Quickstart

SIMPL follows sklearn conventions: configure hyperparameters at init, pass data to fit(). For hyperparameter units, see the Model / Maths section.

from simpl import SIMPL

# 1. Load your data
Y = ...     # spike counts (T, N_neurons)
Xb = ...    # behavioural initialisation positions (T, D)
time = ...  # timestamps (T,) or use time = np.linspace(0, T_end, len(Y)) if length is known but timestamps aren't

# 2. Configure the model (no data, no computation)
model = SIMPL(
    speed_prior=0.4,        # "0.4m/s" prior on latent speed
    behavior_prior=None,    # (optional) soft tether to the initial behaviour/stimulus
    kernel_bandwidth=0.02,  # "2 cm" kernel bandwidth for KDE spike smoothing
    bin_size=0.02,          # "2 cm" spatial bin size for environment discretisation
)

# 3. Fit
model.fit(
    Y,                      # spike counts
    Xb,                     # behavioural initialisation positions
    time,                   # timestamps
    n_iterations=5,
    )

# 4. Access results
model.X_           # final decoded latent positions, shape (T, D)
model.F_           # final receptive fields, shape (N_neurons, *env_dims)
model.results_     # full xarray.Dataset with metrics, likelihoods, and baselines, across iterations.

# 5. Plot results
model.plot_fitting_summary()  # Shows bits-per-spike metric and spike-latent mutual information. 

# (optional) Resume training if not yet converged
model.fit(Y, Xb, time, n_iterations=5, resume=True)

Prediction

Decode new spikes using the fitted receptive fields (no behavioural input needed). The new data must be binned at the same dt as the training data.

Y_new = ...  # new spike counts (T_new, N_neurons)
X_decoded = model.predict(Y_new)
model.prediction_results_  # xr.Dataset with rich results (mu_s, sigma_s, log-likelihoods, etc.)