In vitro and in vivo measures of evoked excitatory and inhibitory conductance dynamics in sensory cortices

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1 Journal of Neuroscience Methods 169 (2008) In vitro and in vivo measures of evoked excitatory and inhibitory conductance dynamics in sensory cortices C. Monier, J. Fournier, Y. Frégnac Unité de Neurosciences Intégratives et Computationnelles (UNIC), UPR CNRS 2191, Gif-sur-Yvette Cedex, France Received 13 September 2007; received in revised form 2 November 2007; accepted 10 November 2007 Abstract In order to better understand the synaptic nature of the integration process operated by cortical neurons during sensory processing, it is necessary to devise quantitative methods which allow one to infer the level of conductance change evoked by the sensory stimulation and, consequently, the dynamics of the balance between excitation and inhibition. Such detailed measurements are required to characterize the static versus dynamic nature of the non-linear interactions triggered at the single cell level by sensory stimulus. This paper primarily reviews experimental data from our laboratory based on direct conductance measurements during whole-cell patch clamp recordings in two experimental preparations: (1) in vitro, during electrical stimulation in the visual cortex of the rat and (2) in vivo, during visual stimulation, in the primary visual cortex of the anaesthetized cat. Both studies demonstrate that shunting inhibition is expressed as well in vivo as in vitro. Our in vivo data reveals that a high level of diversity is observed in the degree of interaction (from linear to non-linear) and in the temporal interplay (from push pull to synchronous) between stimulus-driven excitation (E) and inhibition (I). A detailed analysis of the E/I balance during evoked spike activity further shows that the firing strength results from a simultaneous decrease of evoked inhibition and increase of excitation. Secondary, the paper overviews the various computational methods used in the literature to assess conductance dynamics, measured in current clamp as well as in voltage clamp in different neocortical areas and species, and discuss the consistency of their estimations Elsevier B.V. All rights reserved. Keywords: Excitation; Inhibition; Conductance measurement; Cat visual cortex; Voltage-clamp; In vivo patch clamp 1. Introduction A basic feature in the connectivity of neocortical networks is the profusion of synaptic contacts, established both locally within a given cortical area and across distinct cortical areas (White, 1989). Each pyramidal neuron (the major type of excitatory cell in cortex) receives approximately 10 4 synaptic inputs, of which about 75% are excitatory and 25% inhibitory. Recurrent connectivity between pyramidal cells is expressed, within a given cortical lamina as well as across laminae, as a dense plexus of local horizontal and vertical interconnections. GABAergic inhibitory interneurons, although far less numerous, but having multiple subtypes, seem to control the dynamics of this unstable recurrent excitatory assembly at various target locations (review in Markram et al., 2004; Monyer and Markram, Corresponding author. address: monier@unic.cnrs-gif.fr (C. Monier). 2004; Silberberg and Markram, 2007). In addition to this dominant interlaced pattern of excitation and inhibition originating from the cortex itself, subsets of both types of cells are directly innervated by excitatory thalamic relay neurons, which are the main source of extrinsic input to the neocortex (Binzegger et al., 2004). Axons from the thalamus make stronger and more frequent excitatory connections onto inhibitory interneurons than onto excitatory cells, and their activation produces robust disynaptic feedforward inhibition of cells that receive concomitant direct thalamocortical excitation (Agmon and Connors, 1992; Cruikshank et al., 2007; Gil and Amitai, 1996). One might therefore expect that the selective firing of any single neuron is the concerted result at any point in time of the dynamic balance between a large numbers of co-active synaptic afferents, mostly intrinsic to cortex. Indeed, intracellular recordings in vivo have revealed consistently that cortical neurons are subjected to an intense ongoing synaptic bombardment (Azouz and Gray, 1999; Bringuier et al., 1997; Paré et al., 1998). Although differences were observed /$ see front matter 2007 Elsevier B.V. All rights reserved. doi: /j.jneumeth

2 324 C. Monier et al. / Journal of Neuroscience Methods 169 (2008) depending on the type of anaesthetic used, the resting conductance is generally higher in the intact brain than in partially deafferented networks in vitro (review in Destexhe et al., 2003). Thus, neocortical networks most likely operate in a highconductance state, i.e. with a leak conductance three to five times larger than the resting synaptic conductance (Paré et al., 1998 but see Waters and Helmchen, 2006). This, in turn, is expected to change the integrative properties of the neurons (Bernander et al., 1991; Destexhe and Paré, 1999; Rudolph and Destexhe, 2003), by reducing the apparent membrane time constant and allowing faster transients in membrane potential dynamics. An important issue in the mammalian sensory neocortex is to determine the functional impact of this high conductance resting state on the processing of sensory information itself. Since the first intracellular recordings in visual cortex (Creutzfeldt and Ito, 1968; Innocenti and Fiore, 1974), many electrophysiological studies have shown that the membrane potential strongly fluctuates in response to visual stimuli. However, no definite canonical generative mechanism has been yet identified since these fluctuations could potentially result from the interplay of a diversity of conductances. For instance, the push pull arrangement hypothesized in Simple receptive fields supposes that an increase in excitation will correspond to an in-phase decrease in inhibition, and vice versa (Ferster, 1988; Heggelund, 1986). In contrast, the dominance of recurrent circuit architecture predicts that most of the time excitation and inhibition should occur conjointly (Ben-Yishai et al., 1995; Douglas et al., 1995; Somers et al., 1995; Suarez et al., 1995). In addition, these models of visual cortex suggest that response selectivity can arise from recurrent networks operating at high gain. However, such networks operate close to instability and respond slowly to rapidly changing stimuli. Theoretical studies show that divisive inhibition, acting through interneurons that are themselves divisively inhibited, can stabilize network activity for any arbitrarily large excitatory coupling (Chance and Abbott, 2000). From a theoretical computational perspective, two alternative regimes may be envisioned: (1) the total input conductance of the cell does not change significantly during sensory stimulation. In this case, the ratio between the evoked synaptic conductance and the resting conductance is low or negligible, the excitatory and inhibitory currents add algebraically and the input integration process may be considered as linear; (2) the evoked synaptic conductance increase is in the same range or larger than the resting conductance, leading to a regime where excitatory and inhibitory synaptic inputs interact non-linearly. In other words, if the evoked synaptic input fluctuations are small when compared to the resting conductance, the inputs can be modeled as currents; in the opposite case, the conductance increases must be taken into account. The amplitude of depolarization and/or hyperpolarization in the evoked voltage response, when recorded in current clamp mode, results from the combined integration of both excitatory and inhibitory inputs and depends on multiple parameters: the voltage at rest, the time constant of the membrane, the leak conductance, the amplitude of excitatory and inhibitory conductances, their kinetics and the degree of temporal overlap between their respective recruitment, as well as their reversal potentials. In order to understand the nature of the full integration process, it is thus necessary to devise methods that allow one to infer, from the current or voltage recordings: (1) the dynamics of the balance between excitation and inhibition (E/I), (2) the level of conductance increase evoked by the sensory stimulation, and (3) if possible, to characterize the static versus dynamic nature of the non-linear interaction process. Consensus on these points has been hindered up to now by the fact that different methods have been used in vitro and in vivo to estimate the E/I balance, and seldom compared together. A classical method, mostly applicable in vitro, consists of dissecting out pharmacologically the excitation from the inhibition and thereafter comparing the relative amplitudes of the remaining components (for example Varela et al., 1999). The disadvantage of this method is that the diffuse blockade of a class of receptors by antagonist bath application disrupts the integrity of the network under study and ignores the impact of all types (pre pre, pre post) of interactions between excitation and inhibition. In vivo studies rarely rely on iontophoretic approaches (but see Nelson et al., 1994; Sillito, 1975) but usually infer the dominant presence of inhibition and excitation from the peak amplitudes of evoked hyperpolarization and/or depolarisation, respectively (Berman et al., 1991; Ferster, 1986; Pei et al., 1994; Volgushev et al., 1993). Such approaches have been unable to detect the presence of inhibition when concurrent with excitation (see, for a systematic comparative survey, Monier et al., 2003). Thus, although the membrane potential change may reflect in a qualitative way the ratio between excitation and inhibition, it remains impossible from knowledge solely of the mean membrane potential dynamics to deduce the amplitude of the global input conductance change, this measure being crucial to understand the dynamic regime under which the neuronal network operates. A quantification step, reflecting more directly the functional impact of synaptic input on the spike trigger mechanism, is to measure conductance changes seen at the soma. Detailed simulations have shown that the increase in conductance due to the activity of inhibitory basket cells should be visible from the cell body of pyramidal cells (Koch et al., 1990). These authors estimated that the shunting inhibitory effect would significantly reduce the amplitude of the excitatory postsynaptic potential for somatic input conductance increases larger than 30%. Experimentally, evidence for or against shunting inhibition is still a matter of debate. As early as 40 years ago, large increases in input conductance (up to 300%) were demonstrated in cortical neurons (Dreifuss et al., 1969), both after electrical stimulation of the cortical surface and during exogenous iontophoretic application of GABA. Nevertheless, the first measurements of input conductance performed in vivo, using current pulse injection or electrical stimulation of thalamic afferents, revealed only limited relative changes in input conductance (5 20%) during visual stimulation (Berman et al., 1991; Carandini and Ferster, 1997; Douglas et al., 1988; Ferster and Jagadeesh, 1992; Pei et al., 1991). These negative reports were not in agreement with findings in vitro where Berman et al. (1989, 1991), using the

