Cuneate spiking neural network learning to classify naturalistic texture stimuli under varying sensing conditions.

Clicks: 255
ID: 73209
2019
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Ranked #22 of 55 articles by views in neural networks : the official journal of the international neural network society

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Abstract
We implemented a functional neuronal network that was able to learn and discriminate haptic features from biomimetic tactile sensor inputs using a two-layer spiking neuron model and homeostatic synaptic learning mechanism. The first order neuron model was used to emulate biological tactile afferents and the second order neuron model was used to emulate biological cuneate neurons. We have evaluated 10 naturalistic textures using a passive touch protocol, under varying sensing conditions. Tactile sensor data acquired with five textures under five sensing conditions were used for a synaptic learning process, to tune the synaptic weights between tactile afferents and cuneate neurons. Using post-learning synaptic weights, we evaluated the individual and population cuneate neuron responses by decoding across 10 stimuli, under varying sensing conditions. This resulted in a high decoding performance. We further validated the decoding performance across stimuli, irrespective of sensing velocities using a set of 25 cuneate neuron responses. This resulted in a median decoding performance of 96% across the set of cuneate neurons. Being able to learn and perform generalized discrimination across tactile stimuli, makes this functional spiking tactile system effective and suitable for further robotic applications.
Reference Key
rongala2019cuneateneural Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Rongala, Udaya B;Mazzoni, Alberto;Spanne, Anton;Jörntell, Henrik;Oddo, Calogero M;
Journal neural networks : the official journal of the international neural network society
Year 2019
DOI
S0893-6080(19)30388-0
URL
Keywords
spiking neural network cuneate neurons neurorobotics primary afferents synaptic weight learning tactile sensing

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