{ "info": { "author": "Jonathan Cohen, Princeton University, Intel", "author_email": "jdc@princeton.edu", "bugtrack_url": null, "classifiers": [ "Development Status :: 3 - Alpha", "Intended Audience :: Developers", "License :: OSI Approved :: Apache Software License", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6", "Topic :: Software Development :: Build Tools" ], "description": ".. image:: https://badge.fury.io/py/psyneulink.svg\n :target: https://badge.fury.io/py/psyneulink\n.. image:: https://travis-ci.org/PrincetonUniversity/PsyNeuLink.svg?branch=master\n :target: https://travis-ci.org/PrincetonUniversity/PsyNeuLink\n.. image:: https://mybinder.org/badge.svg\n :target: https://mybinder.org/v2/gh/PrincetonUniversity/PsyNeuLink/master\n\n\nWelcome to PsyNeuLink\n=====================\n\nDocumentation is available at https://princetonuniversity.github.io/PsyNeuLink/\n\nPurpose\n-------\n\nPsyNeuLink is an open-source, software environment written in Python, and designed for the needs of\nneuroscientists, psychologists, computational psychiatrists and others interested in learning about and building\nmodels of the relationship between brain function, mental processes and behavior.\n\nPsyNeuLink can be used as a \"block modeling environment\", in which to construct, simulate, document, and exchange\ncomputational models of neural mechanisms and/or psychological processes at the subsystem and system levels.\nA block modeling environment allows components to be constructed that implement various, possibly disparate\nfunctions, and then link them together into a system to examine how they interact. In PsyNeuLink, components are\nused to implement the function of brain subsystems and/or psychological processes, the interaction of which can then\nbe simulated at the system level.\n\nThe purpose of PsyNeuLink is to make it as easy as possible to create new and/or import existing models, and\nintegrate them to simluate system-level interactions. It provides a suite of core components for\nimplementing models of various forms of processing, learning, and control, and its Library includes examples that\ncombine these components to implement published models. As an open source project, its suite of components is meant\nto be enhanced and extended, and its library is meant to provide an expanding repository of models, written in a\nconcise, executable, and easy to interpret form, that can be shared, compared, and extended by the scientific community.\n\nWhat PsyNeuLink **IS**\n----------------------\n\nIt is:\n\n - *open source*, freeing users of the costs or restrictions associated with proprietary software.\n\n ..\n\n - *computationally general* --\u00a0it can be used to implement, seamlessly integrate, and simulate interactions among\n disparate components that vary in their granularity of representation and function (from individual neurons or\n neural populations to functional subsystems and abstract cognitive functions) and at any time scale of execution.\n\n ..\n\n - *integrative* -- it provides a standard and accessible environment for model comparison, sharing, and documentation;\n\n ..\n\n - *extensible* -- it has an interface (API) that allows it to be used with other powerful tools for implementing\n individual components, such as:\n\n * Neuron and Nengo (biophysically realistic models of neuronal function)\n * Emergent (broad class of neurally-plausible connectionist models);\n * Pytorch and TensorFlow (ODE's, deep learning);\n * ACT-R (symbolic, production system models).\n\n.. note::\n PsyNeuLink is alpha software, that is still being actively developed. Although it is useable, and most of the\n documented functionality is available, some features may not yet be fully implemented and/or subject to\n modification. Please report any bugs and/or suggestions for development to psyneulinkhelp@princeton.edu.\n\nWhat PsyNeuLink is **NOT**\n--------------------------\n\nThe longterm goal of PsyNeuLink is to provide an environment that integrates comptutational modeling of brain function\nand behavior at all levels of analysis. While it is designed to be fully general, and can in principle be used to\nimplement models at any level, it is still under development, and current efficiency considerations make it more\nsuitable for some of forms of modeling than others. In its present form, it is well suited to the creation of\nsimple to moderately complex models, and for the integration of disparate models into a single environment, while in\nit is presently less well suited to efforts involving massively large computations, such as:\n\n - extensive model fitting\n - large scale simulations\n - highly detailed biophysical models of neurons or neuronal populations\n\nOther packages currently better suited to such applications are:\n`Emergent `_ for biologically-inspired neural network models\n`Pytorch `_ and `TensorFlow `_ (for deep learning models);\n`HDDM `_ (for Drift Diffusion Models);\n`ACT-R `_ (for production system models);\n`Genesis `_,\n`Neuron `_,\nand `Nengo `_ (for biophysically-realistic models of neuronal function).\n\nThese packages are good for elaborate and detailed models of a particular form. In contrast, the focus in designing\nPsyNeuLink has been to make it as flexible and easy to use as possible, with the ability to integrate components\nconstructed in other packages (including some of the ones listed above) into a single environment. These are\ncharacteristics that are often (at least in the initial stages of development) in tension with efficiency (think:\ninterpreted vs. compiled).\n\nThat said, priorities for ongoing development of PsyNeuLink are:\n i) acceleration, using just-in-time compilation methods and parallelization;\n ii) enhancement of the API to facilitate wrapping modules from other packages for integration into the PsyNeuLink\n environment (examples currently exist for Pytorch and Emergent);\n iii) integration of tools for parameter estimation, model comparison and data fitting; and\n iv) a graphic interface for the construction of models and realtime display of their execution.