000901914 001__ 901914
000901914 005__ 20240313094935.0
000901914 037__ $$aFZJ-2021-03901
000901914 088__ $$2arXiv$$a2105.05002
000901914 1001_ $$0P:(DE-Juel1)176921$$aDasbach, Stefan$$b0$$eCorresponding author$$ufzj
000901914 245__ $$aProminent characteristics of recurrent neuronal networks are robust against low synaptic weight resolution
000901914 260__ $$c2021
000901914 3367_ $$0PUB:(DE-HGF)25$$2PUB:(DE-HGF)$$aPreprint$$bpreprint$$mpreprint$$s1636122875_17329
000901914 3367_ $$2ORCID$$aWORKING_PAPER
000901914 3367_ $$028$$2EndNote$$aElectronic Article
000901914 3367_ $$2DRIVER$$apreprint
000901914 3367_ $$2BibTeX$$aARTICLE
000901914 3367_ $$2DataCite$$aOutput Types/Working Paper
000901914 520__ $$aThe representation of the natural-density, heterogeneous connectivity of neuronal network models at relevant spatial scales remains a challenge for Computational Neuroscience and Neuromorphic Computing. In particular, the memory demands imposed by the vast number of synapses in brain-scale network simulations constitutes a major obstacle. Limiting the number resolution of synaptic weights appears to be a natural strategy to reduce memory and compute load.  In this study, we investigate the effects of a limited synaptic-weight resolution on the dynamics of recurrent spiking neuronal networks resembling local cortical circuits, and develop strategies for minimizing deviations from the dynamics of networks with high-resolution synaptic weights. We mimic the effect of a limited synaptic weight resolution by replacing normally distributed synaptic weights by weights drawn from a discrete distribution, and compare the resulting statistics characterizing firing rates, spike-train irregularity, and correlation coefficients with the reference solution. We show that a naive discretization of synaptic weights generally leads to a distortion of the spike-train statistics. Only if the weights are discretized such that the mean and the variance of the total synaptic input currents are preserved, the firing statistics remains unaffected for the types of networks considered in this study. For networks with sufficiently heterogeneous in-degrees, the firing statistics can be preserved even if all synaptic weights are replaced by the mean of the weight distribution. We conclude that even for simple networks with non-plastic neurons and synapses, a discretization of synaptic weights can lead to substantial deviations in the firing statistics, unless the discretization is performed with care and guided by a rigorous validation process. For the network model used in this study, the synaptic weights can be replaced by low-resolution weights without affecting its macroscopic dynamical characteristics, thereby saving substantial amounts of memory.
000901914 536__ $$0G:(DE-HGF)POF4-5231$$a5231 - Neuroscientific Foundations (POF4-523)$$cPOF4-523$$fPOF IV$$x0
000901914 536__ $$0G:(EU-Grant)785907$$aHBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907)$$c785907$$fH2020-SGA-FETFLAG-HBP-2017$$x1
000901914 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x2
000901914 536__ $$0G:(DE-HGF)SO-092$$aACA - Advanced Computing Architectures (SO-092)$$cSO-092$$x3
000901914 536__ $$0G:(DE-Juel1)jinb33_20191101$$aBrain-Scale Simulations (jinb33_20191101)$$cjinb33_20191101$$fBrain-Scale Simulations$$x4
000901914 536__ $$0G:(DE-Juel1)PHD-NO-GRANT-20170405$$aPhD no Grant - Doktorand ohne besondere Förderung (PHD-NO-GRANT-20170405)$$cPHD-NO-GRANT-20170405$$x5
000901914 7001_ $$0P:(DE-Juel1)145211$$aTetzlaff, Tom$$b1$$ufzj
000901914 7001_ $$0P:(DE-Juel1)144174$$aDiesmann, Markus$$b2$$ufzj
000901914 7001_ $$0P:(DE-Juel1)162130$$aSenk, Johanna$$b3$$ufzj
000901914 8564_ $$uhttps://arxiv.org/abs/2105.05002
000901914 909CO $$ooai:juser.fz-juelich.de:901914$$pec_fundedresources$$pVDB$$popenaire
000901914 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)176921$$aForschungszentrum Jülich$$b0$$kFZJ
000901914 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)145211$$aForschungszentrum Jülich$$b1$$kFZJ
000901914 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)144174$$aForschungszentrum Jülich$$b2$$kFZJ
000901914 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)162130$$aForschungszentrum Jülich$$b3$$kFZJ
000901914 9131_ $$0G:(DE-HGF)POF4-523$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5231$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vNeuromorphic Computing and Network Dynamics$$x0
000901914 9141_ $$y2021
000901914 920__ $$lno
000901914 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x0
000901914 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lTheoretical Neuroscience$$x1
000901914 9201_ $$0I:(DE-Juel1)INM-10-20170113$$kINM-10$$lJara-Institut Brain structure-function relationships$$x2
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000901914 980__ $$aI:(DE-Juel1)INM-6-20090406
000901914 980__ $$aI:(DE-Juel1)IAS-6-20130828
000901914 980__ $$aI:(DE-Juel1)INM-10-20170113
000901914 980__ $$aUNRESTRICTED
000901914 981__ $$aI:(DE-Juel1)IAS-6-20130828