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

Journal Article

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 timedependent plasticity (STDP). As result, they are commonly used to model cultured neural network, and memristorbased 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 biologicallyplausible sensorimotor system. © 2013 IEEE.

Full Text

Duke Authors

Cited Authors

  • Zhang, X; Xu, Z; Henriquez, C; Ferrari, S

Published Date

  • January 1, 2013

Published In

Start / End Page

  • 6798 - 6805

Electronic International Standard Serial Number (EISSN)

  • 2576-2370

International Standard Serial Number (ISSN)

  • 0743-1546

Digital Object Identifier (DOI)

  • 10.1109/CDC.2013.6760966

Citation Source

  • Scopus