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Towards Cortex Size...
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Johansson, ChristopherKTH,Numerisk Analys och Datalogi, NADA
(author)
Towards Cortex Sized Artificial Neural Systems
- Article/chapterEnglish2007
Publisher, publication year, extent ...
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Elsevier BV,2007
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printrdacarrier
Numbers
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LIBRIS-ID:oai:DiVA.org:kth-6236
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https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-6236URI
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https://doi.org/10.1016/j.neunet.2006.05.029DOI
Supplementary language notes
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Language:English
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Summary in:English
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Subject category:ref swepub-contenttype
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Subject category:art swepub-publicationtype
Notes
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QC 20100902
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We propose, implement, and discuss an abstract model of the mammalian neocortex. This model is instantiated with a sparse recurrently connected neural network that has spiking leaky integrator units and continuous Hebbian learning. First we study the structure, modularization, and size of neocortex, and then we describe a generic computational model of the cortical circuitry. A characterizing feature of the model is that it is based on the modularization of neocortex into hypercolumns and minicolumns.Both a floating- and fixed-point arithmetic implementation of the model are presented along with simulation results. We conclude that an implementation on a cluster computer is not communication but computation bounded. A mouse and rat cortex sized version of our model executes in 44% and 23% of real-time respectively. Further, an instance of the model with 1.6 x 10(6) units and 2 x 10(11) connections performed noise reduction and pattern completion. These implementations represent the current frontier of large-scale abstract neural network simulations in terms of network size and running speed.
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Added entries (persons, corporate bodies, meetings, titles ...)
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Lansner, AndersKTH,Numerisk Analys och Datalogi, NADA(Swepub:kth)u12s8cr8
(author)
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KTHNumerisk Analys och Datalogi, NADA
(creator_code:org_t)
Related titles
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In:Neural Networks: Elsevier BV20:1, s. 48-610893-60801879-2782
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