Package topo :: Package analysis :: Module featureresponses :: Class FeatureResponses

Class FeatureResponses

Systematically vary input pattern feature values and collate the responses.

Each sheet has a DistributionMatrix for each feature that will be tested. The DistributionMatrix stores the distribution of activity values for each unit in the sheet for that feature. For instance, if the features to be tested are orientation and phase, we will create a DistributionMatrix for orientation and a DistributionMatrix for phase for each sheet. The orientation and phase of the input are then systematically varied (when measure_responses is called), and the responses of each unit to each pattern are collected into the DistributionMatrix.

The resulting data can then be used to plot feature maps and tuning curves, or for similar types of feature-based analyses.

Nested Classes

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Inherited from param.parameterized.Parameterized: __metaclass__

Instance Methods

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__init__(self, features, **params)
x.__init__(...) initializes x; see x.__class__.__doc__ for signature

source code

initialize_featureresponses(self, features)
Create an empty DistributionMatrix for each feature and each sheet.

source code

sheets_to_measure(self)
Return a list of the Sheets in the current simulation for which to collect responses.

source code

measure_responses(self, pattern_presenter, param_dict, features, display)
Present the given input patterns and collate the responses.

source code

present_permutation(self, permutation)
Present a pattern with the specified set of feature values.

source code

_update(self, current_values)

source code

Inherited from param.parameterized.Parameterized: __getstate__, __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

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Inherited from param.parameterized.Parameterized: params, print_param_defaults

Class Variables

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repetitions = param.Integer(default= 1, bounds= (1, None), doc...
How many times each stimulus will be presented.

_fullmatrix = {}
dict() -> new empty dictionary.

name = <param.parameterized.String object at 0xa0d002c>
String identifier for this object.

Inherited from base.functionfamily.PatternDrivenAnalysis: post_analysis_session_hooks, post_presentation_hooks, pre_analysis_session_hooks, pre_presentation_hooks

Inherited from param.parameterized.Parameterized: print_level

Properties

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Inherited from object: __class__

Method Details

[hide private]

init(self, features, **params)
(Constructor)

source code

x.__init__(...) initializes x; see x.__class__.__doc__ for signature

Overrides: object.__init__: (inherited documentation)

Class Variable Details

[hide private]

repetitions

How many times each stimulus will be presented.

Each stimulus is specified by a particular feature combination, and need only be presented once if the network has no other source of variability. If results differ for each presentation of an identical stimulus (e.g. due to intrinsic noise), then this parameter can be increased so that results will be an average over the specified number of repetitions.

Value:

param.Integer(default= 1, bounds= (1, None), doc= """
        How many times each stimulus will be presented.

        Each stimulus is specified by a particular feature
        combination, and need only be presented once if the network
        has no other source of variability.  If results differ for
        each presentation of an identical stimulus (e.g. due to
        intrinsic noise), then this parameter can be increased
...

_fullmatrix

dict() -> new empty dictionary.
dict(mapping) -> new dictionary initialized from a mapping object's
    (key, value) pairs.
dict(seq) -> new dictionary initialized as if via:
    d = {}
    for k, v in seq:
        d[k] = v
dict(**kwargs) -> new dictionary initialized with the name=value pairs
    in the keyword argument list.  For example:  dict(one=1, two=2)

Value:

{}

Class FeatureResponses

__init__(self, features, **params) (Constructor)

repetitions

_fullmatrix

init(self, features, **params)
(Constructor)