{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T18:30:26Z","timestamp":1780511426252,"version":"3.54.1"},"reference-count":18,"publisher":"MIT Press","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Neural Computation"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>The intrinsic electrophysiological properties of single neurons can be described by a broad spectrum of models, from realistic Hodgkin-Huxley-type models with numerous detailed mechanisms to the phenomenological models. The adaptive exponential integrate-and-fire (AdEx) model has emerged as a convenient middle-ground model. With a low computational cost but keeping biophysical interpretation of the parameters, it has been extensively used for simulations of large neural networks. However, because of its current-based adaptation, it can generate unrealistic behaviors. We show the limitations of the AdEx model, and to avoid them, we introduce the conductance-based adaptive exponential integrate-and-fire model (CAdEx). We give an analysis of the dynamics of the CAdEx model and show the variety of firing patterns it can produce. We propose the CAdEx model as a richer alternative to perform network simulations with simplified models reproducing neuronal intrinsic properties.<\/jats:p>","DOI":"10.1162\/neco_a_01342","type":"journal-article","created":{"date-parts":[[2020,11,30]],"date-time":"2020-11-30T15:04:45Z","timestamp":1606748685000},"page":"41-66","source":"Crossref","is-referenced-by-count":44,"title":["Conductance-Based Adaptive Exponential Integrate-and-Fire Model"],"prefix":"10.1162","volume":"33","author":[{"given":"Tomasz","family":"G\u00f3rski","sequence":"first","affiliation":[{"name":"Department of Integrative and Computational Neuroscience, Paris-Saclay Institute of Neuroscience, Centre National de la Recherche Scientifique, Gif-sur-Yvette 91190, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Damien","family":"Depannemaecker","sequence":"additional","affiliation":[{"name":"Department of Integrative and Computational Neuroscience, Paris-Saclay Institute of Neuroscience, Centre National de la Recherche Scientifique, Gif-sur-Yvette 91190, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alain","family":"Destexhe","sequence":"additional","affiliation":[{"name":"Department of Integrative and Computational Neuroscience, Paris-Saclay Institute of Neuroscience, Centre National de la Recherche Scientifique, Gif-sur-Yvette 91190, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"281","reference":[{"key":"B1","author":"Allen Brain Institute","year":"2015","journal-title":"Allen cell types database."},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1152\/jn.1993.70.4.1420"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.1162\/089976603322385063"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1152\/jn.00686.2005"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1038\/283673a0"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2006.10.047"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1113\/jphysiol.1971.sp009366"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.1016\/S0006-3495(61)86902-6"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.1162\/089976602320264015"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2003.820440"},{"key":"B11","volume-title":"Dynamical systems in neuroscience: The geometry of excitability and bursting","author":"Izhikevich E.","year":"2007"},{"issue":"3","key":"B12","first-page":"506","volume":"18","author":"Krinskii V. 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