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This project is a quest for conscious artificial intelligence. A number of prototypes will be developed as the project progresses.
This project has 2 subprojects:
Object Pascal based CAI NEURAL API - https://github.com/joaopauloschuler/neural-api
Python based K-CAI NEURAL API - https://github.com/joaopauloschuler/k-neural-api
A video from the first prototype has been made:
http://www.youtube.com/watch?v=qH-IQgYy9zg
Above video shows a popperian agent collecting mining ore from 3...
Multiscale Neuroscience and Systems Biology Simulator
Moose is the core of a modern software platform for the simulation of neural systems ranging from subcellular components and biochemical reactions to complex models of single neurons, large networks, and systems-level processes.
We have moved Github.com. This should be your source for the latest version of the code.
Mullpy is a machine-learning library that mainly aim to solve multi-label problems. It is classifier independent, has many ensemble capabilities (diversity methods like bagging, random subspaces, etc.) and automated results presentation (Excel, images as ROC or class-separated info, etc.). It is fully configurable. At the moment supports Neural Networks and classifiers defined in files. It is working on python3.3.
NeMo is a high-performance spiking neural network simulator which simulates networks of Izhikevich neurons on CUDA-enabled GPUs. NeMo is a C++ class library, with additional interfaces for pure C, Python, and Matlab.
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This is a c-library that provides tools for advanced
analysis of electrophysiological data. It features
denoising, unsupervised classification, time-frequency
analysis, phase-space analysis, neural networks, time-warping and
more.
Brian is a new simulator for spiking neural networks available on almost all platforms. The motivation for this project is that a simulator should not only save the time of processors, but also the time of scientists.
ptsa (pronounced pizza) is a Python module for performing time series analysis. Although it is specifically designed with neural data in mind (EEG, MEG, fMRI, etc...), the code should be applicable to almost any type of time series.
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