NormalGenerator

class spikeometric.datasets.NormalGenerator(n_neurons: int, mean: float, std: float, sparsity=0.5, glorot=False, rng=None)

Bases: spikeometric.datasets.connectivity_generator.ConnectivityGenerator

Generates a dataset of connectivity matrices W0 with an equal number of excitatory and inhibitory neurons with a normal distribution of weights.

Example

>>> from spikeometric.datasets import NormalGenerator, ConnectivityDataset
>>> generator = NormalGenerator(n_neurons=6, mean=0, std=1, sparsity=0.5, glorot=True)
>>> generator.save(10, "data/w0/6_neurons_10_networks_0_mean_1_std_0.5_sparsity_glorot_0_seed")
>>> dataset = ConnectivityDataset("data/w0/6_neurons_10_networks_0_mean_1_std_0.5_sparsity_glorot_0_seed")
>>> data = dataset[0]
>>> data
Data(edge_index=[2, 13], num_nodes=6, W0=[13])
>>> data.W0
tensor([ 0.6270,  0.5048,  0.4300, -0.7440, -0.2918, -1.1955, -0.4468,  0.0000,
         0.0000,  0.0000,  0.0000,  0.0000,  0.0000])
>>> data.edge_index
tensor([[0, 0, 1, 3, 3, 4, 5, 0, 1, 2, 3, 4, 5],
        [2, 4, 3, 1, 5, 5, 4, 0, 1, 2, 3, 4, 5]])

n_neurons (int):

The number of neurons in a network (must be even)

mean (float):

The mean of the normal distribution from which the weights are drawn

std (float):

The standard deviation of the normal distribution

sparsity (float):

For each edge, the probability that it will be set to 0

glorot (bool):

If True, the weights will be divided by the square root of the number of neurons

rng (torch.Generator):

The random number generator to use

generate_W0()

Generates a connectivity matrix W0 of size n_neurons x n_neurons with a normal distribution of weights where Dale’s law is applied to generate an equal number of excitatory and inhibitory neurons. Sparsity is applied by setting a fraction of the edges to 0.