Adaptive structure evolution and biologically plausible synaptic
plasticity for recurrent spiking neural networks
- URL: http://arxiv.org/abs/2304.01015v1
- Date: Fri, 31 Mar 2023 07:36:39 GMT
- Title: Adaptive structure evolution and biologically plausible synaptic
plasticity for recurrent spiking neural networks
- Authors: Wenxuan Pan, Feifei Zhao, Yi Zeng, Bing Han
- Abstract summary: Spiking Neural Network (SNN) based Liquid State Machine (LSM) serves as a suitable architecture to study brain-inspired intelligence.
This paper presents a novel LSM learning model that integrates adaptive structural evolution and multi-scale biological learning rules.
- Score: 6.760855795263126
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: The architecture design and multi-scale learning principles of the human
brain that evolved over hundreds of millions of years are crucial to realizing
human-like intelligence. Spiking Neural Network (SNN) based Liquid State
Machine (LSM) serves as a suitable architecture to study brain-inspired
intelligence because of its brain-inspired structure and the potential for
integrating multiple biological principles. Existing researches on LSM focus on
different certain perspectives, including high-dimensional encoding or
optimization of the liquid layer, network architecture search, and application
to hardware devices. There is still a lack of in-depth inspiration from the
learning and structural evolution mechanism of the brain. Considering these
limitations, this paper presents a novel LSM learning model that integrates
adaptive structural evolution and multi-scale biological learning rules. For
structural evolution, an adaptive evolvable LSM model is developed to optimize
the neural architecture design of liquid layer with separation property. For
brain-inspired learning of LSM, we propose a dopamine-modulated
Bienenstock-Cooper-Munros (DA-BCM) method that incorporates global long-term
dopamine regulation and local trace-based BCM synaptic plasticity. Comparative
experimental results on different decision-making tasks show that introducing
structural evolution of the liquid layer, and the DA-BCM regulation of the
liquid layer and the readout layer could improve the decision-making ability of
LSM and flexibly adapt to rule reversal. This work is committed to exploring
how evolution can help to design more appropriate network architectures and how
multi-scale neuroplasticity principles coordinated to enable the optimization
and learning of LSMs for relatively complex decision-making tasks.
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