mlech26l/ncps

Health v2 · maintenance only

Adoption not part of the score

2345 stars · 378 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A Python package providing PyTorch and TensorFlow/Keras implementations of Neural Circuit Policies (NCPs), including liquid time-constant (LTC) and closed-form continuous-time (CfC) recurrent neural network models. It enables building sparse, biologically inspired, interpretable RNNs with configurable wiring architectures.

Use cases

• train liquid time-constant recurrent networks in pytorch
• build sparse interpretable neural controllers for autonomous driving
• model irregularly sampled time-series with continuous-time RNNs
• implement closed-form continuous-time networks in keras
• replace LSTMs with compact wired neural networks
• train Atari agents with reinforcement learning using NCPs
• stack NCP layers with other neural network layers

When to choose

• you need compact, interpretable recurrent models for control or time-series tasks
• you want continuous-time RNNs that handle irregular time sampling
• you work in PyTorch or TensorFlow/Keras and want published LTC/CfC implementations
• model interpretability and small parameter counts matter more than raw scale

When to avoid

• you need large-scale transformer or LLM workloads
• you want a general-purpose AutoML or model zoo rather than specific RNN architectures
• your stack is JAX or another framework without custom porting

Facets

library · maturity active

machine-learning deep-learning machine-learning deep-learning artificial-intelligence autonomous-vehicles time-series python neural-circuit-policies liquid-time-constant-networks recurrent-neural-networks pytorch tensorflow keras continuous-time-neural-networks interpretable-ai

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