Table 1 Summary of information on the monkey visual cortex dataset as well as the resulting network from JGC analysis on edges between distinct neurons.

From: Jacobian Granger causality for count and binary data with applications to causal network inference

ID

Neurons

Stimulus

Edges (% Sparsity)

(+,-)

Shared

% Edges Shared

Expt1

Expt2

Expt1

Expt2

Edges

Expt1

Expt2

Monkey 1

74

Noise

302 (5.59%)

327 (6.05%)

114, 188

162, 165

26

8.61%

7.95%

Natural

364 (6.74%)

340 (6.29%)

55, 309

83, 257

46

12.64%

13.53%

Monkey 2

123

Noise

403 (2.69%)

377 (2.51%)

127, 276

103, 274

21

5.21%

5.57%

Natural

433 (2.89%)

406 (2.71%)

134, 299

126, 280

35

8.08%

8.62%

  1. The “Neurons” column indicates the total number of observed neurons in the system, which indicates the network size N. Under the “Stimulus” column, “Noise” refers to white noise, while “Natural” refers to a movie of a natural scene, both of which were presented for 30 s. Sparsity here is computed as the fraction between the number of inferred edges and the total number of possible pairwise edges between distinct neurons (for a network of size N, this number is given by \(N^2-N\)). The (+,-) column breaks down the inferred edges into excitatory and inhibitory connections respectively. This table analyzes connections between distinct neurons; the corresponding analysis on self-connections is found in Table 2.
Back to article page