000943377 001__ 943377
000943377 005__ 20240313095020.0
000943377 037__ $$aFZJ-2023-00976
000943377 041__ $$aEnglish
000943377 1001_ $$0P:(DE-Juel1)188317$$aBöttcher, Joshua$$b0$$eCorresponding author
000943377 1112_ $$a2nd Advanced Online & Onsite Course & Symposium on Artificial Intelligence & Neuroscience$$cSiena$$d2022-09-17 - 2022-09-22$$gACAIN 2022$$wItaly
000943377 245__ $$aConnectivity patterns that enable working memory in recurrent networks with dendritic subunits
000943377 260__ $$c2022
000943377 3367_ $$033$$2EndNote$$aConference Paper
000943377 3367_ $$2DataCite$$aOther
000943377 3367_ $$2BibTeX$$aINPROCEEDINGS
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000943377 3367_ $$0PUB:(DE-HGF)6$$2PUB:(DE-HGF)$$aConference Presentation$$bconf$$mconf$$s1704457942_19939$$xAfter Call
000943377 520__ $$aMaintain a working memory of observed features is a crucial neuronal computation. In spiking networks, this computation depends upon the existence of signals within neurons that persist after spike generation. Commonly, these signals are thought to be plasticity-related. Here, we hypothesize that the dendritic subunit forms a complementary substrate for working memory. Dendritic subunits support the generation of N-Methyl-D-Aspartate driven depolarizations (NMDA-spikes), which have a time-scale that outlasts the membrane time-scale by a factor of five. Moreover, voltage dynamics within dendritic subunits are unperturbed by somatic spikes, thus allowing temporal information to persist within neurons.To investigate the capacity of dendritic subunits to retain stimulus information, we reduce a layer 5 pyramidal cell to a prototypical model with dendritic subunits, representing distal basal compartments, and recurrently connect these excitatory (E) neurons to each other's dendrites. Inhibitory (I) interneurons provide global inhibition to balance the network. As NMDA-spikes are generated by coincident inputs to a dendritic subunit, we find that introducing modularity in the E-to-E connections, and increasing the degree of intraneural clustering -- such that neurons from the same cluster preferentially target the same dendritic subunit -- elicits NMDA-spikes in the network. Interestingly, NMDA-spikes also emerge at a fixed modularity when intra-neural clustering is increased, showing that this quantity -- often ignored in connectivity studies -- is crucial in determining the emergent network state. Finally, we quantify memory retention in the network by computing the capacity of linear readout units to reconstruct past inputs based on the current network state. We find that network states with NMDA-spikes can retain past inputs for up to $\sim$200 ms longer than network states without them. Thus, our results show that spiking networks can maintain a working memory even without synaptic plasticity,  through the activity of dendritic subunits.
000943377 536__ $$0G:(DE-HGF)POF4-5232$$a5232 - Computational Principles (POF4-523)$$cPOF4-523$$fPOF IV$$x0
000943377 536__ $$0G:(DE-Juel-1)HiRSE_PS-20220812$$aHelmholtz Platform for Research Software Engineering - Preparatory Study (HiRSE_PS-20220812)$$cHiRSE_PS-20220812$$x1
000943377 7001_ $$0P:(DE-Juel1)176595$$aSchulte to Brinke, Tobias$$b1
000943377 7001_ $$0P:(DE-Juel1)151166$$aMorrison, Abigail$$b2
000943377 7001_ $$0P:(DE-Juel1)186881$$aWybo, Willem$$b3
000943377 909CO $$ooai:juser.fz-juelich.de:943377$$pVDB
000943377 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)188317$$aForschungszentrum Jülich$$b0$$kFZJ
000943377 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)176595$$aForschungszentrum Jülich$$b1$$kFZJ
000943377 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)151166$$aForschungszentrum Jülich$$b2$$kFZJ
000943377 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)186881$$aForschungszentrum Jülich$$b3$$kFZJ
000943377 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$$x0
000943377 9141_ $$y2023
000943377 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x0
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000943377 981__ $$aI:(DE-Juel1)IAS-6-20130828