V1 properties data assemblies and stimulus sets

mkgu_packaging/dicarlo/marques/marques_cavanaugh2002a.py

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+
+import numpy as np
+from marques_utils import gen_sample
+from brainio_collection.packaging import package_data_assembly
+from brainio_base.assemblies import DataAssembly
+
+ASSEMBLY_NAME = 'movshon.Cavanaugh2002a'
+SIZE_STIM_NAME = 'dicarlo.Marques2020_size'
+
+PROPERTY_NAMES = ['surround_suppression_index', 'strongly_suppressed', 'grating_summation_field', 'surround_diameter',
+                  'surround_grating_summation_field_ratio']
+
+
+def collect_data():
+    n_neurons = 190
+
+    # Surround suppression index (n=190 neurons, foveal eccentricity, response > 5spk/s)
+    surround_suppression_index_bins = np.linspace(0, 2, num=11)
+    surround_suppression_index_hist = np.array([66, 44, 38, 23, 16, 3, 0, 0, 0, 0])
+    surround_suppression_index = gen_sample(surround_suppression_index_hist, surround_suppression_index_bins,
+                                            scale='linear')
+
+    # Strongly suppressed (n=190 neurons, foveal eccentricity, response > 5spk/s)
+    strongly_suppressed_bins = np.linspace(0, 1, num=3)
+    strongly_suppressed_hist = np.array([42, 148])
+    strongly_suppressed = np.concatenate((np.zeros(strongly_suppressed_hist[0]),
+                                          np.ones(strongly_suppressed_hist[1]))).reshape((-1, 1))
+
+    # Grating summation field (n=148 neurons & suppression > 10%)
+    grating_summation_field_bins = np.array([0.0625, 0.125, 0.25, 0.5, 1.0, 2.0, 4.0, 8.0, 16.0])
+    grating_summation_field_hist = np.array([0, 9, 30, 55, 42, 10, 2, 0])
+    grating_summation_field = gen_sample(grating_summation_field_hist, grating_summation_field_bins, scale='log2')
+    filler = np.zeros((n_neurons - grating_summation_field_hist.sum(), 1))
+    filler[:] = np.nan
+    grating_summation_field = np.concatenate((filler, grating_summation_field))
+
+    # Surround diameter (n=148 neurons & suppression > 10%)
+    surround_diameter_bins = np.array([0.0625, 0.125, 0.25, 0.5, 1.0, 2.0, 4.0, 8.0, 16.0])
+    surround_diameter_hist = np.array([0, 0, 2, 20, 33, 49, 40, 4])
+    surround_diameter = gen_sample(surround_diameter_hist, surround_diameter_bins, scale='log2')
+    filler = np.zeros((n_neurons - surround_diameter_hist.sum(), 1))
+    filler[:] = np.nan
+    surround_diameter = np.concatenate((filler, surround_diameter))
+
+    # Surround diameter / Grating summation field (n=148 neurons, foveal eccentricity & suppression > 10%)
+    surround_grating_summation_field_ratio_bins = np.array([1, 2, 4, 8, 16, 32])
+    surround_grating_summation_field_ratio_hist = np.array([69, 38, 31, 10, 0])
+    surround_grating_summation_field_ratio = gen_sample(surround_grating_summation_field_ratio_hist,
+                                                        surround_grating_summation_field_ratio_bins, scale='log2')
+    filler = np.zeros((n_neurons - surround_grating_summation_field_ratio_hist.sum(), 1))
+    filler[:] = np.nan
+    surround_grating_summation_field_ratio = np.concatenate((filler, surround_grating_summation_field_ratio))
+
+    # Create DataAssembly with single neuronal properties and bin information
+    assembly = np.concatenate((surround_suppression_index, strongly_suppressed, grating_summation_field,
+                               surround_diameter, surround_grating_summation_field_ratio), axis=1)
+
+    assembly = DataAssembly(assembly, coords={'neuroid_id': ('neuroid', range(assembly.shape[0])),
+                                              'region': ('neuroid', ['V1'] * assembly.shape[0]),
+                                              'neuronal_property': PROPERTY_NAMES},
+                            dims=['neuroid', 'neuronal_property'])
+
+    assembly.attrs['number_of_trials'] = 20
+
+    for p in assembly.coords['neuronal_property'].values:
+        assembly.attrs[p+'_bins'] = eval(p+'_bins')
+
+    return assembly
+
+
+def main():
+    assembly = collect_data()
+    assembly.name = ASSEMBLY_NAME
+    print('Packaging assembly')
+    package_data_assembly(assembly, assembly_identifier=assembly.name,
+                          stimulus_set_identifier=SIZE_STIM_NAME, assembly_class='PropertyAssembly',
+                          bucket_name='brainio.contrib')
+
+
+if __name__ == '__main__':
+    main()
+