3 C. Monier et al. / Journal of Neuroscience Methods 169 (2008) same technique (current pulse injection), found large conductance increases during electrically evoked hyperpolarization in slices of rat and cat visual cortex. Similarly, Connors et al. (1988) reported 200% changes in input conductance during electrically evoked inhibition in cortical pyramidal cells in neocortical slices. These different findings long supported the view that inhibition and excitation would interact in different ways in the in vitro slice and the intact in vivo network. We have, during the past 10 years, re-examined the issue of conductance dynamics during sensory processing by applying, both in vitro and in vivo, a conductance measurement method based on somatic current clamp and voltage-clamp recordings. This method allows, for any delay relative to the stimulus onset, the continuous monitoring of changes in visually evoked conductance. Its application in area 17 of the anaesthetized cat revealed, in some cells, large conductance increases and a high diversity in the observed temporal phase patterns between evoked excitatory and inhibitory conductance changes (Borg- Graham et al., 1998; Monier et al., 2003). This original method, first engineered in voltage-clamp mode (Borg-Graham et al., 1996), has since then been reproduced by many independent research teams in in vivo studies, mostly in current clamp but also in voltage clamp. These findings have been confirmed in the visual cortex of the cat in current-clamp (Anderson et al., 2000, 2001; Hirsch et al., 1998; Marino et al., 2005; Priebe and Ferster, 2005, 2006), in the rat auditory cortex in voltageclamp (Tan et al., 2004; Wehr and Zador, 2003, 2005; Zhang et al., 2003), in the rat barrel cortex in current clamp (Higley and Contreras, 2006; Wilent and Contreras, 2004, 2005) and in the ferret prefrontal cortex in current and voltage clamp (Haider et al., 2006). This conductance measurement method has been also applied in vitro in the ferret prefrontal cortex (Shu et al., 2003), in the rat visual cortex (Le Roux et al., 2006) and in the mouse somatosensory thalamocortical slice (Cruikshank et al., 2007). This paper primarily reviews experimental data from our laboratory based on direct conductance measurements during whole-cell patch clamp recordings in two experimental preparations: (1) in vivo, during visual stimulation, in the primary visual cortex of the anaesthetized cat (Borg-Graham et al., 1998; Monier et al., 2003), and (2) in vitro, during electrical stimulation in the visual cortex of the rat. Both studies demonstrate that shunting inhibition is expressed as well in vivo as in vitro. Our in vivo data reveals that a high level of diversity is observed in the degree of interaction (from linear to non-linear) and in the temporal interplay (from push pull to synchronous) between stimulus-driven excitation (E) and inhibition (I). A detailed analysis of the E/I balance during the evoked spike activity further shows that the firing strength results from a simultaneous decrease of evoked inhibition and increase of excitation. Secondary, the paper overviews the various computational methods used in the literature to assess conductance dynamics and compare the various assumptions associated with each method. It also underlines possible methodological reasons that may explain why the functional role of shunting inhibition has been contradicted to such an extent in the past. 2. Materials and methods 2.1. In vitro preparation Parasagittal slices containing primary visual cortex were obtained from 20- to 25-day-old Wistar rats as described by Edwards et al. (1989). Briefly, rats were decapitated and brains were quickly removed and placed in cold (5 C) artificial extracellular solution, in accordance with guidelines of the American Neuroscience Association. Slices of 350 m thickness were cut on a vibratome and then incubated for at least 1 h at 36 C in extracellular solution containing (in mm): 126 NaCl, 26 NaHCO 3, 10 glucose, 2 CaCl 2, 1.5 KCl, 1.5 MgSO 4 and 1.25 KH 2 PO 4, which was bubbled with a mixture of 95% O 2 5% CO 2 (ph 7.5, osmolarity 310/330 mosm). All extracellular drug applications were delivered through perfusion and were added to the bathing solution for at least 15 min before recording. 2-amino-5-phosphonovalerianic acid (APV), bicuculline, 6-cyano-7-nitroquino-xaline-2,3-dione (CNQX) and picrotoxin were obtained from Sigma (St Louis, MO). Slices were perfused continuously and viewed with standard optics using a 40 long-working-distance water immersion lens of a Zeiss microscope on an X Y translation stage with a videoenhanced differential interference contrast system. Pyramidal neurons, identified on the basis of the shape of their soma and the proximal part of the apical dendrite, were recorded in layer 5 using the whole-cell configuration of patch-clamp techniques. Somatic whole-cell recordings were performed at room temperature using borosilicate glass pipettes (3 5 M in the bath) filled with an internal solution (in mm): 140 K-gluconate, 10 Hepes, 4 ATP, 2 MgCl 2, 0.4 GTP, 0.5 EGTA, ph adjusted to 7.3 with KOH and the osmolarity adjusted to 285 mosm. For some experiments, the quaternary lidocaine derivative QX-314 (Tocris, Bristol, UK) was added (3 mm) in the internal patch electrode solution to block sodium conductance, hence spike initiation, and GABAb receptor activation. The internal pipette solution was also supplemented with 1% Neurobiotin (Vector) to label some of the pyramidal neurons and reconstruct their morphology. After giga-seal attachment, whole cell configuration was achieved with low access resistance. All membrane potential values obtained with this filling solution were corrected offline by 10 mv in order to subtract the junction potential (Neher, 1992). After capacitance neutralization, bridge balancing was done on-line under current clamp to make initial estimates of the access resistance (R s ). These values were checked and revised as necessary off-line by fitting subthreshold hyperpolarizing current clamp responses to the sum of two exponentials (R s = 13.1 ± 5M (4 25 M ), n = 177). The access resistance was not compensated in voltage-clamp mode. Electrical stimulations ( A, 0.2 ms duration) were delivered using 1 M impedance bipolar tungsten electrodes (TST33A10KT, WPI) with a tip separation of 125 m. Tungsten bipolar electrodes were positioned in (i) white matter (WM), at around 500 m from the recording site and along the same radial columnar axis, (ii) layer 4, near the recording site, (iii) in the top of layer 2/3, at a lateral distance of around 800 m from the recording site. The frequency of input stimulation was set at

4 326 C. Monier et al. / Journal of Neuroscience Methods 169 (2008) Hz and five to eight trials were repeated for a given holding current or potential. Current clamp and voltage-clamp modes were carried out using an Axopatch 1D (Axon Instrument, USA). Recordings were filtered at 2 khz, stored and analyzed with specialized in-house dedicated software (Acquis1 TM :Gérard Sadoc-UNIC-CNRS) In vivo preparation Most of the data presented here are new off-line measurements made on sets of previous recordings realized in collaboration with Lyle Graham and published elsewhere (Borg- Graham et al., 1998; Monier et al., 2003). Cells in the primary visual cortex of anaesthetized (Althesin) and paralyzed cats (for details on the surgical preparation, see Bringuier et al., 1997) were recorded intracellularly using an Axoclamp 2A amplifier. Blind whole-cell patch recordings were made in vivo with 3 5 M glass patch electrodes filled with the same solution as that used in vitro. The seal resistance in attached mode was between 1 and 8 G. Only recordings with an access resistance lower than 40 M were selected for further voltage-clamp analysis (average value of R s = 21.8 ± 13 M, n = 217 cells). The estimate of access resistance was revised as often as necessary over the course of the experiment and off-line, by fitting the response to subthreshold hyperpolarizing current steps to the sum of two exponentials. Three-millimeter artificial pupils were used and appropriate corrective optical lenses were added. The receptive field of each cell was quantitatively characterized using sparse noise mapping. Receptive fields were classified as simple or complex using classical criteria based on the space time separation between On and Off responses. Orientation and direction tuning curves were measured with moving bars (direction of motion perpendicular to orientation) swept across the full extent of the subthreshold receptive field, and using random sequences of 8 or 12 directions (angular step: 45 and 30, respectively) repeated 10 times. In one case, orientation and direction tuning curve were equally measured with drifting gratings (with optimal spatial and temporal frequencies and optimal size) in 12 different directions. In addition, 1D-profile mapping across the receptive field width with optimally oriented bars (1 s) flashed in different positions was carried out. For data analysis, we quantified the following response components: spiking activity and spike suppression, membrane potential depolarization and hyperpolarization. For subthreshold activity, spike events were removed from the raw record and replaced by the low-pass filtered membrane potential. The depolarizing and hyperpolarizing evoked components were defined on the basis of a quantitative amplitude selection criterion as the integral of voltage, respectively, above and below the mean depolarizing and hyperpolarizing fluctuations in the resting potential measured during spontaneous activity. To determine the statistical significance of responses calculated over the whole period of visual stimulation, the mean of each component, defined by its integral normalized by the effective time during which its presence was detected (see above amplitude selection criterion), was compared with the normalized mean of this component during spontaneous activity prior to the stimulus, using a paired Student s t-test. All data processing and visual stimulation protocols were carried out using specialized in-house dedicated software (Acquis1 TM :Gérard Sadoc-UNIC- CNRS) Continuous estimation of the synaptic conductance in voltage-clamp Data were analyzed using a method based on the continuous measurement of conductance dynamics during stimulus-evoked synaptic response, whose principle has been described previously (Borg-Graham et al., 1998; Monier et al., 2003). To estimate conductances, the neuron is considered as the pointconductance model of a single-compartment cell, described by the following general membrane equation: dv m (t) C m = G leak (V m (t) E leak ) G exc (t)(v m (t) E exc ) dt G inh (t)(v m (t) E inh ) + I inj where C m denotes the membrane capacitance, I inj the injected current, G leak the leak conductance and E leak is the leak reversal potential. G exc (t) and G inh (t) are the excitatory and inhibitory conductances, with respective reversal potentials E exc and E inh. I V plots are commonly used to characterize input conductance and cellular excitability in a static way and can be characterized in voltage clamp or current clamp mode. The present study is mainly performed in voltage-clamp mode, which minimizes distortion of synaptic events by transient voltagedependent channels and capacitance near to the recording site (the derivative of the voltage is consider to be zero). Our method aims at a dynamic measure of input conductance, phase-locked to the time of the electrical stimulation, and relies on raw stimulus-locked I/V measurements made at each point in time: the current value includes both evoked and resting components, and the holding potential is corrected for the ohmic drop through the access resistance (V hc (t)=v h (t) I(t) R s ). In this situation, the slope of the best linear I/V fit gives the total input conductance of the cell G in (t) at time t. In order to compare different approaches that have been previously used in vitro (Haider et al., 2006; Shu et al., 2003), we have also computed the best third order polynomial fit. In this latter case the total input conductance is estimated as the tangent at the membrane potential value where the current is found to be null. The synaptically evoked component ( G syn (t)) is then measured by subtracting the resting conductance observed in the absence of stimulation (i.e. at a negative delay) from the total conductance: G syn (t) = G in (t) G rest (t) or G syn (t) = G in (t) (G leak + G synrest (t)) The synaptic reversal potential of the synaptic conductance increase (E syn ) is taken as the voltage of the intersection between the IV-curve during the synaptic response and the IV-curve during the resting condition. G syn (t) can be expressed directly in

