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Hierarchical Chunking of Sequential Memory on Neuromorphic Architecture with Reduced Synaptic Plasticity
Guoqi Li1, Lei Deng1, Dong Wang1
1Department of Precision Instrument, Center for Brain Inspired Computing Research, Tsinghua University Beijing, China.
Frontiers in Computational Neuroscience
|January 10, 2017
Summary
This study introduces a hierarchical chunking of sequential memory (HCSM) model, explaining how grouping items improves memory. The model demonstrates chunking reduces synaptic plasticity needs for efficient sequential memory encoding.
Area of Science:
- Neuroscience
- Cognitive Science
- Neuromorphic Engineering
Background:
- Sequential memory performance is enhanced by chunking, a cognitive phenomenon where items are grouped.
- Understanding the neural mechanisms and computational advantages of chunking is crucial for advancing memory research.
Purpose of the Study:
- To develop a bio-plausible hierarchical chunking of sequential memory (HCSM) model.
- To elucidate the relationship between hierarchical chunking, synaptic plasticity, and neuromorphic engineering.
- To investigate the role of chunking in reducing synaptic plasticity requirements for memory tasks.
Main Methods:
- Developed a hierarchical chunking of sequential memory (HCSM) model.
- Linked hierarchical chunking mechanisms with principles of synaptic plasticity.
- Integrated neuromorphic engineering concepts, including memristor behavior, for model validation.
- Simulated a hardware version of the HCSM model using realistic memristor characteristics.
Main Results:
- The HCSM model explains how chunking improves sequential memory performance.
- Chunking reduces the need for high-precision, wide dynamic range synapses.
- Simulations confirmed that chunking enables memory tasks with low-precision synapses, mimicking narrow dynamic range memristor behavior.
- The study reveals the functional role of chunking in encoding sequential memory.
Conclusions:
- Hierarchical chunking is a key mechanism for efficient sequential memory.
- Neuromorphic implementations can leverage chunking to reduce hardware requirements for synaptic plasticity.
- This work provides insights into brain-inspired computing and neuromorphic architectures for memory.
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