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Previous Article | Next Article
The Journal of Neuroscience, May 1, 1999, 19(9):3567-3579
Patterns of Synchronization in the Superior Colliculus of Anesthetized Cats
Michael Brecht, Wolf Singer, and Andreas K. Engel Max-Planck-Institut für Hirnforschung, 60528 Frankfurt, Germany
ABSTRACT
TOP
ABSTRACT
INTRODUCTION
Sensorimotor transformations in the mammalian superior colliculus (SC) are mediated by large sets of distributed neurons. For such distributed coding systems, stimulus superposition poses problems attributable to the merging of neural populations coding for different stimuli. Such superposition problems could be overcome by synchronization of neuronal discharges, because it allows the selection of a subset of distributed responses for further joint processing. To assess the putative role of such a temporal binding mechanism in the SC, we have applied correlation analysis to visually evoked collicular activity. We performed recordings of single-unit and multiunit activity in the SC of anesthetized and paralyzed cats with multiple electrodes. Autocorrelation analysis revealed that collicular neurons often discharged in broad (20-100 msec) bursts or with an oscillatory patterning in the
- and
-frequency range. Significantly modulated cross-correlograms were observed in 50% (128 of 258) of the collicular multiunit recording pairs, and for these pairs significant correlations occurred in 44% of the stimulation epochs. For the single-unit pairs, significant interactions were observed in 14 of 48 cases studied (29%). Collicular cross-correlograms were often oscillatory, and these oscillations covered a broad frequency range of up to 100 Hz, with a predominance of oscillation frequencies in the
Key words: synchronization; cell assembly; oscillation; cat; superior colliculus; correlation analysis
INTRODUCTION
A wide variety of experimental findings support the idea that sensorimotor transformations in the mammalian superior colliculus (SC) are mediated by large populations of distributed neurons (McIlwain, 1991
). The large size of the receptive and movement fields of collicular cells implies that individual sensory stimuli and orienting movements are associated with the activation of a large set of widely distributed neurons. Experiments based on focal pharmacological inactivation of parts of the SC demonstrate directly the distributed nature of collicular motor commands (Lee et al., 1988
However, in distributed coding systems ambiguities arise if multiple nearby stimuli are to be encoded because of the possibility that the respective populations of activated neurons overlap. If neural populations coding for different stimuli merge, this results in a loss of stimulus-specific information (von der Malsburg, 1986
The goal of the present study was to investigate whether response synchronization occurs also in the SC and, if so, whether it exhibits a stimulus dependence compatible with a putative function in the disambiguation of population responses. To this end, we investigated with multielectrode recordings the temporal relations among collicular responses by applying cross-correlation techniques to visually evoked activity in the SC of anesthetized cats. To test the hypothesis that synchronization serves the disambiguation of overlapping populations, we analyzed whether collicular synchronization reflects the gestalt laws of perceptual grouping (Gray et al., 1989
MATERIALS AND METHODS
Preparation. Data were recorded from 15 adult anesthetized cats. Anesthesia was induced with ketamine and xylazine (10 and 2.5 mg/kg, respectively) and was maintained with a mixture of 70% N2O and 30% O2 supplemented by halothane (0.6-1%). After tracheotomy, the animal was placed in a stereotactic head holder. A craniotomy was performed, and the skull was cemented to a metal rod. After completion of all surgical procedures the ear and eye bars were removed, and the halothane was reduced to a level of 0.4%-0.6%. After we had assured that the level of anesthesia was stable and sufficiently deep to prevent any vegetative reactions to somatic stimulation, the animals were paralyzed with pancuronium bromide (0.2 mg · kg
1 · hr
Recording. Collicular activity was recorded either with a multielectrode drive, which allowed independent movement of up to six electrodes or, alternatively, with one or two fixed electrode arrays. Most of the recordings were performed with the multielectrode drive, and in these experiments a guide tube was used to target the electrodes to the SC. In these cases, all electrode tips generally had a small separation of <1 mm in the horizontal plane. In contrast, the fixed electrode array used in other measurements consisted of two to five microelectrodes whose spacing was between 0.2 and 3 mm. The recorded signals were amplified, bandpass-filtered, and fed through a Schmitt trigger to obtain transistor transistor logic pulses, which signaled spike timing.
