  With the increase in interest in deployment of robots in unstructured
environments to work alongside humans, the development of human-like sense of
touch for robots becomes important. In this work, we implement a multi-channel
neuromorphic tactile system that encodes contact events as discrete spike
events that mimic the behavior of slow adapting mechanoreceptors. We study the
impact of information pooling across artificial mechanoreceptors on
classification performance of spatially non-uniform naturalistic textures. We
encoded the spatio-temporal activation patterns of mechanoreceptors through
gray-level co-occurrence matrix computed from time-varying mean spiking
rate-based tactile response volume. We found that this approach greatly
improved texture classification in comparison to use of individual
mechanoreceptor response alone. In addition, the performance was also more
robust to changes in sliding velocity. The importance of exploiting precise
spatial and temporal correlations between sensory channels is evident from the
fact that on either removal of precise temporal information or altering of
spatial structure of response pattern, a significant performance drop was
observed. This study thus demonstrates the superiority of population coding
approaches that can exploit the precise spatio-temporal information encoded in
activation patterns of mechanoreceptor populations. It, therefore, makes an
advance in the direction of development of bio-inspired tactile systems
required for realistic touch applications in robotics and prostheses.