5 C. Monier et al. / Journal of Neuroscience Methods 169 (2008) absolute measurement units (ns) or in relative units (%) when compared to the conductance at rest (named G syn (t)(%)). As a first qualitative step, and in order to determine from E syn (t) the type of synaptic input underlying the conductance changes, we computed dynamic phase plots of G syn (t) against E syn (t) over time. In order to compare synaptic activation profiles across cells, we extracted the peak value of the evoked synaptic conductance ( G peak ) in absolute or relative units and the apparent synaptic reversal potential at this peak (E syn Gpeak ). The mean value of the evoked synaptic response was averaged over a 200 ms window for the in vitro experiments. For in vivo experiments, each response was integrated during the whole stimulation duration (1 3 s) for the moving bar tuning protocols and for a period of 500 ms following the onset and the offset for the flashed bar protocols. In order to address the problem of the correction of access resistance for the voltage-clamp recordings (VC), a bootstrap method was used. This method theoretically makes it possible to estimate the mean and standard deviation of the slope of the regression without any assumptions about the actual statistics of the studied population. In the present case, the bootstrap method consists of repeating a calculation of linear (or polynomial) regression on subsets of values that are derived from the original set of data points, at any given time T. Each set is obtained by a random sampling of the actual data set, with substitution and thus possible repetition, with the constraint that there must be at least one data point for each holding potential, and that the total number of sampled points equals the total number of actual data points, that is the actual number of trials over the different holding potentials. The mean and standard deviation of the distribution of the slopes calculated over all the regressions are then estimated on the basis of at least 200 regressions. This computation is realized on both visual activity segments where each trace is temporally synchronized with the appearance of the visual stimulation, and spontaneous activity periods (recorded without visual stimulation and with shuffling across trials) Estimation of the leak conductance The temporal profile of the conductance observed at rest is decomposed arbitrarily into two components, one constant or static (G leak ) and the other strictly positive or null, reflecting the dynamic changes above baseline (G synrest (t)). The global variance of G rest (t) is approximated by σ 2 G rest = σ 2 G rest + σ2 G rest, where σ G 2 rest is the variance of the mean of G rest(t) and σg 2 rest is the mean of the variances of G rest (t) at each point in time (calculated with a bootstrap method). Assuming a Gaussian distribution, one can thus derive an estimator of G leak, defined here as the lower boundary of the G rest value distribution: G leak = G rest (t) σg 2 rest Since, in vitro, the global spontaneous activity was very low with our extracellular solution, G synrest (t) was set to 0, and G leak and E leak were assumed, respectively, equal to G rest and E rest. In vivo, the reversal potential E leak wasfixedat 80 mv (Paré et al., 1998). Note that this value is more negative than the resting potentials found in vivo Decomposition of the global synaptic conductance Assuming that the evoked conductance change measured at the soma reflects the composite synaptic input effective in driving the cell (since visible at the soma and presumably at the axon hillock), E syn (t) characterizes the effective balance between excitation and inhibition over time. The global synaptic conductance (G syn (t)=g in (t) G leak ) was further decomposed into three conductance components corresponding to the activation of one type of excitatory synapse and two types of inhibitory synapses, each associated with known and fixed reversal potentials. The reversal potentials were set at 0 mv for excitatory (E exc ), 80 mv for chloride conductance (E inha ) and 95 mv for potassium conductances (E inhb ). In order to make the equation system solvable, G syn (t) was expressed, at any point in time, as the sum of two at most of these three components. The choice was dictated by the value of the synaptic reversal potential (E syn (t)): if E syn (t) >E exc then G syn (t) = G exc (t) if E inha <E syn (t) <E exc then G syn (t) = G inha (t) + G exc (t) if E inhb <E syn (t) <E inha then G syn (t) = G inhb (t) + G inha (t) if E syn (t) <E inhb then G syn (t) = G inhb (t) The evoked excitatory and inhibitory conductance components ( G exc (t), G inha (t), G inhb (t)) were obtained by subtracting the mean resting conductance levels (G excrest, G inharest, G inhbrest ) from the corresponding global synaptic conductance component (for example: G exc (t)=g exc (t) G excrest ). Note here that the net evoked conductance change (excitatory or inhibitory) can become negative. Significance criteria were reached when the measured change was 2.96 times larger than the standard deviation of the resting conductance component value. Onset latencies were determined from stimulus onset to the time at which the conductance waveform began to deviate significantly from the average baseline value Direct extraction of excitatory and inhibitory conductance based on solving the conductance model equation The method for extracting conductances from voltage-clamp recordings can also be applied to current-clamp data. However, since, in this case, the derivative of the voltage can no longer be considered as null, conductances must be estimated by taking into account the capacitive current passing through the membrane. At each time point, a linear system composed of as many equations as these are applied current levels with two variables, is be solved by doing a bidimensional regression over G exc (t)

6 328 C. Monier et al. / Journal of Neuroscience Methods 169 (2008) and G inh (t): G inh (t)(vm 1 (t) E inh) + G exc (t)(vm 1 (t) E exc) = Iinj 1 C dvm 1 (t) m G leak (Vm 1 dt (t) E leak) G inh (t)(vm 2 (t) E inh) + G exc (t)(vm 2 (t) E exc) = Iinj 2 C dvm 2 (t) m G leak (Vm 2 dt (t) E leak)... G inh (t)(vm k (t) E inh) + G exc (t)(vm k (t) E exc) = Iinj k C dvm k (t) m G leak (Vm k dt (t) E leak) The capacitance C m is obtained from the time constant of the exponential fit of the membrane voltage in response to a test hyperpolarizing pulse applied at rest and the leak conductance is estimated as described above. The use of the derivative terms has already been introduced in several studies (Priebe and Ferster, 2005; Wilent and Contreras, 2005) but this explicit description of the conductance measurement method has the advantage of clarifying the effects produced by different assumptions. For instance, assuming that the membrane time constant is very fast (i.e. C m is very small) is equivalent to solving the same system without the derivative terms. Moreover, instead of extracting directly two types of conductances, one can also rewrite the equations (by linear combinations) to estimate the global conductance and synaptic reversal potential, which allows decomposition of the global conductance in more than two terms according to the values taken by E syn (t). When applied to voltage-clamp measurements, the derivative terms are set to zero, giving: G inh (t)(vhc 1 E inh) + G exc (t)(vhc 1 E exc) = Iin 1 (t) G leak(vhc 1 E leak) G inh (t)(vhc 2 E inh) + G exc (t)(vhc 2 E exc) = Iin 2 (t) G leak(vhc 2 E leak)... G inh (t)(vhc k E inh) + G exc (t)(vhc k E exc) = Iin k (t) G leak(vhc k E leak) where V hc is the holding voltage (corrected for the ohmic drop) and I in is the measured current Reconstruction of the membrane potential trajectory The computation of the excitatory and inhibitory conductances on the basis of the voltage-clamp measurements allows to predict the membrane potential trajectory (V rec (t)) that would have been observed in current clamp. This is done by solving numerically the following differential equation: dv rec (t) C m = G leak (V rec (t) E leak ) G syn (t)(v rec (t) dt E syn (t)) + I inj C m is estimated from τ m (the membrane time constant, C m = G leak τ m ) of the cell measured at rest with a small step of hyperpolarizing current. G syn (t) takes in account inhibitory and excitatory components (for simplicity the GABAb component is not considered here): G syn (t) = G exc (t) + G inh (t) or G syn (t) = G excrest (t) + G exc (t) + G inhrest (t) + G inh (t) We also computed the membrane potential trajectories that would have been produced if the stimulus evoked exclusively inhibition (V inh (t)) or excitation (V exc (t)). For V inh when G exc (t) is significant: G syn (t)=g inh (t)+ G excrest (t) else G syn (t)=g inh (t)+g exc (t). For V exc when G inh (t) is significant: G syn (t)=g exc (t)+ G inhrest (t) else G syn (t)=g inh (t)+g exc (t). From these three membrane potential values (integrated over 200 ms following the stimulus onset for in vitro and integrated over 50 ms in vivo), the M factor was extracted (Koch et al., 1990): M = V rec V inh V exc V rest This calculation was performed only when the evoked G exc (t) and G inh (t) were both significant. The M ratio quantifies the degree of non-linear interaction between excitation and inhibition: if one assumes that the effects of excitation and inhibition add linearly, M is equal to 1 since V rec = V inh + V exc Vrest. If excitation and inhibition interact non-linearly, M drops towards zero. In order to check the coherence between the different estimates, we calculated the root mean square error ( RMS error ) between the actual mean voltage measured in current clamp ( V m (t)) and that predicted (V rec (t)) from the excitatory and inhibitory conductances estimated in voltage clamp or current clamp (Wehr and Zador, 2003): RMS error = 1 T ( V m (t) V rec (t)) 2 T 0 In addition we computed the synaptic currents underlying the fluctuations of the voltage record in order to directly visualize the strength and the temporal impact of each type of synaptic input: I inh (t) = G inh (V rec (t) E inh ) I exc (t) = G exc (V rec (t) E exc ) 3. Results 3.1. Intrinsic excitability properties of cortical neurons In vitro recordings Patch-clamp recordings were obtained from pyramidal neurons (n = 177), the somata of which were exclusively located in layer 5 of rat visual cortex (P20 P25). Excitability properties of cortical neurons were characterized by the pattern of discharge in

