7,280 research outputs found
Can retinal ganglion cell dipoles seed iso-orientation domains in the visual cortex?
It has been argued that the emergence of roughly periodic orientation
preference maps (OPMs) in the primary visual cortex (V1) of carnivores and
primates can be explained by a so-called statistical connectivity model. This
model assumes that input to V1 neurons is dominated by feed-forward projections
originating from a small set of retinal ganglion cells (RGCs). The typical
spacing between adjacent cortical orientation columns preferring the same
orientation then arises via Moir\'{e}-Interference between hexagonal ON/OFF RGC
mosaics. While this Moir\'{e}-Interference critically depends on long-range
hexagonal order within the RGC mosaics, a recent statistical analysis of RGC
receptive field positions found no evidence for such long-range positional
order. Hexagonal order may be only one of several ways to obtain spatially
repetitive OPMs in the statistical connectivity model. Here, we investigate a
more general requirement on the spatial structure of RGC mosaics that can seed
the emergence of spatially repetitive cortical OPMs, namely that angular
correlations between so-called RGC dipoles exhibit a spatial structure similar
to that of OPM autocorrelation functions. Both in cat beta cell mosaics as well
as primate parasol receptive field mosaics we find that RGC dipole angles are
spatially uncorrelated. To help assess the level of these correlations, we
introduce a novel point process that generates mosaics with realistic nearest
neighbor statistics and a tunable degree of spatial correlations of dipole
angles. Using this process, we show that given the size of available data sets,
the presence of even weak angular correlations in the data is very unlikely. We
conclude that the layout of ON/OFF ganglion cell mosaics lacks the spatial
structure necessary to seed iso-orientation domains in the primary visual
cortex.Comment: 9 figures + 1 Supplementary figure and 1 Supplementary tabl
Geometry and dimensionality reduction of feature spaces in primary visual cortex
Some geometric properties of the wavelet analysis performed by visual neurons
are discussed and compared with experimental data. In particular, several
relationships between the cortical morphologies and the parametric dependencies
of extracted features are formalized and considered from a harmonic analysis
point of view
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The role of HG in the analysis of temporal iteration and interaural correlation
Video-rate volumetric neuronal imaging using 3D targeted illumination
Fast volumetric microscopy is required to monitor large-scale neural ensembles with high spatio-temporal resolution. Widefield fluorescence microscopy can image large 2D fields of view at high resolution and speed while remaining simple and costeffective. A focal sweep add-on can further extend the capacity of widefield microscopy by enabling extended-depth-of-field (EDOF) imaging, but suffers from an inability to reject out-of-focus fluorescence background. Here, by using a digital micromirror device to target only in-focus sample features, we perform EDOF imaging with greatly enhanced contrast and signal-to-noise ratio, while reducing the light dosage delivered to the sample. Image quality is further improved by the application of a robust deconvolution algorithm. We demonstrate the advantages of our technique for in vivo calcium imaging in the mouse brain.This work was funded by the National Institutes of Health (R21EY026310) and the National Science Foundation (CBET-1508988). The authors wish to thank E. McCarthy and Prof. M.J. Baum for providing mouse brain slices used in this manuscript, and A. I. Mohammed for providing in vivo mouse brain samples in the early stages of this work. (R21EY026310 - National Institutes of Health; CBET-1508988 - National Science Foundation)Published versio
Macroscopic equations governing noisy spiking neuronal populations
At functional scales, cortical behavior results from the complex interplay of
a large number of excitable cells operating in noisy environments. Such systems
resist to mathematical analysis, and computational neurosciences have largely
relied on heuristic partial (and partially justified) macroscopic models, which
successfully reproduced a number of relevant phenomena. The relationship
between these macroscopic models and the spiking noisy dynamics of the
underlying cells has since then been a great endeavor. Based on recent
mean-field reductions for such spiking neurons, we present here {a principled
reduction of large biologically plausible neuronal networks to firing-rate
models, providing a rigorous} relationship between the macroscopic activity of
populations of spiking neurons and popular macroscopic models, under a few
assumptions (mainly linearity of the synapses). {The reduced model we derive
consists of simple, low-dimensional ordinary differential equations with}
parameters and {nonlinearities derived from} the underlying properties of the
cells, and in particular the noise level. {These simple reduced models are
shown to reproduce accurately the dynamics of large networks in numerical
simulations}. Appropriate parameters and functions are made available {online}
for different models of neurons: McKean, Fitzhugh-Nagumo and Hodgkin-Huxley
models
Stochastic Optical Reconstruction Microscopy Imaging of Microtubule Arrays in Intact Arabidopsis thaliana Seedling Roots
Super-resolution fluorescence microscopy has generated tremendous success in revealing detailed subcellular structures in animal cells. However, its application to plant cell biology remains extremely limited due to numerous technical challenges, including the generally high fluorescence background of plant cells and the presence of the cell wall. In the current study, stochastic optical reconstruction microscopy (STORM) imaging of intact Arabidopsis thaliana seedling roots with a spatial resolution of 20–40 nm was demonstrated. Using the super-resolution images, the spatial organization of cortical microtubules in different parts of a whole Arabidopsis root tip was analyzed quantitatively, and the results show the dramatic differences in the density and spatial organization of cortical microtubules in cells of different differentiation stages or types. The method developed can be applied to plant cell biological processes, including imaging of additional elements of the cytoskeleton, organelle substructure, and membrane domains
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