Displaying 1 to 9 from 9 results

python-in-neuroscience-tutorials - Collection of tutorials about methods of computational neuroscience using Python

  •    Jupyter

The view the tutorials you can use the excellent nbviewer. To go to the table of contents follow the link. (You can also open them on Github, but nbviewer gives a nicer user experience).

eFEL - Electrophys Feature Extraction Library

  •    C++

The Electrophys Feature Extraction Library (eFEL) allows neuroscientists to automatically extract features from time series data recorded from neurons (both in vitro and in silico). Examples are the action potential width and amplitude in voltage traces recorded during whole-cell patch clamp experiments. The user of the library provides a set of traces and selects the features to be calculated. The library will then extract the requested features and return the values to the user. The core of the library is written in C++, and a Python wrapper is included. At the moment we provide a way to automatically compile and install the library as a Python module. Instructions on how to compile the eFEL as a standalone C++ library can be found here.

SimulationTutorials - Public tutorials around electrophysiological simulations

  •    Jupyter

On November 10th 2017, we present a breakout session "Data-Driven Neurophysiology and Neuronal Modelling". It is part of the Society for Neuroscience (SfN) Annual Meeting Preconference Sessions, SHORT COURSE 2: Neuroinformatics in the Age of Big Data: Working With the Right Data and Tools. The content of this tutorial is located here.

BluePyOpt - Blue Brain Python Optimisation Library

  •    Python

The Blue Brain Python Optimisation Library (BluePyOpt) is an extensible framework for data-driven model parameter optimisation that wraps and standardises several existing open-source tools. It simplifies the task of creating and sharing these optimisations, and the associated techniques and knowledge. This is achieved by abstracting the optimisation and evaluation tasks into various reusable and flexible discrete elements according to established best-practices.




open-computational-neuroscience-resources - A publicly-editable collection of open computational neuroscience resources

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Computational neuroscience means one of two things: 1. analysis of neuroscientific data, whether it be fMRI imaging data, electrode recordings from a mouse running in a maze, statistical modeling of that data, or something else, and 2. simulation of neural systems, including modeling many compartments of a single neuron, or large networks of model neurons with simple individual behavior. These endeavors require expensive data from wet-lab experiments, but much of the work can be accomplished using everyday, consumer-grade laptop and desktop computers! Indeed, the biggest barrier to entry is not hardware, data, or expense, but rather time and passion to learn the tools needed for such computational science. Coupled with the great tools coming out of the modern Data Science movement, open data, open simulation models, and open analysis and simulation tools for computational neuroscience make it easier than ever to learn or even contribute to the study of the brain! The resources below should be more than enough to provide anyone with the means to begin learning or working in computational neuroscience, at no cost other than time and a modern computer. Note: This is intended as a list of resources to help with neuroscientific pursuits, as opposed to artificial intelligence pursuits. More broadly, I've made a similar repo-list of general open science resources here.

open-science-resources - A publicly-editable collection of open science resources, including tools, datasets, meta-resources, etc

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Scientific data and tools should, as much as possible, be free as in beer and free as in freedom. The vast majority of science today is paid for by taxpayer-funded grants; at the same time, the incredible successes of science are strong evidence for the benefit of collaboration in knowledgable pursuits. Within the scientific academy, sharing of expertise, data, tools, etc. is prolific, but only recently with the rise of the Open Access movement has this sharing come to embrace the public. Even though most research data is never shared, both the public and even scientists in their own fields are often unaware of just much data, tools, and other resources are made freely available for analysis! This list is a small attempt at bringing light to data repositories and computational science tools that are often siloed according to each scientific discipline, in the hopes of spurring along both public and professional contributions to science. These categories are very non-exclusive, as many resources could fit multiple categories. If you're interested in computational neuroscience specifically, I've made a similar repo-list of open computational neuroscience resources here.

auryn - Auryn: A fast simulator for spiking neural networks with synaptic plasticity

  •    C++

Auryn is Simulator for recurrent spiking neural networks with synaptic plasticity. It comes with the GPLv3 (please see COPYING). will run the Vogels Abbott benchmark, a balanced network model with conductance based synapses. Spiking activity is written to files with the extension 'ras'.

cochlea - Inner ear models in Python

  •    Python

The package contains state-of-the-art biophysical models, which give realistic approximation of the auditory nerve activity. The models are implemented using the original code from their authors whenever possible. Therefore, they return the same results as the original models. We made an effort to verify it with unit testing (see tests directory for details).


thorns - Spike analysis software

  •    Python

With thorns you can analyze and display spike trains generated by neurons. It can be useful for the analysis of experimental and simulation data using Python. For example, you can easily calculate peristimulus time histogram (PSTH), interspike time histogram (ISIH), vector strength (VS), entrainment and visualize action potentials with raster plot. waves is a submodule with some useful signal processing and generation functions, e.g. generate ramped tone, amplitude modulation tone, FFT filter, set level (dB_SPL).






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