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University Laboratory of Physiology, Oxford, United Kingdom
Submitted 29 April 2004; accepted in final form 11 December 2004
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMETHODSRESULTSDISCUSSIONACKNOWLEDGMENTSREFERENCES |
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONACKNOWLEDGMENTSREFERENCES |
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; Sherman and Guillery 1996, 2002; Usrey et al. 1999). However, retinal afferents do not represent the large majority of inputs to the dLGN. Dominating the dLGN's afferent population are the 85–90% of synapses that provide "modulating" influences on geniculate processing (e.g., Sherman and Guillery 1996, 2002). Although these modulatory inputs cannot change fundamental receptive field (RF) properties established by retinal drivers, they can produce subtle alterations in the response properties of dLGN neurons, such increasing or decreasing the magnitude of visually evoked responses (e.g., Uhlrich et al. 1995, 2002). Thus due to modulatory inputs onto dLGN cells, the thalamic relay of visual information from retina to cortex becomes a dynamic operation.
One important way in which modulatory inputs to the dLGN can alter the retinocortical transfer of visual information is by changing the firing mode of geniculate relay cells. These neurons, like all thalamic relay cells, can fire action potentials in two different modes: "burst" and "tonic." As demonstrated by in vitro recordings (e.g., Jahnsen and Llinas 1984; Kim et al. 2001; Ramcharan et al. 2000a; Sherman 2001; Zhan et al. 1999), firing mode is controlled by a T-type calcium channel that produces IT, a voltage- and time-dependent current. IT is inactivated at relatively depolarized membrane potentials (>60 mV). In this state, depolarizing inputs stimulating a geniculate relay cell will produce conventional, tonic action potentials. If the cell's membrane potential is relatively hyperpolarized for >100 ms, however, IT is de-inactivated. Cell stimulation then produces a depolarizing waveform carried predominantly by calcium ions, known as a low-threshold spike (LTS). This LTS is usually accompanied by a burst of sodium-based action potential firing. Modulatory geniculate inputs that produce (long-lasting) depolarizations or hyperpolarizations of cell membrane potentials can therefore switch relay cell responses from burst to tonic firing, and vice versa.
One of the most important situations in which such burst-tonic switches occur is the transition between sleep and wakefulness. Thalamocortical relay neurons in naturally sleeping animals display rhythmic bouts of bursting ( McCarley et al. 1983; Weyand et al. 2001), which in vitro studies have established are due to cyclic interactions between IT and a depolarizing, hyperpolarization-activated current Ih ( McCormick and Bal 1997). With the transition from slow-wave sleep to wakefulness, modulatory inputs from the brain stem produce a progressive depolarization of thalamocortical relay cells. This inactivates IT, decreasing burst firing and promoting tonic responses ( McCarley et al. 1983; McCormick and Bal 1997).
Because rhythmic burst firing is most prominent during slow-wave sleep ( McCarley et al. 1983; Weyand et al. 2001) and because during slow-wave sleep the responsiveness of dLGN neurons to RF stimulation is decreased ( Livingstone and Hubel 1981), it might seem that burst firing in thalamocortical relay cells is unimportant as a carrier of sensory information. However, studies in the past decade have begun to uncover the possibility that both burst and tonic firing modes represent specific signals in sensory processing. Lu et al. (1992) used intracellular recordings in vivo to demonstrate that burst responses occur in the cat dLGN during periods of visual stimulation. These recordings also showed that burst spikes could be accurately identified purely on the basis of temporal patterns in spike firing ( Lu et al. 1992), allowing less techni-cally demanding extracellular recordings to confirm that bursting, like tonic firing, occurs during sensory stimulation in anesthetized (e.g., Guido et al. 1992) and awake behaving (e.g., Fanselow et al. 2001; Guido and Weyand 1995; Ramcharan et al. 2000b; Swadlow and Gusev 2001; Weyand et al. 2001) animals. Furthermore, while burst and tonic dLGN spikes carry similar amounts of visual information ( Reinagel et al. 1999), recordings in the cat dLGN have shown that the type of visual information transmitted by the two firing modes can be significantly different. Among other differences, burst spikes in the cat follow temporal changes in a sinusoidal stimulus less faithfully ( Guido et al. 1992; Lu et al. 1992), offer better stimulus detection ( Guido et al. 1995), are more tightly tuned for TF ( Mukherjee and Kaplan 1995), and have more reliable timing ( Guido and Sherman 1998) than their tonic counterparts. Changes in firing mode in thalamic relay cells might therefore affect the information relayed by those cells to cortex.
Although studies in the cat have identified several differences in the stimulus coding properties of burst and tonic spikes in the dLGN ( Guido and Sherman 1998; Guido et al. 1992, 1995; Lu et al. 1992; Mukherjee and Kaplan 1995) (see DISCUSSION), the comprehensive set of data gathered in our laboratory concerning the response properties of mouse dLGN neurons ( Grubb and Thompson 2003) offers an opportunity to compare burst and tonic firing across a large range of visual response characteristics. As well as providing a full and detailed description of the functional differences between these two firing modes, such a comparison will also offer an indication as to whether such differences are conserved across mammalian species. We know that the properties of the thalamic IT current and its accompanying burst activity are similar across different mammals (guinea pig: Jahnsen and Llinas 1984; McCormick and Feeser 1990; cat: Zhan et al. 1999; monkey: Ramcharan et al. 2000a; mouse: Kim et al. 2001), but it is unknown whether the distinct visual response characteristics of burst and tonic firing ( Guido and Sherman 1998; Guido et al. 1992, 1995; Lu et al. 1992; Mukherjee and Kaplan 1995) are a common feature of mammalian visual processing at thalamic level. The mouse dLGN data presented here represent a first step toward addressing this issue.
