, Sezione di Cagliari, 09042 Monserrato, Italy.
Physical Review. E
|November 20, 2024
Summary
This study presents a theoretical framework for understanding learning and memory consolidation through structural plasticity in neural networks. The model captures key biological features and simulates synaptic changes, offering insights into network learning capabilities.
Area of Science:
- Computational Neuroscience
- Theoretical Neuroscience
- Systems Neuroscience
Background:
- Structural plasticity is vital for learning and memory.
- Existing models often simplify neural network complexity.
Purpose of the Study:
- Develop a theoretical framework for learning via structural plasticity.
- Incorporate realistic neural network features like firing rates and connectivity.
- Analyze synaptic stabilization, pruning, and reorganization.
Main Methods:
- Utilized a mean-field approach for theoretical modeling.
- Developed a phenomenological model of neural networks.
- Incorporated probability distributions of neuron firing rates and response selectivity.
- Modeled probabilistic connection rules and noisy stimuli.
- Simulated synaptic stabilization, pruning, and reorganization.
Main Results:
- The framework successfully computes learning and memory metrics.
- Model performance was validated against firing-rate-based network simulations.
- Analyzed the impact of training patterns and parameter variations on network capabilities.
Conclusions:
- The theoretical framework provides a robust tool for studying structural plasticity in learning.
- The model's ability to capture biological realism enhances understanding of neural network dynamics.
- This work contributes to the theoretical underpinnings of memory consolidation mechanisms.
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