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- import numpy as np
- from conmorph.connections import save_overlap
- from conmorph.neurons import NeuronType, NeuralTissue
- from interneuron_polarity.model.morphology.positions import Uniform, Deterministic, UniformOrientation
- from interneuron_polarity.model.morphology.shapes import Ellipsoid, NeuralProcess
- from interneuron_polarity.model.morphology.volume import NeuralVolume
- ### Define the neural volume
- voxel_size = 10
- v_x = 300
- v_y = 300
- v_z = 300
- V = NeuralVolume(v_x, v_y, v_z, voxel_size)
- ### Define neural types with their somata distribution and the axon and dendritic tree shapes
- somata_distribution = Uniform(np.array([0,0,0]), np.array([v_x, v_y, v_z]))
- excitatory_axon_shape = Ellipsoid(100, 100)
- excitatory_axon_orientation = Deterministic(np.array([0, 0, 1]))
- excitatory_axon = NeuralProcess(excitatory_axon_shape, excitatory_axon_orientation)
- excitatory_dendrite_shape = Ellipsoid(100, 100)
- excitatory_dendrite_orientation = Deterministic(np.array([0, 0, 1]))
- excitatory_dendrite = NeuralProcess(excitatory_dendrite_shape, excitatory_dendrite_orientation)
- circular_inhibitory_axon_shape = Ellipsoid(80, 80)
- polar_inhibitory_axon_shape = Ellipsoid(200, 50) #approximately same volume
- inhibitory_axon_orientation = UniformOrientation(0, np.pi/2.0, 0, 2*np.pi)
- circular_inhibitory_axon = NeuralProcess(circular_inhibitory_axon_shape, inhibitory_axon_orientation)
- polar_inhibitory_axon = NeuralProcess(polar_inhibitory_axon_shape, inhibitory_axon_orientation)
- inhibitory_dendrite_shape = Ellipsoid(100, 100)
- inhibitory_dendrite_orientation = Deterministic(np.array([0, 0, 1]))
- inhibitory_dendrite = NeuralProcess(inhibitory_dendrite_shape, inhibitory_dendrite_orientation)
- excitatory_population = NeuronType(somata_distribution, excitatory_axon, excitatory_dendrite)
- inhibitory_populations = [NeuronType(somata_distribution, inhibitory_axon, inhibitory_dendrite) for inhibitory_axon in [circular_inhibitory_axon, polar_inhibitory_axon]]
- ### Calculate the axon-dendrite overlap between all pairs of neurons
- number_of_excitatory_neurons = 500
- number_of_inhibitory_neurons = 250
- tissues = [ NeuralTissue(V, excitatory_population, inhibitory_population) for inhibitory_population in inhibitory_populations]
- file_path = "/home/pfeiffer/Projects/subiculum_interneuron_polarity/data"
- number_of_overlap_matrices = 2
- for tissue, type in zip(tissues, ["circular_inhibitory_neurons", "polar_inhibitory_neurons"]):
- for idx in range(number_of_overlap_matrices):
- print("Calculate {:s} overlap matrix {:d}/{:d}".format(type, idx+1, number_of_overlap_matrices))
- file_name = "{:s}_overlap_matrix_{:d}.pickle".format(type, idx)
- path_to_store = file_path + "/" + file_name
- overlap_matrix = tissue.calculate_overlap_matrix(number_of_excitatory_neurons, number_of_inhibitory_neurons)
- save_overlap(path_to_store, overlap_matrix)
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