Perhaps more importantly, though, studying the visual coding properties of burst and tonic firing in the mouse thalamus opens up the study of firing mode function to genetic manipulation techniques. In mammals, these techniques are most advanced in mice, where they can be invaluable tools for investigating neuronal function at the molecular level. In particular, because the switch between burst and tonic thalamic firing modes can be determined by a single ion channel, investigations of firing mode function might especially benefit from approaches involving genetic manipulation. Mice that lack the T-type calcium channel underlying IT have been generated ( Kim et al. 2001), but before the consequences of this mutation on visual processing at the thalamic or cortical level can be investigated, we need to know the probable contribution of this channel in the normal visual system. We can start to address this issue by investigating the basic visual response properties of burst and tonic firing in the wild-type mouse dLGN. In addition, genetic manipulations might be extremely useful in dissecting the molecular components underlying the control of response mode in thalamic relay cells. Various modulatory systems have been proposed to switch thalamic relay cells from burst to tonic mode or vice versa (e.g., Godwin et al. 1996; Lu et al. 1993; Uhlrich et al. 2002). Manipulating the receptors or other molecular machinery involved in this modulation could be extremely informative concerning the circuitry that controls or does not control thalamocortical information transmission.
As an essential precursor to such studies, we demonstrate here that burst-like events can be identified in extracellularly recorded responses of mouse dLGN cells and that these events have some properties expected of thalamic burst firing. We go on to show that burst firing in the mouse dLGN occurs during visual stimulation. Finally, we investigate the visual response characteristics of burst and tonic spikes in the mouse dLGN across a broad range of stimulus parameters.
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METHODS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONACKNOWLEDGMENTSREFERENCES |
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Animals were adult (>3 mo of age) pigmented C57Bl/6J mice of either sex weighing 20–36 g (Harlan Olac, Oxon, UK). Mice were housed under a 12:12-h cycle of light and dark. All procedures were conducted under the auspices of United Kingdom Home Office project and personal licenses held by the authors.
Electrophysiology
We carried out electrophysiological recordings as described previously ( Grubb and Thompson 2003; Grubb et al. 2003). Mice were first anesthetized with an intraperitoneal injection of 25% fentanyl and fluanisone (Hypnorm, Janssen Animal Health, Bucks, UK) and 25% midazolam, Down, UK), administered at 2.7 µl/g. Surgical anesthesia was maintained throughout the setup procedures by subsequent applications of this induction dose. To prevent the accumulation of bronchial secretions, 5 µl atropine sulfate (600 µg/ml, Animalcare, Yorks, UK) was given subcutaneously. A tracheotomy was then performed ( Schwarte et al. 2000), and a small plastic tube (1 mm OD, 0.5 mm ID, SIMS Portex, Kent, UK) inserted and secured by ligation around the trachea. Application of a spot of cyanoacrylate glue added extra stability by attaching the tube to the skin of the upper thorax.
The animal was situated in a custom-built head holder and secured using a bite bar and ear bars before the tracheal tube was connected to a respiratory pump (MiniVent 845, Hugo Sachs Elektronik, March-Hugstetten, Germany). Pump rate (usually 120–140 strokes/min) and stroke volume (usually 150–200 µl) were adjusted to maintain the expired carbon dioxide level, monitored with a Capstar-100 CO2 analyzer (CWE, Ardmore, PA), at 2.5–4%. Once on the pump, the mice breathed a 1:3 mixture of oxygen and nitrous oxide, along with 1–1.5% halothane for maintained anesthesia. The mice were not paralyzed at any stage, allowing continued assessment of anesthetic depth via the paw pinch response. Throughout the experiment, heart rate was monitored via an ECG (
5 Hz was normal). Core body temperature, measured with a rectal probe, was maintained at 37°C by combining a high ambient temperature with heat from a thermostatically coupled blanket (NP 50-7061-R, Harvard Apparatus, Kent, UK).
A circular craniotomy 0.5 mm in diameter was made unilaterally above the presumptive location of the dLGN, 2.5 mm posterior and 2 mm lateral of the Bregma suture ( Paxinos and Franklin 2001). A small durotomy was made in the center of this exposed zone and, after any whiskers occluding the eyes had been trimmed, a tungsten-in-glass recording electrode with a 5- to 10-µm exposed tip (Alan Ainsworth, Northants, UK) was lowered vertically into the brain using a microdrive. Changes in background activity while advancing through the brain were used to locate the electrode relative to the dLGN ( Grubb and Thompson 2003). Once in the dLGN, the electrode was advanced or retracted in 5-µm steps. Signals were amplified locally by a FET headstage, further amplified by a Neurolog preamp (NL104; typically 10–20 k), then filtered (Neurolog NL125, low-frequency 150 Hz, high-frequency 3,000 Hz) for display on an oscilloscope. A Schmidt trigger was used to isolate responses of single neurons by adjusting the signal threshold; the trigger was also connected to a storage oscilloscope that enabled us to monitor spike shapes and to ensure that no spikes were seen in the refractory period. To isolate the responses of single units, electrode position was adjusted until spikes were deemed to have the same shape, amplitude, and rise time. Responses of optic tract fibers—typically large and fast, with high levels of spontaneous firing and a lack of burst activity—were easily distinguished from cell soma responses. Spike events were then time-stamped with a resolution of 100 µm and were linked to the timing of visual stimulus changes using in-house software written by D. Smyth.
