In this work, a spiking neural network (SNN) is proposed for approximating differential sensorimotor maps of robotic systems. The computed model is used as a local Jacobian-like projection that relates changes in sensor space to changes in motor space. The SNN consists of an input (sensory) layer and an output (motor) layer connected through plastic synapses, with inter-inhibitory connections at the output layer. Spiking neurons are modeled as Izhikevich neurons with a synaptic learning rule based on spike-timing-dependent plasticity. Feedback data from proprioceptive and exteroceptive sensors are encoded and fed into the input layer through a motor babbling process. As the main challenge to building an efficient SNN is to tune its parameters, we present an intuitive tuning method that considerably reduces the number of neurons and the amount of data required for training. Our proposed architecture represents a biologically plausible neural controller that is capable of handling noisy sensor readings to guide robot movements in real-time. Experimental results are presented to validate the control methodology with a vision-guided robot.

中文翻译：

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用于基于传感器的机器人控制的差分映射尖峰神经网络

在这项工作中，提出了一个尖峰神经网络 (SNN) 来逼近机器人系统的差分感觉运动图。计算模型用作局部 Jacobian-like 投影，将传感器空间的变化与电机空间的变化联系起来。SNN 由通过塑料突触连接的输入（感觉）层和输出（运动）层组成，在输出层具有抑制间连接。尖峰神经元被建模为具有基于尖峰时间依赖性可塑性的突触学习规则的 Izhikevich 神经元。来自本体感受器和外感受器的反馈数据被编码，并通过一个马达的咝咝声过程输入到输入层。由于构建高效 SNN 的主要挑战是调整其参数，我们提出了一种直观的调整方法，可以大大减少训练所需的神经元数量和数据量。我们提出的架构代表了一种生物学上合理的神经控制器，能够处理嘈杂的传感器读数以实时指导机器人运动。实验结果用于验证视觉引导机器人的控制方法。