\n\nEnvironment Overview\n--------------------\n\nPsyNeuLink is written in Python, and conforms to the syntax, coding standards and modular organization shared by\nmost Python packages. BasicsAndSampler provides an orientation to PsyNeuLinks Components, some examples of what\nPsyNeuLink models look like, and some of its capabilities. QuickReference provides an overview of how PsyNeuLink is\norganized and some of its basic principles of operation. The Tutorial provides an interactive guide to the\nconstruction of models using PsyNeuLink. Core contains the fundamental objects used to build PsyNeuLink models, and\nLibrary contains extensions, including speciality components, implemented compositions, and published models.\n\nInstallation\n------------\n\nPsyNeuLink is compatible with python versions >= 3.5, and is available through `PyPI `__:\n\n::\n\n pip install psyneulink\n\nAll prerequisite packages will be automatically added to your environment.\n\nIf you downloaded the source code, navigate to the cloned directory in a terminal,\nswitch to your preferred python3 environment, then run\n\n::\n\n pip install .\n\nDependencies that are automatically installed (except those noted as optional) include:\n\n * numpy (version <1.16)\n * matplotlib\n * toposort (version 1.4)\n * typecheck-decorator (version 1.2)\n * pillow\n * llvmlite\n * mpi4py (optional)\n * autograd (optional)\n\nLists of required packages for PsyNeuLink, developing PsyNeuLink, and running the PsyNeuLink tutorial are also\nstored in pip-style `requirements.txt`, `dev_requirements.txt`, and `tutorial_requirements.txt` in the source code.\n\nPsyNeuLink is an open source project maintined on `GitHub `_. The repo can be cloned\nfrom `here `_.\n\nIf you have trouble installing the package, or run into other problems, please contact psyneulinkhelp@princeton.edu.\n\nTutorial\n--------\n\nPsyNeuLink includes a tutorial (/tutorial/PsyNeuLink Tutorial.ipynb), that provides examples of how to create basic Components\nin PsyNeuLink, and combine them into Processes and a System. The examples include construction of a simple\ndecision making process using a Drift Diffusion Model, a neural network model of the Stroop effect, and a\nbackpropagation network for learning the XOR problem.\n\nThe tutorial can be run in a browser by clicking the badge below, or `this link `__.\n\n.. image:: https://mybinder.org/badge.svg\n :target: https://mybinder.org/v2/gh/PrincetonUniversity/PsyNeuLink/master\n\nTo run the tutorial locally, you must run python 3.5 and install additional packages:\n\n::\n\n pip install psyneulink[tutorial]\n\nor if you downloaded the source:\n\n::\n\n pip install .[tutorial]\n\n\nTo access the tutorial, make sure you fulfill the requirements mentioned above, download the tutorial notebook (/tutorial/PsyNeuLink Tutorial.ipynb), then run the terminal command\n\n::\n\n jupyter notebook\n\n\nOnce the notebook opens in your browser, navigate to the location where you saved the tutorial notebook, and\nclick on \"PsyNeuLink Tutorial.ipynb\".\n\nHelp and Issue Reporting\n------------------------\n\nHelp is available at psyneulinkhelp@princeton.edu.\n\nIssues can be reported at https://github.com/PrincetonUniversity/PsyNeuLink/issues.\n\nContributors\n------------\n\n*(in alphabetical order)*\n\n* **Allie Burton**, Princeton Neuroscience Institute, Princeton University\n* **Jonathan D. Cohen**, Princeton Neuroscience Institute, Princeton University\n* **Peter Johnson**, Princeton Neuroscience Institute, Princeton University\n* **Justin Junge**, Department of Psychology, Princeton University\n* **Kristen Manning**, Princeton Neuroscience Institute, Princeton University\n* **Katherine Mantel**, Princeton Neuroscience Institute, Princeton University\n* **Dillon Smith**, Princeton Neuroscience Institute, Princeton University\n* **Markus Spitzer**, Princeton Neuroscience Institute, Princeton University\n* **David Turner**, Princeton Neuroscience Institute, Princeton University\n* **Jan Vesely**, Department of Computer Science, Rutgers University\n* **Changyan Wang**, Princeton Neuroscience Institute, Princeton University\n* **Nate Wilson**, Princeton Neuroscience Institute, Princeton University\n\nWith substantial and greatly appreciated assistance from:\n\n* **Abhishek Bhattacharjee**, Department of Computer Science, Rutgers University\n* **Mihai Capota**, Intel Labs, Intel Corporation\n* **Bryn Keller**, Intel Labs, Intel Corporation\n* **Susan Liu**, Princeton Neuroscience Institute, Princeton University\n* **Garrett McGrath**, Princeton Neuroscience Institute, Princeton University\n* **Sebastian Musslick**, Princeton Neuroscience Institute, Princeton University\n* **Amitai Shenhav**, Cognitive, Linguistic, & Psychological Sciences, Brown University\n* **Michael Shvartsman**, Princeton Neuroscience Institute, Princeton University\n* **Ben Singer**, Princeton Neuroscience Institute, Princeton University\n* **Ted Willke**, Intel Labs, Intel Corporation\n\nLicense\n-------\n\n::\n\n Princeton University licenses this file to You under the Apache License, Version 2.0 (the \"License\");\n you may not use this file except in compliance with the License. You may obtain a copy of the License at:\n http://www.apache.org/licenses/LICENSE-2.0\n Unless required by applicable law or agreed to in writing, software distributed under the License is distributed\n on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n See the License for the specific language governing permissions and limitations under the License.\n\n\n", "description_content_type": "", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://github.com/PrincetonUniversity/PsyNeuLink", "keywords": "cognitive modeling", "license": "Apache", "maintainer": "", "maintainer_email": "", "name": "psyneulink", "package_url": "https://pypi.org/project/psyneulink/", "platform": "", "project_url": "https://pypi.org/project/psyneulink/", "project_urls": { "Homepage": "https://github.com/PrincetonUniversity/PsyNeuLink" }, "release_url": 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