Although we did not apply contact lenses, optical quality in our setup was good and did not degrade over the course of experiments. As described previously ( Grubb and Thompson 2003), no significant correlations were found between time from experiment start (
13 h) and any measures of spatial tuning in dLGN neurons. Furthermore, within animals, there were no significant differences in these measures between cells recorded in the first versus the second half of experiments (paired t-test or Wilcoxon paired test, depending on normality, P > 0.05). Eye movements under halothane anesthesia were rare and small ( Grubb and Thompson 2003; cf. Dräger 1975; Gordon and Stryker 1996; Wagor et al. 1980). As described previously ( Grubb and Thompson 2003), remapping of RFs 0.5–1 h after their initial localization revealed a mean shift (±SE) in position of only 3.6 ± 0.5° (n = 11). In addition, over a time scale of 10 min, response features such as phase-locked raster plots (Fig. 5), sinusoidal dependence of cell responses on stimulus phase (Fig. 6), and regular shifts in response phase with increasing temporal frequency (Fig. 5), all suggest that RF position changed very little. Moreover, in a study where properties of burst and tonic firing were always compared within individual tuning curve experiments, small eye movements were unlikely to affect the present findings.
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Visually responsive neurons were first identified using a hand-held ophthalmoscope. Monochrome visual stimuli were then presented on a cathode ray tube monitor under the control of a Visual Stimulus Generator 2/4 graphics card (Cambridge Research Systems, Cambridge, UK); this had a pseudo-15-bit analog output allowing precise control of the luminance of each pixel on the display and correction for expansive luminance nonlinearities. Stimuli could be presented with 256 linearly spaced gray levels. The display comprised 800 x 600 pixels and at viewing distances between 135 and 400 mm subtended 53–111 x 41–95°. With maximum and minimum luminances of 99 and 2cd/m2, respectively, the display was the main source of illumination in an otherwise dim room. The frame rate of the monitor was 160 Hz.
The display was initially placed with a given cell's presumptive RF at its center; mapping with reverse correlation ( Grubb and Thompson 2003) then confirmed this location, or directed movement of the monitor in order that the display covered the entire RF area. Only cells the RFs of which could be localized in this way were subjected to subsequent analysis of response characteristics. Stimuli were presented with both eyes open, but responses in all cells were most likely driven entirely by the contralateral eye: not only are all wild-type mouse dLGN cells monocularly driven ( Grubb et al. 2003), but in these investigations, the display was often placed in positions that could be viewed only by the contralateral eye. Furthermore, although it is still possible that interactions between the two eyes could modulate the responses of single mouse dLGN neurons, such interactions are unlikely to affect present analyses in which burst and tonic response properties were compared within cells.
For quantitative investigation of the visual response properties of burst and tonic firing, sinusoidal monochromatic vertical gratings were presented covering the full extent of the display. These gratings were drifting in all experiments except the null test, in which they were stationary and sinusoidally contrast-modulated. In all experiments, all grating parameters were kept constant except the one under study; gratings varying in this parameter were presented in a pseudorandom sequence. Grating parameters in spatial frequency (SF) and temporal frequency (TF) tuning assessments were varied systematically and logarithmically to produce 8 stimuli ranging between 0.01 and 1 c/° inclusive for SF assessments, and between 0.5 and 16 Hz inclusive for TF assessments. In contrast and null phase test assessments, grating parameters were varied systematically and linearly to produce 12 stimuli ranging between 1 and 100% inclusive for contrast assessments and between –180 and +150° inclusive for null phase tests. Every stimulus repeat also contained a blank stimulus at mean luminance from which spontaneous activity levels were calculated. Each presentation of a grating stimulus in a given repeat occurred for seven cycles; of these only responses from the last five cycles were used to discard any flash responses to the initial presentation of the stimulus. The exception was assessing TF tuning. In these experiments, stimuli 1 Hz were presented for seven cycles (with the 1st 2 discarded) as usual. However, to ensure capture of enough responses to high TF stimuli, stimuli >1 Hz were presented for seven seconds, with the first 2 s being discarded to allow for flash responses. At least two repeats of each 7-cycle (or 7 s) stimulus were presented to every cell, although in the majority of cases four repeats were utilized.
Histology
Each experiment was ended with an intraperitoneal injection of 0.3 ml pentobarbitone sodium (Sagatal, Rhône Mérieux, Essex, UK). The mouse was then transcardially perfused with phosphate-buffered saline (Sigma, Dorset, UK) followed by 4% paraformaldehyde (TAAB Laboratories, Berks, UK) in 0.1 M phosphate buffer. The brain was removed, sunk in 30% sucrose for cryoprotection, and sectioned coronally at 30–50 µm on a freezing microtome. Sections were then mounted from distilled water onto gelatinized slides. Subsequent staining for cell bodies with cresyl violet allowed the identification of small (6 µA for 6 s) electrolytic lesions made at precise locations on successful penetrations. Locating these lesions made it possible to reconstruct electrode tracks and localize recorded units. Only cells unequivocally located within the dLGN were included in subsequent physiological analyses.
Data analysis
IDENTIFYING BURST AND TONIC RESPONSE COMPONENTS.
Spikes forming part of bursts triggered by low-threshold calcium spikes (LTS bursts) were identified from temporal patterns in the firing of dLGN cells. From intracellular recordings in the cat dLGN, Lu et al. (1992) developed criteria that could reliably identify bursts driven by LTS activity; we applied the same criteria here. Specifically, the first action potential in a burst had a preceding silent period of 
100 ms and a following interspike interval (ISI) of 4 ms. Any subsequent spikes with preceding ISIs of 4 ms were designated as additional action potentials in a burst. All spikes that did not form part of LTS bursts were designated as part of tonic firing.
RHYTHMICITY.
