Spike-based indirect training of a spiking neural network-controlled virtual insect

Spiking neural networks (SNNs) have been shown capable of replicating the spike patterns observed in biological neuronal networks, and of learning via biologically-plausible mechanisms, such as synaptic time-dependent plasticity (STDP). As result, they are commonly used to model cultured neural network, and memristor-based neuromorphic computer chips that aim at replicating the scalability and functionalities of biological circuitries. These examples of SNNs, however, do not allow for the direct manipulation of the synaptic strengths (or weights) as required by existing training algorithms. Therefore, this paper presents an indirect training algorithm that, instead, is designed to manipulate input spike trains (stimuli) that can be implemented by patterns of blue light, or controlled input voltages, to induce the desired synaptic weights changes via STDP. The approach is demonstrated by training an SNN to control a virtual insect that seeks to reach a target location in an obstacle populated environment, without any prior control or navigation knowledge. The simulation results illustrate the feasibility and efficiency of the proposed indirect training algorithm for a biologically-plausible sensorimotor system.