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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},
}