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EFFECTIVE ENCODING/DECODING OF SPIKING SIGNALS FROM AN ARTIFICIAL TOUCH SENSOR
 

Summary: EFFECTIVE ENCODING/DECODING OF SPIKING SIGNALS
FROM AN ARTIFICIAL TOUCH SENSOR
Luca Leonardo Bologna1, Romain Brasselet1, Marco Maggiali2, Angelo Arleo1
1Adaptive NeuroComputation Group, CNRS - University Pierre&Marie Curie, 75005 Paris, France
2Department of Robotics, Brain and Cognitive Sciences, Italian Institute of Technology, Genoa, Italy
luca.bologna@upmc.fr, rbrassel@snv.jussieu.fr, marco.maggiali@iit.it, angelo.arleo@upmc.fr
ABSTRACT
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 spik-
ing 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,

  

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

 

Collections: Biology and Medicine