mkgu_packaging/dicarlo/marques/marques_devalois1982a.py

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+
+import numpy as np
+from marques_utils import gen_sample
+from brainio_collection.packaging import package_data_assembly
+from brainio_base.assemblies import DataAssembly
+
+ASSEMBLY_NAME = 'devalois.DeValois1982a'
+ORIENTATION_STIM_NAME = 'dicarlo.Marques2020_orientation'
+
+
+def collect_data():
+    # Preferred orientation data
+    preferred_orientation_hist = np.array([110, 83, 100, 92])
+    preferred_orientation_bins = np.linspace(-22.5, 157.5, 5)
+
+    preferred_orientation = gen_sample(preferred_orientation_hist, preferred_orientation_bins, scale='linear')
+
+    # Create DataAssembly with single neuronal properties and bin information
+    assembly = DataAssembly(preferred_orientation, coords={'neuroid_id': ('neuroid',
+                                                                          range(preferred_orientation.shape[0])),
+                                                           'region': ('neuroid', ['V1'] * preferred_orientation.shape[0]),
+                                                           'neuronal_property': ['preferred_orientation']},
+                            dims=['neuroid', 'neuronal_property'])
+
+    assembly.attrs['number_of_trials'] = 20
+
+    for p in assembly.coords['neuronal_property'].values:
+        assembly.attrs[p+'_bins'] = eval(p+'_bins')
+
+    return assembly
+
+
+def main():
+    assembly = collect_data()
+    assembly.name = ASSEMBLY_NAME
+    print('Packaging assembly')
+    package_data_assembly(assembly, assembly_identifier=assembly.name, stimulus_set_identifier=ORIENTATION_STIM_NAME,
+                          assembly_class='PropertyAssembly', bucket_name='brainio.contrib')
+
+
+if __name__ == '__main__':
+    main()
+

mkgu_packaging/dicarlo/marques/marques_devalois1982b.py

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+
+import numpy as np
+from marques_utils import gen_sample
+from brainio_collection.packaging import package_data_assembly
+from brainio_base.assemblies import DataAssembly
+
+ASSEMBLY_NAME = 'devalois.DeValois1982b'
+SPATIAL_FREQUENCY_STIM_NAME = 'dicarlo.Marques2020_spatial_frequency'
+
+
+def collect_data():
+    # Peak spatial frequency data
+    peak_spatial_frequency_bins = np.array([0.35, 0.5, 0.7, 1.0, 1.4, 2.0, 2.8, 4.0, 5.6, 8.0, 11.2, 16.0, 22.4])
+    # peak_spatial_frequency_simple_hist = np.array([0, 4, 4, 8, 25, 33, 26, 28, 12, 5, 2, 1])
+    # peak_spatial_frequency_complex_hist = np.array([0, 0, 0, 9, 9, 7, 10, 23, 12, 8, 3, 3])
+    # peak_spatial_frequency_hist = (peak_spatial_frequency_simple_hist + peak_spatial_frequency_complex_hist)
+    #
+    # peak_spatial_frequency = gen_sample(peak_spatial_frequency_hist, peak_spatial_frequency_bins, scale='log2')
+
+    peak_spatial_frequency_simple_foveal_hist = np.array([0, 4, 4, 8, 25, 33, 26, 28, 12, 5, 2, 1])
+    peak_spatial_frequency_complex_foveal_hist = np.array([0, 0, 0, 9, 9, 7, 10, 23, 12, 8, 3, 3])
+    peak_spatial_frequency_simple_parafoveal_hist = np.array([2, 4, 10, 12, 18, 7, 18, 3, 4, 0, 0, 0])
+    peak_spatial_frequency_complex_parafoveal_hist = np.array([1, 2, 1, 2, 5, 15, 13, 9, 3, 2, 0, 0])
+
+    peak_spatial_frequency_hist = (
+                peak_spatial_frequency_simple_foveal_hist + peak_spatial_frequency_complex_foveal_hist +
+                peak_spatial_frequency_simple_parafoveal_hist + peak_spatial_frequency_complex_parafoveal_hist)
+
+    peak_spatial_frequency = gen_sample(peak_spatial_frequency_hist, peak_spatial_frequency_bins, scale='log2')
+
+    # Create DataAssembly with single neuronal properties and bin information
+    assembly = DataAssembly(peak_spatial_frequency, coords={'neuroid_id': ('neuroid',
+                                                                           range(peak_spatial_frequency.shape[0])),
+                                                            'region': ('neuroid', ['V1'] *
+                                                                       peak_spatial_frequency.shape[0]),
+                                                            'neuronal_property': ['peak_spatial_frequency']},
+                            dims=['neuroid', 'neuronal_property'])
+
+    assembly.attrs['number_of_trials'] = 20
+
+    for p in assembly.coords['neuronal_property'].values:
+        assembly.attrs[p+'_bins'] = eval(p+'_bins')
+
+    return assembly
+
+
+def main():
+    assembly = collect_data()
+    assembly.name = ASSEMBLY_NAME
+    print('Packaging assembly')
+    package_data_assembly(assembly, assembly_identifier=assembly.name,
+                          stimulus_set_identifier=SPATIAL_FREQUENCY_STIM_NAME, assembly_class='PropertyAssembly',
+                          bucket_name='brainio.contrib')
+
+
+if __name__ == '__main__':
+    main()
+