Mirror neuron | EPFL Graph Search

A mirror neuron is a neuron that fires both when an organism acts and when the organism observes the same action performed by another. Thus, the neuron "mirrors" the behavior of the other, as though the observer were itself acting. Mirror neurons are not always physiologically distinct from other types of neurons in the brain; their main differentiating factor is their response patterns. By this definition, such neurons have been directly observed in humans and primate species, and in birds. In humans, brain activity consistent with that of mirror neurons has been found in the premotor cortex, the supplementary motor area, the primary somatosensory cortex, and the inferior parietal cortex. The function of the mirror system in humans is a subject of much speculation. Birds have been shown to have imitative resonance behaviors and neurological evidence suggests the presence of some form of mirroring system. To date, no widely accepted neural or computati

Clustering and control for adaptation uncovers time-warped spike time patterns in cortical networks in vivo

James Bryden Isbister

How information in the nervous system is encoded by patterns of action potentials (i.e. spikes) remains an open question. Multi-neuron patterns of single spikes are a prime candidate for spike time encoding but their temporal variability requires further characterisation. Here we show how known sources of spike count variability affect stimulus-evoked spike time patterns between neurons separated over multiple layers and columns of adult rat somatosensory cortex in vivo. On subsets of trials (clusters) and after controlling for stimulus-response adaptation, spike time differences between pairs of neurons are "time-warped" (compressed/stretched) by trial-to-trial changes in shared excitability, explaining why fixed spike time patterns and noise correlations are seldom reported. We show that predicted cortical state is correlated between groups of 4 neurons, introducing the possibility of spike time pattern modulation by population-wide trial-to-trial changes in excitability (i.e. cortical state). Under the assumption of state-dependent coding, we propose an improved potential encoding capacity.

2021

Shedding of neurexin 3 beta ectodomain by ADAM10 releases a soluble fragment that affects the development of newborn neurons

Erika Myrna Borcel Novoa, Mitko Dimitrov, Patrick Fraering, Marine Krzisch, Magda Maria Palczynska, Giorgio Ulrich

Neurexins are transmembrane synaptic cell adhesion molecules involved in the development and maturation of neuronal synapses. In the present study, we report that Nrxn3 beta is processed by the metalloproteases ADAM10, ADAM17, and by the intramembrane-cleaving protease.-secretase, producing secreted neurexin3 beta (sNrxn3 beta) and a single intracellular domain (Nrxn3 beta-ICD). We further completed the full characterization of the sites at which Nrxn3 beta is processed by these proteases. Supporting the physiological relevance of the Nrxn3 beta processing, we demonstrate in vivo a significant effect of the secreted shedding product sNrxn3 beta on the morphological development of adult newborn neurons in the mouse hippocampus. We show that sNrxn3 beta produced by the cells of the dentate gyrus increases the spine density of newborn neurons whereas sNrxn3 beta produced by the newborn neuron itself affects the number of its mossy fiber terminal extensions. These results support a pivotal role of sNrxn3 beta in plasticity and network remodeling during neuronal development.

Nature Publishing Group2016

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

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

Dimitri Bruno Marie Rodarie

EPFL2022