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

Summary: Neuromimetic encoding/decoding of spatiotemporal spiking signals
from an artificial touch sensor
Luca Leonardo Bologna, Romain Brasselet, Marco Maggiali, Angelo Arleo
Abstract-- A framework to discriminate tactile stimuli deliv-
ered 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

  

Source: Arleo, Angelo - Laboratory of Neurobiology of Adaptive Processes, Université Pierre-et-Marie-Curie, Paris 6

 

Collections: Biology and Medicine