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001024013 0247_ $$2doi$$a10.12751/NNCN.BC2023.174
001024013 037__ $$aFZJ-2024-01911
001024013 041__ $$aEnglish
001024013 1001_ $$0P:(DE-Juel1)198649$$aEbrahimzadeh, pezhman$$b0$$eCorresponding author
001024013 1112_ $$aBerstein Conference 2023$$cBerlin$$d2023-09-26 - 2023-09-29$$wGermany
001024013 245__ $$aAttractor states in spiking neural networks
001024013 260__ $$bG-Node$$c2023
001024013 300__ $$a1
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001024013 520__ $$aConsidering the brain as a dynamical system allows for a rigorous analysis of some aspects of brain dynamics regarding the working memory and their relation to spike patterns. The concept of attractor networks describes one of the dynamical mechanisms in which the brain maintains persistent activity via creating locally stable attractor states. In this work, we analyze the interplay between the excitatory\inhibitory electrical and chemical synaptic connections as means of creation [and annihilation] mechanism of different dynamical regimes and their respective attractor states considering a network of spiking neurons. The exponentially-decaying external input is introduced as means of a vector field driving the system into a specific attractor. Based on the topological analysis of the state space of the input-driven system, computational capabilities of the attractor states and link to working memory are discussed.
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001024013 650_7 $$2Other$$aComputational Neuroscience
001024013 650_7 $$2Other$$aNetworks and dynamical systems
001024013 7001_ $$0P:(DE-Juel1)176778$$aBouhadjar, Younes$$b1$$ufzj
001024013 7001_ $$0P:(DE-Juel1)133935$$aSchiek, Michael$$b2$$ufzj
001024013 7001_ $$0P:(DE-Juel1)188145$$aStrachan, John P.$$b3$$ufzj
001024013 7001_ $$0P:(DE-Juel1)188273$$aNeftci, Emre$$b4$$ufzj
001024013 773__ $$a10.12751/NNCN.BC2023.174
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001024013 9141_ $$y2024
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001024013 9201_ $$0I:(DE-Juel1)PGI-14-20210412$$kPGI-14$$lNeuromorphic Compute Nodes$$x0
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