Sequential Predictive Attention for the Representation of SpiKing Signals

Navigation

Verified details

These details have been verified by PyPI
Maintainers
Avatar for skatchn from gravatar.com skatchn

Unverified details

These details have not been verified by PyPI
Project links
Meta
  • License: GNU GENERAL PUBLIC LICENSE Version 3, 29 June 2007 Copyright (C) 2007 Free Software Foundation, Inc...
  • Author: Nicolas Skatchkovsky
  • Tags Computational neuroscience, Artificial Intelligence, GenAI
  • Requires: Python >=3.9
  • Provides-Extra: scripts
Classifiers

Project description

SPARKS 🎆

This is the official repository for SPARKS: a Sequential Predictive Autoencoder for the Analysis of spiKing Signals.

SPARKS includes a novel self-attention mechanism using Hebbian learning to generate reliable latent representations from single spike timings. SPARKS trains a variational autoencoder with a novel criterion inspired by predictive coding for temporal coherence.

sparks is implemented in PyTorch and includes demos for a quickstart. It can perform supervised or unsupervised to produce low-dimensional latent embeddings which allows to gain biological insights from neural data.

Make sure to 👀 watch or ⭐️ star this repository to keep updated!

Reference

  • 📄 Preprint:

Available now on BiorXiv!

License

  • SPARKS is an open source software under a GLPv3 license.

Project details

Verified details

These details have been verified by PyPI
Maintainers
Avatar for skatchn from gravatar.com skatchn

Unverified details

These details have not been verified by PyPI
Project links
Meta
  • License: GNU GENERAL PUBLIC LICENSE Version 3, 29 June 2007 Copyright (C) 2007 Free Software Foundation, Inc...
  • Author: Nicolas Skatchkovsky
  • Tags Computational neuroscience, Artificial Intelligence, GenAI
  • Requires: Python >=3.9
  • Provides-Extra: scripts
Classifiers

Release history Release notifications | RSS feed

This version

0.1.1

0.1.0

0.0.1

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

sparks_ai-0.1.1.tar.gz (26.3 MB view details)

Uploaded Source

Built Distribution

sparks_ai-0.1.1-py3-none-any.whl (26.1 MB view details)

Uploaded Python 3

File details

Details for the file sparks_ai-0.1.1.tar.gz.

File metadata

  • Download URL: sparks_ai-0.1.1.tar.gz
  • Upload date:
  • Size: 26.3 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.10.9

File hashes

Hashes for sparks_ai-0.1.1.tar.gz
Algorithm Hash digest
SHA256 a3bee694629237e8a858bae70ce74dad8a2bed54eb7aae2fff69e4189a3a928e
MD5 2893e1bccba16a8af12fd4bb8b5bf02c
BLAKE2b-256 a79367fba1d3b86289c71c84c69de975613f6835dee26eb9918e5033582381bf

See more details on using hashes here.

File details

Details for the file sparks_ai-0.1.1-py3-none-any.whl.

File metadata

  • Download URL: sparks_ai-0.1.1-py3-none-any.whl
  • Upload date:
  • Size: 26.1 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.10.9

File hashes

Hashes for sparks_ai-0.1.1-py3-none-any.whl
Algorithm Hash digest
SHA256 2ab2fa45b4bff27b5f0f9c56a69b2454deebffec752fc5cd1238b1ebe4ad6be2
MD5 bf18fce23009b5f91b301250df94869b
BLAKE2b-256 cbbeff3c460c174843ad00726a14fd5284d0edf70868c4e63c7854209cabdcb7

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page