Optimizing and Benchmarking Machine Learning and Traditional Synaptic Event Detection Pipelines in Neurophysiology Experiments.
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| Title: | Optimizing and Benchmarking Machine Learning and Traditional Synaptic Event Detection Pipelines in Neurophysiology Experiments. |
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| Authors: | Sevigny JP; Department of Psychology, University of Illinois at Chicago, Chicago, Illinois 60607.; Graduate Program in Neuroscience, University of Illinois at Chicago, Chicago, Illinois 60607., Schrank S; Department of Psychology, University of Illinois at Chicago, Chicago, Illinois 60607., Donka RM; Department of Psychology, University of Illinois at Chicago, Chicago, Illinois 60607., Aguilar OD; Department of Psychology, University of Illinois at Chicago, Chicago, Illinois 60607., Yunus NI; Department of Psychology, University of Illinois at Chicago, Chicago, Illinois 60607., Valchinova MR; Department of Psychology, University of Illinois at Chicago, Chicago, Illinois 60607., Fyke Z; Graduate Program in Neuroscience, University of Illinois at Chicago, Chicago, Illinois 60607.; Department of Biology, University of Illinois at Chicago, Chicago, Illinois 60607., Zak JD; Graduate Program in Neuroscience, University of Illinois at Chicago, Chicago, Illinois 60607.; Department of Biology, University of Illinois at Chicago, Chicago, Illinois 60607., Roitman JD; Department of Psychology, University of Illinois at Chicago, Chicago, Illinois 60607.; Graduate Program in Neuroscience, University of Illinois at Chicago, Chicago, Illinois 60607., Sparta DR; Department of Psychology, University of Illinois at Chicago, Chicago, Illinois 60607 dsparta@uic.edu.; Graduate Program in Neuroscience, University of Illinois at Chicago, Chicago, Illinois 60607. |
| Source: | ENeuro [eNeuro] 2026 Apr 17; Vol. 13 (5). Date of Electronic Publication: 2026 Apr 17 (Print Publication: 2026). |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Society for Neuroscience Country of Publication: United States NLM ID: 101647362 Publication Model: Electronic-Print Cited Medium: Internet ISSN: 2373-2822 (Electronic) Linking ISSN: 23732822 NLM ISO Abbreviation: eNeuro Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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