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172 Calibrage de modèles impulsionnels Sommaire 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . 173 6.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 174 6.2.1 Vectorized optimization . . . . . . . . . . . . . . . . . . 174 6.2.2 Distributed computing . . . . . . . . . . . . . . . . . . . 176 6.2.3 GPU implementation . . . . . . . . . . . . . . . . . . . 177 6.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 178 6.3.1 Fitting models of cortical and brainstem neurons . . . . 178 6.3.2 Reducing complex biophysical models to simple phenomenological models . . . . . . . . . . . . . . . . . . . . . 180 6.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . 181
6.1 Introduction 173 Fitting neuron models to spike trains Cyrille Rossant, Dan F. M. Goodman, Bertrand Fontaine, Jonathan Platkiewicz, Anna K. Magnusson and Romain Brette Abstract Computational modeling is increasingly used to understand the function of neural circuits in systems neuroscience. These studies require models of individual neurons with realistic input-output properties. Recently, it was found that spiking models can accurately predict the precisely timed spike trains produced by cortical neurons in response to somatically injected currents, if properly fitted. This requires fitting techniques that are efficient and flexible enough to easily test different candidate models. We present a generic solution, based on the Brian simulator (a neural network simulator in Python), which allows the user to define and fit arbitrary neuron models to electrophysiological recordings. It relies on vectorization and parallel computing techniques to achieve efficiency. We demonstrate its use on neural recordings in the barrel cortex and in the auditory brainstem, and confirm that simple adaptive spiking models can accurately predict the response of cortical neurons. Finally, we show how a complex multicompartmental model can be reduced to a simple effective spiking model. 6.1 Introduction An increasing number of studies in systems neuroscience rely on computational modeling to understand how function emerges from the interaction of individual neurons. Although the neuron models used in these studies are usually well established models (e.g. Hodgkin-Huxley, integrate-and-fire models and variations), their parameters are generally gathered from a number of measurements in different neurons and preparations. For example, the model of Rothman and Manis (Rothman et Manis 2003), an established biophysical model of neurons in the cochlear nucleus (CN) of the auditory brainstem, includes ionic channels with properties measured in different neurons of the CN of guinea pigs, combined with a sodium channel and an Ih current derived from previous measurements in mammalian neurons in other areas (including the thalamus). This situation is hardly avoidable for practical reasons, but it raises two questions : 1) channel properties from heterogeneous neuron types, species or ages may not be compatible, and 2) there could be functionally relevant correlations between parameter values within the same neuron type (e.g. maximal conductances), which would be missed if the information about channels came from several independent neurons. Therefore, it seems desirable to obtain models which are fitted for specific neurons. If these models are to be used for network modelling, then the main goal is to predict the spike trains in response to an arbitrary input. Recently, it was found that simple phenomenological spiking models, such as integrate-and-fire models
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THÈSE DE DOCTORAT DE L’UNIVERSIT
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Computational role of correlations
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viii différent, indispensable. Je
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x 3.4 Fiabilité de la décharge ne
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xiv 3.8 Fiabilité neuronale in vit
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xvi 9.5 Quality and stability of el
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2 entrée sens (perception) périph
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4 3. L’étude de la sensibilité
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Contexte scientifique 7
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10 Sommaire 1.1 Introduction . . .
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12 Anatomie et physiologie du syst
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46 Modélisation impulsionnelle de
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104 Oscillations, corrélations et
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222 Détection de coïncidences dan
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244 A B 20 mV 100 ms C EPSP spike D
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246 A B Neuron resistance (MΩ) El
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248 A 20 mV B C D -15 mV threshold
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250 Discussion
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252 Discussion de haute conductance
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254 Discussion Implications sur la
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256 Discussion des neurones aux co
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258 exemples incluent un modèle r
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262 Réponse à London et al. Somma
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264 Réponse à London et al. a b 2
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266 Réponse à London et al. spike
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268 Publications 2. Rossant, C. & B
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270 Bibliographie Allen, P., Fish,
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272 Bibliographie Bar-Gad, I., Rito
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274 Bibliographie Brette, R. (2003)
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276 Bibliographie Buzsáki, G. (200
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280 Bibliographie El Boustani, S. e
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292 Bibliographie Lim, D., Ong, Y.,
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296 Bibliographie Nickolls, J., Buc
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300 Bibliographie Robbe, D. et Buzs
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302 Bibliographie Schneidman, E., B
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304 Bibliographie Smith, P. (1995).
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306 Bibliographie Takeda, K. et Fun
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310 Bibliographie Weliky, M. et Kat
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