In most of the experiments, we recorded multiunit activity. In these measurements, the Schmitt trigger threshold was adjusted to exceed the noise limit at least twofold. In a number of experiments we additionally studied single-unit activity. In these cases we separated the spikes of different cells by a real-time template-matching algorithm (MSD spike sorter, Alpha-Omega). For several reasons, the analysis of collicular correlation patterns was greatly facilitated by including multiunit activity (for a detailed discussion of the advantages and potential pitfalls of multiunit cross-correlation analysis, see Bedenbaugh and Gerstein, 1997
Receptive fields were mapped onto a tangent screen, and the ocular dominance, the orientation tuning, and the direction preferences of each multiunit or single-unit recording were assessed with hand-held stimuli. For quantitative measurements visual stimuli were projected onto the tangent screen via a computer-controlled optic bench. Typically, moving bars served as stimuli. Multiunit clusters or single units with overlapping or nearby receptive fields were activated with a single bar, whereas cells with distant receptive fields were activated with two coherently moving bars. Generally, the bar stimuli were positioned outside the receptive fields (RFs) close to the RF border and moved through the RF in a 3 sec period. In a few cases computer-generated flow fields were presented on a 21 inch monitor. Responses were recorded for at least 10 stimulus repetitions, and, if not specified otherwise, the data from such blocks of 10 trials were combined for analysis.
Data analysis. For all responses, peristimulus time histograms, auto- and cross-correlation functions, as well as their first-order shift predictors were computed. Cross-correlograms were computed with a bin width of 1 msec. The contribution of stimulus coordination to the observed correlation patterns was assessed by shift predictor correlograms. To obtain the first-order shift predictor, cross-correlograms were computed from responses to successive stimuli rather than from simultaneous responses to the same stimulus, as was the case for the cross-correlation functions (Perkel et al., 1967
250 msec), these correlograms from shifted trials were almost always (>95% of cases) flat (also see Fig. 1); i.e., the fitting procedure described below did not discover significant peaks in such correlograms. Usually, the correlograms were computed over the whole response epoch, which generally consisted of a 3 sec period during which a bar stimulus moved across the receptive field.
According to our experience it is useful to restrict the analysis to correlograms with a sufficient filling. Therefore the following criteria were applied. Correlograms for multiunit recordings were included in the analysis only if the firing rates of each cell cluster exceeded 10 Hz and if the correlogram had an average entry of at least two coincidences per bin. Because the shift predictors were flat, we did not have to subtract them to distinguish between stimulus-locked and internally generated correlations. In this respect our procedure differs from that of other authors (Ghose and Freeman, 1992
In addition to computation of averaged correlograms, a sliding window analysis was applied to a subset of the data to analyze the time course of collicular correlations during the stimulus presentation. In these cases, a short analysis window (100 or 400 msec duration) was moved over the responses in steps of 50 or 100 msec. The correlograms obtained from each of those overlapping windows were plotted as a two-dimensional array, where the x-axis denotes the time course of the responses. The amplitude of the correlograms was normalized by the geometric mean of firing rates and displayed in a color-coded manner. Finally, to assess the frequency contents of neuronal interactions, power spectra were computed. To exclude contributions to the frequency spectrum by stimulus-locked response components, the power spectra were computed from averaged cross-correlograms of which the respective shift predictor had been subtracted.
To quantify the modulation of the correlograms, generalized Gabor functions (damped cosine functions) were fitted to the correlograms; this method is described in detail by König (1994)
2 reduction). This requirement leads to a rejection of fitting functions, which would be judged to be poor according to the visual inspection of the fit (also see Young et al., 1992
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Figure 1. Analysis and classification of correlation patterns. A, Example of a successfully fitted cross-correlation function. The black line represents the generalized Gabor function that was fitted to the data. B, Parameters derived from the fitted function for characterizing the cross-correlation pattern. The amplitudes of the central peak (A) and the first side peak (S) are measured from the offset (O). The strength of the correlation and the oscillatory patterning can then be quantified by calculating the RMA of the center peak, i.e., the ratio A/O and the relative modulation of the side peak S/O, respectively. Error bars indicate the estimates of the SEs for the central peak and the first satellite peak. C, First-order shift predictor of the cross-correlogram shown in A. For computation of the shift predictor, spike trains are taken from successive stimulus presentations.
