scholarly journals Experience-dependent development of dendritic arbors in mouse visual cortex

2019 ◽  
Author(s):  
Sarah E.V. Richards ◽  
Anna R. Moore ◽  
Alice Y. Nam ◽  
Shikhar Saxena ◽  
Suzanne Paradis ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Large Scale ◽  
Three Dimensional ◽  
Small Gtpase ◽  
Negative Regulator ◽  
Early Postnatal Development ◽  
Dendritic Segment ◽  
Dendritic Arbors ◽  
High Utility ◽  
Mouse Visual Cortex

AbstractDespite the importance of dendritic arbors for proper neuronal function, our knowledge of how sensory experience influences these structures during postnatal cortical development is incomplete. We present a large-scale dataset of 849 three-dimensional reconstructions of pyramidal neuron basal arbors collected across early postnatal development in the mouse visual cortex. We found that the basal arbor underwent a 45% increase in total length between postnatal day 7 (P7) and P30. Surprisingly, comparisons of dark-reared and typically-reared mice revealed that only 15% of arbor length could be attributed to visual experience. Furthermore, we characterized the role of the activity-regulated small GTPase Rem2, showing that Rem2 is an experience-dependent negative regulator of dendritic segment number during the visual critical period. These data establish a detailed, quantitative analysis of the basal arbor that has high utility for understanding circuit development and providing a framework for computationalists wishing to generate anatomically accurate neuronal models.

scholarly journals A Scalable and Modular Automated Pipeline for Stitching of Large Electron Microscopy Datasets

2021 ◽  
Author(s):  
Gayathri Mahalingam ◽  
Russel Torres ◽  
Daniel Kapner ◽  
Eric T Trautman ◽  
Tim Fliss ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Visual Cortex ◽  
High Throughput ◽  
Serial Section ◽  
Large Scale ◽  
Software Pipeline ◽  
Array Tomography ◽  
Automated Quality Control ◽  
Mouse Visual Cortex ◽  
The Brain

Serial section Electron Microscopy can produce high throughput imaging of large biological specimen volumes. The high-resolution images are necessary to reconstruct dense neural wiring diagrams in the brain, so called connectomes. A high fidelity volume assembly is required to correctly reconstruct neural anatomy and synaptic connections. It involves seamless 2D stitching of the images within a serial section followed by 3D alignment of the stitched sections. The high throughput of ssEM necessitates 2D stitching to be done at the pace of imaging, which currently produces tens of terabytes per day. To achieve this, we present a modular volume assembly software pipeline ASAP(Assembly Stitching and Alignment Pipeline) that is scalable and parallelized to work with distributed systems. The pipeline is built on top of the Render [18] services used in the volume assembly of the brain of adult Drosophila melanogaster [2]. It achieves high throughput by operating on the meta-data and transformations of each image stored in a database, thus eliminating the need to render intermediate output. The modularity of ASAP allows for easy adaptation to new algorithms without significant changes to the workflow. The software pipeline includes a complete set of tools to do stitching, automated quality control, 3D section alignment, and rendering of the assembled volume to disk. We also implemented a workflow engine that executes the volume assembly workflow in an automated fashion triggered following the transfer of raw data. ASAP has been successfully utilized for continuous processing of several large-scale datasets of the mouse visual cortex and human brain samples including one cubic millimeter of mouse visual cortex [1, 25]. The pipeline also has multi-channel processing capabilities and can be applied to fluorescence and multi-modal datasets like array tomography.

scholarly journals Experience-Dependent Development of Dendritic Arbors in Mouse Visual Cortex

2020 ◽  
Vol 40 (34) ◽  
pp. 6536-6556
Author(s):  
Sarah E.V. Richards ◽  
Anna R. Moore ◽  
Alice Y. Nam ◽  
Shikhar Saxena ◽  
Suzanne Paradis ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Dependent Development ◽  
Dendritic Arbors ◽  
Mouse Visual Cortex

Three-dimensional clusters of functional GABAergic neurons in the mouse visual cortex