Rhythmicity in spontaneous bursting was assessed after Weyand et al. (2001). For each 5-s presentation of a 51 cd/m2 blank screen during which at least four bursts were fired, we calculated the SD of interburst intervals, the latter being defined as the time between the first spikes of consecutive bursts. This SD is a direct measure of burst rhythmicity—perfect rhythmic bursting should produce a SD of 0. To assess the statistical significance of each SD measure, we employed a Monte Carlo approach ( Weyand et al. 2001). For each blank stimulus presentation, 999 simulations were run in which an identical number of bursts were randomly distributed within the 5-s interval. Standard deviations of interburst intervals were calculated for each simulation and were ranked alongside the original, measured value. The rank of the value of the observed SD then gave a statistical measure of burst rhythmicity: significant rhythmicity (P < 0.05) was detected if the observed value fell within the 50 lowest simulated values.
FOURIER ANALYSIS.
Fourier analysis was applied separately to a given cell's tonic and burst spikes. Burst or tonic responses to a given grating stimulus were first binned according to their poststimulus time. One hundred twenty eight bins were used per stimulus cycle, producing a poststimulus time histogram (PSTH) for each stimulus. A fast Fourier transform of these data then converted these PSTH data into amplitudes (in spikes/s) and phases (in degrees or radians) of harmonics of the stimulus' modulation frequency ( Hochstein and Shapley 1976).
RESPONSE LINEARITY.
Linearity of spatial summation was assessed using a linearity measure first applied by Hochstein and Shapley (1976). Over the many stimulus phases presented in a single modified null test, the mean second harmonic (F2) response amplitude was divided by the maximum fundamental harmonic (F1) response amplitude. Linearity values <1 are indicative of linear spatial summation, while linearity values greater than one are indicative of nonlinear spatial summation.
Another measure of response linearity, "rectification," was calculated over a given tuning curve as follows
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; Lu et al. 1992).
CURVE FITTING.
All curve fits were carried out using a least-squares minimalization algorithm ( Grubb and Thompson 2003).
Plots of stimulus SF versus cell F1 response amplitude were fitted with a difference of Gaussians curve ( Grubb and Thompson 2003; Rodieck 1965; So and Shapley 1981)
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is SF, b is baseline response, kc is the area under the RF center's Gaussian function, ks is the relative area under the RF surround's Gaussian function, and rc and rs are the radii of the center and surround Gaussian functions, respectively, at the point each mechanism has reached 1/e of its peak.
Plots of stimulus TF versus cell F1 response amplitude were fitted with an atheoretical function comprising two half-Gaussians ( Grubb and Thompson 2003)
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is TF, p is peak TF, a is the response amplitude at optimum TF, s is the Gaussian spread, b1 is the baseline on the low-frequency side of the curve, and b2 is the baseline on the high-frequency side of the curve. Plots of stimulus TF versus cell F1 response phase were simply fitted with a straight line.
Plots of stimulus Michelson contrast versus cell F1 response amplitude were fitted using a hyperbolic function ( Albrecht and Hamilton 1982; Grubb and Thompson 2003) of the form
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SIGNAL DETECTION ANALYSIS.
Receiver operating characteristic (ROC) curves were derived from probability distributions of spike counts obtained during a sampling episode 
of visually driven (s) and spontaneous (n) activity ( Guido et al. 1995). s comprised half the temporal period of the stimulus and was centered on the phase of the F1 response component. n comprised an equal period during the presentation of a blank screen (51 cd/m2). For all spikes fired to a given stimulus, a criterion domain was set corresponding to the full range of spike counts (including 0) occurring for s and n. ROC curves were then obtained by plotting, for each criterion level, the probability P(false alarm) of obtaining a criterion response during n versus the probability P(hit) of obtaining a response during s. To produce a nonparametric measure of discrimination performance ( Green and Swets 1966; Macmillan and Creelman 1991), the area underneath each ROC curve was then calculated using simple geometry.
MEASURES OF SPIKE TIMING RELIABILITY.
Spike timing reliability was assessed using two measures. The first, "reliability," was applied to all burst or tonic spikes fired to a given stimulus, and represented the mean deviation of spike times from their F1 phase, normalized by the stimulus period
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First-spike reliability was applied only to the first burst or tonic spikes fired during a particular stimulus cycle and was calculated as the SD of first-spike latencies. Latencies were taken from the start of a half-cycle period centered on the F1 component of either burst or tonic firing, as appropriate
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10. Statistical analysis
Sample sizes in this study were large enough (n > 30) to reliably assess their Normality using a Kolmogorov-Smirnov test. Those deemed Normal were described using the means ± SE and were compared using parametric tests. Those deemed non-normal were described using the median and were compared using nonparametric tests. Paired tests were used to compare burst and tonic spikes fired by the same cell to the same stimuli. All comparison tests were two-tailed, with the level of significance set at 0.05.
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RESULTS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONACKNOWLEDGMENTSREFERENCES |
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Visual response properties of burst and tonic firing were assessed in 102 mouse dLGN cells, all with classic geniculate ON- or OFF-center receptive fields (see Grubb and Thompson 2003). Responses were recorded from cells with RFs at visual eccentricities ranging from 0 to 92° (means ± SE: 44 ± 3°). Because the response properties of mouse dLGN cells change very little with increasing visual eccentricity ( Grubb and Thompson 2003), we did not limit our analysis to cells with RFs in particular regions of visual space.
Burst firing in the responses of cells to sinusoidal grating stimuli was identified on the basis of temporal patterns of spikes. The first spikes in bursts were preceded by 100 ms of silence and were followed by another spike within 4 ms. Subsequent spikes in bursts followed at ISIs of 4 ms ( Lu et al. 1992) (see METHODS). Figure 1 illustrates the way in which the responses of mouse dLGN cells to drifting sinusoidal grating stimuli were analyzed using a double interspike interval (ISI) plot, revealing distinct temporal patterns of firing associated with burst responses. For each spike, subsequent ISI is plotted against preceding ISI. The distribution of points depends on the temporal properties of the stimulus and, more importantly, the balance between burst and tonic firing. The significance of the latter can be seen by comparing Fig. 1, A and C, in which example cells fire almost all of their spikes as part of burst or tonic firing, respectively. Figure 1B illustrates the more common occurrence in which both burst and tonic spikes contribute to a cell's responses.
