001     1024013
005     20250203103453.0
024 7 _ |a 10.12751/NNCN.BC2023.174
|2 doi
037 _ _ |a FZJ-2024-01911
041 _ _ |a English
100 1 _ |a Ebrahimzadeh, pezhman
|0 P:(DE-Juel1)198649
|b 0
|e Corresponding author
111 2 _ |a Berstein Conference 2023
|c Berlin
|d 2023-09-26 - 2023-09-29
|w Germany
245 _ _ |a Attractor states in spiking neural networks
260 _ _ |c 2023
|b G-Node
300 _ _ |a 1
336 7 _ |a CONFERENCE_PAPER
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336 7 _ |a Conference Paper
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336 7 _ |a INPROCEEDINGS
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336 7 _ |a Output Types/Conference Paper
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336 7 _ |a Contribution to a conference proceedings
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520 _ _ |a Considering the brain as a dynamical system allows for a rigorous analysis of some aspects of brain dynamics regarding the working memory and their relation to spike patterns. The concept of attractor networks describes one of the dynamical mechanisms in which the brain maintains persistent activity via creating locally stable attractor states. In this work, we analyze the interplay between the excitatory\inhibitory electrical and chemical synaptic connections as means of creation [and annihilation] mechanism of different dynamical regimes and their respective attractor states considering a network of spiking neurons. The exponentially-decaying external input is introduced as means of a vector field driving the system into a specific attractor. Based on the topological analysis of the state space of the input-driven system, computational capabilities of the attractor states and link to working memory are discussed.
536 _ _ |a 5234 - Emerging NC Architectures (POF4-523)
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588 _ _ |a Dataset connected to DataCite
650 _ 7 |a Computational Neuroscience
|2 Other
650 _ 7 |a Networks and dynamical systems
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700 1 _ |a Bouhadjar, Younes
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700 1 _ |a Schiek, Michael
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700 1 _ |a Strachan, John P.
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700 1 _ |a Neftci, Emre
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773 _ _ |a 10.12751/NNCN.BC2023.174
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913 1 _ |a DE-HGF
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|v Neuromorphic Computing and Network Dynamics
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914 1 _ |y 2024
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