{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T19:09:51Z","timestamp":1790795391892,"version":"4.1.0"},"reference-count":54,"publisher":"MIT Press - Journals","issue":"8","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Neural Computation"],"published-print":{"date-parts":[[2020,8]]},"abstract":"<jats:p> Understanding how rich dynamics emerge in neural populations requires models exhibiting a wide range of behaviors while remaining interpretable in terms of connectivity and single-neuron dynamics. However, it has been challenging to fit such mechanistic spiking networks at the single-neuron scale to empirical population data. To close this gap, we propose to fit such data at a mesoscale, using a mechanistic but low-dimensional and, hence, statistically tractable model. The mesoscopic representation is obtained by approximating a population of neurons as multiple homogeneous pools of neurons and modeling the dynamics of the aggregate population activity within each pool. We derive the likelihood of both single-neuron and connectivity parameters given this activity, which can then be used to optimize parameters by gradient ascent on the log likelihood or perform Bayesian inference using Markov chain Monte Carlo (MCMC) sampling. We illustrate this approach using a model of generalized integrate-and-fire neurons for which mesoscopic dynamics have been previously derived and show that both single-neuron and connectivity parameters can be recovered from simulated data. In particular, our inference method extracts posterior correlations between model parameters, which define parameter subsets able to reproduce the data. We compute the Bayesian posterior for combinations of parameters using MCMC sampling and investigate how the approximations inherent in a mesoscopic population model affect the accuracy of the inferred single-neuron parameters. <\/jats:p>","DOI":"10.1162\/neco_a_01292","type":"journal-article","created":{"date-parts":[[2020,6,10]],"date-time":"2020-06-10T22:41:18Z","timestamp":1591828878000},"page":"1448-1498","source":"Crossref","is-referenced-by-count":16,"title":["Inference of a Mesoscopic Population Model from Population Spike Trains"],"prefix":"10.1162","volume":"32","author":[{"given":"Alexandre","family":"Ren\u00e9","sequence":"first","affiliation":[{"name":"Department of Physics, University of Ottawa, Ottawa K1N 6N5, Canada; Max Planck Research Group Neural Systems Analysis, Center of Advanced European Studies and Research (caesar), Bonn 53175, Germany; and Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain Structure-Function Relationships (INM-10), J\u00fclich Research Centre, J\u00fclich 52425, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andr\u00e9","family":"Longtin","sequence":"additional","affiliation":[{"name":"Department of Physics, University of Ottawa, Ottawa K1N 6N5, Canada, and Brain and Mind Research Institute, University of Ottawa, Ottawa K1H 8M5, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jakob H.","family":"Macke","sequence":"additional","affiliation":[{"name":"Max Planck Research Group Neural Systems Analysis, Center of Advanced European Studies and Research (caesar), Bonn 53175, Germany, and Computational Neuroengineering, Department of Electrical and Computer Engineering, Technical University of Munich, Munich 80333, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"281","reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1005545"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1016\/j.conb.2017.06.003"},{"key":"B3","author":"Betancourt M. 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