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dc.contributor.authorSheik, Sen
dc.contributor.authorCoath, Men
dc.contributor.authorIndiveri, Gen
dc.contributor.authorDenham, SLen
dc.contributor.authorWennekers, Ten
dc.contributor.authorChicca, Een
dc.date.accessioned2017-02-13T13:57:05Z
dc.date.available2017-02-13T13:57:05Z
dc.date.issued2012en
dc.identifier.urihttp://hdl.handle.net/10026.1/8454
dc.description.abstract

Many sounds of ecological importance, such as communication calls, are characterized by time-varying spectra. However, most neuromorphic auditory models to date have focused on distinguishing mainly static patterns, under the assumption that dynamic patterns can be learned as sequences of static ones. In contrast, the emergence of dynamic feature sensitivity through exposure to formative stimuli has been recently modeled in a network of spiking neurons based on the thalamo-cortical architecture. The proposed network models the effect of lateral and recurrent connections between cortical layers, distance-dependent axonal transmission delays, and learning in the form of Spike Timing Dependent Plasticity (STDP), which effects stimulus-driven changes in the pattern of network connectivity. In this paper we demonstrate how these principles can be efficiently implemented in neuromorphic hardware. In doing so we address two principle problems in the design of neuromorphic systems: real-time event-based asynchronous communication in multi-chip systems, and the realization in hybrid analog/digital VLSI technology of neural computational principles that we propose underlie plasticity in neural processing of dynamic stimuli. The result is a hardware neural network that learns in real-time and shows preferential responses, after exposure, to stimuli exhibiting particular spectro-temporal patterns. The availability of hardware on which the model can be implemented, makes this a significant step toward the development of adaptive, neurobiologically plausible, spike-based, artificial sensory systems.

en
dc.format.extent17 - ?en
dc.languageengen
dc.language.isoengen
dc.subjectSTDPen
dc.subjectaddress event representationen
dc.subjectauditoryen
dc.subjectmismatchen
dc.subjectneuromorphic VLSIen
dc.subjectspectro-temporal featuresen
dc.subjectunsupervised learningen
dc.titleEmergent Auditory Feature Tuning in a Real-Time Neuromorphic VLSI System.en
dc.typeJournal Article
plymouth.author-urlhttps://www.ncbi.nlm.nih.gov/pubmed/22347163en
plymouth.volume6en
plymouth.publication-statusPublished onlineen
plymouth.journalFront Neuroscien
dc.identifier.doi10.3389/fnins.2012.00017en
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/00 Groups by role
plymouth.organisational-group/Plymouth/00 Groups by role/Academics
plymouth.organisational-group/Plymouth/Faculty of Health and Human Sciences
plymouth.organisational-group/Plymouth/Faculty of Health and Human Sciences/School of Psychology
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering/School of Computing, Electronics and Mathematics
plymouth.organisational-group/Plymouth/PS - Doctoral College
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA04 Psychology, Psychiatry and Neuroscience
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA11 Computer Science and Informatics
plymouth.organisational-group/Plymouth/Research Groups
plymouth.organisational-group/Plymouth/Research Groups/Centre for Brain, Cognition and Behaviour (CBCB)
plymouth.organisational-group/Plymouth/Research Groups/Centre for Brain, Cognition and Behaviour (CBCB)/Brain
dc.publisher.placeSwitzerlanden
dcterms.dateAccepted2012-01-19en
dc.identifier.eissn1662-453Xen
dc.rights.embargoperiodNot knownen
rioxxterms.versionofrecord10.3389/fnins.2012.00017en
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2012en
rioxxterms.typeJournal Article/Reviewen


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