TY - JOUR TI - Temporal derivative computation in the dorsal raphe network revealed by an experimentally driven augmented integrate-and-fire modeling framework AU - Harkin, Emerson F AU - Lynn, Michael B AU - Payeur, Alexandre AU - Boucher, Jean-François AU - Caya-Bissonnette, Léa AU - Cyr, Dominic AU - Stewart, Chloe AU - Longtin, André AU - Naud, Richard AU - Béïque, Jean-Claude A2 - Uchida, Naoshige A2 - Huguenard, John R A2 - Roeper, Jochen A2 - Morikawa, Hitoshi A2 - Miller, Paul VL - 12 PY - 2023 DA - 2023/01/19 SP - e72951 C1 - eLife 2023;12:e72951 DO - 10.7554/eLife.72951 UR - https://doi.org/10.7554/eLife.72951 AB - By means of an expansive innervation, the 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. Yet the network computations that emerge from the excitability and connectivity features of the DRN are still poorly understood. To gain insight into these computations, we began by carrying out a detailed electrophysiological characterization of genetically identified mouse 5-HT and somatostatin (SOM) neurons. We next developed a single-neuron modeling framework that combines the realism of Hodgkin-Huxley models with the simplicity and predictive power of generalized integrate-and-fire models. We found that feedforward inhibition of 5-HT neurons by heterogeneous SOM neurons implemented divisive inhibition, while endocannabinoid-mediated modulation of excitatory drive to the DRN increased the gain of 5-HT output. Our most striking finding was that the output of the DRN encodes a mixture of the intensity and temporal derivative of its input, and that the temporal derivative component dominates this mixture precisely when the input is increasing rapidly. This network computation primarily emerged from prominent adaptation mechanisms found in 5-HT neurons, including a previously undescribed dynamic threshold. By applying a bottom-up neural network modeling approach, our results suggest that the DRN is particularly apt to encode input changes over short timescales, reflecting one of the salient emerging computations that dominate its output to regulate behavior. KW - serotonin KW - dorsal raphe KW - single neuron models KW - spiking neural networks KW - adaptation KW - medial prefrontal cortex JF - eLife SN - 2050-084X PB - eLife Sciences Publications, Ltd ER -