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001044515 005__ 20250729202321.0
001044515 037__ $$aFZJ-2025-03252
001044515 041__ $$aEnglish
001044515 1001_ $$0P:(DE-Juel1)195833$$aSchutzeichel, Lars$$b0$$eCorresponding author$$ufzj
001044515 1112_ $$a29th International Conference on Statistical Physics$$cFlorence$$d2025-07-13 - 2025-07-18$$gStatPhys29$$wItaly
001044515 245__ $$aRecurrent network dynamics underlying transient sensory stimulus representations in mice
001044515 260__ $$c2025
001044515 3367_ $$033$$2EndNote$$aConference Paper
001044515 3367_ $$2BibTeX$$aINPROCEEDINGS
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001044515 502__ $$cRWTH Aachen
001044515 520__ $$aDifferent stimuli elicit different transient neural responses in the brain. How is theinformation represented in the parallel neuronal activity and how is it reshaped by thedynamics of local recurrent circuits? We investigate these questions in Neuropixels recordings of awake behaving mice and recurrent neural network models by inferring the stimulusclass from the network activity.We employ methods from statistical physics of disordered systems to derive a two-replica mean-field theory that reduces complex network dynamics to three dynamicalquantities that fully determine the separability of stimulus representations. These dynamical quantities are the mean population activity $R$ and the overlaps $Q^{=}$ and $Q^{\neq}$,representing response variability within or across stimulus classes, respectively.Mean-field theory predicts the time evolution of $R$, $Q^{=}$, and $Q^{\neq}$ and enables us to quantitatively explain experimental observables. The analytical theory predicts the temporaldynamics of stimulus separability as an interplay of firing rate dynamics, controlled byinhibitory balancing, and overlaps, governed by chaotic dynamics.The analysis of mutual information of an optimally trained readout on the populationsignal reveals a trade-off between more information conveyed with an increasing numberof stimuli, and stimuli becoming less separable due to their increased overlap in the finite-dimensional neuronal space. We find that the experimentally observed small populationactivity $R$ lies in a regime where information grows with the number of stimuli, which issharply separated from a second regime, in which information converges to zero, revealinga crucial advantage of sparse coding.
001044515 536__ $$0G:(DE-HGF)POF4-5231$$a5231 - Neuroscientific Foundations (POF4-523)$$cPOF4-523$$fPOF IV$$x0
001044515 536__ $$0G:(DE-HGF)POF4-5232$$a5232 - Computational Principles (POF4-523)$$cPOF4-523$$fPOF IV$$x1
001044515 536__ $$0G:(GEPRIS)368482240$$aGRK 2416 - GRK 2416: MultiSenses-MultiScales: Neue Ansätze zur Aufklärung neuronaler multisensorischer Integration (368482240)$$c368482240$$x2
001044515 536__ $$0G:(GEPRIS)533396241$$aDFG project G:(GEPRIS)533396241 - Evolutionäre Optimierung neuronaler Netzwerkdynamik auf eine empfängerspezifische interareale Kommunikation (533396241)$$c533396241$$x3
001044515 7001_ $$0P:(DE-HGF)0$$aBauer, Jan$$b1
001044515 7001_ $$0P:(DE-Juel1)178725$$aBouss, Peter$$b2$$ufzj
001044515 7001_ $$0P:(DE-Juel1)175146$$aMusall, Simon$$b3$$ufzj
001044515 7001_ $$0P:(DE-Juel1)156459$$aDahmen, David$$b4$$ufzj
001044515 7001_ $$0P:(DE-Juel1)144806$$aHelias, Moritz$$b5$$ufzj
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001044515 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$$x1
001044515 9141_ $$y2025
001044515 9201_ $$0I:(DE-Juel1)IBI-3-20200312$$kIBI-3$$lBioelektronik$$x0
001044515 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lComputational and Systems Neuroscience$$x1
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