Serves for training, testing, finetuning, and deploying models, with well-documented examples for all of these tasks. Caffe is a program allowing extension to new formats, network layers, and loss functions. It can be useful for state-of-the-art deep learning algorithms and a collection of reference models. Moreover, this tool fits industry and internet-scale media needs by CUDA graphics processing unit (GPU) computation.
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Proposes a simulator-independent language for building neuronal network models. PyNN makes it possible to write a simulation script once, using the Python programming language, and run it without modification on any supported simulator (currently NEURON, NEST, PCSIM, Brian and the Heidelberg VLSI neuromorphic hardware). PyNN increases the productivity of neuronal network modelling by providing high-level abstraction, by promoting code sharing and reuse, and by providing a foundation for simulator-agnostic analysis, visualization and data-management tools. PyNN increases the reliability of modelling studies by making it much easier to check results on multiple simulators.
Aims to support neural simulations conceived under python. NeuroTools intends to supply an additional assistance for tasks which are not provided by simulation engines, for instance into the fields of parameterization, instrumentation or visualization. This package is composed of nine modules encompassing several tools to assist users in plotting and image processing as well as in manipulation and calculation using spike trains.
A toolbox that facilitates importing and exporting models represented in the Systems Biology Markup Language (SBML) in and out of the MATLAB environment and provides functionality that enables an experienced user of either SBML or MATLAB to combine the computing power of MATLAB with the portability and exchangeability of an SBML model. SBMLToolbox supports all levels and versions of SBML.
Allows users to handle vision science computation. ISETBIO is an open-source software that performs calculations, through the optics, from the scene radiance and into the front end of biological visual systems. It authorizes to estimate a wide range of factors such as the optics and retinal image, bipolar responses or the capture of light by the photopigment.
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Python in neuroscience
Tools (3):
Nengo, Topographica, PyNN
Topics (1):
Computational neuroscience modelling