首页|Tuning Synaptic Connections Instead of Weights by Genetic Algorithm in Spiking Policy Network

Tuning Synaptic Connections Instead of Weights by Genetic Algorithm in Spiking Policy Network

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Learning from interaction is the primary way that biological agents acquire knowledge about their environment and them-selves.Modern deep reinforcement learning(DRL)explores a computational approach to learning from interaction and has made signi-ficant progress in solving various tasks.However,despite its power,DRL still falls short of biological agents in terms of energy efficiency.Although the underlying mechanisms are not fully understood,we believe that the integration of spiking communication between neur-ons and biologically-plausible synaptic plasticity plays a prominent role in achieving greater energy efficiency.Following this biological intuition,we optimized a spiking policy network(SPN)using a genetic algorithm as an energy-efficient alternative to DRL.Our SPN mimics the sensorimotor neuron pathway of insects and communicates through event-based spikes.Inspired by biological research show-ing that the brain forms memories by creating new synaptic connections and rewiring these connections based on new experiences,we tuned the synaptic connections instead of weights in the SPN to solve given tasks.Experimental results on several robotic control tasks demonstrate that our method can achieve the same level of performance as mainstream DRL methods while exhibiting significantly higher energy efficiency.

Spiking neural networksgenetic evolutionbio-inspired learningagent & cognitive architecturesrobotic control

Duzhen Zhang、Tielin Zhang、Shuncheng Jia、Qingyu Wang、Bo Xu

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School of Artificial Intelligence,University of Chinese Academy of Sciences,Beijing 100049,China

Institute of Automation,Chinese Academy of Sciences(CAS),Beijing 100190,China

Center for Excellence in Brain Science and Intelligence Technology,Chinese Academy of Sciences(CAS),Shanghai 200031,China

Beijing Nova Program,ChinaStrategic Priority Research Program of Chinese Academy of Sciences,ChinaShanghai Municipal Science and Technology Major Project,ChinaYouth Innovation Promotion Association of the Chinese Academy of Sciences,China

20230484369XDA270104042021SHZDZX

2024

10.1007/s11633-023-1481-1

机器智能研究(英文)
中国科学院自动化所

机器智能研究(英文)

CSTPCDEI
影响因子：0.49
ISSN：2731-538X
年,卷(期)：2024.21(5)