2010 ◽  
Vol 68 ◽  
pp. e152-e153
Author(s):  
Teppei Ebina ◽  
Kazuhiro Sohya ◽  
Yuchio Yanagawa ◽  
Tadaharu Tsumoto
Keyword(s):  
Visual Cortex ◽  
Three Dimensional ◽  
Gabaergic Neurons ◽  
Mouse Visual Cortex

scholarly journals Large-scale algorithmic search identifies stiff and sloppy dimensions in synaptic architectures consistent with murine neocortical wiring

2021 ◽  
Author(s):  
Tarek Jabri ◽  
Jason N MacLean
Keyword(s):  
Visual Cortex ◽  
Large Scale ◽  
Normal Range ◽  
Dynamical Regimes ◽  
Algorithmic Search ◽  
Short And Long Term ◽  
Mouse Visual Cortex ◽  
Parameter Values ◽  
Connectivity Parameter

Complex systems can be defined by "sloppy" dimensions, meaning that their behavior is unmodified by large changes to specific parameter combinations, and "stiff" dimensions whose changes result in considerable modifications. In the case of the neocortex, sloppiness in synaptic architectures would be crucial to allow for the maintenance of spiking dynamics in the normal range despite a diversity of inputs and both short- and long-term changes to connectivity. Using simulations on neural networks with spiking matched to murine visual cortex, we determined the stiff and sloppy parameters of synaptic architectures across three classes of input (brief, continuous, and cyclical). Large-scale algorithmically-generated connectivity parameter values revealed that specific combinations of excitatory and inhibitory connectivity are stiff and that all other architectural details are sloppy. Stiff dimensions are consistent across a range of different input classes with self-sustaining synaptic architectures occupying a smaller subspace as compared to the other input classes. We also find that experimentally estimated connectivity probabilities from mouse visual cortex are similarly stiff and sloppy when compared to the architectures that we identified algorithmically. This suggests that simple statistical descriptions of spiking dynamics are a sufficient and parsimonious description of neocortical activity when examining structure-function relationships at the mesoscopic scale. Moreover, this study provides further evidence of the importance of the interrelationship of excitatory and inhibitory connectivity to establish and maintain stable spiking dynamical regimes in neocortex.

scholarly journals Theory of Neuronal Perturbome: Linking Connectivity to Coding via Perturbations

2020 ◽  
Author(s):  
Sadra Sadeh ◽  
Claudia Clopath
Keyword(s):  
Visual Cortex ◽  
Large Scale ◽  
Neuronal Networks ◽  
Single Neuron ◽  
Population Level ◽  
Prima Facie ◽  
The Road ◽  
Experimental Findings ◽  
Mouse Visual Cortex

SummaryTo unravel the functional properties of the brain, we need to untangle how neurons interact with each other and coordinate in large-scale recurrent networks. One way to address this question is to measure the functional influence of individual neurons on each other by perturbing them in vivo. Application of such single-neuron perturbations in mouse visual cortex has recently revealed feature-specific suppression between excitatory neurons, despite the presence of highly specific excitatory connectivity, which was deemed to underlie feature-specific amplification. Here, we studied which connectivity profiles are consistent with these seemingly contradictory observations, by modelling the effect of single-neuron perturbations in large-scale neuronal networks. Our numerical simulations and mathematical analysis revealed that, contrary to the prima facie assumption, neither inhibition-dominance nor broad inhibition alone were sufficient to explain the experimental findings; instead, strong and functionally specific excitatory-inhibitory connectivity was necessary, consistent with recent findings in the primary visual cortex of rodents. Such networks had a higher capacity to encode and decode natural images in turn, which was accompanied by the emergence of response gain nonlinearities at the population level. Our study provides a general computational framework to investigate how single-neuron perturbations are linked to cortical connectivity and sensory coding, and paves the road to map the perturbome of neuronal networks in future studies.

