Temporal derivative computation in the dorsal raphe network revealed by an experimentally-driven augmented integrate-and-fire modeling framework

Abstract

AbstractBy means of an expansive innervation, the relatively few phylogenetically-old serotonin (5-HT) neurons of the dorsal raphe nucleus (DRN) are positioned to enact coordinated modulation of circuits distributed across the entire brain in order to adaptively regulate behavior. In turn, the activity of the DRN is driven by a broad set of excitatory inputs, yet the resulting network computations that naturally emerge from the excitability and connectivity features of the various cellular elements of the DRN are still unknown. To gain insight into these computations, we developed a flexible experimental and computational framework based on a combination of automatic characterization and network simulations of augmented generalized integrate-and-fire (aGIF) single-cell models. This approach enabled the examination of causal relationships between specific excitability features and identified population computations. We found that feedforward inhibition of 5-HT neurons by heterogeneous DRN somatostatin (SOM) neurons implemented divisive inhibition, while endocannabinoid-mediated modulation of excitatory drive to the DRN increased the gain of 5-HT output. The most striking computation that arose from this work was the ability of 5-HT output to linearly encode the derivative of the excitatory inputs to the DRN. This network computation primarily emerged from the prominent adaptation mechanisms found in 5-HT neurons, including a previously undescribed dynamic threshold. This novel computation in the DRN provides a potential mechanism underlying some of the functions recently ascribed to 5-HT in the context of reinforcement learning.