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Spatio-temporal spike pattern classification in neuromorphic systems


Sheik, S; Pfeiffer, M; Stefanini, F; Indiveri, G (2013). Spatio-temporal spike pattern classification in neuromorphic systems. In: Lepora, Nathan F; Mura, Anna; Krapp, Holger G; Verschure, Paul F M J; Prescott, Tony J. Biomimetic and Biohybrid Systems. Berlin: Springer, 262-273.

Abstract

Spike-based neuromorphic electronic architectures offer an attractive solution for implementing compact efficient sensory-motor neural processing systems for robotic applications. Such systems typically comprise event-based sensors and multi-neuron chips that encode, transmit, and process signals using spikes. For robotic applications, the ability to sustain real-time interactions with the environment is an essential requirement. So these neuromorphic systems need to process sensory signals continuously and instantaneously, as the input data arrives, classify the spatio-temporal information contained in the data, and produce appropriate motor outputs in real-time. In this paper we evaluate the computational approaches that have been proposed for classifying spatio-temporal sequences of spike-trains, derive the main principles and the key components that are required to build a neuromorphic system that works in robotic application scenarios, with the constraints imposed by the biologically realistic hardware implementation, and present possible system-level solutions.

Abstract

Spike-based neuromorphic electronic architectures offer an attractive solution for implementing compact efficient sensory-motor neural processing systems for robotic applications. Such systems typically comprise event-based sensors and multi-neuron chips that encode, transmit, and process signals using spikes. For robotic applications, the ability to sustain real-time interactions with the environment is an essential requirement. So these neuromorphic systems need to process sensory signals continuously and instantaneously, as the input data arrives, classify the spatio-temporal information contained in the data, and produce appropriate motor outputs in real-time. In this paper we evaluate the computational approaches that have been proposed for classifying spatio-temporal sequences of spike-trains, derive the main principles and the key components that are required to build a neuromorphic system that works in robotic application scenarios, with the constraints imposed by the biologically realistic hardware implementation, and present possible system-level solutions.

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Additional indexing

Item Type:Book Section, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Date:2013
Deposited On:12 Feb 2014 17:05
Last Modified:05 Apr 2016 17:33
Publisher:Springer
Series Name:Lecture Notes in Computer Science
Number of Pages:12
ISSN:0302-9743
ISBN:978-3-642-39802-5
Additional Information:Second International Conference, Living Machines 2013, London, UK
Publisher DOI:https://doi.org/10.1007/978-3-642-39802-5_23

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