Histology. At the end of each experiment a lethal dose of sodium pentothal was given, and the animal was perfused through the heart with warm saline followed by cold (4-8°C) fixative (4% paraformaldehyde in PBS). The brain was removed, frozen, and cut in the frontal plane into 60 µm sections. These sections were alternately stained for cell bodies (Nissl) and fibers (Gallyas method). Small lesions (electrode tip-negative, 12 sec DC current, 12 µA) made after each recording penetration allowed the reconstruction of the recording track. SC laminae were classified according to the method of Kanaseki and Sprague (1974)
RESULTS
Recording sites and pairs
The majority of the results reported here are based on multiunit recordings from 315 collicular sites. Cross-correlation patterns were computed for 258 recording pairs. The vast majority of the multiunit recording sites were localized in the superficial collicular layers: 173 were located in the stratum griseum superficiale; 54 were located in the stratum opticum; and 68 recording sites could not be attributed unambiguously, but the majority of these were presumably located in the superficial SC laminae. Eighteen multiunit recording sites were located in the intermediate collicular layers, in the stratum griseum intermediale. In addition to these multiunit recordings, we analyzed 45 well isolated single units from which we collected a sufficient number of spikes to compute meaningful correlograms. In this sample cross-correlation patterns were studied for 48 pairs.
Collicular correlation patterns
The autocorrelation functions of SC multiunits displayed in Figure 2 give a survey of the temporal structure of collicular activity. As shown in the averaged autocorrelograms for multiunits in Figure 2, we often observed cell clusters that discharged in bursts, which lasted 20-100 msec (Fig. 2A,B). In Figure 2, C and D, correlograms of single-unit activity are illustrated. In general, high-frequency oscillations in the
range (as illustrated in Fig. 2D) were encountered only rarely. When oscillations occurred, their frequencies often varied from trial to trial and therefore were difficult to assess in averages. Figure 2E-H illustrates selected single-sweep autocorrelograms of a multiunit recording (compare the corresponding averaged correlogram shown in Fig. 2A), which exhibited a pronounced variability of firing patterns. From Figure 2E-H it is clear that also the firing rate of the cell group exhibited substantial variability. In this case as well as in other examples studied, we did not observe any obvious relationship between firing rates and temporal firing pattern.
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Figure 2. Autocorrelograms computed for multi- and single-unit recordings from different sites in superficial SC layers. A, B, Averaged autocorrelograms of multiunit activity. The black line indicates the shift predictor. A very broad peak is seen in A, but in the autocorrelogram shown in B only a center peak towers above the shift predictor, which represents the spurious rate-dependent and stimulus-locked coincidences. C, D, Averaged autocorrelograms of single-unit activity. The cell shown in C had a long refractory period and exhibited a tendency to fire in regular intervals. D, Single-unit autocorrelogram with a high-frequency oscillatory modulation. E-H, Autocorrelograms computed from individual stimulation epochs (3 sec duration) recorded successively from the same cell cluster as in A. Note the variability of the temporal structure in successive responses to the same stimulus. In C-F and H, the black line represents the generalized Gabor function that was fitted to the data.
Significant cross-correlations were observed in approximately half (128 of 258, 49.6%) of the collicular multiunit recording pairs. For those recording pairs in which at least one significantly modulated correlogram was detected, correlations occurred in 44% of the stimulation epochs (each epoch comprising 10 stimulus repetitions, whereby we recorded on average 9 such stimulation epochs per recording pair). Figure 3 illustrates examples of cross-correlograms obtained in multiunit and single-unit recording pairs. Figure 3 also shows that subtracting the respective shift predictors from the correlograms did not affect their modulation (except for slightly increasing the noise level). This indicates that the correlations were attributable to intrinsic neuronal interactions rather than to stimulus locking of the respective responses. Moreover, the frequency spectra of the cross-correlograms are provided. As far as can be inferred from the small number of single-unit pairs, the correlation patterns of single and multiple units appeared to be similar. In 14 of 48 (29%) single-unit pairs the correlations reached significance. The only notable difference was that anticorrelated responses (i.e., those with a central trough in the correlogram) were seen twice in the single-unit pairs but never in the multiunit recordings.
