Neuron

Summary

Within a nervous system, a neuron, neurone, or nerve cell is an electrically excitable cell that fires electric signals called action potentials across a neural network. Neurons communicate with other cells via synapses - specialized connections that commonly use minute amounts of chemical neurotransmitters to pass the electric signal from the presynaptic neuron to the target cell through the synaptic gap. The neuron is the main component of nervous tissue in all animals except sponges and placozoa. Non-animals like plants and fungi do not have nerve cells. Neurons are typically classified into three types based on their function. Sensory neurons respond to stimuli such as touch, sound, or light that affect the cells of the sensory organs, and they send signals to the spinal cord or brain. Motor neurons receive signals from the brain and spinal cord to control everything from muscle contractions to glandular output. Interneurons connect neurons to other neurons within the same region

Official source

https://en.wikipedia.org/wiki/Neuron

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Data-driven whole mouse brain modeling for multi-scale simulations

Dimitri Bruno Marie Rodarie

In this thesis, we present a data-driven iterative pipeline to generate, simulate and validate point-neuron models of the whole mouse brain. The ultimate goal is to replicate close loop experiments with a virtual body in a virtual world. This pipeline was origi-nally created by a PhD student of the Blue Brain Project and this pioneer work has yielded the first versions of the model and their simulations. My objective is to refine, extend and consolidate the previous version of the workflow and in particular to extend the repertoire of neuron types by integrating and consolidating available literature data.The pipeline has four main parts. First, we define a pair of reference atlases to create a spatial reference for every dataset that we want to integrate. Second, we create a cell atlas with estimates of the number and density of the major cell types in each brain region. Then, we build a connectivity atlas that describes the connections of each neuron of the mouse brain and their properties. Finally, we assign point-neuron parameters to each of the neuron types, and synaptic parameters to each of the connection types of our model to obtain a point-neuron network of the mouse brain. This network can be simulated to perform mouse brain experi-ments in-silico.In this thesis, we evaluate the quality of the different reference atlases, released by the Allen Institute for Brain Science and pro-vide methods to mitigate the impact of artifacts in the original images. We further extend the cell atlas with density estimates of more inhibitory neuron types. To do so, we introduce a new method to combine estimates of inhibitory neuron counts from the literature into a consistent framework. We also estimate inhibitory neuron density in regions where no literature data are avai-lable, using collected literature estimates and gene expression data from in situ hybridization image stacks. Our approach can be further extended to other cell types and provides a resource to build more detailed circuits of the mouse brain. Next, we refine the connectivity atlas, including literature findings on short-range connectivity. Additionally, we construct a database to store data collected on neuron types point-neuron parameters and synaptic parameters. With the results of the previous steps, we generate a new version of the whole mouse brain point-neuron network. We tested this new model against its previous version using three benchmark experiments. We explore the possibility to embed and connect detailed circuit reconstructions of the mouse brain to our point neuron model and co-simulate them. Finally, we constrain a 3D skeleton model of the mouse with joint constraints from the literature and attach muscle to its bones to create a musculoskeletal model of the mouse body that can be simulated in close-loop with our model to reproduce motor control experiments.

EPFL2022

Rostro-caudal coordination of spinal motor output is essential for locomotion. Most spinal interneurons project axons longitudinally to govern locomotor output, yet their connectivity along this axis remains unclear. In this study, we use larval zebrafish to map synaptic outputs of a major inhibitory population, V1 (Eng1(+)) neurons, which are implicated in dual sensory and motor functions. We find that V1 neurons exhibit long axons extending rostrally and exclusively ipsilaterally for an average of 6 spinal segments; however, they do not connect uniformly with their post-synaptic targets along the entire length of their axon. Locally, V1 neurons inhibit motor neurons (both fast and slow) and other premotor targets, including V2a, V2b, and commissural premotor neurons. In contrast, V1 neurons make robust long-range inhibitory contacts onto a dorsal horn sensory population, the commissural primary ascending neurons (CoPAs). In a computational model of the ipsilateral spinal network, we show that this pattern of short-range V1 inhibition to motor and premotor neurons underlies burst termination, which is critical for coordinated rostro-caudal propagation of the loco motor wave. We conclude that spinal network architecture in the longitudinal axis can vary dramatically, with differentially targeted local and distal connections, yielding important consequences for function.

CELL PRESS2021

The way in which cortical microcircuit components -- most importantly neurons -- and their connectivity -- the network -- shape and constrain emergent dynamics is a long-standing question in neuroscience. Experimentally observed dynamical properties can often be explained by circuit models with different simplifying assumptions for the underlying neuron models and their network structure, such as deterministic synapse models describing stochastic synapses, or a uniform network structure describing heterogeneous synaptic connectivity. Intrinsic neural variability, for example, can emerge from both stochastic synaptic properties (noise) and deterministic network dynamics (chaos). It is therefore often not clear if models with ad hoc simplifying assumptions for various biological details provide correct explanations for the emergence of cortical dynamics. In this thesis, we set out to advance our understanding of how detailed biological properties of cortical neurons and their network structure shape emergent dynamics by studying a model of a prototypical neocortical microcircuit that was reconstructed using all relevant available biological data. To make our predictions as biologically accurate as possible, we used a "zero tweak" strategy wherein parameters in the model were not adjusted to replicate specific experimentally observed dynamical properties, but instead were constrained by biological data. This allowed us to characterize the effect of two biological properties that are often abstracted away in ad hoc simplifications: stochastic synaptic transmission and a heterogeneous network structure with complex higher-order connectivity. Studying the model, we made several important predictions: (1) Stochastic synaptic transmission, in an interplay with recurrent network dynamics, causes rapid chaotic divergence of spontaneous activity. (2) Synaptic noise overshadows other local cellular noise sources. (3) Amid the noise and chaos, neurons can reliably respond to external inputs with millisecond spike-time precision. (4) This reliable response goes beyond mere feedforward suppression of recurrent dynamics and is driven by the circuit at a near-critical excitation-inhibition balance. (5) An abundance of high-dimensional cliques of all-to-all connected neurons which shape correlations between neurons in a hierarchical manner. (6) This effect is strongly reduced when synaptic connectivity is replaced by a rejected null model with reduced higher-order network structure. We conclude that a detailed representation of cellular noise sources and high-dimensional network structure is imperative to accurately model emergent cortical network dynamics. Models that make ad hoc simplifying assumptions need to carefully justify the exclusion of such details.

EPFL2019

Data-driven whole mouse brain modeling for multi-scale simulations

Dimitri Bruno Marie Rodarie

EPFL2022

EPFL2019