Comp Neuro: https://neuralensemble.org/
- Brian2 - best for prototyping, general comp neuro abstract modelling
- Elephant - working with actual brain data
- NEST - for REALLY big simulations - im talking about supercomputer level
- Nengo (maybe, I dont know if it is still relavent)
- TransformerLens - good for Mechanistic Interpretability research in ai
Misc:
Ai:
ChatGPT Generated
Computational Neuroscience
| Library | Why You Need It |
|---|---|
| Brian2 | THE simulator for spiking neural networks — fast, very Pythonic, fantastic for experimenting with neuron models. |
| NEURON + LFPy | If you need detailed, biologically-realistic neuron modeling (ion channels, dendrites, etc.) — used in “heavy” neuroscience papers. |
| NEST | Large-scale spiking neural network simulations. Good if you’re scaling up to networks of thousands/millions of neurons. |
| Elephant | Data analysis for neuroscience (spike trains, time series, STA, etc.). Works well with Neo. |
| Neo | Data structures for electrophysiology data (standardizes how spikes, events, analog signals, etc. are stored). |
| BindsNET | Deep learning + spiking neural network toolkit. If you want SNNs and ML connections. |
| PyNN | Abstraction layer to write one code that can run on Brian2, NEST, NEURON, etc. |
If you only want a “working core” you can expand later:
numpy,scipy,matplotlib,pandasbrian2,neo,elephantsympy,jaxseaborn,h5py,scikit-learndask(for larger data)
General
| Rank | Library | Primary Use Case | Learning Source |
|---|---|---|---|
| 1 | NumPy | Scientific Computing | documentation |
| 2 | Pandas | Data Analysis | documentation |
| 3 | Matplotlib | Data Visualization | documentation |
| 4 | SciPy | Scientific Computing | |
| 5 | Scikit-learn | Machine Learning | |
| 6 | TensorFlow | Machine Learning/AI | |
| 7 | Keras | Machine Learning/AI | |
| 8 | PyTorch | Machine Learning/AI | |
| 9 | Flask | Web Development | |
| 10 | Django | Web Development | |
| 11 | Requests | HTTP for Humans | |
| 12 | BeautifulSoup | Web Scraping | |
| 13 | Selenium | Web Testing/Automation | |
| 14 | PyGame | Game Development | |
| 15 | SymPy | Symbolic Mathematics | |
| 16 | Pillow | Image Processing | |
| 17 | SQLAlchemy | Database Access | |
| 18 | Plotly | Interactive Visualization | |
| 19 | Dash | Web Applications | |
| 20 | Jupyter | Interactive Computing | |
| 21 | FastAPI | Web APIs | |
| 22 | PySpark | Big Data Processing | |
| 23 | NLTK | Natural Language Processing | |
| 24 | spaCy | Natural Language Processing | |
| 25 | Tornado | Web Development | |
| 26 | Streamlit | Data Apps | |
| 27 | Bokeh | Data Visualization | |
| 28 | PyTest | Testing Framework | |
| 29 | Celery | Task Queuing | |
| 30 | Gunicorn | WSGI HTTP Server | |
Neuroscience Tools
- Nengo - Library for creating and simulating large-scale brain models.
- Nitime - Timeseries analysis for neuroscience data.
- Nilearn - Module for performing statistical learning/machine learning on NeuroImaging data.
- DIPY - Toolbox for analysis of MR diffusion imaging.
- MNE-Python - Community-driven software for processing time-resolved neural signals including electroencephalography (EEG) and magnetoencephalography (MEG).
- NiBabel - Provides read and write access to some common medical and neuroimaging file formats.
- PsychoPy - Package for running psychology and neuroscience experiments. It allows for creating psychology stimuli in Python.
- NuPic - Numenta Platform for Intelligent Computing is an implementation of Hierarchical Temporal Memory (HTM), a theory of intelligence based strictly on the neuroscience of the neocortex.
- Brian2 - Free, open source simulator for spiking neural networks.
- expyriment - Platform-independent lightweight Python library for designing and conducting timing-critical behavioural and neuroimaging experiments.
- BindsNET - Package for simulating spiking neural networks for reinforcement & machine learning.
- SpikeInterface - Framework designed to unify spike-sorting technologies
- NiMARE - NiMARE is a Python package for neuroimaging meta-analyses