Populations of Spiking Neurons for Reservoir Computing: Closed Loop Control of a Compliant Quadruped

On robot compliance. A cerebellar control approach

The work presented here is a novel biological approach for the compliant control of a robotic arm in real time (RT). We integrate a spiking cerebellar network at the core of a feedback control loop performing torque-driven control. The spiking cerebellar controller provides torque commands allowing for accurate and coordinated arm movements. To compute these output motor commands, the spiking cerebellar controller receives the robot's sensorial signals, the robot's goal behavior, and an instructive signal. These input signals are translated into a set of evolving spiking patterns representing …

Stable Lifelong Learning: Spiking neurons as a solution to instability in plastic neural networks

Synaptic plasticity poses itself as a powerful method of self-regulated unsupervised learning in neural networks. A recent resurgence of interest has developed in utilizing Artificial Neural Networks (ANNs) together with synaptic plasticity for intra-lifetime learning. Plasticity has been shown to improve the learning capabilities of these networks in generalizing to novel environmental circumstances. However, the long-term stability of these trained networks has yet to be examined. This work demonstrates that utilizing plasticity together with ANNs leads to instability beyond the pre-specifie…

IzhiRISC-V——一种基于RISC-V且带有自定义ISA扩展的处理器，用于采用Izhikevich神经元的脉冲神经网络处理

IzhiRISC-V -- a RISC-V-based Processor with Custom ISA Extension for Spiking Neuron Networks Processing with Izhikevich Neurons

针对通用RISC-V处理器处理脉冲神经网络时因重复使用基本指令更新神经元导致能效优势被削弱的问题，提出IzhiRISC-V处理器，通过自定义神经形态ISA扩展及现有ALU的定制硬件扩展实现脉冲神经元更新，为构建大规模系统奠定基础。

突触运动适应：脉冲神经网络中用于自适应机器人控制的三因子学习规则

Synaptic motor adaptation: A three-factor learning rule for adaptive robotic control in spiking neural networks

本文提出 SMA 算法，利用经梯度下降元优化的三因子突触可塑性规则，使四足机器人仅凭机载传感实现实时在线运动适应，性能达到现有先进方法水平。

Learning to Walk: Spike Based Reinforcement Learning for Hexapod Robot Central Pattern Generation

Learning to walk -- i.e., learning locomotion under performance and energy constraints continues to be a challenge in legged robotics. Methods such as stochastic gradient, deep reinforcement learning (RL) have been explored for bipeds, quadrupeds and hexapods. These techniques are computationally intensive and often prohibitive for edge applications. These methods rely on complex sensors and pre-processing of data, which further increases energy and latency. Recent advances in spiking neural networks (SNNs) promise a significant reduction in computing owing to the sparse firing of neuros and h…

星形胶质细胞调控的神经形态中枢模式发生器用于足式机器人运动控制

Astrocyte Regulated Neuromorphic Central Pattern Generator Control of Legged Robotic Locomotion

针对CPG步态涌现可塑性机制不明的问题，提出星形胶质细胞调控的SNN-CPG，通过奖励调制STDP让四足机器人习得小跑步态，并较强化学习方法节省23.3倍计算功耗。