I have a question regarding the implementation of the Liquid Time-Constant (LTC) network in the ncps package. Specifically, I am trying to understand the roles of sensory and motor neurons in the context of an LTC model.
Here is a sample model that I'm working with:

from ncps.tf import LTC

def build_ltc_model(input_shape):
    model = Sequential()
    model.add(LTC(units=10, return_sequences=True, input_shape=input_shape))
    model.add(LTC(units=5, return_sequences=False))
    model.add(Dense(1))
    model.compile(loss=MeanSquaredError(), optimizer=Adam(learning_rate=learning_rate), metrics=[RootMeanSquaredError()])
    model.summary()
    return model

Question

In the above model, the LTC layers are defined with integer values for units (10 and 5, respectively). I would like to know:

1. What constitutes the sensory neurons at each LTC layer?
2. What constitutes the motor neurons at each LTC layer?

In my understanding, sensory neurons are those that map the input features to the internal state, while motor neurons are those that map the internal state to the output. However, I am unclear on how this applies when specifying units as an integer without custom wiring. In other words, I cannot make any connection between these two parameters and my model:

• input_mapping – Mapping applied to the sensory neurons. Possible values None, "linear", "affine" (default "affine").
• output_mapping – Mapping applied to the motor neurons. Possible values None, "linear", "affine" (default "affine").

Any clarification or examples would be greatly appreciated. Thank you in advance!

Best regards,
Reza

@raminmh
@mlech26l