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Figure 3. Cross-correlograms computed for multiunit (A-H) and single-unit (I-L) recordings from different sites in superficial SC layers. A, Top diagram, The two recorded cell groups were located in the right SC, both in the lower stratum griseum superficiale. The distance between the two recording sites was ~1.5 mm. Bottom diagram, Schematic plot of the receptive fields of the two recording sites and the stimulus. Note that the receptive field of cell group 1 extended substantially into the ipsilateral visual field (dashed line, vertical meridian). B, The cross-correlogram for the two multiunit responses shows a broad peak. C, This broad peak remains after subtracting the shift predictor. D, Power spectrum of the cross-correlogram computed after subtracting the first-order shift predictor as shown in C. E, Top diagram, The two recorded cell groups were nearby in the right SC, both in the stratum griseum superficiale. Bottom diagram, Plot of the receptive fields of the two recording sites and the stimulus. F, The cross-correlogram shows an oscillatory modulation at a frequency of ~13 Hz, which remains after subtracting the shift predictor (G). H, Power spectrum of the cross-correlogram calculated after subtraction of the first-order shift predictor as shown in G. I, Top diagram, The two recorded single units were isolated by template matching from an electrode in the lower stratum opticum. Bottom diagram, Plot of the receptive fields of the two cells and the stimulus. J, The cross-correlogram shows an oscillatory modulation at a frequency of 33 Hz, and this modulation remains after subtracting the shift predictor (K). L, Power spectrum of this interaction computed after subtracting the first-order shift predictor as shown in K. Where present, the black line superimposed to the correlograms represents the generalized Gabor function that was fitted to the data. , Phase shift of the Gabor function; f, oscillation frequency.
Figure 4 summarizes the statistics of collicular interactions for multiunit data. As described in Materials and Methods, the strength of the interactions was quantified by the RMA (central peak amplitude divided by offset) of the correlograms. The histogram of RMA values for the data sample is displayed in Figure 4A. The mean RMA of the respective best modulated correlogram for each recording pair in which correlations occurred was 0.67; the RMA for all correlograms with significant modulation averaged 0.49. As indicated by significant satellite peaks, many of the cross-correlograms were oscillatory. Of the most strongly modulated correlograms for a given pair of recording sites, 44% showed an oscillatory modulation, and for all correlograms this fraction was 23%. The fact that in most cases the number of satellite peaks was confined to one or two indicates that the oscillatory patterning of the discharges was not strictly periodic. Oscillation frequencies covered a broad range from 5 to 100 Hz, albeit in most cases being in the range between 5 and 40 Hz (Fig. 4B). In most of the correlograms the central peak was located around zero phase lag (Fig. 4C). Of the correlograms exhibiting the strongest modulation for a given recording pair, 64% had peak shifts of <5 msec. As summarized in Figure 4D, there was a strong positive correlation between the degree of receptive field overlap of the respective units and the incidence of significant correlations: the more the fields overlapped, the higher the probability of observing a significant interaction.
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Figure 4. Statistics of multiunit cross-correlations. The data in A-C refer to the most strongly modulated correlogram for each recording pair. A, Distribution of relative modulation amplitudes (as defined in Fig. 1) for cross-correlograms with a significant center peak. B, Distribution of oscillation frequencies. C, Cumulative distribution of the absolute values of phase shifts (AbsPhase) for cross-correlograms with significant center peaks. Note that in 64% of the correlograms the phase shift is smaller <5 msec. D, Probability of synchronization as a function of RF overlap. These data were calculated only for a subset of our data (178 recording pairs). The incidence is higher for cells with overlapping receptive fields (p < 0.01).
Dynamic aspects of collicular correlations
To assess the development of collicular correlation patterns over time, we used a sliding window analysis in which a short (100-400 msec) analysis window was moved in small time steps along the responses. An example for such a time-resolved analysis of correlated oscillatory responses is shown in Figure 5. In this case, synchronization started rapidly and was sustained throughout the response. In many cases studied in this way, synchronization could be observed within the first 100 msec of the responses. Another characteristic aspect of collicular cross-correlations was their variability across trials. As exemplified in Figure 6, both the strength of the interaction and the dominant oscillation frequency could vary, despite the fact that stimulus conditions were unchanged. The firing rates of the cell groups also exhibited a notable variability. Again, as for the autocorrelations (Fig. 2E-H), we did not observe any obvious relationship between firing rate and firing pattern.
