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001006561 005__ 20240313095010.0
001006561 037__ $$aFZJ-2023-01709
001006561 1001_ $$0P:(DE-Juel1)174585$$aNestler, Sandra$$b0$$eCorresponding author$$ufzj
001006561 1112_ $$aCosyne 2023$$cMontreal$$d2023-03-09 - 2023-03-14$$gCosyne$$wCanada
001006561 245__ $$aNeuronal extraction of statistical patterns embedded in time series
001006561 260__ $$c2023
001006561 3367_ $$033$$2EndNote$$aConference Paper
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001006561 520__ $$aNeuronal systems need to process temporal signals. Here, we hypothesize that temporal (co-)fluctuations – corresponding to high-order statistics beyond the average activity – are relevant for computation. The proposed biologically inspired feedforward neuronal model is able to extract information from up to the third order cumulant to perform time series classification. This model relies on a nonlinear gain function to combine the different statistical orders of the inputs, after a usual weighted summation. In addition to the afferent synaptic weights, the nonlinear gain function is optimized, which enables the combination of cumulants in a synergistic manner to maximize the classification accuracy. The approach is demonstrated both on synthetic and real world datasets of multivariate time series to test the classification performance and to interpret the tunable gain function. Moreover, we show that our biological scheme makes a better use of the number of trainable parameters as compared to a classical machine-learning scheme. Our findings emphasize the benefit of biological neuronal architectures, paired with dedicated learning algorithms, for the processing of information embedded in higher-order statistical cumulants of temporal (co-)fluctuations.
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001006561 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x3
001006561 536__ $$0G:(DE-HGF)SO-092$$aACA - Advanced Computing Architectures (SO-092)$$cSO-092$$x4
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001006561 536__ $$0G:(DE-Juel-1)PF-JARA-SDS005$$aSDS005 - Towards an integrated data science of complex natural systems (PF-JARA-SDS005)$$cPF-JARA-SDS005$$x6
001006561 7001_ $$0P:(DE-Juel1)144806$$aHelias, Moritz$$b1$$ufzj
001006561 7001_ $$0P:(DE-Juel1)184621$$aGilson, Matthieu$$b2
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001006561 9131_ $$0G:(DE-HGF)POF4-523$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5232$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vNeuromorphic Computing and Network Dynamics$$x1
001006561 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)POF4-5234$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vNeuromorphic Computing and Network Dynamics$$x2
001006561 9141_ $$y2023
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001006561 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x0
001006561 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lTheoretical Neuroscience$$x1
001006561 9201_ $$0I:(DE-Juel1)INM-10-20170113$$kINM-10$$lJara-Institut Brain structure-function relationships$$x2
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