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100 ms of silence and are followed by another spike within 4 ms. Spikes within bursts, with pre- and post-ISIs of 4 ms, form the majority of spikes in zone 2, while the x-axis position of the cluster in zone 3 is due to these spikes being mostly the last spikes in bursts—they follow within 4 ms of a previous spike but are themselves followed by >4 ms of nonspiking "dead" time. In the example cell in Fig. 1A, dominated by burst responses, these zones contain almost all of the spikes fired. However, the temporal structure of the stimulus also affects the positioning of clusters in double ISI plots: assuming a neuron fires spikes to every stimulus cycle, maximum pre- and post-ISIs are constrained by the grating's temporal frequency (TF). In the example cell in Fig. 1C, dominated by tonic responses, the majority of spikes fall outside zones 1–3 but are constrained by the stimulus' TF. The example cell in Fig. 1B, in contrast, displays a complex ISI pattern indicative of important contributions from both burst and tonic firing. This produces a broad distribution of post-ISIs in zone 3: while many of these last spikes in bursts are the last fired in a cycle and are thus constrained largely by stimulus TF (zone 4), many others are followed by tonic firing and thus possess much shorter post-ISIs (zone 5). Similarly, within spikes fired as part of tonic firing, spikes clustered in zone 6 are the last spikes fired in a cycle, and those clustered in zone 7 are the first fired in a cycle.
Firing in mouse dLGN cells thus conforms to particular temporal patterns, some of which correspond well to those predicted by the properties of intracellularly recorded LTS bursts in the cat dLGN ( Lu et al. 1992). Separating mouse dLGN cell responses into burst and tonic components on the basis of the preceding criteria is therefore nonarbitrary: it is effective in classifying preexisting temporal patterns of neuronal firing. However, this classification is not quite perfect. Figure 1 shows that "tonic" spikes can sometimes occupy ISI clusters (zones 2 and 3) dominated by burst firing. Indeed, Lu et al. (1992) concede that their criteria are conservative, allocating to tonic firing 5–10% of spikes that actually form part of bursts.
Characteristics of burst firing in the mouse dLGN
Bursting appears to be a well conserved thalamic phenomenon. As assessed in vitro, the properties of the IT current and its accompanying burst activity are similar across thalamic nuclei ( Jahnsen and Llinas 1984; Ramcharan et al. 2000a) and across different mammalian species (guinea pig: Jahnsen and Llinas 1984; McCormick and Feeser 1990; cat: Zhan et al. 1999; monkey: Ramcharan et al. 2000a; mouse: Kim et al. 2001). Furthermore, it was shown in the previous section that distinct patterns of firing in the mouse dLGN can be identified by criteria used to identify burst spikes in other species (cat: Lu et al. 1992; monkey: Ramcharan et al. 2000b). However, we wanted to be more confident that the bursting criteria used here were identifying the same LTS-based phenomenon studied in other species. We therefore investigated whether certain features of burst firing reported to occur in those species also existed in the mouse dLGN.
Bursts are longer if preceded by greater periods of silence
It is known from in vitro recordings in the cat ( Zhan et al. 1999) and monkey ( Ramcharan et al. 2000a) that the number of spikes in a burst is not related to the strength of the depolarizing input stimulus but rather to the hyperpolarization of the membrane potential when that depolarizing stimulus arrives. In vivo, membrane hyperpolarization should depend on the degree of inhibition on a cell. Such inhibition will vary over time, but might have most chance to hyperpolarize a cell when that cell has been silent for an extended period. One might therefore predict that, although nonspiking "dead time" and membrane hyperpolarization need not go hand in hand, the number of spikes in bursts in vivo might be greater when bursts are preceded by longer periods of silence. In fact, across all bursts recorded in all experiments in all cells here, a small but significant positive relationship was noted between burst length and the duration of the preburst silent period (Spearman r = 0.16, P < 0.0001, n = 42,400). These data were summarized by taking the median preburst silent period for different burst lengths (Fig. 2A). For bursts consisting of 5 spikes, which represented >99% of all bursts recorded, there is a clear increase in the median period of preburst silence with increasing burst length. For larger bursts, this relationship breaks down. This could be due to the relatively small sample of long bursts. Alternatively, Rowe and Fischer (2001) used paired retinal and geniculate recordings to show that the first spikes in thalamic bursts are usually stereotypical events triggered by a single retinal spike but that later spikes in long bursts can be triggered directly by subsequent retinal action potentials. In other words, long bursts in vivo may not be long because of a large initial membrane hyperpolarization ( Zhan et al. 1999), but because they are triggered by two or more retinal action potentials closely spaced in time. If the latter is true, the relationship between preburst dead time and burst length might be expected to break down for the very longest bursts recorded.
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It has been previously reported that burst length in the cat ( McCarley et al. 1983) and human ( Radhakrishnan et al. 1999) thalamus is correlated with the duration of the first within-burst ISI. This was confirmed for the mouse dLGN here. Across all bursts there was a strong negative relationship between the first ISI within a burst and burst length (Spearman r = –0.41, P < 0.0001, n = 42,400). Taking median initial intraburst ISI values for each burst length also reveals that bursts are longer when the first intraburst ISI is shorter (Fig. 2B).
