Hauptseite > Publikationsdatenbank > Joint excitatory-inhibitory clustering supports realistic spiking statistics and signal propagation > print

001     1047420
005     20260122203304.0
037 _ _ |a FZJ-2025-04289
041 _ _ |a English
100 1 _ |a Van Dael, Lies
|0 P:(DE-Juel1)206720
|b 0
|e Corresponding author
|u fzj
111 2 _ |a Bernstein Conference
|c Frankfurt am Main
|d 2025-09-29 - 2025-10-02
|w Germany
245 _ _ |a Joint excitatory-inhibitory clustering supports realistic spiking statistics and signal propagation
260 _ _ |c 2025
336 7 _ |a Conference Paper
|0 33
|2 EndNote
336 7 _ |a INPROCEEDINGS
|2 BibTeX
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336 7 _ |a Poster
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520 _ _ |a The connectivity among billions of neurons underlies the brain’s cognitive and information processing functions. Fully characterizing the single-neuron structural connectivity of the entire brain is technically not feasible. Furthermore, most studies focus on a single scale (population, area or single-neuron level); thus, bridging brain scales remains a challenge in neuroscience. Experimental data indicate that neuronal connections in the cerebral cortex are clustered, with stronger connections within clusters and weaker ones across them. Previous models linked such clustering to observed cortical activity. However, the mechanisms governing large-scale cortical dynamics - specifically reliable inter-area signal propagation, while maintaining stable activity and realistic spiking statistics- remain unclear. This study examines how joint clustering of excitatory and inhibitory cells contributes to explaining these dynamics. We hypothesize that local cortical circuits form joint clusters of excitatory and inhibitory neurons, and explore how this affects resting-state activity, inter-area signal transmission, and trial-to-trial variability. Building on a previously developed unclustered spiking neural network model of all vision-related areas in one hemisphere of the macaque cortex, each cortical area is modeled by a 1 mm2 microcircuit with biologically realistic neuron and synapse densities, avoiding downscaling artifacts. We extend this by subdividing each area equally into a number of joint excitatory-inhibitory clusters. Figure 1 schematically shows this organization.Validation against in vivo resting-state data reveals that the model with joint clusters of excitatory and inhibitory cells matches observed activity better than the unclustered model, in terms of firing behavior, firing rate distributions, and inter-spike interval variability. This type of clustering also enables reliable signal propagation across areas in feedforward and feedback directions, with biologically plausible response latencies. Finally, the clustered model reproduces the experimentally observed phenomenon of reduced trial-to-trial variability in response to stimulus onset.To conclude, these results demonstrate that joint clustering of excitatory and inhibitory neurons is a plausible organizational principle of local cortical circuits. This architecture simultaneously supports resting-state spiking statistics, inter-area signal propagation, and trial-to-trial variability dynamics.
536 _ _ |a 5231 - Neuroscientific Foundations (POF4-523)
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536 _ _ |a HBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907)
|0 G:(EU-Grant)785907
|c 785907
|f H2020-SGA-FETFLAG-HBP-2017
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536 _ _ |a HBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)
|0 G:(EU-Grant)945539
|c 945539
|f H2020-SGA-FETFLAG-HBP-2019
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536 _ _ |a GRK 2416 - GRK 2416: MultiSenses-MultiScales: Neue Ansätze zur Aufklärung neuronaler multisensorischer Integration (368482240)
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536 _ _ |a DFG project G:(GEPRIS)313856816 - SPP 2041: Computational Connectomics (313856816)
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536 _ _ |a Brain-Scale Simulations (jinb33_20220812)
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700 1 _ |a Pronold, Jari
|0 P:(DE-Juel1)165321
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700 1 _ |a Shimoura, Renan
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700 1 _ |a Rostami, Vahid
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700 1 _ |a Morales-Gregorio, Aitor
|0 P:(DE-Juel1)176593
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700 1 _ |a van Albada, Sacha
|0 P:(DE-Juel1)138512
|b 5
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913 1 _ |a DE-HGF
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|v Neuromorphic Computing and Network Dynamics
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914 1 _ |y 2025
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920 1 _ |0 I:(DE-Juel1)IAS-6-20130828
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980 _ _ |a poster
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