A list of spike to imaging forward models (N=4) and imaging deconvolution methods (N=12) and their application on simultaneous recorded single-neuron electrophysiology and fluorescence data. S2F models: Linear model Quadratic model Hill function model Sigmoid model F2S models: Nonngegative Wiener Filter Sequential Monte Carlo method Fast OOPSI (FOOPSI) Finite Rate of Innovation (FRI) Linear Peeling Nonlinear Peeling Constrained OOPSI (COOPSI) AR1 Constrained OOPSI (COOPSI) AR2 Constrained OOPSI (COOPSI) AR3 MCMC COOPSI MLSpike Suite2
One of the most important building blocks of the brain–machine interface (BMI) based on neuronal spi...
<p>A: Gain and phase of the input filter as a function of frequency. The neuronal morphology varied...
As multi-electrode and imaging technology begin to provide us with simultaneous recordings of large ...
<p>(A) Illustration showing linear combination of incoming-edge neighbors fluorescence traces and re...
This paper describes a parametric deconvolution method(PDPS) appropriate for a particular class of s...
<p>(<b>A</b>): QGIF model, (<b>B</b>): FHN model with high SNR, (<b>C):</b> FHN model with low SNR. ...
<p>This repository contains the two fluorescence imaging datasets used in the publication "Blind Spa...
We propose mathematical models to analyze two nervous system phenomena. The first is a model of the ...
We examine the problem of estimating the spike trains of multiple neurons from voltage traces record...
International audienceRecent advances in multi-electrodes array acquisition has made it possible tor...
One of the central problems in systems neuroscience is to understand how neural spike trains convey ...
Multiphoton calcium fluorescence imaging has gained prominence as a valuable tool for the study of b...
Single neuron models have a long tradition in computational neuroscience. Detailed bio-physical mode...
AbstractNeural models that simulate single spike trains can help us understand the basic principles ...
The temporal waveform of neural activity is commonly estimated by low-pass filtering spike train dat...
One of the most important building blocks of the brain–machine interface (BMI) based on neuronal spi...
<p>A: Gain and phase of the input filter as a function of frequency. The neuronal morphology varied...
As multi-electrode and imaging technology begin to provide us with simultaneous recordings of large ...
<p>(A) Illustration showing linear combination of incoming-edge neighbors fluorescence traces and re...
This paper describes a parametric deconvolution method(PDPS) appropriate for a particular class of s...
<p>(<b>A</b>): QGIF model, (<b>B</b>): FHN model with high SNR, (<b>C):</b> FHN model with low SNR. ...
<p>This repository contains the two fluorescence imaging datasets used in the publication "Blind Spa...
We propose mathematical models to analyze two nervous system phenomena. The first is a model of the ...
We examine the problem of estimating the spike trains of multiple neurons from voltage traces record...
International audienceRecent advances in multi-electrodes array acquisition has made it possible tor...
One of the central problems in systems neuroscience is to understand how neural spike trains convey ...
Multiphoton calcium fluorescence imaging has gained prominence as a valuable tool for the study of b...
Single neuron models have a long tradition in computational neuroscience. Detailed bio-physical mode...
AbstractNeural models that simulate single spike trains can help us understand the basic principles ...
The temporal waveform of neural activity is commonly estimated by low-pass filtering spike train dat...
One of the most important building blocks of the brain–machine interface (BMI) based on neuronal spi...
<p>A: Gain and phase of the input filter as a function of frequency. The neuronal morphology varied...
As multi-electrode and imaging technology begin to provide us with simultaneous recordings of large ...