Impact of Network Structure and Cellular Response on Spike Time Correlations
Figure 2
Iterative construction of the linear approximation to network activity.
(A) An example recurrent network. (B)–(D) A sequence of graphs determines the successive approximations to the output of neuron 1. Processes defined by the same iteration of Eq. (11) have equal color. (B) In the first iteration of Eq. (11), the output of neuron 1 is approximated using the unperturbed outputs of its neighbors. (C) In the second iteration the results of the first iteration are used to define the inputs to the neuron. For instance, the process depends on the base process
which represents the unperturbed output of neuron 1. Neuron 4 receives no inputs from the rest of the network, and all approximations involve only its unperturbed output,
. (D) Cells 3 and 4 are not part of recurrent paths, and their contributions to the approximation are fixed after the second iteration. However, the recurrent connection between cells 1 and 2 implies that subsequent approximations involve contributions of directed chains of increasing length.