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000890909 1001_ $$0P:(DE-Juel1)176594$$aJiang, Han-Jia$$b0$$ufzj
000890909 1112_ $$aThe 5th HBP Student Conference$$cvirtual$$d2021-02-01 - 2021-02-04$$wvirtual
000890909 245__ $$aA Multi-layer Microcircuit Model of Somatosensory Cortex with Multiple Interneuron Classes
000890909 260__ $$c2021
000890909 3367_ $$033$$2EndNote$$aConference Paper
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000890909 502__ $$cUniversity of Cologne
000890909 520__ $$aThree major classes of GABAergic inhibitory interneurons play critical and distinct roles in the regulation of cortical network dynamics and signal processing [1, 2]. The mechanisms of regulation are linked to the anatomical and physiological diversity of these interneurons. While experimental studies provide realistic observations, neuronal network simulations with parameters from experimental data can aid in direct exploration of the mechanisms and conveniently test hypotheses. Therefore, we develop a microcircuit model of mouse somatosensory (barrel) cortex incorporating three major classes of interneurons as a tool to study cortical network dynamics and sensory signal processing.The simulation software NEST 2.16.0 [3] is used to create a multi-layer (L2/3, L4, L5, and L6) cortical microcircuit model adapted from [4]. The neuron model is the leaky-integrate-and-fire neuron model with exponentially decaying post-synaptic currents (PSC). The network includes populations of excitatory (Exc) cells and three classes of interneurons: parvalbumin (PV), somatostatin (SOM) and vasoactive intestinal peptide (VIP) cells. The layer-specific numbers of each cell type are based on the data of a mouse barrel column [5, 6]. Cell-type-specific membrane parameters are according to the measurements in [7]. Probabilities of recurrent connections are determined from experimental data of paired recordings or algorithmic estimates by [8]. Each neuron receives optimized background inputs with cell-type-specific numbers of connections. Two hundred thalamic cells are created and connected to Exc and PV cells with layer-specific probabilities from [9] to test the network responses to a 10-ms transient thalamic input of 100 spikes/s. The weights of excitatory recurrent connections are set according to local intracortical unitary excitatory postsynaptic potentials (uEPSPs) [5], while those of inhibitory connections are multiplied by a relative inhibitory strength (g). The background and thalamic inputs are only excitatory, with weights according to thalamocortical uEPSPs [10]. Synaptic short-term plasticities (STPs) are included in the recurrent connections according to the Tsodyks model [11]. The STP parameters of different connections are individually fitted to match the experimentally measured STPs.The ground-state firing rates of the populations in the optimized model are comparable to those of in vivo data from [12], although some deviations remain (Figure 1A). The lower firing rates of VIP cells and Exc cells in L5 and L6 compared to the in vivo data are possibly due to the incomplete STP and connectivity data (data not shown), respectively. The changes of firing rates in L2/3 in response to activation of PV, SOM, and VIP cells show their respective roles of inhibition and disinhibition (Figure 2A), as observed in most experimental studies. Over a range of external input and relative inhibitory strengths, the model is able to fulfill the criteria on firing rates, spiking irregularity, and pairwise correlations of spike counts derived in [13] (Figure 1B). In addition, the model with STPs shows clearer multi-layer spiking responses to simulated transient thalamic input as compared to a version without STPs (Figure 2B). Further mechanistic analysis of the model may reveal the mechanisms behind these results and explore the specific roles of different interneuron types in state-dependent modulation of sensory signal processing. Specifically, with the experimental data of the thalamic input responsible for the whisker sensation [9], this model is useful in simulating  somatosensory inputs and provides a tool to analyze the roles of interneurons in its regulation and signal processing.
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000890909 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$x1
000890909 7001_ $$0P:(DE-Juel1)138512$$avan Albada, Sacha$$b1$$eCorresponding author$$ufzj
000890909 8564_ $$uhttps://juser.fz-juelich.de/record/890909/files/poster_HJ_5thHBP.pdf$$yOpenAccess
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000890909 9141_ $$y2021
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