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000809498 037__ $$aFZJ-2016-02592
000809498 041__ $$aEnglish
000809498 1001_ $$0P:(DE-Juel1)156383$$aRostami, Vahid$$b0$$eCorresponding author$$ufzj
000809498 1112_ $$a9th Bernstein Sparks Workshop$$cGöttingen$$d2016-05-25 - 2016-05-27$$wGermany
000809498 245__ $$aPairwise maximum-entropy models: bimodality, bistability, non-ergodicityproblems, and their elimination via inhibition
000809498 260__ $$c2016
000809498 3367_ $$033$$2EndNote$$aConference Paper
000809498 3367_ $$2BibTeX$$aINPROCEEDINGS
000809498 3367_ $$2DRIVER$$aconferenceObject
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000809498 520__ $$aThe pairwise maximum-entropy model [1,2], applied to experimental neuronal data of populations of 200 andmore neurons, is very likely to give a bimodal probability distribution for the population-averaged activity. Wehave provided evidence for this claim, starting from an experimental dataset and then looking at summarizeddata from the literature. The first mode is the one observed in the data. The second mode (unobserved)can appear at very high activities (even 90% of the population simultaneously active) and its height increaseswith population size. This bimodality has several undesirable consequences:1.The presence of two modes is unrealistic in view of observed neuronal activity.2.The prediction of a high-activity mode is unrealistic on neurobiological grounds.3.Boltzmann learning becomes non-ergodic, hence the pairwise model found by this method is not themaximum entropy distribution; similarly, solving the inverse problem by common variants of mean-fieldapproximations has the same problem.4.The Glauber dynamics associated with the model is either unrealistically bistable, or does not reflect thedistribution of the pairwise model.
000809498 536__ $$0G:(DE-HGF)POF2-331$$a331 - Signalling Pathways and Mechanisms in the Nervous System (POF2-331)$$cPOF2-331$$fPOF II$$x0
000809498 536__ $$0G:(DE-HGF)POF3-574$$a574 - Theory, modelling and simulation (POF3-574)$$cPOF3-574$$fPOF III$$x1
000809498 536__ $$0G:(DE-HGF)POF3-571$$a571 - Connectivity and Activity (POF3-571)$$cPOF3-571$$fPOF III$$x2
000809498 7001_ $$0P:(DE-Juel1)165939$$aMana, PierGianLuca$$b1$$ufzj
000809498 7001_ $$0P:(DE-Juel1)144806$$aHelias, Moritz$$b2$$ufzj
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000809498 9131_ $$0G:(DE-HGF)POF2-331$$1G:(DE-HGF)POF2-330$$2G:(DE-HGF)POF2-300$$3G:(DE-HGF)POF2$$4G:(DE-HGF)POF$$aDE-HGF$$bGesundheit$$lFunktion und Dysfunktion des Nervensystems$$vSignalling Pathways and Mechanisms in the Nervous System$$x0
000809498 9131_ $$0G:(DE-HGF)POF3-574$$1G:(DE-HGF)POF3-570$$2G:(DE-HGF)POF3-500$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bKey Technologies$$lDecoding the Human Brain$$vTheory, modelling and simulation$$x1
000809498 9131_ $$0G:(DE-HGF)POF3-571$$1G:(DE-HGF)POF3-570$$2G:(DE-HGF)POF3-500$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bKey Technologies$$lDecoding the Human Brain$$vConnectivity and Activity$$x2
000809498 9141_ $$y2016
000809498 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x0
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