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PyTorch and TensorFlow implementation of NCP, LTC, and CfC wired neural models
| Date | Stars |
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| 2026-07-24 | 2334 |
| 2026-07-25 | 2334 |
| 2026-07-28 | 2334 |
| 2026-07-30 | 2334 |
| 2026-08-06 | 2334 |
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<div align="center"><img src="https://raw.githubusercontent.com/mlech26l/ncps/master/docs/img/banner.png" width="800"/></div> # Neural Circuit Policies (for PyTorch and TensorFlow) [](https://zenodo.org/badge/latestdoi/290199641)      ## 📜 Papers [Neural Circuit Policies Enabling Auditable Autonomy (Open Access)](https://publik.tuwien.ac.at/files/publik_292280.pdf). [Closed-form continuous-time neural networks (Open Access)](https://www.nature.com/articles/s42256-022-00556-7) Neural Circuit Policies (NCPs) are designed sparse recurrent neural networks loosely inspired by the nervous system of the organism [C. elegans](http://www.wormbook.org/chapters/www_celegansintro/celegansintro.html). The goal of this package is to making working with NCPs in PyTorch and keras as easy as possible. [📖 Docs](https://ncps.readthedocs.io/en/latest/index.html) ```python import torch from ncps.torch import CfC rnn = CfC(20,50) # (input, hidden units) x = torch.randn(2, 3, 20) # (batch, time, features) h0 = torch.zeros(2,50) # (batch, units) output, hn = rnn(x,h0) ``` ## Installation ```bash pip install ncps ``` ## 🔖 Colab Notebooks We have created a few Google Colab notebooks for an interactive introduction to the package - [Google Colab (Pytorch) Basic usage](https://colab.research.google.com/drive/1VWoGcpyqGvrUOUzH7ccppE__m-n1cAiI?usp=sharing) - [Google Colab (Tensorflow): Basic usage](https://colab.research.google.com/drive/1IvVXVSC7zZPo5w-PfL3mk1MC3PIPw7Vs?usp=sharing) - [Google Colab (Tensorflow): Processing irregularly sampled time-series](https://colab.research.google.com/drive/1wBojTMMMVWl2WbF6hASbST1-XhK_xs5u?usp=sharing) - [Google Colab (Tensorflow) Stacking NCPs with other layers](https://colab.research.google.com/drive/1-mZunxqVkfZVBXNPG0kTSKUNQUSdZiBI?usp=sharing) ## End-to-end Examples - [Quickstart (torch and tf)](https://ncps.readthedocs.io/en/latest/quickstart.html) - [Atari Behavior Cloning (torch and tf)](https://ncps.readthedocs.io/en/latest/examples/atari_bc.html) - [Atari Reinforcement Learning (tf)](https://ncps.readthedocs.io/en/latest/examples/atari_ppo.html) ## Usage: Models and Wirings The package provides two models, the liquid time-constant (LTC) and the closed-form continuous-time (CfC) models. Both models are available as ```tf.keras.layers.Layer``` or ```torch.nn.Module``` RNN layers. ```python from ncps.torch import CfC, LTC input_size = 20 units = 28 # 28 neurons rnn = CfC(input_size, units) rnn = LTC(input_size, units) ``` The RNNs defined above consider fully-connected layers, i.e., as in LSTM, GRUs, and other RNNs. The distinctiveness of NCPs is their structured wiring diagram. To combine the LTC or CfC model with a ```python from ncps.torch import CfC, LTC from ncps.wirings import AutoNCP wiring = AutoNCP(28, 4) # 28 neurons, 4 outputs input_size = 20 rnn = CfC(input_size, wiring) rnn = LTC(input_size, wiring) ```  ## Tensorflow The Tensorflow bindings are available via the ```ncps.tf``` module. ```python from ncps.tf import CfC, LTC from ncps.wirings import AutoNCP units = 28 wiring = AutoNCP(28, 4) # 28 neurons, 4 outputs input_size = 20 rnn1 = LTC(units) # fully-connected LTC rnn2 = CfC(units) # fully-connected CfC rnn3 = LTC(wiring) # NCP wired LTC rnn4 = CfC(wiring) # NCP wired CfC ``` We can then combine the NCP cell with arbitrary ```tf.keras.layers```, for instance to build a powerful image sequence classifier: ```python from ncps.wirings import AutoNCP from ncps.tf import LTC import tensorflow as tf height, width, channels = (78, 200, 3) ncp = LTC(Aut
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:b098b7075a0b2251, topic:tensorflow