On Goodness of Fit Tests For Models of Neuronal Spike Trains Considered as Counting Processes

After an elementary derivation of the "time transformation", mapping a counting process onto a homogeneous Poisson process with rate one, a brief review of Ogata's goodness of fit tests is presented and a new test, the "Wiener process test", is proposed. This test is based on a straightforward application of Donsker's Theorem to the intervals of time transformed counting processes. The finite sample properties of the test are studied by Monte Carlo simulations. Performances on simulated as well as on real data are presented. It is argued that due to its good finite sample properties, the new test is both a simple and a useful complement to Ogata's tests. Warnings are moreover given against the use of a single goodness of fit test

Supplementary Material for: Homogeneity and identity tests for unidimensional Poisson processes with an application to neurophysiological peri-stimulus time histograms–R version

R version of the Supplementary material for "Homogeneity and identity tests for unidimensional Poisson processes with an application to neurophysiological peri-stimulus time histograms.

SIMONE: a realistic neural network simulator to reproduce MEA-based recordings

International audienceContemporary multielectrode arrays (MEAs) used to record extracellular activity from neural tissues can deliver data at rates on the order of 100 Mbps. Such rates require efficient data compression and/or preprocessing algorithms implemented on an application specific integrated circuit (ASIC) close to the MEA. We present SIMONE (Statistical sIMulation Of Neuronal networks Engine), a versatile simulation tool whose parameters can be either fixed or defined by a probability distribution. We validated our tool by simulating data recorded from the first olfactory relay of an insect. Different key aspects make this tool suitable for testing the robustness and accuracy of neural signal processing algorithms (such as the detection, alignment, and classification of spikes). For instance, most of the parameters can be defined by a probabilistic distribution, then tens of simulations may be obtained from the same scenario. This is especially useful when validating the robustness of the processing algorithm. Moreover, the number of active cells and the exact firing activity of each one of them is perfectly known, which provides an easy way to test accuracy

Optogenetic Light Sensors in Human Retinal Organoids

Optogenetic technologies paved the way to dissect complex neural circuits and monitor neural activity using light in animals. In retinal disease, optogenetics has been used as a therapeutic modality to reanimate the retina after the loss of photoreceptor outer segments. However, it is not clear today which ones of the great diversity of microbial opsins are best suited for therapeutic applications in human retinas as cell lines, primary cell cultures and animal models do not predict expression patterns of microbial opsins in human retinal cells. Therefore, we sought to generate retinal organoids derived from human induced pluripotent stem cells (hiPSCs) as a screening tool to explore the membrane trafficking efficacy of some recently described microbial opsins. We tested both depolarizing and hyperpolarizing microbial opsins including CatCh, ChrimsonR, ReaChR, eNpHR 3.0, and Jaws. The membrane localization of eNpHR 3.0, ReaChR, and Jaws was the highest, likely due to their additional endoplasmic reticulum (ER) release and membrane trafficking signals. In the case of opsins that were not engineered to improve trafficking efficiency in mammalian cells such as CatCh and ChrimsonR, membrane localization was less efficient. Protein accumulation in organelles such as ER and Golgi was observed at high doses with CatCh and ER retention lead to an unfolded protein response. Also, cytoplasmic localization was observed at high doses of ChrimsonR. Our results collectively suggest that retinal organoids derived from hiPSCs can be used to predict the subcellular fate of optogenetic proteins in a human retinal context. Such organoids are also versatile tools to validate other gene therapy products and drug molecules

Automatic Spike Train Analysis and Report Generation. An Implementation with R, R2HTML and STAR.

Multi-electrode arrays (MEA) allow experimentalists to record extracellularly from many neurons simultaneously for long durations. They therefore often require that the data analyst spends a considerable amount of time first sorting the spikes, then doing again and again the same basic analysis on the different spike trains isolated from the raw data. This spike train analysis also often generates a considerable amount of figures, mainly diagnostic plots, that need to be stored (and/or printed) and organized for efficient subsequent use. The analysis of our data recorded from the first olfactory relay of an insect, the cockroach Periplaneta americana, has led us to settle on such “routine ” spike train analysis procedures: one applied to spontaneous activity recordings, the other used with recordings where an olfactory stimulation was repetitively applied. We have developed a group of functions implementing a mixture of common and original procedures and producing graphical or numerical outputs. These functions can be run in batch mode and do moreover produce an organized report of their results in an HTML file. A R package: STAR (Spike Train Analysis with R) makes these functions readily available to the neurophysiologists community. Like R, STAR is open source and free. We believe that our basic analysis procedures are of general interest but they can also be very easily modified to suit user specific needs

Data set from Pouzat and Chaffiol (2009) Journal of Neuroscience Methods 181:119.

<p>1</p> <p>1</p> <p>1This is the data set of Cockroach first olfactory relay recordings used in Pouzat and Chaffiol (2009) Automatic Spike Train Analysis and Report Generation. An Implementation with R, R2HTML and STAR <em>Journal of Neuroscience Methods</em> <strong>181</strong>: 119-1443. These data are also included in the R package STAR. The data are in HDF5 format.</p

Supplementary Material for: Homogeneity and identity tests for unidimensional Poisson processes for neurophysiological peri-stimulus time histograms

This file contains the complete code required to reproduce the analysis of Pouzat, Chaffiol and Bar-Hen (2015) "Homogeneity and identity tests for unidimensional Poisson processes for neurophysiological peri-stimulus time histograms" in both R and Python

Etude de la précision de la décharge des neurones sensibles à la phéromone dans le lobe antennaire d'un papillon de nuit Agrotis Ipsilon

National audienc