A novel organic neuromorphic device performing pattern classification is presented and demonstrated. It features an artificial soma capable of dendritic integration from three pre-synaptic neurons. The time-response of the interface between electrolytic solutions and organic mixed ionic-electronic conductors is proposed as the sole computational feature for pattern recognition, and it is easily tuned in the organic dendritic integrator by simply controlling electrolyte ionic strength. The classifier is benchmarked in speech-recognition experiments, with a sample of 14 words, encoded either from audio tracks or from kinematic data, showing excellent discrimination performances in a planar, miniaturizable, fully passive device, designed to be promptly integrated in more complex architectures where on-board pattern classification is required.
An organic artificial soma for spatio-temporal pattern recognition via dendritic integration / Di Lauro, M.; Rondelli, F.; De Salvo, A.; Corsini, A.; Genitoni, M.; Greco, P.; Murgia, M.; Fadiga, L.; Biscarini, F.. - In: NEUROMORPHIC COMPUTING AND ENGINEERING. - ISSN 2634-4386. - 4:2(2024), pp. 024001-024001. [10.1088/2634-4386/ad3a96]
An organic artificial soma for spatio-temporal pattern recognition via dendritic integration
Biscarini F.
2024
Abstract
A novel organic neuromorphic device performing pattern classification is presented and demonstrated. It features an artificial soma capable of dendritic integration from three pre-synaptic neurons. The time-response of the interface between electrolytic solutions and organic mixed ionic-electronic conductors is proposed as the sole computational feature for pattern recognition, and it is easily tuned in the organic dendritic integrator by simply controlling electrolyte ionic strength. The classifier is benchmarked in speech-recognition experiments, with a sample of 14 words, encoded either from audio tracks or from kinematic data, showing excellent discrimination performances in a planar, miniaturizable, fully passive device, designed to be promptly integrated in more complex architectures where on-board pattern classification is required.File | Dimensione | Formato | |
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