.png){kind=icon}
返回
Brain-inspired learning rules for spiking neural network-based control: a tutorial
.png){kind=icon}
PRE
AI
DOI：10.1007/s13534-024-00436-6.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Robotic systems rely on spatio-temporal information to solve control tasks. With advancements in deep neural networks, reinforcement learning has significantly enhanced the performance of control tasks by leveraging deep learning techniques. However, as deep neural networks grow in complexity, they consume more energy and introduce greater latency. This complexity hampers their application in robotic systems that require real-time data processing. To address this issue, spiking neural networks, which emulate the biological brain by transmitting spatio-temporal information through spikes, have been developed alongside neuromorphic hardware that supports their operation. This paper reviews brain-inspired learning rules and examines the application of spiking neural networks in control tasks. We begin by exploring the features and implementations of biologically plausible spike-timing-dependent plasticity. Subsequently, we investigate the integration of a global third factor with spike-timing-dependent plasticity and its utilization and enhancements in both theoretical and applied research. We also discuss a method for locally applying a third factor that sophisticatedly modifies each synaptic weight through weight-based backpropagation. Finally, we review studies utilizing these learning rules to solve control tasks using spiking neural networks.
Keyword:
Spiking neural networks
Spike-timing-dependent plasticity
R-STDP
Neuromorphic computing
Control problem
Reinforcement learning
期刊
IF:
2.8
论文数:
533
被引数:
1.5K
机构
引用论文
分享收藏A cooperative switch determines the sign of synaptic plasticity in distal dendrites of neocortical pyramidal neurons.png)
NEURON
OAAI
NEURONIF15
OAAI分享收藏A biologically plausible supervised learning method for spiking neural networks using the symmetric STDP rule使用对称STDP规则的尖峰神经网络的生物学合理监督学习方法NEURAL NETWORKS
OAAI
NEURAL NETWORKSIF6.3
OAAI分享收藏
查看更多内容