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Figure 5. Sliding window analysis of a collicular cross-correlation pattern. A, The schematic plot (top) illustrates the receptive fields of the two recording sites (A. C., area centralis) and the stimulus. B, A 400-msec-long cross-correlation window was moved in steps of 100 msec over the stimulation period. The x-axis denotes the time course of the responses, whereby each time displays the results of a 400-msec-long cross-correlation window centered on the respective time bin. The y-axis corresponds to the time shift of the stacked correlograms. The amplitude of the correlograms was normalized by the geometric mean of firing rates and displayed with a color code. Dashed gray lines indicate onset and end of the movement of the bar stimulus. The peristimulus time histograms of the two responses are shown above and below the sliding window plot. Data were recorded from two cell clusters in the stratum griseum superficiale. Note the rapid onset of the synchronous oscillation. C, Power spectrum of this interaction computed for the cross-correlogram (averaged over the whole stimulation period) after subtracting the first-order shift predictor.
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Figure 6. Intertrial variability of collicular cross-correlation patterns. The two recorded cell groups were located in the right SC, both in the stratum griseum superficiale. Ten successive trials were recorded with the same light bar stimulus. A, Peristimulus time histograms of the two recorded responses. Both cell groups responded vigorously to the forward (Window 1) and backward (Window 2) stimulus movement. B, C, Averaged cross-correlograms for the forward and backward responses, respectively. Both correlograms show a prominent center peak and significant oscillatory components with a frequency of ~10 Hz. D, E, Variability of the temporal structure across the individual trials. Because of noise, only a few responses showed a significant modulation. In these cases, the continuous lines indicate the generalized Gabor function fitted to the data. f, Dominant frequency of oscillatory modulation.
Spatial organization of collicular synchronization
In all experiments synchronization probability depended critically on the horizontal separation of the recordings sites. For recording pairs with horizontal electrode separations of
3 mm, we never observed correlations in the superficial layers beyond electrode separations of 2.5 mm. In the cat SC, receptive field size increases massively with depth (Meredith and Stein, 1990
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Figure 7. Incidence of synchronization as a function of recording depth in the SC. A, Histological reconstruction (in a parasagittal plane) of the penetrations of three microelectrodes. The electrodes were advanced simultaneously into the SC, whereby penetrations A and B reached the deeper collicular layers. Circles indicate recording sites, and lines are drawn between recording sites for which synchronization was observed, whereas cells at points not linked with lines were not synchronized. The thickness of the lines indicates the correlation probability, i.e., the percentage of stimulation epochs in which synchronization occurred. The incidence of synchronization increased with depth and was higher between the two less distant electrodes, B and C, than between electrodes A and B. No significant synchronization was seen between electrodes A and C. B, Receptive fields of selected recording sites. The dashed receptive field borders indicate cells without clearly demarcated response zones. Note the increase of receptive field size with depth. AC, Area centralis.
To assess the contribution of direct retinal drive on intracollicular correlation patterns, we compared the distribution of synchronization patterns in the ipsilateral and the contralateral visual field representation. In the cat SC there is a substantial representation of the ipsilateral visual field. In this portion of the SC, visual responses are mediated mainly by corticotectal projections rather than by direct retinal input (Antonini et al., 1978
In addition to investigating tangential interactions, we wondered whether cells in the superficial and deep collicular layers would interact and show temporal correlations of their responses. Unfortunately, the visual responses of deep-layer cells were very poor in three of five experiments. In three experiments receptive fields could barely be mapped, and responses were sluggish. This made it difficult to collect a sufficient number of visually driven spikes for correlation analysis, and in the few instances in which we succeeded the correlograms were not modulated. However, in two experiments visual responses in the deep layers were vigorous and synchronized with responses in superficial layers. As documented in Figure 7, strong correlations were readily obtained between cells in the stratum griseum superficiale and stratum opticum on the one hand and stratum griseum intermediale on the other. The example shown in Figure 8A-C illustrates that these correlations were robust. A statistical comparison of the 230 multiunit pairs recorded within the superficial layers and the 18 recording pairs recorded between superficial and deep collicular layers revealed that they occurred with a similar incidence (Fig. 8D). We did not observe any systematic difference between intrasuperficial and superficial-deep correlation patterns. Specifically, there was no tendency for a phase lead of one of the layers. However, it must be emphasized that our data set for these comparisons is very small because of the technical difficulties described above.
