src.canns.trainer.hebbian

Classes

AntiHebbianTrainer	Anti-Hebbian trainer for pattern decorrelation and unlearning.
HebbianTrainer	Generic Hebbian trainer with progress reporting.

Module Contents

class src.canns.trainer.hebbian.AntiHebbianTrainer(model, **kwargs)

Bases: HebbianTrainer

Anti-Hebbian trainer for pattern decorrelation and unlearning.

Overview - Implements anti-Hebbian learning rule: “Neurons that fire together, wire apart” - Uses negative weight updates: W <- W - Σ (t t^T) instead of positive - Inherits all functionality from HebbianTrainer (predict, predict_batch, etc.)

Applications - Sparse coding and independent component analysis - Competitive learning networks - Decorrelation and whitening of feature representations - Lateral inhibition modeling - Selective forgetting / pattern unlearning

Learning Rule - For patterns x, compute optional mean activity rho and update:

W <- W - sum_i (x_i - rho)(x_i - rho)^T (note the minus sign)

• If subtract_mean=True, patterns are centered by mean: t = x - rho

• If normalize_by_patterns=True, divide by number of patterns

• All options from HebbianTrainer apply (subtract_mean, zero_diagonal, etc.)

Example

>>> model = AmariHopfieldNetwork(num_neurons=100, activation="tanh")
>>> # Train with Hebbian first
>>> hebb_trainer = HebbianTrainer(model)
>>> hebb_trainer.train(all_patterns)
>>> # Then apply anti-Hebbian to unlearn specific pattern
>>> anti_trainer = AntiHebbianTrainer(model, subtract_mean=False)
>>> anti_trainer.train([pattern_to_forget])

Initialize Anti-Hebbian trainer.

Parameters:

• model (src.canns.models.brain_inspired.BrainInspiredModel) – The model to train

• **kwargs – Additional arguments passed to HebbianTrainer

class src.canns.trainer.hebbian.HebbianTrainer(model, show_iteration_progress=False, compiled_prediction=True, *, weight_attr='W', subtract_mean=True, zero_diagonal=True, normalize_by_patterns=True, prefer_generic=True, state_attr=None, prefer_generic_predict=True, preserve_on_resize=True)

Bases: src.canns.trainer._base.Trainer

Generic Hebbian trainer with progress reporting.

Overview - Uses a model-exposed weight parameter (default attribute name: W) to apply a

standard Hebbian update. If unavailable, falls back to the model’s apply_hebbian_learning.

• Works with models that expose a parameter object with a .value ndarray of shape (N, N) (e.g., bm.Variable).

Generic rule - For patterns x (shape: (N,)), compute optional mean activity rho and update

W <- W + sum_i (x_i - rho)(x_i - rho)^T.

• Options allow zeroing the diagonal and normalizing by number of patterns.

Key options - weight_attr: Name of the weight attribute on the model (default: “W”). - subtract_mean: Whether to center patterns by mean activity rho. - zero_diagonal: Whether to set diagonal of W to zero after update. - normalize_by_patterns: Divide accumulated outer-products by number of patterns. - prefer_generic: Prefer the generic Hebbian rule over model-specific method. - state_attr: Name of the state vector attribute for prediction (default: s; or

model-provided predict_state_attr).

• prefer_generic_predict: Prefer the trainer’s generic predict loop over the model’s predict implementation (falls back automatically when unsupported).

Initialize Hebbian trainer.

Parameters:

• model (src.canns.models.brain_inspired.BrainInspiredModel) – The model to train

• show_iteration_progress (bool) – Whether to show progress for individual pattern convergence

• compiled_prediction (bool) – Whether to use compiled prediction by default (faster but no iteration progress)

• weight_attr (str | None) – Name of model attribute holding the connection weights (default: “W”).

• subtract_mean (bool) – Subtract dataset mean activity (rho) before outer-product.

• zero_diagonal (bool) – Force zero self-connections after update.

• normalize_by_patterns (bool) – Divide accumulated outer-products by number of patterns.

• prefer_generic (bool) – If True, use trainer’s generic Hebbian rule when possible; otherwise call the model’s own implementation if available.

predict(pattern, num_iter=20, compiled=None, show_progress=None, convergence_threshold=1e-10, progress_callback=None)

Predict a single pattern.

Parameters:

• pattern – Input pattern to predict

• num_iter (int) – Maximum number of iterations

• compiled (bool | None) – Override default compiled setting

• show_progress (bool | None) – Override default progress setting

• convergence_threshold (float) – Energy change threshold for convergence

Returns:

Predicted pattern

predict_batch(patterns, num_iter=20, compiled=None, show_sample_progress=True, show_iteration_progress=None, convergence_threshold=1e-10)

Predict multiple patterns with progress reporting.

Parameters:

• patterns (list) – List of input patterns to predict

• num_iter (int) – Maximum number of iterations per pattern

• compiled (bool | None) – Override default compiled setting

• show_sample_progress (bool) – Whether to show progress across samples

• show_iteration_progress (bool | None) – Override default iteration progress setting

• convergence_threshold (float) – Energy change threshold for convergence

Returns:

List of predicted patterns

Return type:

list

train(train_data)

Train the model using Hebbian learning.

Behavior - Preferred path: apply a generic Hebbian update directly to model.<weight_attr>. - Fallback path: call model.apply_hebbian_learning(train_data) if generic path

is unavailable.

Requirements for generic path - Model must expose model.<weight_attr> with a .value array of shape (N, N). - Optionally, models can declare weight_attr property to specify the

attribute name, allowing HebbianTrainer(..., weight_attr=None).

normalize_by_patterns = True

prefer_generic = True

prefer_generic_predict = True

preserve_on_resize = True

state_attr = None

subtract_mean = True

weight_attr = 'W'

zero_diagonal = True