Metadata-Version: 2.4
Name: brain-modules
Version: 0.0.5
Summary: Replicate brain modules' computations with Artificial Neural Networks
Home-page: https://github.com/NeuroAI-Research/brain-modules
Author: Ricky Ding
Author-email: e0134117@u.nus.edu
License: MIT
Keywords: neuroscience,neuroAI,artificial-intelligence,neural-networks,cognitive-modeling,brain-modules
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Requires-Dist: gymnasium
Requires-Dist: torch
Requires-Dist: matplotlib
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## Intro

[My](https://rickyding1997.github.io) approach to `brain simulation`

1. Identify a list of computational modules in the brain
2. For each module
    1. Understand what it computes: input -> output
    2. Get training data (synthetic or real-world)
    3. Train artificial neural networks to replicate its functionality
3. Combine modules

Why?

- Biological plausibility is a **trap**, simulating spikes and neurotransmitters does not help us understand how brain generates intelligence
- Analogy: considering transistor physics is irrelevant to understanding how a computer computes `a + b` -- they are on different **isolated** levels of abstractions, they do not depend on one another to work

- Current works in AI are mainly focused on solving daily-life tasks (text, image/video, game playing) -- I want to use these technologies to understand the brain
- It is well proven that ANNs can produce intelligence (LLMs, RL agents) -- making them qualified to model modules in the brain

## List of `implemented` modules

```bash
pip install brain-modules
```

1. [Hippocampal Place Cells (2014 Nobel Prize)](./docs/hippocampus/1_PlaceCells.md)