ISI duration increases within bursts
Another widely reported feature of thalamic bursting is that ISIs become progressively longer within bursts (e.g., McCarley et al. 1983; Radhakrishnan et al. 1999; Reinagel et al. 1999). This feature was also observed here. Grouping data across bursts in the mouse dLGN, there was a significant positive correlation between within-burst ISI number and ISI duration (Spearman r = 0.22, P < 0.0001, n = 77,384). Taking median ISI duration values for each ISI position also shows a gradual increase in duration from the average first through to the average fourth ISI in a burst (Fig. 2C1). Later within-burst ISIs do not continue the trend but represent <1% of the ISIs analyzed and, as noted in the preceding text, might be expected to be less stereotyped than earlier within-burst ISIs ( Rowe and Fischer 2001). A more powerful demonstration of increasing ISI duration within bursts comes, however, from analyzing differences in within-burst ISIs within individual bursts. Figure 2C2 shows that the median difference in duration between a within-burst ISI and its preceding within-burst ISI is always positive. All median difference values here are significantly greater than zero (Wilcoxon rank sum test, P < 0.05). In other words, any given intraburst ISI tends to be shorter than the intraburst ISI that follows it.
Bursts are stereotyped events
Following Reinagel et al. (1999), the coefficient of variation (CV) of burst length was calculated for each experiment. Defined as the SD divided by the mean of any given sample, the CV should be 1 if sample values vary by chance. Here, most bursts (47%) consist of only two spikes. This stereotyped length is reflected in low CV values in every tuning curve experiment (Fig. 2D). The median CV value across all tuning curve experiments was 0.25, strikingly similar to mean values reported for cat X (0.25) or Y (0.23) cells ( Reinagel et al. 1999).
Rhythmicity of spontaneous burst firing
Spontaneous bursting in the dLGN neurons of naturally sleeping animals often occurs rhythmically ( McCarley et al. 1983; Weyand et al. 2001), with rhythmicity established by cyclic interactions between two depolarizing geniculate currents IT and Ih ( McCormick and Bal 1997). We assessed whether spontaneous bursting is also rhythmic in the mouse dLGN under conditions of halothane anesthesia. Our analysis was based on 5-s periods of spontaneous activity in which the stimulus display consisted of a blank screen at mean luminance (51 cd/m2); such blank presentations occurred once for every repeat of sinusoidal grating stimuli in a given tuning experiment (see METHODS). Rhythmicity was assessed by analyzing interburst intervals during such 5-s spontaneous epochs. For each epoch that contained at least four bursts, we calculated the SD of interburst intervals, with this SD measure being directly related to bursting rhythmicity: perfect rhythmic bursting would produce a SD of zero. We then assessed the statistical significance of each SD rhythmicity measure using Monte Carlo simulations that ranked the observed rhythmicity value among 999 simulated values (see METHODS).
Examples of burst timing in the responses of a single mouse dLGN cell during several 5-s spontaneous epochs are presented in Fig. 3A. These plots show that evenly-spaced bursts produce significant rhythmicity values and also demonstrate that spontaneous bursting in the mouse dLGN could alternate between rhythmic and nonrhythmic behavior over time in the same neuron. The histogram in Fig. 3B presents P values for all 234 spontaneous epochs (involving 45 cells) that contained at least four bursts. Forty four of these epochs (19%, involving 22 cells) were accompanied by significantly rhythmic bursting (P < 0.05). Of the 22 cells showing rhythmic spontaneous bursting, most (n = 12) did so in only a single epoch, although 3 cells had five rhythmic bursting epochs each. Rhythmicity in spontaneous bursting therefore occurs 20% of the time in the dLGN cells of a halothane-anesthetized mouse and is restricted to around half of geniculate neurons under such conditions, although a minority of neurons display rhythmic bursting rather often. This level of rhythmicity is considerably lower than the 47% of rhythmic episodes observed in the naturally sleeping cat ( Weyand et al. 2001). The discrepancy could arise because our spontaneous epochs were 5 s long, whereas Weyand et al. based their analysis on "burst bouts" of 2 s. However, when we also analyzed the rhythmicity of such 2-s burst bouts in our mouse preparation, we observed a reduction in rhythmic episodes: 48 of 438 bouts (11%) displayed significantly rhythmic firing. dLGN cells in anesthetized mice therefore do burst rhythmically but compared with geniculate neurons in naturally sleeping animals do so only rather rarely.
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Neurons in the mouse dLGN fire spikes that can be identified as part of bursts (Fig. 1) and that can be shown to possess features characteristic of burst firing in other species and preparations (Fig. 2). But do these burst spikes encode visual information? A first, crude step toward answering this question comes from a comparison of bursting prevalence during periods of spontaneous and visually driven activity. Every investigation of a dLGN cell's responses to a certain stimulus parameter (known as a "tuning experiment") involved presenting a pseudorandom series of sinusoidal grating stimuli which varied in the parameter under study (spatial frequency, contrast, etc.; see METHODS) and also included the presentation of a blank stimulus at mean luminance. For each such tuning experiment, we calculated the percentage of spikes forming part of bursts (burst %) over all presentations of all grating stimuli (visually driven activity), and over all periods of blank screen presentation (spontaneous activity). We recognize that this approach does not compare matched periods of visually driven and spontaneous spiking, but our use of a percentage measure of bursting prevalence should compensate for this.
Although some cells' visual firing was dominated by burst firing (maximum burst % = 78%; see Fig. 1A), in most experiments bursts occupied a small fraction of all spikes fired (Fig. 4A1). However, burst percentage values for periods of spontaneous activity were usually even lower with many cells not firing any burst spikes in the absence of visual stimulation (Fig. 4A2). Indeed, across all tuning experiments (n = 323 in 102 cells), burst percentage values were significantly higher for visually driven versus spontaneous activity (visual median: 19.8%; spontaneous median: 11.1%; Wilcoxon matched pairs test, P = 0.002; Fig. 4B). Because this analysis was applied across all visual stimuli in a given experiment, not just those producing the highest firing rates, this is a rather clear demonstration that bursting is more prevalent when cells are responding to visual stimuli. This begs the question: exactly what visual information does burst firing carry?
