def ToliasCadena2017Correlation(*regression_args, **regression_kwargs):
loader = AssemblyLoader()
assembly_repetition = loader(average_repetition=False)
assembly = loader(average_repetition=True)
assembly.stimulus_set.name = assembly.stimulus_set_name
similarity_metric = CrossRegressedCorrelation(
regression=corr_regression(
*regression_args, **regression_kwargs),
correlation=pearsonr_correlation(),
crossvalidation_kwargs={'splits': 4, 'stratification_coord': None})
identifier = f'tolias.Cadena2017-correlation'
ceiler = InternalConsistency(split_coord='repetition_id')
def ceiling():
assembly_nonan, stimuli = loader.dropna(
assembly_repetition,
assembly_repetition.attrs['stimulus_set'])
return ceiler(assembly_nonan)
return NeuralBenchmark(identifier=identifier, version=1,
assembly=assembly, similarity_metric=similarity_metric,
ceiling_func=ceiling)
def ToliasCadena2017Mask():
loader = AssemblyLoader()
assembly_repetition = loader(average_repetition=False)
assembly = loader(average_repetition=True)
assembly.stimulus_set.identifier = assembly.stimulus_set_identifier
similarity_metric = CrossRegressedCorrelation(
regression=mask_regression(),
correlation=pearsonr_correlation(),
crossvalidation_kwargs={
'splits': 4,
'stratification_coord': None
})
identifier = f'tolias.Cadena2017-mask'
ceiler = InternalConsistency(split_coord='repetition_id')
return NeuralBenchmark(identifier=identifier,
version=1,
assembly=assembly,
similarity_metric=similarity_metric,
visual_degrees=VISUAL_DEGREES,
number_of_trials=NUMBER_OF_TRIALS,
parent='V1',
bibtex=BIBTEX,
ceiling_func=lambda: ceiler(assembly_repetition))
def AruKuzovkin2018PLS():
assembly_repetition = LazyLoad(
lambda: load_assembly(average_repetitions=False))
assembly = LazyLoad(lambda: load_assembly(average_repetitions=True))
# similarity_metric = CrossRegressedCorrelation(
# regression=pls_regression(),
# correlation=pearsonr_correlation(),
# crossvalidation_kwargs=dict(stratification_coord=None)
# )
similarity_metric = RDMCrossValidated(crossvalidation_kwargs=dict(
stratification_coord=None))
# sub-select the entire IT region
# x = assembly['region']
# idx = x.data == 20
# sub-select only important it electrodes for IT: 1094
# idx = np.zeros((len(assembly.neuroid_id)), dtype=bool)
# idx[1094] = True
# idx[847] = True
#idx = get_IT_MNI()
idx = it_idx
new_assembly = assembly[:, idx, :]
# Can replace the "name" of the brain region
new_assembly = type(new_assembly)(
new_assembly.values,
coords={
coord: (dims, values if coord != 'region' else ['IT'] *
len(new_assembly['region']))
for coord, dims, values in walk_coords(new_assembly)
},
dims=new_assembly.dims)
new_assembly.attrs = assembly.attrs
new_assembly = new_assembly.where(new_assembly['time_bin_start'] < 350,
drop=True)
new_assembly = new_assembly.where(new_assembly['time_bin_start'] > 250,
drop=True)
new_assembly = new_assembly.mean('time_bin')
new_assembly = new_assembly.expand_dims('time_bin')
new_assembly.coords['time_bin'] = pd.MultiIndex.from_tuples(
[(62.5, 156.2)], names=['time_bin_start', 'time_bin_end'])
new_assembly = new_assembly.squeeze('time_bin')
new_assembly.attrs = assembly.attrs
ceiling = Score([1, np.nan],
coords={'aggregation': ['center', 'error']},
dims=['aggregation'])
return NeuralBenchmark(identifier=f'aru.Kuzovkin2018-pls',
version=1,
assembly=new_assembly,
similarity_metric=similarity_metric,
visual_degrees=VISUAL_DEGREES,
ceiling_func=lambda: ceiling,
parent='Kamila',
number_of_trials=1)
