Core Concepts

Configuring Reservoirs

The reservoir is the dynamical core of the ESN. rclib provides RandomSparse for standard ESNs.

res = reservoirs.RandomSparse(
    n_neurons=1000,      # Size of the reservoir
    spectral_radius=0.9, # Scaling of spectral radius
    sparsity=0.1,        # Density of connections
    leak_rate=1.0,       # 1.0 = full update, < 1.0 = leaky integrator
    input_scaling=1.0,   # Scaling of input weights
    include_bias=False,  # Add bias neuron to reservoir
    seed=42              # Random seed for reproducibility
)

Configuring Readouts

The readout maps the high-dimensional reservoir state to the target output.

• Ridge Regression (readouts.Ridge): The standard offline training method. Fast and stable.
• Recursive Least Squares (readouts.Rls): For online, adaptive learning.
• Least Mean Squares (readouts.Lms): A simpler gradient-based online method.

Building the Model

The ESN class acts as a container.

model = ESN(connection_type="serial") # "serial" or "parallel"
model.add_reservoir(res1)
# For deep ESNs:
# model.add_reservoir(res2)
model.set_readout(readout)