A robust sound perception model suitable for neuromorphic implementation
We have recently demonstrated the emergence of dynamic feature sensitivity through exposure to formative stimuli in a real-time neuromorphic system implementing a hybrid analog/digital network of spiking neurons. This network, inspired by models of auditory processing in mammals, includes several mu...
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Published in | Frontiers in neuroscience Vol. 7; p. 278 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
Published |
Switzerland
Frontiers Research Foundation
17.01.2014
Frontiers Media S.A |
Subjects | |
Online Access | Get full text |
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Summary: | We have recently demonstrated the emergence of dynamic feature sensitivity through exposure to formative stimuli in a real-time neuromorphic system implementing a hybrid analog/digital network of spiking neurons. This network, inspired by models of auditory processing in mammals, includes several mutually connected layers with distance-dependent transmission delays and learning in the form of spike timing dependent plasticity, which effects stimulus-driven changes in the network connectivity. Here we present results that demonstrate that the network is robust to a range of variations in the stimulus pattern, such as are found in naturalistic stimuli and neural responses. This robustness is a property critical to the development of realistic, electronic neuromorphic systems. We analyze the variability of the response of the network to "noisy" stimuli which allows us to characterize the acuity in information-theoretic terms. This provides an objective basis for the quantitative comparison of networks, their connectivity patterns, and learning strategies, which can inform future design decisions. We also show, using stimuli derived from speech samples, that the principles are robust to other challenges, such as variable presentation rate, that would have to be met by systems deployed in the real world. Finally we demonstrate the potential applicability of the approach to real sounds. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 ObjectType-Article-2 ObjectType-Feature-1 This article was submitted to Neuromorphic Engineering, a section of the journal Frontiers in Neuroscience. Edited by: André Van Schaik, The University of Western Sydney, Australia Reviewed by: John Harris, University of Florida, USA; Dylan R. Muir, University of Basel, Switzerland |
ISSN: | 1662-4548 1662-453X 1662-453X |
DOI: | 10.3389/fnins.2013.00278 |