7 C. Monier et al. / Journal of Neuroscience Methods 169 (2008) Table 1 General descriptive parameters at rest: global conductance measurements and intrinsic excitability patterns, in vitro and in vivo Cell types Nb of cells Input resistance (M ) Resting potential (mv) Time constant (ms) Spike threshold (mv) Spike width (ms) Spike amplitude (mv) Spike dvratio F/I ((Pa s 1 )/pa) In vitro ± ± ± ± ± ± ± ± 41 In vivo ± ± ± ± ± ± ± ± 141 RS ± ± ± ± ± ± ± ± 80 IB ± ± ± ± ± ± ± ± 96 NIB ± ± ± ± ± ± ± ± 110 FSA ± ± ± ± ± ± ± ± 179 FS ± ± ± ± ± ± ± ± 246 For the in vivo population, cells were classified into the following excitability subcategories by taking into account the shape of the action potential and the temporal profile of their spiking patterns to current pulses: RS, regular spiking; IB, bursting with inactivation; NIB, non-inactivating bursting; FSA, fast spiking adapting; FS, fast spiking. response to test depolarizing current pulses using a current clamp mode protocol. They were correlated when possible with the morphological description obtained by intracellular filling with neurobiotin. The dominant discharge pattern of pyramidal neurons was found to be of the regular type with adaptation (RS), as defined by (McCormick et al., 1985). Among the recorded neurons successfully labelled with neurobiotin (n = 31), all exhibited the typical morphology of pyramidal cells (Larkman and Mason, 1990). The voltage waveform, obtained in response to the intracellular injection of a negative pulse of current of small amplitude and small duration (see Fig. 1B), was fitted with a double exponential, giving a mean input membrane resistance estimate around 200 M (R in = ± 89 M, n = 177) and a membrane time constant around 30 ms ( m = 31.8 ± 11 ms, n = 177). The mean resting potential was 70.0 mv (± 5 mv) and the fluctuations of the voltage stayed very small during the resting condition (σ v = 0.36 ± 0.2 mv, n = 177). Some additional neurons (n = 60) were recorded with QX314 in the intracellular solution. Their input resistances were significantly larger than with the control filling solution (R in = ± 132 M vs ± 89, p < 0.01). A similar observation was made for the membrane time constant (τ m = 42.6 ± 20 ms vs ± 12 ms, p < 0.001). The resting membrane potential was slightly, but significantly, more hyperpolarized (E rest = 71.9 ± 5 mv vs ± 5, p < 0.01). Long pulses of positive currents were also injected to reveal the excitability pattern (see example on Fig. 1). For cells recorded with the control solution, specific shape parameters were measured on the first spike triggered by the intracellular current pulse. The absolute spike threshold, the width at half height and the spike amplitude were, respectively, 50.3 ± 5 mv, 1.7 ± 0.4 ms and 84 ± 10 mv. The asymmetry dv ratio of the spikes was 2.4 ± 0.6 (rising slope, 99.6 ± 20 mv ms 1, fall-off slope 45.8 ± 13 mv ms 1 ). The F/I curves were fitted with a linear function with a mean slope estimate of 70 ± 41 a.p. s 1 na 1 (Table 1) In vivo recordings The present study is based on the quantitative analysis of 217 cells, recorded using patch electrodes, for which the intrinsic properties of cells were characterized in current clamp mode by injecting a short duration hyperpolarizing current pulse, as done in vitro. In contrast with the in vitro situation, these cells were spontaneously active (mean rate: 0.45 ± 0.6 a.p. s 1 ; n = 211) and the level of spontaneous activity varied greatly between cells. The fluctuation of the membrane potential measured in the resting condition was around 4 mv (σ Vm = 3.9 ± 2.3 mv; range: mv, n = 211). 55% of these cells (n = 119) were found to be of the regular spiking type (RS) with a clear adaptation of successive spike intervals; 29% of cells (n = 63) were intrinsic bursting cells (IB) with a progressive inactivation of action potential generation within the burst; 3% of cells (n = 7) were bursting cells without spike inactivation (NIB or chattering), but with a large amplitude AHP (we follow the classification of Baranyi et al., 1993). The rest of the cells qualified as fast spiking cells (FS), since they

8 330 C. Monier et al. / Journal of Neuroscience Methods 169 (2008) Fig. 1. In vitro and in vivo measurements of intrinsic properties of V1 neurons. Measures of input resistance (R in ) and I/V curves are illustrated in pyramidal regular spiking cells (RS), recorded in current clamp mode in vitro and in vivo, with patch electrodes filled with gluconate (with or without QX314). (A) Intracellular membrane potential (V m ) responses of three cortical RS neurons to negative and positive current pulse injections in three different conditions: (left) control internal pipette gluconate solution in vitro; (center) control gluconate solution + QX314 in vitro; (right) control gluconate solution in vivo. Each 1 s current pulse used for the I/V measurement was preceded by a short (200 ms in vitro, 100 ms in vivo) negative pulse at the end of which the input resistance (R in ) was estimated. (B) Zoom on the membrane response to the short negative current pulse (black) fitted by a sum of two exponentials (red): the fast decaying exponential was used to fit the electrode response (access resistance R s and electrode time constant τ electrode ) and the slower one to extract the membrane response (input resistance R in and time constant τ m ). (C) I/V characteristics obtained in the same (see (A)) three recording conditions, showing a significant inflexion of the slope around 50 mv. The slopes of the regression lines fitting each linearity domain below (R1) or above (R2) the rectification point, correspond to the R1 and R2 input resistance values. (D) Mean input resistances (R in, R1 and R2) obtained across the three experimental conditions (see (A)). Right panels, mean membrane time constant (τ m ) and resting potential (E rest ).

9 C. Monier et al. / Journal of Neuroscience Methods 169 (2008) showed thin spikes (< 0.4 ms duration), among which the majority showed spike frequency adaptation (12% of the total sample: n = 25, FSA) and the rest none (2%, n = 4, FS) (see Table 1) In vitro versus in vivo comparison Since we recorded only pyramidal cells in the in vitro preparation, the comparison was limited to the equivalent type of cell in the in vivo preparation, the class of RS pyramidal cells (n = 119). Neurons recorded in vivo had a resting potential more hyperpolarized than in vitro ( 72.8 ± 5 mv vs ± 5) and showed a lower input membrane resistance (R in = 63.4 ± 30 M in vivo vs. 208 M in vitro) and a shorter membrane time constant (τ = 13.5 ± 5msin vivo vs ms in vitro) with a scaling factor in both cases around three. As expected due to the difference of temperature between the in vivo and in vitro preparations, the width of the spike was larger in vitro (1.7 ± 0.4 ms in vitro vs ± 0.2 ms in vivo) but the spiking threshold values were undistinguishable ( 50.3 ± 5 mv in vitro vs ± 5 mv in vivo). For further details, see Table Linearity domain of I/V curves In vitro recordings I/V curves were measured in current clamp using long pulse current injection (Fig. 1A) whose intensity was increased in 50 pa steps (starting from 200 pa, 15 steps on average, range: 10 40, see Fig. 1A). I/V curves were best fitted by two linear segments whose intersection point was chosen to optimize the explained variance and whose abscissa ( 53.7 ± 7 mv) was usually close to the spike initiation threshold ( 50.3 ± 5 mv). The input resistance of the cell (with the control solution) for the higher range of voltage values was on average four times smaller than that measured for voltage values below the rectification point (174.3 ± 76 M, R in (V m < threshold) vs ± 23 M, R in (V m > threshold), ratio: 4.0 ± 3, n = 177, see Fig. 1C and D). For cells recorded with QX314 in the intracellular solution, a rectification in the I/V-curves was observed as well, (R in : ± 109 M (V m < threshold) vs ± 34 M (V m > threshold)), ratio: 3.2 ± 1, rectification point abscissa: 54.7 ± 11 mv, n = 60) but the change in slope was of slightly lesser amplitude (ratio: 3.2 instead of 4.0 for the control solution). These rectifications observed for membrane potential values above the spike threshold seem to be due to the recruitment of voltage dependent potassium conductances, which are not fully blocked by QX314. In a subpopulation of cells (n = 50), we checked the steadystate linearity of the I/V curves in voltage-clamp mode for holding potentials ranging from 100 to 0 mv in steps of 10 mv, during both the resting condition and the synaptic response. In almost all cells, the I/V curves showed a slight rectification for membrane potentials more hyperpolarized than 90 mv or more depolarized than 40 mv, although the precise range over which this non-linear behavior was expressed varied greatly across cells. The average value of the Pearson correlation coefficients calculated from the linear regressions in the resting condition over the full range of holding potentials was 0.94 ± If the regression was limited to holding potentials ranging only between 90 mv and 40 mv, the averaged correlation coefficient value became significantly higher (0.97 ± 0.02, paired t-test, p < 0.001). A similar analysis, when applied to the first 200 ms of the synaptically evoked response for the full range of holding potentials, gave significantly higher correlation coefficients when compared to the resting condition (0.98 ± 0.02 vs ± 0.05, paired t-test, p < 0.001). This suggests that the recruitment of additional synaptic responses by the sensory stimulation tends to linearize the I/V relationship. One should note that (i) the rectification observed in the resting condition had only a negligible effect on the total I/V-curve during synaptic response, due to the fact that the synaptically evoked conductance increase was large compared to the resting conductance (see below), and (ii) that the putative NMDA involvement during synaptic activation does not seem to contribute significantly to a noticeable non-linear behavior at more positive holding potentials. In summary, our linear conductance estimation method was considered valid for cells whose I/V linear correlation coefficient was larger than 0.95 for holding potentials between 90 and 40 mv. Nevertheless, the linearity of the reconstructed I/V curves is increased when measurements are made in voltage clamp with steady holding voltage steps. These observations justify the method we have used for conductance extrapolation, which takes into consideration only the initial linear segment of the I/V curve (thus restricting the analysis to membrane potential values below spike threshold) In vivo recordings As previously observed in vitro, I/V curves obtained in current clamp (see Fig. 1) were best fitted by two linear segments whose intersection point abscissa was found close to the spike threshold (for RS cells: 50.1 ± 3 mv). The input resistance of the cell above this threshold was on average 2.6 times smaller than that measured below spike threshold (35.0 ± 23 M vs ± 29 M, ratio 2.6 ± 3, n = 119, see Table 2). This ratio was smaller than that observed in vitro (4.0 ± 3, t-test, p < 0.001), reflecting most likely a linearization of the behavior of cortical cells by the synaptic bombardment observed in the intact organism preparation. However, in contrast with RS and IB cells, NIB, FSA and FS cells tended to behave more non-linearly, due to a strong potassium conductance mediated rectification characterized by a fast and large AHP (see Table 2) Measurements of the evoked global synaptic conductance change and the apparent composite reversal potential In vitro recordings We recorded synaptic responses in identified pyramidal neurons of layer 5 in current clamp and voltage-clamp modes. Tungsten bipolar electrodes were positioned at different distances from the recording site to focally stimulate different cortical layers and afferent circuits: (a) in white matter (WM), in order to directly stimulate thalamo-cortical fibers, (b) in layer 4, in order to stimulate local recurrent circuits and optimize the recruitment of monosynaptic short-range inhibition and (c) in the

