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src=\"https://raw.githubusercontent.com/jonescompneurolab/jones-website/master/images/frontpage/logos/logo-hnn-medium.png\" width=\"300\"\u003e\n\u003c/h1\u003e\u003cbr\u003e\n\n# hnn-core\n\n[![tests](https://github.com/jonescompneurolab/hnn-core/actions/workflows/unix_unit_tests.yml/badge.svg?branch=master)](https://github.com/jonescompneurolab/hnn-core/actions/?query=branch:master)\n[![CircleCI](https://circleci.com/gh/jonescompneurolab/hnn-core.svg?style=svg)](https://circleci.com/gh/jonescompneurolab/hnn-core)\n[![Codecov](https://codecov.io/gh/jonescompneurolab/hnn-core/branch/master/graph/badge.svg)](https://codecov.io/gh/jonescompneurolab/hnn-core)\n[![PyPI](https://img.shields.io/pypi/dm/hnn-core.svg?label=PyPI%20downloads)](https://pypi.org/project/hnn-core/)\n[![JOSS](https://joss.theoj.org/papers/10.21105/joss.05848/status.svg)](https://doi.org/10.21105/joss.05848)\n\n![HNN-GUI](https://raw.githubusercontent.com/jonescompneurolab/hnn-core/acbcc4a598610dc3be5d4b0b7c59f98251ea7690/.github/images/hnn_gui.png)\n\n# About\n\nThe [Human Neocortical Neurosolver (HNN)](https://hnn.brown.edu) is an open-source\nneural modeling tool designed to help researchers/clinicians interpret human brain\nimaging data. This repository, called **HNN-core**, houses the source code for HNN.\n\nWith only a few lines of code, HNN provides a convenient way to run simulations of an\nanatomically- and biophysically-detailed dynamical system model of human thalamocortical\nbrain circuits. Given its modular, object-oriented design, HNN makes it easy to generate\nand evaluate hypotheses on the mechanistic origin of signals measured with\nmagnetoencephalography (MEG), electroencephalography (EEG), or intracranial\nelectrocorticography (ECoG). A unique feature of the HNN model is that it accounts for\nthe biophysics generating the primary electric currents underlying such data. Simulation\nresults are *directly* comparable to source-localized data (current dipoles in units of\nnano-Ampere-meters), enabling precise tuning of model parameters to match\ncharacteristics of recorded signals. Multimodal neurophysiology data such as local field\npotential (LFP), current-source density (CSD), and spiking dynamics can also be\nsimulated simultaneously with current dipoles.\n\nYou can view [HNN's frontpage here](https://hnn.brown.edu) for an overview of all that\nHNN can do. For how to use HNN, we provide scientific documentation, tutorials, and\nexamples aplenty on our [HNN Textbook website][]. There, we describe the use of HNN in\nstudying the circuit-level origin of some of the most commonly measured MEG/EEG and ECoG\nsignals: event related potentials (ERPs) and low-frequency rhythms (alpha/beta/gamma).\n\nThe HNN API, written in Python and built on top of\n[NEURON](https://nrn.readthedocs.io), is designed to be flexible and serve\nusers with varying levels of coding expertise, while the [HNN\nGUI](https://jonescompneurolab.github.io/textbook/content/04_using_hnn_gui/gui_quickstart.html)\nis designed to be useful to researchers with no formal computational neural modeling or\ncoding experience.\n\nThe terms HNN, HNN-core, and `hnn-core` are effectively equivalent, as they are all\ndifferent names for the same codebase. Historically, HNN-core was developed based on\nthe [original, deprecated HNN repository](https://github.com/jonescompneurolab/hnn), however that\nrepository is **no longer supported or developed**. It is kept online only for the sake\nof scientific reproducibility.\n\nPlease consider supporting HNN development efforts by voluntarily [providing your\ndemographic information\nhere](https://docs.google.com/forms/d/e/1FAIpQLSfN2F4IkGATs6cy1QBO78C6QJqvm9y14TqsCUsuR4Rrkmr1Mg/viewform)!\nNote that any demographic information we collect is anonymized and aggregated for\nreporting on the grants that fund the continued development of HNN. All questions are\nvoluntary.\n\n# Installation\n\nYou can try HNN **in your browser for free, with no local installation required!** At\nthe top of our [Installation Guide][], you can find links that describe how to run HNN\nonline in the cloud, either using Google CoLab notebooks or using the [Neuroscience Gateway\nPortal](https://www.nsgportal.org/).\n\nTo install HNN locally, see our [Installation Guide][] located at the [HNN Textbook\nwebsite][]. The easiest way to install `hnn-core` with the all its dependencies on Mac,\nLinux, or Windows (using \"Windows Subsystem for Linux\"), is to first install the\n[Anaconda Python Distribution](https://www.anaconda.com/download/success) and then run\nthe following commands:\n\n```\nconda create -y -q -n hnn-core-env python=3.12\nconda activate hnn-core-env\nconda install hnn-core-all -c jonescompneurolab -c conda-forge\n```\n\nOur Anaconda packages currently only support Python 3.12. However, installing `hnn-core`\nthrough `pip` currently supports **Python 3.9 through 3.13**, inclusively. Please see\nour [Installation Guide][] for detailed instructions on the various ways you can install\nHNN.\n\n# Usage\n\nOnce you have installed `hnn-core` and the dependencies for the features you want, you\ncan find tutorials, examples, and scientific documentation at our [HNN Textbook\nwebsite][].\n\n# Problems?\n\nYou can use the [GitHub Issues\ntracker](https://github.com/jonescompneurolab/hnn-core/issues) to report bugs. For user\nquestions, installation help, and scientific discussions, please see our [GitHub\nDiscussions page](https://github.com/jonescompneurolab/hnn-core/discussions).\n\n# Interested in Contributing?\n\nContributors are always welcome! Please read our [Contributing Guide][] and make sure to\nabide by our [Code of\nConduct](https://github.com/jonescompneurolab/hnn-core/blob/master/CODE_OF_CONDUCT.md). Our\n[governance structure can be found\nhere](https://jonescompneurolab.github.io/hnn-core/stable/governance.html).\n\n# Citing\n\nIf you use HNN-core in your work, please cite our [publication in\nJOSS](https://doi.org/10.21105/joss.05848):\n\n\u003e Jas et al., (2023). HNN-core: A Python software for cellular and\n\u003e circuit-level interpretation of human MEG/EEG. *Journal of Open Source\n\u003e Software*, 8(92), 5848, \u003chttps://doi.org/10.21105/joss.05848\u003e\n\n# Funding\n\nHNN-core development has been funded in part by the following United States of America federal government grants:\n- NIH R01EB022889 Human Neocortical Neurosolver\n- NIH 1U24NS129945 Dissemination of the Human Neocortical Neurosolver (HNN) software for circuit level interpretation of human MEG/EEG\n- NSF IIS 2424101 Collaborative Research: CRCNS Research Proposal: Uncovering the mechanisms and meaning of brain rhythm frequency shifts during decision making\n\n[Contributing Guide]: https://jonescompneurolab.github.io/hnn-core/stable/contributing.html\n[HNN Textbook website]: https://jonescompneurolab.github.io/textbook/content/preface.html\n[Installation Guide]: https://jonescompneurolab.github.io/textbook/content/01_getting_started/installation.html\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjonescompneurolab%2Fhnn-core","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjonescompneurolab%2Fhnn-core","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjonescompneurolab%2Fhnn-core/lists"}