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URL: http://neuronalarchitects.com/neural-cipher.html
Proper Citation: Neural Cipher (RRID:SCR_001577)
Description: THIS RESOURCE IS NO LONGER IN SERVICE, documented August 17, 2016. C#.NET 4.0 application that interfaces with the Neural Maestro class library to perform Classical and Bayesian logistic polynomial regression analysis for multiple trial and experimental neuroscience / electrophysiological datasets. The idea is to build a neural feature extractor to plug into a neural recognition network based on recordings from patch clamps and/or brain computer interfaces. Frequency based feature sets are collected from the simulation of neuronal cell assemblies and examined for inclusion in combinatorial regressions using the Neural Maestro API. Regressions are evaluated based on different metrics. The application interfaces with a R and MATLAB API and produces a compendium to be published on CRAN.
Abbreviations: Neural Cipher
Resource Type: software resource, data analysis software, software application, data processing software
Keywords: polynomial regression analysis, neuroscience, electrophysiology, recording, patch clamp, brain computer interface, r, matlab, eeg, neuron, classical, bayesian, logistic, multiple trial, experiment
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The NIDDK Information Network (dkNET) serves the needs of basic and clinical investigators by providing seamless access to large pools of data and research resources relevant to the mission of The National Institute of Diabetes Digestive and Kidney Diseases (NIDDK). dkNET is hosted at University of California San Diego, and is supported by NIH NIDDK grant 2U24DK097771-09.
Email: info_at_ dknet.org
