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Associative memory model with arbitrary Hebbian length
Zijian Jiang1, Jianwen Zhou2, Tianqi Hou3
1PMI Lab, School of Physics, Sun Yat-sen University, Guangzhou 510275, People's Republic of China.
Physical Review. E
|January 15, 2022
Summary
Learning in the brain
Area of Science:
- Neuroscience
- Computational Neuroscience
- Cognitive Science
Background:
- Synaptic plasticity underlies learning and memory.
- The brain converts temporal information into spatial representations.
- The role of synaptic integration windows in this process is not fully understood.
Purpose of the Study:
- To investigate how synaptic integration windows influence the conversion of temporal to spatial correlations.
- To explore the impact of Hebbian learning on associative memory models.
- To understand the role of anti-Hebbian learning in memory regulation.
Main Methods:
- Development of a generalized associative memory model for pattern sequences.
- Analytical solution of the model with arbitrary Hebbian lengths.
- Inclusion of an anti-Hebbian component to study memory landscape modification.
Main Results:
- A small Hebbian length significantly enhances correlation conversion.
- Stimulus-induced attractors become highly correlated with stored patterns.
- The anti-Hebbian component reshapes the memory energy landscape.
Conclusions:
- Synaptic Hebbian length is crucial for efficient correlation conversion in associative memory.
- The model provides insights into neural state transitions and memory regulation.
- Establishes a link between associative memory, Hebbian length, and brain's correlation conversion function.
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