10 332 C. Monier et al. / Journal of Neuroscience Methods 169 (2008) Table 2 Summary of in vivo and in vitro measurements of input resistance, made in the linear (below spike threshold) and rectifying (above spike threshold) segments of the I/V curve and their relative ratio Cell types Nb of cells R 1 (V m < threshold) (M ) R 2 (V m > threshold) (M ) R 1 /R 2 ratio In vitro control ± ± ± 3 In vitro QX ± ± ± 1 In vivo RS ± ± ± 3 In vivo IB ± ± ± 1 In vivo NIB ± ± ± 6 In vivo FSA ± ± ± 5 In vivo FS ± ± ± 5 top part of layer 2/3, in order to stimulate more distal inputs. The intensity of the stimulation was adjusted in current clamp mode to induce a subthreshold postsynaptic response and set at half the threshold intensity required to obtain reliable suprathreshold activation. This amplitude of stimulation was around two to three times the amplitude of stimulation necessary to induce a minimal response. Stimulation strength was thus strong enough to activate excitatory and inhibitory circuits, but weak enough to avoid recruiting dominant non-linear processes, linked for instance to NMDA receptor activation or action potential initiation. In current clamp, whatever the stimulation location, and taking into account the intensity of the test stimulation (see above), the evoked synaptic responses mainly exhibited two temporal phases: an early and fast depolarization followed by a hyperpolarization. The full decomposition into simultaneously recruited conductance activation components is required since knowledge solely of the composite potential response does not allow the relative strength and timing of evoked excitation and inhibition to be dissected. The first step of the analysis (see Section 2) isto measure synaptic conductance profiles and the composite (or apparent) reversal potential. Fig. 2 illustrates the membrane current (I m ) measurements, performed in voltage-clamp and whole cell modes, for 5 different holding potentials (between 80 and 40 mv) and averaged over 10 interleaved trials. The voltage abscissa of the intersection point between the I/V curve at a time delay t after the electrical stimulation (i.e. during the synaptic evoked response) and the I/V curve at rest (i.e. before stimulation) gives a continuous estimation of the stimulus-locked dynamics of the composite apparent reversal potential (E syn (t)), monitored from the soma (see Section 2 for more details). In all recordings, the phase plots of the relative change in synaptic drive, G syn (t), versus the apparent reversal potential, E syn (t), presented typically three phases (Fig. 2): (1) in the first phase (1 2 ms), the initial conductance increase was weak but the reversal potential shifted in a few milliseconds to more positive potential values close to 0 mv, indicative of a significant recruitment of AMPAmediated excitation; (2) in a second phase, the conductance increased strongly, while concomitantly the synaptic reversal potential became more negative and converged typically in the neighborhood of 60 to 70 mv; this value is suggestive of dominant GABAa receptor activation; (3) in the last phase, the conductance decreased again and the synaptic reversal potential became slightly more hyperpolarized (around 80 mv). The correlation between the peak conductance value ( G peak ) and its corresponding apparent synaptic reversal, established on the basis of cell-by-cell paired measurements, is illustrated in Fig. 2. The global shape of the correlation between G syn (t) and E syn (t) for the whole population is roughly similar to the phase plots observed on a cell-by-cell basis for a single stimulation. The peak conductance values were observed for reversal potentials ranging between 60 and 70 mv. The increase in peak conductance varied from 100% to up to 1500%. Independent of the stimulation site, the conductance increase reached on average 460 ± 326% (n = 152, 88 cells with control solution and 64 cells with QX314). When the analysis is further constrained as a function of site location, the smallest conductance increases were observed for white matter stimulation (340 ± 265%, 20.7 ± 13 ns, n = 56), while a slightly higher value was observed for stimulation of the layer 2/3 top (483 ± 296%, 26.9 ± 24 ns, n = 65) and the strongest changes were obtained for direct stimulation of the layer 4 neighborhood of the recorded cell (637 ± 392%, 32.0 ± 27 ns, n = 31). However, when taking into account the high variability of the peak of the conductance in any subpopulation, these differences did not reach a statistically significant level. The presence of QX314 in the intracellular solution in order to block fast sodium and GABAb conductances did not affect the absolute increase of the global synaptic conductance (24.7 ± 16 ns with control recording solution (n = 88) vs ± 27 ns with QX314 (n = 64)). However, since the leak conductance was significantly smaller in the presence of QX314 (5.5 ± 3.8 ns vs. 6.9 ± 3.6 ns with the control solution), the relative conductance increase was higher in this former condition (543 ± 404% with QX314 vs. 395 ± 233% with the control solution). The apparent synaptic reversal potential corresponding to the peak conductance value was 62.6 ± 9 mv for all cells (n = 188) and no significant differences were found across stimulation sites (WM: 63.6 ± 10 mv; layer 2/3 top: 62.4 ± 8 mv; layer 4: 62.3 ± 10 mv). The presence of QX314 in the pipette did not produce any significant change in the apparent synaptic reversal potential at the conductance peak ( 61.8 ± 10 mv with QX314 vs ± 8 mv without QX314) In vivo recordings The data analyzed here are taken from a variety of visual protocols. The spatio-temporal structure of the spiking and voltage

11 C. Monier et al. / Journal of Neuroscience Methods 169 (2008) Fig. 2. Continuous measures of input conductance changes, evoked in vitro and in vivo.(a) In vitro response of a layer 5 pyramidal RS cell to electrical stimulation from white matter (WM). (B) In vivo example of a subthreshold response in a complex cell (sensory responses detailed in Fig. 6, cell 8). (a) VC current waveforms (I m ) measured at four (in vivo) and five (in vitro) levels of potential (between 90 and 40 mv). All responses are averages of 10 trials. (b) Time courses of relative change in input conductance G syn (%) and (c) its apparent reversal potential E syn (t), evoked by the stimulation (arrow). (d) I/V characteristics are derived from linear regressions corresponding to the resting state (black circles), the slope of which gives G rest, and during visual activation (red circles), the slope of which gives G in (T). The measure is done at time T marked by a red dotted line in the voltage-clamp I m records. The voltage abscissa axis, V hc, corresponds to the command holding potential corrected for the R s ohmic drop. (e) Phase plot of relative G syn (%) vs. E syn (t) illustrates the time-course of the trajectory of the E/I balance following the stimulation onset. (f) Population analysis ((A) in vitro, cells with and without QX314, each dot representing a given response for a given cell, n = 188; (B) in vivo, n = 300), distributions and correlation between the peak value in the relative conductance increase ( G peak (%)) evoked by the stimulation and the apparent composite reversal potential at which it occurred (E syn G peak ). receptive fields (RF) was mapped using 2D sparse noise (157 cells) or 1D-randomized exploration with an optimally oriented flashed bar across the RF width (46 cells). Orientation selectivity tuning curves of spiking and voltage responses were also determined in response to light bars moving at an optimal speed (49 cells). A diversity of sensory responses was observed in current clamp recording mode, with in general a strong net V m depolarization and spikes for the preferred stimulus, occasionally sharp hyperpolarizing potentials for non-optimal stimuli, but most often a composite sequence of subthreshold depolarizations and hyperpolarizations (see examples in Fig. 4 for moving bars and Fig. 6 for flashed static bars). When the stability of recordings and the value of the access resistance in voltage clamp allowed it, inward/outward current responses were also recorded for the same stimulus sequences under voltage-clamp (VC) conditions imposed at two to four holding potentials or under current clamp (CC) condition with different levels of negative current injection. Quantitative conductance measurements were carried out during directional tuning protocols with moving bars (19 cells, 11 cells in VC only, 4 cells in both VC and CC and 4 cells in CC only) and spatial sensitivity profile protocols with static flashed bars of optimal orientation randomly positioned across the RF width (7 cells, 5 cells in VC only and 2 cells in both

