Self-Organizing Spiking Neural Model for Learning Fault-Tolerant Spatio-Motor Transformations
In this paper, we present a spiking neural model that learns spatio-motor transformations. The model is in the form of a multilayered architecture consisting of integrate and fire neurons and synapses that employ spike-timing-dependent plasticity learning rule to enable the learning of such transformations. We developed a simple 2-degree-of-freedom robot-based reaching task which involves the learning of a nonlinear function. Computer simulations demonstrate the capability of such a model for learning the forward and inverse kinematics for such a task and hence to learn spatio-motor transformations. The interesting aspect of the model is its capacity to be tolerant to partial absence of sensory or motor inputs at various stages of learning. We believe that such a model lays the foundation for learning other complex functions and transformations in real-world scenarios.
learning spatio-motor transformations neurons synapses kinematics robots spike-timing-dependent plasticity (stdp)