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152 Adaptation automatique de modèles impulsionnels Sommaire 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . 153 5.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 155 5.2.1 Vectorization techniques . . . . . . . . . . . . . . . . . . 155 5.2.2 GPU implementation . . . . . . . . . . . . . . . . . . . 159 5.2.3 Distributed computing . . . . . . . . . . . . . . . . . . . 160 5.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160 5.3.1 Model fitting . . . . . . . . . . . . . . . . . . . . . . . . 160 5.3.2 Model reduction . . . . . . . . . . . . . . . . . . . . . . 166 5.3.3 Benchmark . . . . . . . . . . . . . . . . . . . . . . . . . 166 5.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . 168
5.1 Introduction 153 Automatic fitting of spiking neuron models to electrophysiological recordings Cyrille Rossant, Dan F. M. Goodman, Jonathan Platkiewicz and Romain Brette Abstract Spiking models can accurately predict the spike trains produced by cortical neurons in response to somatically injected currents. Since the specific characteristics of the model depend on the neuron, a computational method is required to fit models to electrophysiological recordings. The fitting procedure can be very time consuming both in terms of computer simulations and in terms of code writing. We present algorithms to fit spiking models to electrophysiological data (timevarying input and spike trains) that can run in parallel on graphics processing units (GPUs). The model fitting library is interfaced with Brian, a neural network simulator in Python. If a GPU is present it uses just-in-time compilation to translate model equations into optimized code. Arbitrary models can then be defined at script level and run on the graphics card. This tool can be used to obtain empirically validated spiking models of neurons in various systems. We demonstrate its use on public data from the INCF Quantitative Single-Neuron Modeling 2009 competition by comparing the performance of a number of neuron spiking models. 5.1 Introduction Neurons encode time-varying signals into trains of precisely timed spikes (Mainen et Sejnowski 1995, Brette et Guigon 2003), using a diverse set of ionic channels with specific characteristics. Recently, it was found that simple phenomenological spiking models, such as integrate-and-fire models with adaptation, can in fact predict the response of cortical neurons to somatically injected currents with surprising accuracy in spike timing (Jolivet et al. 2004, Brette et Gerstner 2005, Gerstner et Naud 2009). This unexpected performance is probably related to the fact that detailed conductance-based models with widely diverse ion channel characteristics can in fact have the same properties at neuron level (Goldman et al. 2001). These encouraging results triggered an interest in quantitative fitting of neuron models to experimental recordings, as assessed by the recent INCF Quantitative Single-Neuron Modeling competition. The competition has seen several successful submissions, but there is no available method to systematically fit arbitrary models to experimental data. Such computational tools would be particularly useful for modellers in systems neuroscience, for example, who could use empirically validated models in their studies. We developed a model fitting library, which works with the Brian simulator (Goodman et Brette 2009), and allows the fitting of user-defined spiking models to electrophysiological data consisting of spike trains
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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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xii
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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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76 Codage neuronal et computation S
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202 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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260
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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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278 Bibliographie Dan, Y. et Poo, M
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280 Bibliographie El Boustani, S. e
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282 Bibliographie Fusi, S. et Matti
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286 Bibliographie Henze, D. et Buzs
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288 Bibliographie Jolivet, R., Rauc
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290 Bibliographie Krumin, M. et Sho
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292 Bibliographie Lim, D., Ong, Y.,
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294 Bibliographie McCulloch, W. et
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296 Bibliographie Nickolls, J., Buc
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298 Bibliographie Peyrache, A., Kha
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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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308 Bibliographie Uhlhaas, P., Pipa
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310 Bibliographie Weliky, M. et Kat
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