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- import matplotlib.pyplot as plt
- import noise
- import numpy as np
- from brian2.units import *
- from scripts.spatial_maps.orientation_maps.orientation_map import OrientationMap
- from scripts.interneuron_placement import create_interneuron_sheet_entropy_max_orientation
- from tqdm import tqdm
- from scripts.interneuron_placement import create_grid_of_excitatory_neurons, \
- create_interneuron_sheet_by_repulsive_force
- use_saved_array = False
- trials_per_scale = 1
- N_E = 900
- N_I = 90
- sheet_x = 450 * um
- sheet_y = 450 * um
- inhibitory_axon_long_axis = 100 * um
- inhibitory_axon_short_axis = 25 * um
- number_of_excitatory_neurons_per_row = int(np.sqrt(N_E))
- '''
- Tuning Maps
- '''
- # tuning_label = "Perlin"
- tuning_label = "Orientation map"
- # optimization_label = "Repulsive"
- optimization_label = "Entropy Optimization"
- corr_len_list = range(0,451,15) #For these values maps exist
- ellipse_trial_entropy_list = []
- circle_trial_entropy_list = []
- if not use_saved_array:
- for corr_len in tqdm(corr_len_list, desc="Calculating entropy over scale"): #TODO: Add trials, since the maps are random
- ellipse_single_trial_entropy_list = []
- circle_single_trial_entropy_list = []
- for seed in range(10):
- print(corr_len,seed)
- if tuning_label == "Perlin": #TODO: How to handle scale in Perlin
- tuning_map = lambda x, y: noise.pnoise2(x / 100.0, y / 100.0, octaves=2)*np.pi
- elif tuning_label == "Orientation map":
- map = OrientationMap(number_of_excitatory_neurons_per_row + 1,number_of_excitatory_neurons_per_row + 1,
- corr_len,sheet_x/um,sheet_y/um,seed)
- # map.improve(10)
- try:
- map.load_orientation_map()
- except:
- print('No map yet with {}x{} pixels and {} pixel correllation length and {} seed'.format(map.x_dim, map.y_dim, map.corr_len, map.rnd_seed))
- continue
- tuning_map = lambda x, y: map.tuning(x, y)
- ex_positions, ex_tunings = create_grid_of_excitatory_neurons(sheet_x / um, sheet_y / um,
- number_of_excitatory_neurons_per_row, tuning_map)
- inhibitory_radial_axis = np.sqrt(inhibitory_axon_long_axis * inhibitory_axon_short_axis)
- if optimization_label == "Repulsive":
- inhibitory_axonal_clouds = create_interneuron_sheet_by_repulsive_force(N_I, inhibitory_axon_long_axis / um,
- inhibitory_axon_short_axis / um, sheet_x / um,
- sheet_y / um, random_seed=2, n_iterations=1000)
- inhibitory_axonal_circles = create_interneuron_sheet_by_repulsive_force(N_I, inhibitory_radial_axis / um,
- inhibitory_radial_axis / um, sheet_x / um,
- sheet_y / um, random_seed=2, n_iterations=1000)
- elif optimization_label == "Entropy Optimization":
- inhibitory_axonal_clouds, ellipse_single_trial_entropy = create_interneuron_sheet_entropy_max_orientation(ex_positions, ex_tunings, N_I, inhibitory_axon_long_axis / um,
- inhibitory_axon_short_axis / um, sheet_x / um,
- sheet_y / um, trial_orientations=30)
- inhibitory_axonal_circles, circle_single_trial_entropy = create_interneuron_sheet_entropy_max_orientation(ex_positions, ex_tunings, N_I, inhibitory_radial_axis / um,
- inhibitory_radial_axis / um, sheet_x / um,
- sheet_y / um, trial_orientations=1)
- ellipse_single_trial_entropy_list.append(ellipse_single_trial_entropy)
- circle_single_trial_entropy_list.append(circle_single_trial_entropy)
- ellipse_trial_entropy_list.append(ellipse_single_trial_entropy_list)
- circle_trial_entropy_list.append(circle_single_trial_entropy_list)
- # interneuron_tunings = [inhibitory_axonal_clouds, inhibitory_axonal_circles]
- # plot_neural_sheet(ex_positions, ex_tunings, inhibitory_axonal_clouds)
- np.save('../../simulations/2020_02_27_entropy_over_noise_scale/circle_trial_entropy_list.npy', circle_trial_entropy_list)
- np.save('../../simulations/2020_02_27_entropy_over_noise_scale/ellipse_trial_entropy_list.npy', ellipse_trial_entropy_list)
- else:
- circle_trial_entropy_list = np.load(
- '../../simulations/2020_02_27_entropy_over_noise_scale/circle_trial_entropy_list.npy')
- ellipse_trial_entropy_list = np.load(
- '../../simulations/2020_02_27_entropy_over_noise_scale/ellipse_trial_entropy_list.npy')
- ellipse_entropy_mean = np.array([np.mean(i) for i in ellipse_trial_entropy_list])
- circle_entropy_mean = np.array([np.mean(i) for i in circle_trial_entropy_list])
- ellipse_entropy_std_dev = np.array([np.std(i) for i in ellipse_trial_entropy_list])
- circle_entropy_std_dev = np.array([np.std(i) for i in circle_trial_entropy_list])
- print(ellipse_trial_entropy_list)
- print(ellipse_entropy_std_dev)
- plt.figure()
- plt.plot(corr_len_list,circle_entropy_mean, label='Circle', marker='o',color='C1')
- plt.fill_between(corr_len_list,circle_entropy_mean-circle_entropy_std_dev,circle_entropy_mean+circle_entropy_std_dev,color='C1',alpha=0.4)
- plt.plot(corr_len_list,ellipse_entropy_mean, label='Ellipse', marker='o',color='C2')
- plt.fill_between(corr_len_list,ellipse_entropy_mean-ellipse_entropy_std_dev,ellipse_entropy_mean+ellipse_entropy_std_dev,color='C2',alpha=0.4)
- plt.xlabel('Correlation length')
- plt.ylabel('Entropy')
- plt.legend()
- plt.show()
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