Eventax provides a JAX implementation of the EventProp algorithm using Diffrax and Equinox offering full autograd support, easy extension with custom neuron dynamics, and built-in delay training.
Features
- Fully differentiable implementation via JAX and Diffrax
- Easy extension with custom neuron model dynamics + learnable parameters
- Support for (trainable) synnaptic delays.
- GPU/TPU compatibility through JAX
📦 Installation
pip install eventax
1 maintainer
Metadata
1 maintainer
Release files for eventax 0.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| eventax-0.1.0.tar.gz | 37.1 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| eventax-0.1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 79.1 kB
Release files / eventax-0.1.0.tar.gz
| Download URL | eventax-0.1.0.tar.gz |
|---|---|
| Size | 37.1 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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twine/6.2.0 CPython/3.12.9
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Release files / eventax-0.1.0-py3-none-any.whl
| Download URL | eventax-0.1.0-py3-none-any.whl |
|---|---|
| Size | 42.0 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.12.9
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