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005     20240313103109.0
024 7 _ |a 2128/22664
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037 _ _ |a FZJ-2019-04106
041 _ _ |a English
100 1 _ |a Bouhadjar, Younes
|0 P:(DE-Juel1)176778
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|e Corresponding author
111 2 _ |a International Conference on Neuromorphic Systems
|g ICONS
|c Knoxville
|d 2019-07-23 - 2019-07-25
|w United States
245 _ _ |a Constraints on sequence processing speed in biological neuronal networks
260 _ _ |c 2019
336 7 _ |a Conference Paper
|0 33
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502 _ _ |c RWTH Aachen
520 _ _ |a 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
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536 _ _ |a HBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907)
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700 1 _ |a Diesmann, Markus
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700 1 _ |a Waser, R.
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700 1 _ |a Wouters, Dirk J.
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700 1 _ |a Tetzlaff, Tom
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856 4 _ |u https://juser.fz-juelich.de/record/864291/files/younes_bouhadjar_sequence_learning.pdf
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