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001041227 037__ $$aFZJ-2025-02174
001041227 041__ $$aEnglish
001041227 1001_ $$0P:(DE-Juel1)195833$$aSchutzeichel, Lars$$b0$$eCorresponding author$$ufzj
001041227 1112_ $$aDPG spring meeting$$cRegensburg$$d2025-03-17 - 2025-03-21$$gDPG$$wGermany
001041227 245__ $$aTransient Recurrent Dynamics Shape Representations in Mice
001041227 260__ $$c2025
001041227 3367_ $$033$$2EndNote$$aConference Paper
001041227 3367_ $$2DataCite$$aOther
001041227 3367_ $$2BibTeX$$aINPROCEEDINGS
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001041227 520__ $$aDifferent stimuli evoke transient neural responses, but how is stimulus information represented and reshaped by local recurrent circuits? We address this question using Neuropixels recordings from awake mice and recurrent network models, inferring stimulus classes (e.g., visual or tactile) from activity. A two-replica mean-field theory reduces complex network dynamics to three key quantities: the mean population activity ($R$) and overlaps ($Q^{=}$, $Q^{\neq}$), reflecting response variability within and across stimulus classes. The theory predicts the time evolution of $R$, $Q^{=}$, and $Q^{\neq}$. Validated in experiments, it reveals how inhibitory balancing governs the dynamics of $R$, while chaotic dynamics shape overlaps, providing insights into the mechanisms underlying transient stimulus separation. The analysis of mutual information of an optimally trained population activity readout reveals that sparse coding (small $R$) allows the optimal information representation of multiple stimuli.
001041227 536__ $$0G:(DE-HGF)POF4-5231$$a5231 - Neuroscientific Foundations (POF4-523)$$cPOF4-523$$fPOF IV$$x0
001041227 536__ $$0G:(DE-HGF)POF4-5232$$a5232 - Computational Principles (POF4-523)$$cPOF4-523$$fPOF IV$$x1
001041227 536__ $$0G:(GEPRIS)368482240$$aGRK 2416 - GRK 2416: MultiSenses-MultiScales: Neue Ansätze zur Aufklärung neuronaler multisensorischer Integration (368482240)$$c368482240$$x2
001041227 536__ $$0G:(GEPRIS)533396241$$aDFG project G:(GEPRIS)533396241 - Evolutionäre Optimierung neuronaler Netzwerkdynamik auf eine empfängerspezifische interareale Kommunikation (533396241)$$c533396241$$x3
001041227 7001_ $$0P:(DE-HGF)0$$aBauer, Jan$$b1
001041227 7001_ $$0P:(DE-Juel1)178725$$aBouss, Peter$$b2$$ufzj
001041227 7001_ $$0P:(DE-Juel1)175146$$aMusall, Simon$$b3$$ufzj
001041227 7001_ $$0P:(DE-Juel1)156459$$aDahmen, David$$b4$$ufzj
001041227 7001_ $$0P:(DE-Juel1)144806$$aHelias, Moritz$$b5$$ufzj
001041227 8564_ $$uhttps://www.dpg-verhandlungen.de/year/2025/conference/regensburg/part/soe/session/7/contribution/8?lang=en
001041227 909CO $$ooai:juser.fz-juelich.de:1041227$$pVDB
001041227 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)195833$$aForschungszentrum Jülich$$b0$$kFZJ
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001041227 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)144806$$aForschungszentrum Jülich$$b5$$kFZJ
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001041227 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$$x1
001041227 9141_ $$y2025
001041227 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lComputational and Systems Neuroscience$$x0
001041227 9201_ $$0I:(DE-Juel1)IBI-3-20200312$$kIBI-3$$lBioelektronik$$x1
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