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Figure 8. Interlaminar interactions between superficial and deep SC. A-C, Example of an interlaminar correlation. A, The two cell groups were recorded in the right SC at a horizontal separation of ~1 mm and located in the stratum opticum and stratum griseum intermediale, respectively. B, Schematic plot of the receptive fields of the two recording sites and the stimulus. The dashed RF borders of the deep-layer cell cluster indicate that the borders of the response zone of this cell group could not be clearly mapped. C, Correlogram of the interlaminar interaction. The shift of the center peak indicates that the deep-layer cell cluster fires on average shortly before the superficial layer cells. The black continuous line superimposed to the correlograms represents the generalized Gabor function that was fitted to the data.
Stimulus specificity of collicular correlations
A central question of our study was whether collicular synchronization patterns demarcate assemblies of functionally related cells. To address this issue we activated spatially separate SC cells with either one coherent stimulus or two independent stimuli. Figures 9 and 10 show examples of such experiments. SC multiunits tended to synchronize their responses more strongly if activated with a single coherent stimulus compared with two independent stimuli. These effects occurred without changes of firing rate (compare Figs. 9D,F, 10D,E). In the example displayed in Figure 10, the cell groups had completely nonoverlapping receptive fields. This latter example demonstrates that the synchronization observed in the long-bar condition cannot be attributed to common input from a visual field region shared by both cell groups. Such stimulus-specific changes in synchronization patterns were demonstrable not only in multiunit but also in single-unit recordings and occurred irrespective of whether the correlograms had broad or sharp peaks (data not shown). Figure 11 summarizes the results of 16 such experiments. In 15 of 16 experiments synchronization was stronger in the long-bar as compared with the dual-bar conditions. Moreover, as illustrated in Figure 11, the decrease in synchronization strength for the transition from the long-bar to the two-bar condition was similar irrespective of whether the two bars moved in the same direction or opposite directions.
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Figure 9. Stimulus dependence of collicular synchronization between multiunit clusters with partially overlapping receptive fields. Data are from the same case as that illustrated in Figure 5. A-C, Schematic plots of the receptive fields and the three different stimulation conditions. The cells were activated with a continuous moving light bar, two separate light bars moving in the same direction, or two light bars moving in antiphase across the receptive fields. A, inset, Position of the recording electrodes in the stratum griseum superficiale. D-F, Cross-correlograms computed for the responses obtained with the three stimulus paradigms. The synchronization seen with a continuous moving light bar (D) disappeared if the cells at the two recording sites were activated by two bars moving in the same direction (E) or opposite directions (F). The black continuous line superimposed to the correlogram in D represents the generalized Gabor function that was fitted to the data.
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Figure 10. Stimulus dependence of synchronization between collicular cell clusters with nonoverlapping receptive fields. A-C, Schematic plots of the receptive fields and the three different stimulation conditions. Activity was recorded from two multiunit clusters in the stratum griseum superficiale. Conventions are as in Figure 9. The synchronization seen with a continuous moving light bar (D) disappears if the cells at the two recording sites were activated by two bars moving in the same direction (E) or opposite directions (F).
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Figure 11. Statistics of stimulus dependence. The scatterplot compares the synchronization strength for different stimulation conditions in our data sample (n = 16). Note that we did not test all three stimulation conditions in all measurements. The parameter plotted is the relative modulation amplitude of the center peak (ratio of center peak amplitude over the offset of the correlogram modulation) for the long-bar condition (y-axis) versus the dual-bar conditions (x-axis). Note that in 15 of 16 cases, the strength of synchronization is consistently higher for spatially coherent stimulus configurations.
