Building Network Functions from Signed Feedforward Loops

Connectivity shapes computation by determining how signals propagate and interact within a network. Compared with analyses of individual connections, the feedforward loop (FFL) provides a minimal multipath structure in which direct and indirect pathways converge on the same target through a relay node. FFLs have been widely studied for their computational capacity, and their excitatory–inhibitory (E-I) configurations are known to determine distinct dynamical regimes. However, the role of E-I organization in the functional specialization of FFLs remains unclear. Here, we systematically analyze the dynamics of four E-I configurations of the FFL and characterize how direct and indirect pathways interact to shape computation. Under defined input and background conditions, these dynamics support distinct computational functions, including 1) response initiation, 2) response switching, 3) stopping of ongoing responses, and 4) context-dependent release. We then extend motif regularization with a differentiable signed-motif loss that increases the proportion of selected FFLs in larger networks, translating local computational mechanisms into structural priors for network learning. Behavioral evaluations in MiniHack show that these motif-enriched networks exhibit task-dependent computational biases consistent with their local functional specialization, and that a learned scheduler can coordinate components shaped by different motif priors to improve performance in more complex environments. Together, these results show how E-I organization shapes the functional specialization of FFLs and how local signed pathway dynamics can be transformed into reusable computational building blocks for constructing and composing functions.