12 334 C. Monier et al. / Journal of Neuroscience Methods 169 (2008) Fig. 3. Decomposition of the conductance change into excitatory and inhibitory components. Same conventions as in Fig. 2. (A) Decomposition method. Responses to WM electrical stimulation, recorded in a layer 5 pyramidal cell. From top to bottom: current recordings (I m ) in voltage clamp (VC), global synaptic conductance waveform G syn (t) and its apparent reversal potential E syn (t), the three underlying conductance component waveforms (G exc in red, G inha in blue and G inhb in green), reconstituted V m changes (V rec in black), and reconstituted profiles due to excitation only (V exc in red) and inhibition only (V inh in blue). (B) Blocking excitation. Measure of the relative synaptic conductance increase G syn (%) and the synaptic reversal potential of the inhibitory conductances evoked by stimulating layer 4. (a) Recordings are made with QX314 in the intracellular pipette solution in order to block GABAb conductance (b). The additional application of CNQX and APV completely suppresses the excitatory conductance and reduces the inhibitory conductance (demonstrating a polysynaptic origin of inhibition). (c) Superimposed phase plots G syn (%) vs. E syn (t) in the absence (black) and presence (red) of CNQX + APV, recorded in the same cell. (d) Distribution of the synaptic reversal potential

13 C. Monier et al. / Journal of Neuroscience Methods 169 (2008) VC and CC). In one cell, additional conductance measurements in VC in response to drifting gratings in different directions were carried out (illustrated in Fig. 5) but not pooled with other data. For the global cell population (n = 26), the mean resting conductance was 14.6 ± 9.2 ns and the mean input resistance 97.0 ± 60.6 M. The access resistance was estimated off-line for the VC recordings (mean R in :27± 13 M ; n = 22). In addition, we extracted several measures from the CC V m records: mean and variance of evoked spiking activity and evoked membrane potential depolarization and hyperpolarization. If at least one of these components changed significantly during sensory activation in comparison to the resting case, the response was used in further analyses even if it was not accompanied by a significant global conductance change. With this criterion, three hundred responses from a total of four hundred recorded responses were used (200/200 for moving bars, 100/200 for flashing bars, extending across the discharge field and the surrounding silent receptive field). The mean peak conductance increase (relative to the resting conductance) across cells (one peak value for each cell) was 95.3 ± 61.2% (n = 26). The mean absolute value of the global synaptic conductance peak was 16.1 ± 13.8 ns and the mean reversal potential at which the conductance peak was observed was 62.5 ± 8.4 mv. If we consider now all the significant responses (n = 300, see Fig. 3) the mean conductance change observed at the peak corresponded to a mean increase of 57.5 ± 43.7% (range 5 270%) and absolute conductance values of 10.2 ± 9.7 ns (range ns). The mean composite reversal potential for the peak response remained around 60 mv ( 63.1 ± 8.6 mv) although its value could vary, across stimulations and cells, between 80 and 30 mv. In spite of the variability across cells ( G peak : range: 5 270%, ns), we did not observe a significant dependency of the relative or absolute conductance peak on the stimulus condition, i.e. static versus moving bars. However, whereas flashed stimuli evoked only transient changes in input conductance (lasting for a few tens of ms), longer conductance increases were evoked by moving bars and could last for several hundreds of milliseconds (see examples in Figs. 4 and 5). Although the ohmic drop was systematically compensated offline in our calculations, we checked if the diversity in conductance measures could be explained in part by the differences in access resistance across the different recordings. The weak negative correlation between the peak conductance increase and the access resistance in our experiment (r 2 = 0.21) indicated the tendency that the higher the access resistance was, the weaker the conductance increase. This suggests that our calculation probably underestimated conductance increase for relatively high access resistance conditions. A simple and likely explanation is that the access resistance and the capacitance of the electrode act as a low-pass filter on the conductance estimate Conductance decomposition into excitatory and inhibitory components Methodological issues In order to quantify the balance between excitation and inhibition during afferent stimulation (electrical or visual), the synaptic conductance change was decomposed into components specific to the activation of different types of receptors. The simplest method, used previously in the literature (see for example Anderson et al., 2000), is to linearly decompose the global conductance into two components associated with two distinct reversal potentials, one for the excitation and one for the inhibition. With this simple dual decomposition method, if the apparent synaptic reversal potential is below the assumed inhibitory reversal potential, the excitatory conductance change will be negative and the inhibitory conductance change larger than the global conductance increase. The determination of realistic values for E inh thus becomes crucial in the interpretation of the conductance changes. For instance, in their study of cortical conductance increases produced by thalamic electrical stimulation (see Fig. 14 in Anderson et al., 2000), Anderson et al. concluded these was a suppression of excitation. However, a different interpretation may be given if the synaptic reversal potential (during the period when their measure gives a negative excitatory conductance) reaches values below the preset value of the inhibitory reversal potential used in their decomposition. The inhibitory component is in fact reflecting the composite effect of at least two distinct GABAa and GABAb-mediated inhibitory processes, with two distinct reversal potentials. Considering only the major component (GABAa) or choosing an intermediate value between GABAa and GABAb reversal potentials (as done in Anderson et al., 2000) introduces consequently errors in the estimation of the relative contribution of inhibition and excitation. In the present study, the global synaptic conductance is decomposed into three components G exc (t), G inha (t) and G inhb (t) corresponding, respectively, to the activation of one type of excitatory synapse and two types of inhibitory synapses, each associated with fixed reversal potentials (E exc for the excitation, E inha for the GABAa inhibition and E inhb for the GABAb inhibition). We chose the simplifying assumption that, depending on the actual value of the apparent composite synaptic reversal potential, only two out of the three possible types of synaptic inputs contribute in a dominant manner to synaptic activation (see Section 2). When applying this three-component decomposition method to activation cases similar to those reported by Anderson et al. (2000) (see above), no withdrawal of excita- (E synpeak ) at which the peak conductance increase is observed, for the population of cells recorded in the presence of CNQX + APV (n = 19), with a mode centered around 80 mv. (C) Paired pulse stimulation. (a and b) Examples of conductance change measurements ( G syn (%), G exc and G inh ) and inhibitory conductance reduction evoked by paired pulse stimulation (ISI = 200 ms), from WM (a) and layer 4 (b): (a) control solution; (b) recordings with QX314 in the intracellular pipette solution without (left) or in the presence (right) of CNQX/APV in the bath. (c and d) Population analysis (n = 81) pooling both stimulation sites (WM and layer 4): distribution of the response change ratios for the first vs. the second stimulation pulse (arrows), averaged over the full time course (c) or based on the peak conductance values (d) (global synaptic (black), inhibitory (blue) and excitatory (red)).

14 336 C. Monier et al. / Journal of Neuroscience Methods 169 (2008) Fig. 4. Voltage-clamp measurement of conductance dynamics evoked by moving stimuli. For each cell: light drifting bars were presented 10 times at the preferred (P) and non-preferred (NP) orientations/directions (cross-oriented for cells 2 6 and null-direction for cell 1). Grey inset waveforms, from top to bottom: (1) single trial current clamp (CC) V m responses (black, with truncated spikes) superimposed with the mean ( V m, orange), (2) evoked changes in synaptic conductance ( G syn (%)) measured from VC recordings), (3) inhibitory (G inh in blue) and excitatory (G exc in red) conductance components relative to their respective resting values (thin horizontal line), (4) reconstituted inhibitory and excitatory currents, derived from the evoked conductance changes, (5) reconstituted membrane potential responses, considering solely significant increases in the mean excitatory (V exc ) or inhibitory (V inh ) conductance contributions, and (6) observed average CC measures ( V m in black) superimposed with the reconstructed V m trajectory (V rec, orange), derived from the conductance measurements. In the inset boxes, cross-correlation functions between excitatory and inhibitory conductance waveforms. For cells 1 3 only: spatio-temporal maps of subthreshold visual responses (XT-RT). The color code is, respectively, warm/red for depolarisation and cold/blue for hyperpolarization. All cells shown here have Simple receptive fields with inseparable (cell 1) or separable (cells 2 and 3) XT-RFs. The stimulus (moving light bar in the preferred orientation and direction) is presented in the space time domain in three different positions reached at different times (labelled 1 3 in each RF map).

