Journal of Neurophysiology

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Composite Modulatory Feedforward Loop Contributes to the Establishment of a Network State

Jin-Sheng Wu, Ferdinand S. Vilim, Nathan G. Hatcher, Michael R. Due, Jonathan V. Sweedler, Klaudiusz R. Weiss, Jian Jing


Feedforward loops (FFLs) are one of many network motifs identified in a variety of complex networks, but their functional role in neural networks is not well understood. We provide evidence that combinatorial actions of multiple modulators may be organized as FFLs to promote a specific network state in the Aplysia feeding motor network. The Aplysia feeding central pattern generator (CPG) receives two distinct inputs—a higher-order interneuron cerebral-buccal interneuron-2 (CBI-2) and the esophageal nerve (EN)—that promote ingestive and egestive motor programs, respectively. EN stimulation elicits a persistent egestive network state, which enables the network to temporarily express egestive programs following a switch of input from the EN to CBI-2. Previous work showed that a modulatory CPG element, B65, is specifically activated by the EN and participates in establishing the egestive state by enhancing activity of egestion-promoting B20 interneurons while suppressing activity and synaptic outputs of ingestion-promoting B40 interneurons. Here a peptidergic contribution is mediated by small cardioactive peptide (SCP). Immunostaining and mass spectrometry show that SCP is present in the EN and is released on EN stimulation. Importantly, SCP directly enhances activity and synaptic outputs of B20 and suppresses activity and synaptic outputs of B40. Moreover, SCP promotes B65 activity. Thus the direct and indirect (through B65) pathways to B20 and B40 from SCPergic neurons constitute two FFLs with one functioning to promote egestive output and the other to suppress ingestive output. This composite FFL consisting of the two combined FFLs appears to be an effective means to co-regulate activity of two competing elements that do not inhibit each other, thereby contributing to establish specific network states.

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