Package topo :: Package learningfn :: Module basic :: Class Oja

Class Oja

Oja's rule (Oja, 1982; Dayan and Abbott, 2001, equation 8.16.)

Hebbian rule with soft multiplicative normalization, tending the weights toward a constant sum-squared value over time. Thus this function does not normally need a separate output_fn for normalization.

Nested Classes

[hide private]

Inherited from param.parameterized.Parameterized: __metaclass__

Instance Methods

[hide private]

__call__(self, input_activity, unit_activity, weights, single_connection_learning_rate)
Apply this learning function given the input and output activities and current weights.

source code

Inherited from param.parameterized.Parameterized: __getstate__, __init__, __repr__, __setstate__, __str__, debug, defaults, force_new_dynamic_value, get_param_values, get_value_generator, inspect_value, message, print_param_values, script_repr, set_default, set_dynamic_time_fn, set_param, state_pop, state_push, verbose, warning

Inherited from param.parameterized.Parameterized (private): _add_parameter, _instantiate_param, _set_name, _setup_params

Inherited from object: __delattr__, __format__, __getattribute__, __hash__, __new__, __reduce__, __reduce_ex__, __setattr__, __sizeof__, __subclasshook__

Class Methods

[hide private]

Inherited from param.parameterized.Parameterized: params, print_param_defaults

Class Variables

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alpha = param.Number(default= 0.1, bounds= (0, None))

name = <param.parameterized.String object at 0xb287e6c>
String identifier for this object.

Inherited from param.parameterized.Parameterized: print_level

Properties

[hide private]

Inherited from object: __class__

Method Details

[hide private]

call(self, input_activity, unit_activity, weights, single_connection_learning_rate)
(Call operator)

source code

Apply this learning function given the input and output activities and current weights.

Must be implemented by subclasses.

Overrides: base.functionfamily.LearningFn.__call__: (inherited documentation)

Class Oja

__call__(self, input_activity, unit_activity, weights, single_connection_learning_rate) (Call operator)

call(self, input_activity, unit_activity, weights, single_connection_learning_rate)
(Call operator)