DISCUSSION
Evidence for correlated activity in the SC
The major result of our study is that correlated discharge is a prominent feature of single-unit and multiunit activity in the SC. Our sample of single-unit correlation patterns is small but in good agreement with our multiunit data. The analysis of multiunit activity enabled us to quantify collicular correlation patterns with high temporal and spatial resolution. The virtues of quantifying correlation patterns by fitting of Gabor functions are discussed elsewhere (König, 1994
Comparison with other correlation studies
Numerous electrophysiological studies have described discharge properties of cells in the vertebrate midbrain (Chalupa, 1984
The correlation patterns recorded in the SC of anesthetized cats compare in many respects with the interaction patterns that have been observed in visual cortical areas of cats and monkeys. Like the latter (Eckhorn et al., 1988
Relationship between correlation results and collicular physiology
Some of the cellular properties of collicular neurons might predetermine them for engaging in precisely timed and coordinated discharges. Intracellular recordings demonstrate that collicular neurons have brief time constants of 3-6 msec (Grantyn et al., 1983
As shown here, collicular neurons respond to visual stimuli not with random firing patterns but with precisely synchronized activity that sometimes shows an oscillatory temporal structure. These synchronization patterns are not locked to the stimulus; they appear very early in the response and are somewhat variable from one stimulus presentation to the next. Several observations suggest an intracollicular origin of this temporal patterning. First, it is unlikely that collicular correlations result from shared retinal input, because there were no differences between the correlation patterns recorded in the contralateral and ipsilateral visual field representation, the latter receiving visual input mainly via the retino-cortico-tectal loop involving the corpus callosum (Antonini et al., 1978
Our observation that the probability of collicular synchronization decreases with increasing horizontal separation of recording sites goes well with a host of data indicating that both sensory and motor representations are mapped in coordinates tangential to the laminae (Feldon et al., 1970
It is commonly held that the superficial and deep SC layers form two rather independent structures (Edwards, 1980
Collicular synchronization and assembly coding
If response synchronization serves to disambiguate population codes, it should have the following properties: (1) correlations of discharges should occur with a precision in the millisecond range to define relations with high temporal resolution and a fast time scale; (2) these correlations should occur over physiologically relevant distances, i.e., between cells with overlapping or adjacent receptive fields; and (3) correlations should change dynamically such that only those subsets of simultaneously activated cells synchronize their responses, which represent a common object. The correlation patterns observed here meet these predictions. A large percentage of SC cells showed temporally precise synchronization. Synchronization occurred between nearby cells as well as across distances of 2.5 mm, i.e., half of the diameter of the SC. Collicular synchronization exhibited a dependence on stimulus coherence similar to that shown previously for cortical synchronization (Gray et al., 1989
Admittedly, the congruence between the predictions listed above and the phenomenology of collicular synchronization does not prove that synchronization serves as a binding code. However, the importance of spike timing for SC function is suggested by microstimulation experiments in awake cats in which saccades were elicited by activating with electrical stimuli two or three sites of the SC simultaneously. When the trains of stimulation pulses were precisely synchronized (<5 msec phase lag between the pulses), the vectors of the resulting saccades were close to the average of saccades evoked from each stimulation site alone. On the other hand, when the simultaneously applied trains of stimulation pulses had phase lags of >5 msec, the vectors of the resulting saccades were close to the sum of the individual saccades, as if the stimulation trains had been applied in succession (Brecht et al., 1997
FOOTNOTES Received Nov. 9, 1998; revised Feb. 16, 1999; accepted Feb. 18, 1999.
This work was supported by the Max-Planck-Society, the Minna-James-Heineman Foundation, the Heisenberg Program of the Deutsche Forschungsgemeinschaft and the Institute for Advanced Study (Berlin, Germany), where A.K.E. received a Daimler-Benz fellowship in 1997-1998. We thank S. Neuenschwander for providing the analysis software package, R. Goebel for providing the visual stimulation software, C. Selignow, J. Klon-Lipok, U. Hörbelt, and P. Janson for excellent technical assistance, and R. Ruhl and S. Ruhl for help in preparing the figures.
Correspondence should be addressed to Michael Brecht, Max-Planck-Institut für Hirnforschung, Deutschordenstraße 46, 60528 Frankfurt, Germany.
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