Simulating a complete Tritonia escape swim network using a novel event-based spiking neural network algorithm

Abstract

Tritonia has been studied in the laboratory by several studies, which have led to significant advances in identifying the biological components that participate in the Tritonia escape swim network. There are also studies, which have artificially reproduced the neuronal patterns of the Tritonia escape swim circuit. These studies simulated the interneurons of the swim central pattern generator (CPG) known as dorsal swim interneuron, ventral swim interneuron, and cerebral 2. In this research, other neurons that participate in the Tritonia escape swim network were simulated. In addition to the main CPG components, sensory, ramp, and dorsal/ventral flexion neurons are all included in the neural network (NN). The objective of the study was to artificially reconstruct a more representative image of the Tritonia escape swim NN, its neuronal activities, and synaptic connections. The network was simulated using a spiking neural network (SNN) simulator named Synapse, which has been implemented based on a novel event-based SNN algorithm. After tuning synaptic delays, weights, and membrane potential properties, the expected spike patterns were successfully reproduced for each involved neuron. The spike patterns from this study were validated using the laboratory recorded signals as well as the existing simulated patterns.