Metadata-Version: 1.2
Name: brain-py
Version: 1.1.0b0
Summary: BrainPy: A flexible and extensible framework for brain modeling
Home-page: https://github.com/PKU-NIP-Lab/BrainPy
Author: Chaoming Wang
Author-email: chao.brain@qq.com
License: UNKNOWN
Description: 
        ![Logo](docs/_static/logo.png)
        
        [![LICENSE](https://anaconda.org/brainpy/brainpy/badges/license.svg)](https://github.com/PKU-NIP-Lab/BrainPy)    [![Documentation](https://readthedocs.org/projects/brainpy/badge/?version=latest)](https://brainpy.readthedocs.io/en/latest/?badge=latest)   [![PyPI version](https://badge.fury.io/py/brainpy-simulator.svg)](https://badge.fury.io/py/brainpy-simulator)   [![Build Status](https://travis-ci.com/PKU-NIP-Lab/BrainPy.svg?branch=master)](https://travis-ci.com/PKU-NIP-Lab/BrainPy)
        
        
        
        # Why to use BrainPy
        
        ``BrainPy`` is an integrative framework for computational neuroscience and brain-inspired computation based on Just-In-Time (JIT) compilation (built on the top of [JAX](https://github.com/google/jax) and [Numba](https://github.com/numba/)). Core functions provided in BrainPy includes
        
        - **JIT compilation** for class objects. 
        - **Numerical solvers** for ODEs, SDEs, DDEs, FDEs, and others. 
        - **Dynamics simulation tools** for various brain objects, like neurons, synapses, networks, soma, dendrites, channels, and even more. 
        - **Dynamics analysis tools** for differential equations, including phase plane analysis and bifurcation analysis, continuation analysis and sensitive analysis.
        - **Seamless integration with deep learning models**, and has the speed benefit on JIT compilation.
        - And more ......
        
        `BrainPy` is designed to effectively satisfy your basic requirements: 
        
        - *Easy to learn and use*: BrainPy is only based on Python language and has little dependency requirements. 
        - *Flexible and transparent*: BrainPy endows the users with the fully data/logic flow control. Users can code any logic they want with BrainPy. 
        - *Extensible*: BrainPy allow users to extend new functionality just based on Python coding. For example, we extend the numerical integration with the ability to do numerical analysis. In such a way, the same code in BrainPy can not only be used for simulation, but also for dynamics analysis. 
        - *Efficient running speed*: All codes in BrainPy can be just-in-time compiled (based on [JAX](https://github.com/google/jax) and [Numba](https://github.com/numba/)) to run on CPU or GPU devices, thus guaranteeing its running efficiency. 
        
        
        
        # How to use BrainPy
        
        ## Step 1: installation
        
        ``BrainPy`` is based on Python (>=3.6), and the following packages are required to be installed to use ``BrainPy``:
        
        - NumPy >= 1.15
        - Matplotlib >= 3.3
        
        *The installation details please see documentation: [Quickstart/Installation](https://brainpy.readthedocs.io/en/latest/quickstart/installation.html)*
        
        
        
        **Method 1**: install ``BrainPy`` by using ``pip``:
        
        ```bash
        > pip install -U brain-py
        ```
        
        **Method 2**: install ``BrainPy`` from source:
        
        ```bash
        > pip install git+https://github.com/PKU-NIP-Lab/BrainPy
        >
        > # or
        > pip install git+https://git.openi.org.cn/OpenI/BrainPy
        >
        > # or
        > pip install -e git://github.com/PKU-NIP-Lab/BrainPy.git@V1.0.0
        ```
        
        
        
        **Other dependencies**: you want to get the full supports by BrainPy, please install the following packages:
        
        - `JAX >= 0.2.10`,  needed for "jax" backend and "dnn" module
        - `Numba >= 0.52`,  needed for JIT compilation on "numpy" backend
        - `SymPy >= 1.4`, needed for dynamics "analysis" module and Exponential Euler method
        
        
        
        ## Step 2: useful links
        
        - **Documentation:** https://brainpy.readthedocs.io/
        - **Source code:** https://github.com/PKU-NIP-Lab/BrainPy   or   https://git.openi.org.cn/OpenI/BrainPy
        - **Bug reports:** https://github.com/PKU-NIP-Lab/BrainPy/issues   or   Email to adaduo@outlook.com
        - **Examples from papers**: https://brainmodels.readthedocs.io/en/latest/from_papers.html
        
        
        
        ## Step 3: comprehensive examples
        
        Here list several examples of BrainPy. More detailed examples and tutorials please see [**BrainModels**](https://brainmodels.readthedocs.io).
        
        
        
        ### Neuron models
        
        - [Hodgkin–Huxley neuron model](https://github.com/PKU-NIP-Lab/BrainModels/blob/main/brainmodels/tensor_backend/neurons/HodgkinHuxley_model.py)
        
        
        
        ### Synapse models
        
        - [AMPA synapse model](https://github.com/PKU-NIP-Lab/BrainModels/blob/main/brainmodels/tensor_backend/synapses/AMPA_synapse.py)
        
        
        
        ### Network models
        
        - [Gamma oscillation network model](https://brainmodels.readthedocs.io/en/latest/from_papers/Wang_1996_gamma_oscillation.html)
        - [E/I balanced network model](https://brainmodels.readthedocs.io/en/latest/from_papers/Vreeswijk_1996_EI_net.html)
        - [Continuous attractor network model](https://brainmodels.readthedocs.io/en/latest/from_papers/Wu_2008_CANN.html)
        
        
        
        
        ### Low-dimension dynamics analysis
        
        - [Phase plane analysis of the I<sub>Na,p</sub>-I<sub>K</sub> model](https://brainmodels.readthedocs.io/en/latest/tutorials/dynamics_analysis/NaK_model_analysis.html)
        - [Codimension 1 bifurcation analysis of FitzHugh Nagumo model](https://brainmodels.readthedocs.io/en/latest/tutorials/dynamics_analysis/FitzHugh_Nagumo_analysis.html)
        - [Codimension 2 bifurcation analysis of FitzHugh Nagumo model](https://brainmodels.readthedocs.io/en/latest/tutorials/dynamics_analysis/FitzHugh_Nagumo_analysis.html#Codimension-2-bifurcation-analysis)
        
        
        
        ### Learning through back-propagation
        
        
        
        
Keywords: computational neuroscience,brain-inspired computation,dynamical systems,differential equations,numerical integration
Platform: UNKNOWN
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries
Requires-Python: >=3.6
