Neural Circuit Policies (NCPs) are sparse, interpretable recurrent neural network architectures — derived from Liquid Time-Constant (LTC) networks and wired to resemble biological neural circuits (sensory -> interneuron -> command -> motor).

What Is an NCP?

• Structure: A 4-layer architecture inspired by the C. elegans nematode wiring diagram.
• Sparsity: Extremely sparse connections. A typical NCP might solve a complex driving task with only 19 neurons and 75 synapses.
• Training: Trained via algorithms like BPTT or evolution, then often mapped to ODE solvers.

Why NCPs Matter

• Interpretability: You can look at the weights and say "This neuron activates when the car sees the road edge."
• Efficiency: Can run on extremely constrained hardware (IoT, microcontrollers).
• Generalization: The imposed structure prevents overfitting, leading to better out-of-distribution performance.

Neural Circuit Policies are glass-box AI — proving that we don't need millions of neurons to solve control tasks if we wire the few we have correctly.