15 C. Monier et al. / Journal of Neuroscience Methods 169 (2008) Fig. 5. Voltage-clamp measurement of conductance dynamics evoked by a drifting grating in a simple RF (cell 2, illustrated also in Fig. 4). Drifting gratings (contrast: 0.7, spatial frequency: 0.5 cycle/, temporal frequency: 1.32 cycle s 1 and size: 6 radius) were presented for five cycles in the preferred orientation and direction (left column, P), non-preferred orientation (centre, NPO) or non-preferred direction (right, NPD). The conventions and traces are the same as in Fig. 4. The black indented line below each stimulus delineates the time window during which the stimulus is visible (up = ON; shaded inset) or not (down = OFF). The two bottom panelsgive an expanded view of the onset responses (*). They show a transient V m depolarization (black), a transient increase in excitatory conductance (red), accompanied by a fast and large increase in inhibitory conductance followed by a tonic component of weaker amplitude (see Section 4). tion is found and the conductance change is instead explained by the recruitment of an inhibitory potassium (GABAb) conductance (see Fig. 3A). Of course, this decomposition scheme cannot account for a temporal overlap between the potassium inhibitory conductance and the excitatory conductance changes. An unavoidable limitation of the voltage-clamp method is that the spatial clamp of the cell is inevitably incomplete (Spruston et al., 1993), which affects the reversal values seen from the soma. Consequently, the apparent composite synaptic reversal potential can become more negative than the reversal potential of the inhibition or more positive than the reversal potential of the excitation if the synapses are away from the soma. In the case where the conductance is decomposed linearly with a synaptic reversal potential more negative than the reversal potential of GABAb, the excitatory conductance would be negative and the extrapolated inhibitory conductance would become larger than the global synaptic conductance. In order to overcome this problem, the synaptic conductance was equated

16 338 C. Monier et al. / Journal of Neuroscience Methods 169 (2008) to the excitatory or to the GABAb component (see Section 2), in cases where the reversal potential was found to be, respectively, either above E exc or below E inhb. This ensures that all the global synaptic conductance terms in the decomposition are positive or null. A theoretical possibility (the linear case) is that the excitation and inhibition operate in a push pull arrangement with a similar visibility from the soma. In such situation, a weak or null change in global conductance might be expected during visual stimulation whereas large changes in the apparent reversal potential might be observed and indicate rapid successive reversals in the E/I balance. Since the global conductance change is null, it is also no longer possible to calculate the reversal potential of the composite drive. In the framework of our decomposition method, the only way to overcome this problem is to estimate the leak conductance and subtract it from the global conductance to obtain the global synaptic conductance (which is the sum of the resting synaptic conductance and evoked synaptic conductance). The assumption we made (see Section 2) was to consider that G leak is the constant baseline component and G synrest represents an additive stochastic component, positive in sign since it corresponds to the ongoing bombardment by excitatory and inhibitory conductance events in the absence of any sensory drive. The computational advantage is that the decomposition is made on conductance components which are always positive and thus does not require during sensory or electrical activation a net conductance increase from the resting level to be tractable In vitro measurements of inhibitory synaptic reversal potentials In order to solve the equations detailed in Section 2, the first required step is to estimate Einha, the reversal potential of the GABAa inhibition, mainly mediated by a chloride conductance. A partial clamping of the intracellular chloride concentration is likely to occur in whole cell mode patch recordings. It has been previously reported that complex regulatory mechanisms of the intracellular chloride tend to stabilize the internal concentration between 4 and 10 mm depending on the chloride concentration of the pipette (DeFazio et al., 2000). The estimated GABAa reversal potential depends in vitro on the various extracellular ionic concentrations chosen for the ACSF solution, and in particular that of extracellular potassium and chloride (respectively, 1.5 and mm in the present study). The estimated intracellular chloride concentration under our experimental conditions was 5 mm and the chloride concentration in the external solution was fixed at mm. These values yield a theoretical E Cl value around 80 mv. In order to precisely determine the GABAa reversal potential under our experimental conditions, we pharmacologically blocked the GABAb and excitatory components. The GABAb component (and sodium conductance) was blocked intracellularly by adding (to the filling solution) 3 mm QX314, a quaternary lidocaine derivative, which does not affect GABAa receptor activation (Nathan et al., 1990). AMPA/Kainate and NMDA receptors were blocked by adding to the ACSF bath solution CNQX (25 M) and APV (20 M), respectively. Electrical stimulation was applied to layer 4, with the aim of activating maximally monosynaptic inhibitory inputs (see Fig. 4). After application of CNQX/APV, the synaptic conductance was reduced and the apparent synaptic reversal potential of the peak conductance shifted from 62.1 ± 12 mv to 80.1 ± 3 mv (n = 19, see Fig. 3). The remaining component of synaptic conductance was further abolished by bath application of 10 M bicuculline (n =6)or50 M picrotoxin (n = 2), selective antagonists of the GABAa receptors. Following stimulation of layer 2/3 top, the apparent reversal potential of synaptic responses after application of CNQX/APV was 80.9 ± 1.3 mv (n = 25), a value which is slightly more negative than that found for the layer 4 stimulation. In summary, the reversal potential of GABAa-mediated currents observed in the different stimulation conditions is compatible with the reversal potential of the chloride conductance predicted by the Nernst equation. A possible explanation for the more negative values found when stimulating the top part of layer 2/3 could be that such responses are mediated by a distal inhibitory input, whose spatial location partially escapes the voltage-clamp imposed at the soma (Spruston et al., 1993). Accordingly, we have set E inha to 80 mv for all decomposition of conductances measurements in vitro and in vivo. The other inhibitory reversal potential, corresponding to the activation of a potassium conductance, E inhb, was fixed at 95 mv, in agreement with the standard estimate from the literature In vitro measurements of excitatory reversal potentials Assuming that the global conductance change is solely due to excitatory and inhibitory conductance changes and taking into account the fact that the global I V curves observed during synaptic stimulation and/or blockade of the excitation (CNQX, APV, QX314) were non-rectifying, we extrapolated the view that the IV curve of the excitatory AMPA conductance is also linear. This conclusion could not be tested within our experimental paradigms because the pharmacological blockade of inhibition gives rise to uncontrolled bursts in the spontaneous activity. Using the same preparation, recording set-up and analysis tools as ours, (Le Roux et al., 2006) performed a related control (see Figure D in their supplementary data) where voltage-clamped ramps were applied between 80 and +30 mv in the absence of exogenous application of glutamate. The crossover point between the two linear I V relationships (control vs. glutamate) established that the reversal potential for the excitatory equals 0 mv for our experimental conditions. In the same run of pharmacological experiments, we checked the possible involvement of NMDA conductances in our experimental conditions. We compared synaptic response with QX314 in the intracellular electrode, evoked before and during application of APV (20 M) in the ACSF bath solution (n = 20). The global conductance was not significantly modified during application of APV. Although it cannot be excluded that in some conditions (in particular strong stimulation) in vitro, and supposedly in vivo, an NMDA contribution is expressed, we conclude that in our experimental conditions in vitro no strong NMDA activation was discernable. In addition, the literature reports that

17 C. Monier et al. / Journal of Neuroscience Methods 169 (2008) the voltage sensitivity of NMDA receptors is such that more than one-half of them can be opened only at membrane potentials more positive than 30 mv (Hestrin et al., 1990; Jahr and Stevens, 1990). In order to suppress the NMDA current, we clamped neurons only at membrane potentials more hyperpolarized than 40 mv, and reduced the possibility of recruiting excitatory current non-linearities. Accordingly, the excitatory reversal potential used in our decompositions was fixed at 0mV In vitro measurements of excitatory and inhibitory conductances Balance between excitation and inhibition For cells recorded with the control solution (n = 88) and stimulated either from WM (n = 34) or layer 2/3 (n = 54), the conductance peak value was 6.0 ± 4.0 ns for the excitatory AMPA conductance, 20.0 ± 13.1 ns for the chloride conductance (GABAa) and 0.5 ± 0.4 ns for the potassium conductance (GABAb). Thus, when normalizing each component by the global conductance change, GABAa inhibition was found to be dominant (82.9 ± 5%), relative to excitation (15.3 ± 5%) and GABAb mediated inhibition (2.2 ± 2%). No significant dependency on the electrical stimulation site was found. For recordings done with QX314 in the intracellular recording solution, we checked that our method could appropriately detect the blockade of potassium conductance. Effectively, the percentage of the conductance component mediated with a reversion potential of 95 mv was five times smaller for cells with QX314 than for cells with the control solution (0.4 ± 1% vs. 2.2 ± 2%, p < 0.001). In cells with QX314, the balance between excitation and inhibition was 17.3 ± 7% (excitation) and 82.7 ± 7% (inhibition). Similar findings with QX314 have been previously reported in auditory cortex by Wehr and Zador (2005). They recorded, in the rat A1 cortex in vivo, responses to isolated tones at the optimal frequency, either with or without QX-314 in a potassium-based internal solution. Cells recorded without QX-314 showed a small, slow inhibitory conductance change, whereas cells recorded with QX-314 showed no such slow inhibitory conductance change in response to the same stimuli Kinetics of excitatory and inhibitory conductance changes The onset latency of the evoked synaptic conductance varied as a function of the site of stimulation. The shortest onset latencies were observed for proximal stimulation sites (3.1 ± 0.8 ms), the longest for layer 2/3 stimulation (6.5 ± 1.5 ms) while intermediate values were found for WM stimulation (4.5 ± 1.5 ms). The onset latencies were shorter for excitation than for inhibition (paired statistics) with temporal delay of 0.2 ± 1 ms for proximal, 0.7 ± 1 ms for WM and 1.2 ± 1 ms for layer 2/3 stimulation. The peak latencies were also shorter for G exc than for G inha (layer 4, 6.7 ms vs. 9.8 ± 3 ms; WM: 8.4 ± 2 ms vs ± 3 ms, layer 2/3, 12.1 ± 3 ms vs ± 4 ms, paired t-test, p < 0.001). The temporal shift between excitatory and inhibitory conductance peaks was, on average, 3.5 ± 3 ms. In order to quantify the global shape and kinetics of synaptic conductances, we used three different types of templates (simple alpha function, attenuated alpha function and double alpha function (sum of two alpha functions)). The double alpha function, with 4 fitting parameters, was found to be the best fit for all experimental traces. The mean value and the peak (and latency), derived from the double alpha fit function, were strongly correlated with the direct measures made on the conductance waveforms (r 2 = 0.98 ± 0.05 on average). The first part of the conductance was captured by a fast and large amplitude alpha function, whereas the second part of the curve was fitted by a slow and lower amplitude alpha function. For excitatory and inhibitory conductances (pooling WM and layer 4), the time constant of the fast alpha function was found to be on average almost 5 times faster than that of the slow alpha function, with a peak amplitude times larger. Nevertheless, in 45% of these cells, the excitatory conductance was adequately fitted with a single alpha function. In terms of conductance profiles, the inhibitory evoked change globally had slower kinetics than the excitatory one (5.5 ± 5 ms vs. 3.1 ± 1 ms for the fast alpha function and 30.2 ± 15 ms vs ± 15 ms for the slow alpha function). Similar differences were observed when stimulating layer 2/3: the time constant of G inha was slower than G exc (8.6 ± 3.0 ms vs. 4.9 ± 2.0 ms for the fast alpha function and 47.2 ± 21.0 vs ± 27.0 ms for the slow alpha function). However, on average, the synaptic conductance profiles evoked by stimulation in layer 2/3 had slower kinetics compared with these produced by stimulation in layer 4 or WM Separability between monosynaptic and disynaptic inhibition An important feature reflected in the time-course of the observed conductance changes is that the synaptic response results from the composite activation of monosynaptic and at least disynaptic pathways. The proportion of monosynaptic inhibition can be estimated with a blockade of excitatory transmission with CNQX/APV bath application. As seen in Fig. 3B, for layer 4 stimulation, the inhibitory conductance change was reduced by the blockade of excitatory transmission. On average, about 45 ± 20% (n = 19) of the inhibitory conductance remained during CNQX/APV application, and this inhibition component can be considered to be of monosynaptic origin. For stimulation at the top of layer 2/3, synaptic responses disappeared in around 20% of cells after blockade of excitation, suggesting that, for this contingent of cells, no monosynaptic inhibitory fibers were activated from this layer. For the remaining 80% of cells, the part of monosynaptic inhibition was small (14.0 ± 7.0%, n = 29). For WM electrical stimulations, the application of CNQX/APV suppressed completely all evoked synaptic conductances (n = 3) Modulation of the E/I balance during paired pulse stimulation In order to check the sensitivity of our method, we applied a paired pulse stimulation protocol with a delay of three hundred milliseconds between each successive afferent volley, which is known to produce a sizeable depression of the inhibitory synaptic response in pyramidal cells (Thomson, 2000). When

