Temporal derivative computation in the dorsal raphe network revealed by an experimentally driven augmented integrate-and-fire modeling framework
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...
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| Format: | Article |
| Language: | English |
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eLife Sciences Publications Ltd
2023-01-01
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| Series: | eLife |
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| Online Access: | https://elifesciences.org/articles/72951 |
| _version_ | 1797894037502427136 |
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| author | Emerson F Harkin Michael B Lynn Alexandre Payeur Jean-François Boucher Léa Caya-Bissonnette Dominic Cyr Chloe Stewart André Longtin Richard Naud Jean-Claude Béïque |
| author_facet | Emerson F Harkin Michael B Lynn Alexandre Payeur Jean-François Boucher Léa Caya-Bissonnette Dominic Cyr Chloe Stewart André Longtin Richard Naud Jean-Claude Béïque |
| author_sort | Emerson F Harkin |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-10T07:02:26Z |
| format | Article |
| id | doaj.art-20b69314e853484c8e8cab4b250de1de |
| institution | Directory Open Access Journal |
| issn | 2050-084X |
| language | English |
| last_indexed | 2024-04-10T07:02:26Z |
| publishDate | 2023-01-01 |
| publisher | eLife Sciences Publications Ltd |
| record_format | Article |
| series | eLife |
| spelling | doaj.art-20b69314e853484c8e8cab4b250de1de2023-02-27T15:02:02ZengeLife Sciences Publications LtdeLife2050-084X2023-01-011210.7554/eLife.72951Temporal derivative computation in the dorsal raphe network revealed by an experimentally driven augmented integrate-and-fire modeling frameworkEmerson F Harkin0https://orcid.org/0000-0003-0698-5894Michael B Lynn1https://orcid.org/0000-0003-0760-4555Alexandre Payeur2https://orcid.org/0000-0002-2437-8249Jean-François Boucher3Léa Caya-Bissonnette4https://orcid.org/0000-0002-2893-6949Dominic Cyr5Chloe Stewart6André Longtin7https://orcid.org/0000-0003-0678-9893Richard Naud8https://orcid.org/0000-0001-7383-3095Jean-Claude Béïque9https://orcid.org/0000-0001-7278-4906Brain and Mind Research Institute, Centre for Neural Dynamics, Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, CanadaBrain and Mind Research Institute, Centre for Neural Dynamics, Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, CanadaBrain and Mind Research Institute, Centre for Neural Dynamics, Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, Canada; Department of Physics, University of Ottawa, Ottawa, CanadaBrain and Mind Research Institute, Centre for Neural Dynamics, Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, CanadaBrain and Mind Research Institute, Centre for Neural Dynamics, Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, CanadaBrain and Mind Research Institute, Centre for Neural Dynamics, Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, CanadaBrain and Mind Research Institute, Centre for Neural Dynamics, Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, CanadaBrain and Mind Research Institute, Centre for Neural Dynamics, Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, Canada; Department of Physics, University of Ottawa, Ottawa, CanadaBrain and Mind Research Institute, Centre for Neural Dynamics, Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, Canada; Department of Physics, University of Ottawa, Ottawa, CanadaBrain and Mind Research Institute, Centre for Neural Dynamics, Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, CanadaBy 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.https://elifesciences.org/articles/72951serotonindorsal raphesingle neuron modelsspiking neural networksadaptationmedial prefrontal cortex |
| spellingShingle | Emerson F Harkin Michael B Lynn Alexandre Payeur Jean-François Boucher Léa Caya-Bissonnette Dominic Cyr Chloe Stewart André Longtin Richard Naud Jean-Claude Béïque Temporal derivative computation in the dorsal raphe network revealed by an experimentally driven augmented integrate-and-fire modeling framework eLife serotonin dorsal raphe single neuron models spiking neural networks adaptation medial prefrontal cortex |
| title | Temporal derivative computation in the dorsal raphe network revealed by an experimentally driven augmented integrate-and-fire modeling framework |
| title_full | Temporal derivative computation in the dorsal raphe network revealed by an experimentally driven augmented integrate-and-fire modeling framework |
| title_fullStr | Temporal derivative computation in the dorsal raphe network revealed by an experimentally driven augmented integrate-and-fire modeling framework |
| title_full_unstemmed | Temporal derivative computation in the dorsal raphe network revealed by an experimentally driven augmented integrate-and-fire modeling framework |
| title_short | Temporal derivative computation in the dorsal raphe network revealed by an experimentally driven augmented integrate-and-fire modeling framework |
| title_sort | temporal derivative computation in the dorsal raphe network revealed by an experimentally driven augmented integrate and fire modeling framework |
| topic | serotonin dorsal raphe single neuron models spiking neural networks adaptation medial prefrontal cortex |
| url | https://elifesciences.org/articles/72951 |
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