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208 Détection de coïncidences dans les neurones bruités the linear model, in particular spikes (Figure 8.2d). We use a manually selected threshold criterion (typically around 20 mV) to identify spikes. The compensated membrane potential of the cell, which we use for subthreshold analyses, is Vraw−Ve (Figure 8.2e). The electrode compensation technique subtracts the voltage drop through the electrode that is produced by the injected current. However, it does not impact the filtering effect of the electrode, due to the non-zero response time (time constant τe). For this reason, spike height (difference between peak and spike threshold) varied systematically with the estimated electrode time constant τe (for cells C1- 6 : 64 mV, 49 mV, 33 mV, 75 mV, 79 mV, 57 mV ; electrode time constant was 3 ms for C3 and 1.1 ms for C2, and
8.2 Materials and Methods 209 Current B To generate synaptic inputs with synchrony events (Figure 8.10), we first generate a set of Ne = 4000 (resp. Ni = 1000) independent excitatory (resp. inhibitory) spike trains with Poisson statistics and rate λe=0.65 Hz (resp. λi=1.3 Hz). Each spike triggers an exponentially decaying PSC with time constant τe=3 ms (resp. τi=10 ms ) and peak value we=13 pA (resp. wi=5.7 pA). These values were chosen so that EPSPs and IPSPs are about ±1 mV high (close to the average excitatory PSP size in the mouse auditory cortex in vitro (Oswald et Reyes 2008)). Synchrony events are generated according to a Poisson process with rate λc, and for each event we pick p excitatory synapses at random and make them simultaneously fire. To compensate, a random spike is suppressed for each new spike. For each cell, we test 4 values of p (5, 15, 25, 35) and 3 values of λc (5 Hz, 10 Hz, 20 Hz). The total stimulation lasts about 14 minutes. We calculate the firing rate r for each pair of values (p, λc), and the error bar corresponds to the standard deviation assuming Poisson statistics, that is, r/T , where T is the duration of the block. Current C We also measure the firing rate of neurons in response to spike trains with specified pairwise correlation c (Figure 8.3c and Figure 8.13). The current is the same as in the previous paragraph, except that correlations are introduced differently (Kuhn et al. 2003, Brette 2009) : we generate a single reference Poisson spike train with rate λc/c, and for each target spike train, we copy every spike of the reference spike train with probability c. Correlations are only introduced in excitatory inputs. The stimulation lasts about 5 minutes, during which we successively test 11 values of c regularly spaced between 0 and 0.01. The blocks are randomized, so that c does not increase monotonously during the stimulation. 8.2.5 Neuron models Models were simulated with the Brian simulator (Goodman et Brette 2009). We used integrate-and-fire models, where the membrane potential V (t) is governed by the following equation : dV (t) τm dt = El − V (t) + I(t) where τm = 5 ms is the membrane time constant (taking into account the increase conductance in vivo (Destexhe et al. 2003)), El = -65 mV is the resting potential and I(t) is the input (current times membrane resistance). The neuron fires when V (t) reaches the threshold θ=-55 mV, and is then reset to El and clamped at this value for a refractory period of 5 ms. Despite their simplicity, such simple models can predict the spiking responses of cortical neurons to time-varying currents injected at the soma with surprising accuracy (Gerstner et Naud 2009, Rossant et al. 2011b).
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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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102 Codage neuronal et computation
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104 Oscillations, corrélations et
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152 Adaptation automatique de modè
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Page 218 and 219: 202 Détection de coïncidences dan
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Page 266 and 267: 250 Discussion
Page 268 and 269: 252 Discussion de haute conductance
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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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284 Bibliographie Graupner, M. et B
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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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