  Motion detection is a primary task required for robotic systems to perceive
and navigate in their environment. Proposed in the literature bioinspired
neuromorphic Time-Difference Encoder (TDE-2) combines event-based sensors and
processors with spiking neural networks to provide real-time and
energy-efficient motion detection through extracting temporal correlations
between two points in space. However, on the algorithmic level, this design
leads to loss of direction-selectivity of individual TDEs in textured
environments. Here we propose an augmented 3-point TDE (TDE-3) with additional
inhibitory input that makes TDE-3 direction-selectivity robust in textured
environments. We developed a procedure to train the new TDE-3 using
backpropagation through time and surrogate gradients to linearly map input
velocities into an output spike count or an Inter-Spike Interval (ISI). Our
work is the first instance of training a spiking neuron to have a specific ISI.
Using synthetic data we compared training and inference with spike count and
ISI with respect to changes in stimuli dynamic range, spatial frequency, and
level of noise. ISI turns out to be more robust towards variation in spatial
frequency, whereas the spike count is a more reliable training signal in the
presence of noise. We performed the first in-depth quantitative investigation
of optical flow coding with TDE and compared TDE-2 vs TDE-3 in terms of
energy-efficiency and coding precision. Results show that on the network level
both detectors show similar precision (20 degree angular error, 88% correlation
with ground truth). Yet, due to the more robust direction-selectivity of
individual TDEs, TDE-3 based network spike less and hence is more
energy-efficient. Reported precision is on par with model-based methods but the
spike-based processing of the TDEs provides allows more energy-efficient
inference with neuromorphic hardware.
