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@INPROCEEDINGS{Bttcher:943377,
      author       = {Böttcher, Joshua and Schulte to Brinke, Tobias and
                      Morrison, Abigail and Wybo, Willem},
      title        = {{C}onnectivity patterns that enable working memory in
                      recurrent networks with dendritic subunits},
      reportid     = {FZJ-2023-00976},
      year         = {2022},
      abstract     = {Maintain 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.},
      month         = {Sep},
      date          = {2022-09-17},
      organization  = {2nd Advanced Online $\&$ Onsite Course
                       $\&$ Symposium on Artificial
                       Intelligence $\&$ Neuroscience, Siena
                       (Italy), 17 Sep 2022 - 22 Sep 2022},
      subtyp        = {After Call},
      cin          = {INM-6},
      cid          = {I:(DE-Juel1)INM-6-20090406},
      pnm          = {5232 - Computational Principles (POF4-523) / Helmholtz
                      Platform for Research Software Engineering - Preparatory
                      Study $(HiRSE_PS-20220812)$},
      pid          = {G:(DE-HGF)POF4-5232 / $G:(DE-Juel-1)HiRSE_PS-20220812$},
      typ          = {PUB:(DE-HGF)6},
      url          = {https://juser.fz-juelich.de/record/943377},
}