Neuromimetic encoding/decoding of spatiotemporal spiking signals from an artificial touch sensor

A framework to discriminate tactile stimuli delivered to an artificial touch sensor is presented. Following a neuromimetic approach, we encode the signals from a 24-capacitive sensor fingertip into spiking activity through a network of leaky integrate-and-fire neurons. The activity resulting from the stimulation of the touch sensor through Braille-like dot patterns is then analysed by means of a newly defined Information measure which explicitly takes into consideration the metrics of the spike train space. Results show that an optimal discrimination of the entire set of 26 stimuli (i.e. 100% correct classification) is reached early after the stimulus onset. Interestingly, the method proves to be effective with both statically and dynamically delivered stimulation which are hard to decode because of the similarity between encoded firing activity given to the proximity of the patterns presented. The decoding analysis allowed us to corroborate the working hypothesis that human tactile discrimination relies on optimal encoding/decoding processes already at the level of the primary stage neurons in the somatosensory pathway.