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

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Cuneate spiking neural network learning to classify naturalistic texture stimuli under varying sensing conditions. / Rongala, Udaya B.; Mazzoni, Alberto; Spanne, Anton; Jörntell, Henrik; Oddo, Calogero M.

In: Neural Networks, Vol. 123, 2020, p. 273-287.

Research output: Contribution to journalArticle

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TY - JOUR

T1 - Cuneate spiking neural network learning to classify naturalistic texture stimuli under varying sensing conditions

AU - Rongala, Udaya B.

AU - Mazzoni, Alberto

AU - Spanne, Anton

AU - Jörntell, Henrik

AU - Oddo, Calogero M.

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

KW - Cuneate neurons

KW - Neurorobotics

KW - Primary afferents

KW - Spiking neural network

KW - Synaptic weight learning

KW - Tactile sensing

U2 - 10.1016/j.neunet.2019.11.020

DO - 10.1016/j.neunet.2019.11.020

M3 - Article

VL - 123

SP - 273

EP - 287

JO - Neural Networks

JF - Neural Networks

SN - 1879-2782

ER -