Spatio-temporal Spike Pattern Classification in Neuromorphic Systems
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Date
2013
Publication Type
Conference Paper
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yes
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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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published
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Book title
Biomimetic and Biohybrid Systems
Journal / series
Volume
8064
Pages / Article No.
262 - 273
Publisher
Springer
Event
2nd International Conference on Biomimetic and Biohybrid Systems (Living Machines 2013)
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Methods
Software
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Organisational unit
03453 - Douglas, Rodney J. (emeritus)