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Investigations into the relative timing of burst and tonic spikes during presentation of sinusoidal gratings suggest that burst spikes might carry important information about stimulus onset. Figure 5A shows the burst and tonic responses of a mouse dLGN cell to a sinusoidal grating of optimum spatial and temporal frequencies. As often reported by others (e.g., Guido et al. 1992, 1995; Lu et al. 1992) burst spikes, when they occur, tend to occur at the start of a response to a given stimulus cycle. Tonic spikes tend to occur a little later. This precedence of burst firing can have two possible mechanisms: burst spikes can have shorter latencies than tonic spikes or they can be triggered by earlier phases of the stimulus. The relative contribution of each of these candidate mechanisms can be assessed using plots of stimulus temporal frequency (TF) versus cell F1 response phase. The slope of a line relating these two variables can be used as a measure of response latency ( Grubb and Thompson 2003; Hawken et al. 1996). In addition, the y intercept of the line can give an indication of relative response phase (e.g., Guido et al. 1992; Mukherjee and Kaplan 1995). In mouse dLGN cells, burst responses to low TFs are usually phase advanced relative to tonic responses to the same stimuli. At high TFs, though, response phases for the two firing modes tend to be similar (Fig. 5B). This makes the TF-F1 phase line steeper for burst firing, meaning that burst spikes possess longer latencies than tonic spikes. Across all TF experiments, burst latencies were significantly longer than the latencies of tonic spikes fired to the same stimuli (burst median: 106 ms; tonic: 90 ms; Wilcoxon matched pairs test, P = 0.001; Fig. 5C). However, burst spikes still occur earlier than tonic spikes because they are triggered by an earlier phase of the stimulus. Phase advances for burst spikes, taken by subtracting tonic from burst y-intercept values in each experiment, were almost all positive, with the sample median being significantly different from zero (more positive values reflect earlier phases; 1-sample t-test, P < 0.0001; Fig. 5D). Despite their longer latency, burst spikes usually carry the first stimulus information relayed to cortex.
Response linearity
When all spikes are taken into consideration, almost all mouse dLGN cells sum inputs in a linear manner across their RFs ( Grubb and Thompson 2003). It is unlikely that such linear behavior could have a substantially nonlinear component, so it was reassuring to note that in almost all mouse dLGN cells both burst and tonic spikes show linear spatial summation. The plots in Fig. 6A display typical results of modified null tests ( Hochstein and Shapley 1976) applied separately to the burst and tonic components of cells' responses. These tests involve displaying stationary sinusoidal gratings the contrast of which is sinusoidally modulated in time, at various spatial phases across a cell's RF. Null tests were always initially applied with stimuli at a cell's optimal spatial frequency (SF) but were often repeated at higher SFs because nonlinearities of spatial summation can become more prevalent as SF increases (e.g., Hochstein and Shapley 1976). Twenty-eight cells were tested at optimal SF, 12 at twice optimal SF, and 3 at three times optimal SF. Temporal frequency of these stimuli was 1 Hz (11 cells) or optimal (17 cells), maximum stimulus contrast was 70%. By convention, fundamental (F1) response amplitudes are represented as negative in null test plots when their phase differs by 90–270° from that of the maximum response; this explains why burst and tonic plots from the same cell sometimes look like mirror images of each other.
In cells that sum inputs linearly across their RFs, responses to counterphased stimuli should be largest at the fundamental (F1) stimulus harmonic and the amplitude of this harmonic should vary sinusoidally with stimulus phase ( Hochstein and Shapley 1976). In addition, a linearly summating cell should possess two null phases where excitatory and inhibitory inputs are perfectly balanced across the RF ( Enroth-Cugell and Robson 1966). These null phases are represented by zero crossings in a null test plot ( Hochstein and Shapley 1976). In almost all cases in our mouse dLGN sample (27/28 cells, 42/43 null tests, Fig. 6A, 1 and 2), the F1 components of both burst and tonic firing were largest, varied sinusoidally in amplitude with stimulus phase, and crossed the zero line at 2 null locations. Such behavior was accompanied by linear "linearity" values (see METHODS) ( Hochstein and Shapley 1976) of <1.
One cell showed evidence of SF-dependent nonlinear spatial summation but did so in both response modes (Fig. 6A3). In both burst and tonic firing, although appearing to possess two null phases, the F2 response component of this neuron to stimuli at twice optimal SF was generally larger than the F1 component and varied sinusoidally with stimulus phase. Such a response pattern is characteristic of a RF with overlapping, nonantagonistic On and Off subregions ( Lennie and Perry 1981). While this cell displayed evidence of such ON-OFF nonlinearities in both its burst and tonic firing (Fig. 6A3), a high maximum F1 response in its burst firing meant that only its tonic component possessed a linearity value greater than unity, at 1.64.
Across all null test experiments, linearity values of burst spikes were not significantly different from those of tonic spikes fired to the same stimuli (burst means ± SE: 0.42 ± 0.02; tonic: 0.37 ± 0.04; paired t-test, P = 0.17; Fig. 6B). Both burst firing and tonic firing in the responses of mouse dLGN cells therefore sum spatial inputs in an equally linear manner.
