"Single-neuron model in cortical context" is the fourth seminar in the series Pathways to the 2023 IHP thematic project Random Processes in the Brain.
Title: Single-neuron model in cortical context
Date: 2022-05-31 at 10:00 GMT-3
Speaker: Markus Diesmann,
Affiliation: Jülich Research Centre
Abstract: In the preparation of the 2023 IHP thematic program "Random Processes in the Brain" the question came up how relevant the single-neuron model is for cortical dynamics and function. Given the plethora of single-neuron models available, insight into their differential effects on the network level would give theoreticians guidance on which model to choose for which research question. The purpose of this talk is to outline a small project approaching this question which could be carried out in the framework of the thematic program in a collaboration of several labs. The talk first presents a well-studied full-density network model of the cortical microcircuit as a suitable reference network. The proposal is to replace the original single-neuron model by alternative common single-neuron models and to quantify the impact on the network level. For this purpose the presentation reviews a range of common single-neuron models as candidates and a set of measures like firing rate, irregularity, and the power spectrum. It seems achievable that all relevant neuron models can be formulated in the domain-specific language NESTML and data analysis be carried out in the Elephant framework such that a reproducible digital workflow for the project can be constructed. A minimal scope of the investigation covers a static network in a stationary state. However, there are indications in the literature that the conventional constraints on network activity are weak. Furthermore, hypotheses on the function of the cortical microcircuit depend on the transient interaction between cortical layers, synaptic plasticity, and a separation of dendritic and somatic compartments. Therefore, we need to carefully debate how the scope of an initial exploration can usefully be restricted.
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