Intrinsic Plasticity for Robust Event-Based Optic Flow Estimation

Abstract

Event cameras and spiking neural networks (SNNs) allow for a highly bio-inspired, low-latency and power efficient implementation of optic flow estimation. Just recently, a hierarchical SNN was proposed in which motion selectivity is learned from raw event data in an unsupervised manner using spike-timing-dependent plasticity (STDP). However, real-life applications of this SNN are currently still limited by the fact that the exact choice of neuron parameters depends on the spatiotemporal properties of the input. Furthermore, tuning the network is a challenging task due to the high degree of coupling between the various parameters. Inspired by neurons in biological brains that modify their intrinsic parameters through a process called intrinsic plasticity, this research proposes update rules which adapt the voltage threshold and maximum synaptic delay during inference. This allows applying the already trained network to a wider range of operating conditions and simplifies the tuning process. Starting with a detailed parameter analysis, primary functions and undesired side effects are assigned to each parameter. The update rules are then designed in such a way as to eliminate these side effects. Unlike existing update rules for the voltage threshold, this work does not attempt to keep the firing activity of output neurons within a specific range, but instead aims to adjust the threshold such that only the correct output maps spike. In particular, the voltage threshold is adapted such that output spikes occur in no more than two maps per retinotopic location. The maximum synaptic delay is adapted such that the resulting apparent pixel velocities of the input match those of the data used during training. A sensitivity analysis is presented which illustrates the effects of newly introduced parameters on the network performance. Furthermore, the adapted network is tested on real event data recorded onboard a drone avoiding obstacles. Due to the difficulties in matching the output of the adapted SNN to the ground truth data, quantitative results are inconclusive. However, qualitative results show a clear improvement in both the density and correctness of optic flow estimates.