18 340 C. Monier et al. / Journal of Neuroscience Methods 169 (2008) stimulating WM (n = 50), the conductance increase evoked by the second stimulation was reduced by 35% (32% ± 20% for the peak conductance and 35 ± 19% for the mean of the conductance, see Fig. 3C). The composite apparent reversal potential of the peak shifted towards a more depolarized potential by 5.9 ± 11 mv (E syn : 64.3 ± 9 mv for the first stimulation, 58.4 ± 11 mv for the second). All these difference were statistically significant (paired t-test: p < 0.01). When stimulating layer 4 (n = 31), the observed modulations were of the same magnitude (reduction of 26 ± 23% for the peak conductance, 28 ± 25% for the conductance mean and a shift of +4.3 mv for the composite reversal potential). The decrease in the evoked conductance change and the shift in the apparent reversal potential of the peak of the conductance increase towards more depolarized values, both suggest that the relative contribution of inhibition is decreased during paired-pulse stimulation. The decomposition method permits this differential modulation to be quantified. In the histograms of the Fig. 3C, in order to simplify the presentation, we pooled GABAa and GABAb inhibitory changes into a single component and combined the effects of paired stimulation in layer 4 and WM that were not significantly different. On average, and in spite of variability across cells, the peak of the excitatory conductance was slightly but significantly depressed (88 ± 26%, paired t-test, p < 0.01). In contrast, the mean value of the excitatory conductance did not change significantly (98 ± 30%, p = 0.27). The depression seen after the second stimulation pulse in the global conductance increase was mainly expressed in the inhibitory conductance component (WM stimulation: 64 ± 22% and 61 ± 20%, layer 4 stimulation: 70 ± 23% and 68 ± 25% for peak and mean values, respectively, see Fig. 3C). Because of the mixed recruitment of monosynaptic and polysynaptic inhibition, the depression of the inhibitory conductance component could have been produced by a reduction of the excitatory drive of the inhibitory interneuron or by a depression of inhibitory synapses directly targeting the recorded pyramidal neuron. In order to distinguish between the monosynaptic and polysynaptic nature of the paired-pulse change in inhibition, we blocked excitation and polysynaptic inhibition (mediated by an excitatory drive) with CNQX/APV (n = 21) and observed a similar level of depression of the inhibitory conductance component (69 ± 17% for the peak and 68 ± 25% for the mean). We conclude that most of the regulatory effect concerns monosynaptically evoked inhibition. In summary, during paired-pulse stimulation of intracortical or WM afferents in vitro, the balance between the excitatory and inhibitory drive (G exc /G inha ) increases from 20.3% ± 18.9% in response to the first shock to 30.8% ± 26.3% in response to the second shock. This effect results mostly from a down-regulation of monosynaptic inhibitory activation In vivo measurements of excitatory and inhibitory conductances Estimation of the leak conductance and the E/I balance at rest The assumption we made in the methods section was to consider that G leak is the lower boundary, constant component of G rest and G synrest represents an additive positive or null stochastic component. The intrinsic standard deviation of the resting conductance derived from our VC recordings (n = 22) ranged from 0.5 to 8.0 ns (mean: 2.6 ± 2.0 ns). The mean resting conductance was 14.6 ± 9.2 ns (R rest : 97.0 ± 60.6 M ) and could be decomposed for each cell into the sum of a G leak term (mean: 8.6 ± 4.2 ns, R leak : ± M ) and a synaptic resting conductance component G synrest (mean: 6.0 ± 4.3 ns). Thus, according to our conventions, the synaptic conductance resulting from ongoing bombardment corresponded to roughly half of the global input conductance at rest (45.1 ± 22.1%). Taking into account the value of the mean resting potential ( 73.5 ± 5.8 mv) and assuming a leak reversal potential around 80 mv (Paré et al., 1998), we can further estimate the apparent synaptic reversal potential of the global synaptic conductance and decompose it during spontaneous activity (in the absence of any visual stimulation) into three receptor activation specific components (AMPA: G excrest = 1.0 ± 0.9 ns; GABAa: G iarest = 4.9 ± 3.6 ns; GABAb: G ibrest = 0.7 ± 0.21 ns). Since the global inhibitory conductance amounts to 4.93 ± 3.6 ns, G excrest and G inhrest represent, respectively, 21.3 ± 17% and 79.4 ± 16% of the resting synaptic conductance (E/I ratio of 1/4) Decomposition of evoked excitatory and inhibitory components During visual stimulation, the global synaptic conductance can be decomposed again into three components, G ex, G inha, and G inhb. The evoked synaptic conductance is calculated for each component by subtracting the mean value at rest ( G ex (t)=g ex (t) G exrest and G inh (t)=g inh (t) G inhrest ). This convention implies that both net terms, G ex (t) and G inh (t), can become negative when the level of activation during visual stimulation is lower than that already present during spontaneous activity. The sum of the positive component of the evoked synaptic conductances ( G ex + G inha + G inhb )was found to be larger than the global synaptic conductance increase G syn derived previously from the global I/V method (113%). This indicates that the different evoked conductance components have to be integrated over the whole time-course of the sensory activation since direct estimates from average conductance changes would lead to an underestimation of the different synaptic components. The diversity in the levels of changes observed in excitatory and inhibitory conductances is illustrated in Fig. 4 for moving bars with optimal and non-optimal orientations in 6 cells and in Fig. 6 for static light bars flashed in the center of the discharge field in 4 other cells. In Fig. 4, three cells (cell 1, 5 and 6) present large increases in conductance whereas the three other (cells 2, 3 and 4) exhibit changes of a lesser amplitude. In spite of this diversity in the global conductance change strength, the increase of excitation is generally much lower that of inhibition. Large fluctuations in the temporal profile of the inhibitory conductance were commonly observed, while the excitatory conductance waveforms presented fluctuations of only limited amplitude. A second form of diversity is seen in the sign and degree of temporal co-variation between excitatory and inhibitory conductances, evolving sometimes in synchrony (non-preferred stimuli (NP) in

19 C. Monier et al. / Journal of Neuroscience Methods 169 (2008) Fig. 6. Voltage-clamp measurement of conductance dynamics evoked by static stimuli four different cells is illustrated (cells 7 10). Light bars were flashed ten times in the same RF position, with the same (optimal) orientation, for 1 s duration. The black indented line below each stimulus delineates the time window during which the stimulus is visible (up = ON; grey inset) or not (down = OFF). The conventions and traces are the same as in Fig. 4. cells 1, 3, 5 and 6 in Fig. 4; but also preferred stimulus (P) in cells 5 and 6), sometimes in phase opposition (preferred stimulus (P) in cells 1, 2, 3 and 4 in Fig. 4). For static stimuli, the evoked changes were qualitatively similar to those produced by white matter stimulation in the in vitro preparation, sometimes with an early excitatory drive followed by a large inhibitory conductance increase (see Off response in cell 9 in Fig. 6). However a high diversity of interaction patterns between excitation and inhibition was observed across cells, as shown in Fig. 6: the response (Off, here) in cell 10 is mainly dom-