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| Home > Publications database > Multi-Scale Spiking Network Model of Human Cerebral Cortex |
| Poster (Other) | FZJ-2023-02823 |
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2023
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Please use a persistent id in citations: doi:10.34734/FZJ-2023-02823
Abstract: The structure of the brain plays a crucial role in shaping its activity. However, the link between structural connectivity and observed neuronal activity remains not fully understood. Previous research utilizing a large-scale spiking network model of leaky integrate-and-fire neurons has addressed this question for macaque cortex [1,2]. In this study, we employ the same framework to investigate human cortex and present a large-scale spiking network model that links the cortical network structure to the resting-state activity of neurons, populations, layers, and areas.Our approach integrates data on cortical architecture, cellular morphologies, and local and cortico-cortical connectivity into a multi-scale framework to predict connection probabilities between neurons based on their types and locations within areas and layers. We represent each cortical area with a 1 mm2 area-specific microcircuit incorporating the full density of neurons and synapses. For this first model version, the laminar thicknesses and neuron densities are derived from the von Economo and Koskinas atlas [3]. The connectivity on the area level is informed by diffusion tensor imaging (DTI) data [4], while predictions on laminar connectivity patterns are derived from predictive connectomics based on macaque data that express regularities of laminar connectivity patterns as a function of cortical architecture. We use the Potjans and Diesmann [5] model as a basis for the local connectivity, scaling it according to cytoarchitectonic data. To map inter-area synapses to target cells, which may have their cell body in a different layer compared to the synapse location, we assign synapses in proportion to the layer- and cell-type-specific dendritic lengths determined from human neuron morphologies [6]. The model contains approximately 4 million neurons and 50 billion synapses and is simulated on JURECA-DC using the NEST simulator.Simulations of the model reveal a state with asynchronous and irregular activity that deviates from experimental recordings in terms of spiking activity and inter-area functional connectivity. By increasing the strength of the inter-area synapses, a state is reached that captures aspects of both microscopic and macroscopic resting-state activity of human cortex measured via electrophysiological recordings from medial frontal cortex and fMRI [7]. Furthermore, we used our model to track the effect of a single additional spike through the large-scale network. We find that a single-spike perturbation spreads rapidly across the entire network within 50-75 ms, comparable to visual response latencies in macaque cortex [8], suggesting that the cortical network allows individual spikes to play a role in fast sensory processing and behavioral responses. Overall, the model serves as a basis for the investigation of multi-scale structure-dynamics relationships in human cortex.
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