This title appears in the Scientific Report :
2019
Please use the identifier:
http://hdl.handle.net/2128/24492 in citations.
Please use the identifier: http://dx.doi.org/10.1145/3354265.3354281 in citations.
Constraints on sequence processing speed in biological neuronal networks
Constraints on sequence processing speed in biological neuronal networks
Sequence processing has been proposed to be the universal computation performed by the neocortex. The Hierarchical Temporal Memory (HTM) model provides a mechanistic implementation of this form of processing. While the model accounts for a number of neocortical features, it is based on networks of h...
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Personal Name(s): | Bouhadjar, Younes (Corresponding author) |
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Diesmann, Markus / Waser, R. / Wouters, Dirk J. / Tetzlaff, Tom | |
Contributing Institute: |
Jara-Institut Brain structure-function relationships; INM-10 Computational and Systems Neuroscience; IAS-6 Computational and Systems Neuroscience; INM-6 |
Published in: |
Proceedings of the International Conference on Neuromorphic Systems - ICONS '19 - ACM Press New York, New York, USA, 2019. - ISBN 9781450376808 - doi:10.1145/3354265.3354281 |
Imprint: |
ACM Press New York, New York, USA
2019
|
Physical Description: |
1-9 |
DOI: |
10.1145/3354265.3354281 |
Conference: | International Conference on Neuromorphic Systems, Knoxville (TN), 2019-07-23 - 2019-07-25 |
Document Type: |
Contribution to a book Contribution to a conference proceedings |
Research Program: |
Doktorand ohne besondere Förderung Human Brain Project Specific Grant Agreement 2 Advanced Computing Architectures Theory, modelling and simulation |
Link: |
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Publikationsportal JuSER |
Please use the identifier: http://dx.doi.org/10.1145/3354265.3354281 in citations.
Sequence processing has been proposed to be the universal computation performed by the neocortex. The Hierarchical Temporal Memory (HTM) model provides a mechanistic implementation of this form of processing. While the model accounts for a number of neocortical features, it is based on networks of highly abstract neuron and synapse models updated in discrete time. Here, we reformulate the model in terms of a network of spiking neurons with continuous-time dynamics to investigate how neuronal parameters such as cell-intrinsic time constants and synaptic weights constrain the sequence-processing speed. |