Although burst and tonic firing do not differ in terms of their linearity of spatial summation, they do differ on a very different measure of response linearity, rectification. The response of a linear neuron to a sinusoidal grating stimulus should be sinusoidal, with a dominant F1 component (e.g., Shapley and Lennie 1985). However, even when a neuron displays wholly linear spatial summation its response to a sinusoid may not be sinusoidal because of response rectification: when spontaneous activity is low and visual responses strong and transient, cells cannot decrease their responses to the nonpreferred half cycle of a given grating, and may not precisely follow the temporal form of the grating's preferred half cycle. Such response rectification acts to increase all nonfundamental harmonics in a cell's responses. Because burst spikes tend to be high-frequency and transient, one might expect their rectification to be greater. Indeed, over all tuning experiments (n = 332 in 102 cells), the "rectification" of burst spikes (Eq. 1) was significantly greater than that of tonic spikes fired to the same stimuli (burst median: 0.46; tonic: 0.36; Wilcoxon matched pairs test, P < 0.0001; Fig. 6C). Thus although both burst and tonic spikes display linear spatial summation, burst spikes are significantly more nonlinear in terms of response rectification.
Spatial frequency tuning
As well as being identical with regard to spatial summation, burst and tonic spikes in the mouse dLGN are no different with regard to their SF tuning properties. SF tuning was assessed by fitting separate Difference of Gaussians (DoG) curves (Eq. 2) to the F1 amplitudes of burst and tonic responses to sinusoidal gratings of various SFs. Temporal frequency of these stimuli was 1 Hz, contrast was 70%. The DoG equation ( Rodieck 1965, So and Shapley 1981) models the center and surround mechanisms of a cell as symmetrical, antagonistic Gaussian functions, and can provide measures of the strengths and sizes of a given cell's center and surround RF regions. Because a DoG curve also provides a smooth fit to raw data, it can also be used to precisely calculate a cell's SF optimum (or peak) and cutoff (taken as the high SF at which response amplitude decayed to 1% of its maximum). However, due to the large size of RFs in the mouse dLGN, coupled with the limited size of our stimulus display at appropriate viewing distances, we were unable to accurately describe a cell's full low-SF roll-off (see Grubb and Thompson 2003).
As shown in Fig. 7A, DoG curves fitted to burst and tonic responses sometimes differed in their relative amplitudes (though not consistently in favor of either burst or tonic firing), but were usually similar in shape. We included in our analysis all cells (n = 61) in which the DoG function accounted for 80% of the variance of both burst and tonic raw data. In this sample, there were no significant differences in spatial tuning between the two response modes. Burst and tonic spikes fired to the same stimuli did not differ in terms of kc, the strength of the RF center (burst median: 7.59; tonic: 13.42; Wilcoxon matched pairs test, P = 0.43), ks, the relative strength of the RF surround (burst median: 0.98; tonic: 0.981; Wilcoxon matched pairs test, P = 0.06), rc, the radius of the RF center (burst median: 5.0°; tonic: 4.5°, Wilcoxon matched pairs test, P = 0.38), rs, the radius of the RF surround (burst median: 13.6°; tonic: 17.1°, Wilcoxon matched pairs test, P = 0.96), peak SF (burst means ± SE: 0.033 ± 0.003c/°; tonic: 0.032 ± 0.002c/°; paired t-test, P = 0.64; Fig. 7B), or SF cutoff (burst means ± SE: 0.17 ± 0.01; tonic: 0.16 ± 0.01; paired t-test, P = 0.56; Fig. 7C).
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Burst and tonic spikes differ with respect to their temporal tuning. Temporal frequency (TF) tuning was assessed by presenting drifting sinusoidal gratings of various TFs at optimal SF and 70% contrast. F1 response amplitudes of burst and tonic firing to these stimuli were then separately fitted with two-half-Gaussian equations (Eq. 3). These function
, 1 spike. In poststimulus time histograms (PSTHs; right) spikes are binned across all stimulus cycles according to their poststimulus time (pst). The "all spikes" PSTH possesses 2 peaks. Separating responses into burst and tonic components shows that these 2 peaks correspond to these 2 different firing modes. Burst spikes (middle) are concentrated at the start of the cell's response to the stimulus, whereas tonic spikes (bottom) tend to occur later in the stimulus cycle. B: dissecting possible factors underlying the precedence of burst spikes. In these plots of temporal frequency (TF) vs. F1 response phase, calculated separately for a given cell's burst and tonic spikes, the slope of the line relating the 2 variables can be taken as a measure of response latency and the y intercept of this line can give an indication of relative response phase (e.g., Guido et al. 1992
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, show F1 response amplitudes to drifting sinusoidal gratings of various SFs; —, the best fit to these data of a Difference of Gaussians model (see METHODS). Note the different y-axis scales for the 3 examples, and the logarithmic x axis. Across these examples, and the mouse dLGN sample as a whole, the shape of SF tuning curves tended to be very similar for burst and tonic components of a given cell's responses. In some cells, burst responses were stronger than tonic responses (A1). More commonly, tonic firing was stronger than burst firing (A2). In other cells, burst and tonic tuning curves were similar in amplitude as well as shape (A3). Spatial tuning parameters for all 3 cells: cell 1: burst firing: kc 18.4, ks 0.67, rc 2.8°, rs 4.0°, peak 0.063 c/°, cutoff 0.27c/°; tonic firing: kc 6.7, ks 0.70, rc 2.9°, rs 6.0°, peak 0.064 c/°, cutoff 0.25 c/°; cell 2: burst firing: kc 7.4, ks 0.69, rc 3.8°, rs 17.0°, peak 0.031 c/°, cutoff 0.18 c/°; tonic firing: kc 12.4, ks 1.0, rc 2.6°, rs 22.9°, peak 0.029 c/°, cutoff 0.26 c/°; cell 3: burst firing: kc 5.2, ks 0.96, rc 3.8°, rs 12.4°, peak 0.041 c/°, cutoff 0.19 c/°; tonic firing: kc 4.3, ks 1.20, rc 3.5°, rs 14.0°, peak 0.04 c/°, cutoff 0.20 c/°. B: burst and tonic firing modes do not have different peak SFs.