Module cells 

RNN Cell Implementations

This module provides single-timestep RNN cells for Neural Circuit Policies. These cells process one timestep at a time and are wrapped by the higher-level RNN layers in crate::rnn for sequence processing.

Cell Types

Cell	Description	Use Case
CfCCell	Closed-form Continuous-time	Fast, efficient, recommended
LTCCell	Liquid Time-Constant	Biologically accurate, slower
WiredCfCCell	CfC with multi-layer wiring	Complex architectures
LSTMCell	Standard LSTM	Mixed memory augmentation

When to Use Cells Directly

Most users should use the higher-level CfC or LTC layers which handle sequence processing automatically. Use cells directly when you need:

• Custom sequence processing logic
• Integration with other frameworks
• Fine-grained control over state management

CfC Operating Modes

The CfCCell supports three operating modes via CfcMode:

Default Mode (Recommended)

h = tanh(ff1) × (1 - σ(t)) + tanh(ff2) × σ(t)

Gated interpolation between two feedforward paths. Best balance of expressiveness and stability.

Pure Mode

h = a - a × exp(-t × (|w_τ| + |ff1|)) × ff1

Direct ODE solution without gating. More biologically plausible but can be less stable for some tasks.

NoGate Mode

h = tanh(ff1) + tanh(ff2) × σ(t)

Simplified mode using addition instead of interpolation. Useful for tasks where gating adds unnecessary complexity.

Tensor Shapes

All cells expect 2D tensors for single-timestep processing:

Tensor	Shape	Description
input	[batch, input_size]	Input features
hidden_state	[batch, hidden_size]	Previous hidden state
output	[batch, hidden_size]	Cell output
new_state	[batch, hidden_size]	Updated hidden state

Example: Using CfCCell Directly

use ncps::cells::{CfCCell, CfcMode};
use burn::tensor::Tensor;

let device = Default::default();
let cell = CfCCell::<Backend>::new(16, 32, &device)
    .with_mode(CfcMode::Default);

// Process single timestep
let input: Tensor<Backend, 2> = /* [batch, 16] */;
let hidden: Tensor<Backend, 2> = Tensor::zeros([batch, 32], &device);

let (output, new_hidden) = cell.forward(input, hidden, 1.0);
// output: [batch, 32]
// new_hidden: [batch, 32]

Input/Output Mapping Modes

LTCCell supports different input/output mapping strategies via MappingMode:

• Affine: Linear transformation with bias (most expressive)
• Linear: Linear transformation without bias
• None: Direct pass-through (fastest)

Re-exports

pub use cfc_cell::CfCCell;

pub use cfc_cell::CfcMode;

pub use lstm_cell::LSTMCell;

pub use ltc_cell::LTCCell;

pub use ltc_cell::MappingMode;

pub use wired_cfc_cell::WiredCfCCell;

Modules

cfc_cell

Closed-form Continuous-time (CfC) Cell Implementation

lstm_cell

ltc_cell

Liquid Time-Constant (LTC) Cell Implementation

wired_cfc_cell

Wired CfC Cell Implementation