scholarly journals Theory of neuronal perturbome in cortical networks

2020 ◽  
Vol 117 (43) ◽  
pp. 26966-26976 ◽  
Author(s):  
Sadra Sadeh ◽  
Claudia Clopath
Keyword(s):  
Visual Cortex ◽  
Large Scale ◽  
Neuronal Networks ◽  
Single Neuron ◽  
Population Level ◽  
Prima Facie ◽  
The Road ◽  
Experimental Findings ◽  
Mouse Visual Cortex

To unravel the functional properties of the brain, we need to untangle how neurons interact with each other and coordinate in large-scale recurrent networks. One way to address this question is to measure the functional influence of individual neurons on each other by perturbing them in vivo. Application of such single-neuron perturbations in mouse visual cortex has recently revealed feature-specific suppression between excitatory neurons, despite the presence of highly specific excitatory connectivity, which was deemed to underlie feature-specific amplification. Here, we studied which connectivity profiles are consistent with these seemingly contradictory observations, by modeling the effect of single-neuron perturbations in large-scale neuronal networks. Our numerical simulations and mathematical analysis revealed that, contrary to the prima facie assumption, neither inhibition dominance nor broad inhibition alone were sufficient to explain the experimental findings; instead, strong and functionally specific excitatory–inhibitory connectivity was necessary, consistent with recent findings in the primary visual cortex of rodents. Such networks had a higher capacity to encode and decode natural images, and this was accompanied by the emergence of response gain nonlinearities at the population level. Our study provides a general computational framework to investigate how single-neuron perturbations are linked to cortical connectivity and sensory coding and paves the road to map the perturbome of neuronal networks in future studies.

Three-dimensional analysis of visually responsive Parvalbumin-positive neurons in the mouse visual cortex

2011 ◽  
Vol 71 ◽  
pp. e82
Author(s):  
Teppei Ebina ◽  
Kazuhiro Sohya ◽  
Rui Kimura ◽  
Yin Shu-Ting ◽  
Hirofumi Ozeki ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Dimensional Analysis ◽  
Three Dimensional ◽  
Mouse Visual Cortex

scholarly journals A large-scale, standardized physiological survey reveals higher order coding throughout the mouse visual cortex

2018 ◽  
Author(s):  
Saskia E. J. de Vries ◽  
Jerome Lecoq ◽  
Michael A. Buice ◽  
Peter A. Groblewski ◽  
Gabriel K. Ocker ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Large Scale ◽  
Sensory Information ◽  
Natural Scenes ◽  
Visual Coding ◽  
Adult Mice ◽  
Cortical Responses ◽  
Visual Cortical ◽  
Mouse Visual Cortex ◽  
Hierarchical Computation

SummaryTo understand how the brain processes sensory information to guide behavior, we must know how stimulus representations are transformed throughout the visual cortex. Here we report an open, large-scale physiological survey of neural activity in the awake mouse visual cortex: the Allen Brain Observatory Visual Coding dataset. This publicly available dataset includes cortical activity from nearly 60,000 neurons collected from 6 visual areas, 4 layers, and 12 transgenic mouse lines from 221 adult mice, in response to a systematic set of visual stimuli. Using this dataset, we reveal functional differences across these dimensions and show that visual cortical responses are sparse but correlated. Surprisingly, responses to different stimuli are largely independent, e.g. whether a neuron responds to natural scenes provides no information about whether it responds to natural movies or to gratings. We show that these phenomena cannot be explained by standard local filter-based models, but are consistent with multi-layer hierarchical computation, as found in deeper layers of standard convolutional neural networks.

scholarly journals A large-scale standardized physiological survey reveals functional organization of the mouse visual cortex

2019 ◽  
Vol 23 (1) ◽  
pp. 138-151 ◽  
Author(s):  
Saskia E. J. de Vries ◽  
Jerome A. Lecoq ◽  
Michael A. Buice ◽  
Peter A. Groblewski ◽  
Gabriel K. Ocker ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Large Scale ◽  
Functional Organization ◽  
Mouse Visual Cortex
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