Chapter 1 Cognition, gamma oscillations and neuronal synchrony

Advances in Clinical Neurophysiology (Supplements to Clinical Neurophysiology Vol. 54) Editors: R.C. Reisin. M.R Nuwer, M. Hallett, C. Medina 2002 Elsevier Science B.V. All rights reserved.

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Chapter 1

The Berger Lecture

Cognition, gamma oscillations and neuronal synchrony Wolf Singer Max Planck Institutefor Brain Research, D-60528 Frankfurt/Main (Germany)

Perceptual objects consist ofunique, often highly complex constellations of features and require for their adequate neuronal representation that the respective features and the spatial and/or temporal relations among these features be encoded and represented by neuronal responses. Although the variety of basic feature dimensions that nervous systems exploit to classify perceptual objects is limited, the diversity of possible constellations is, for all practical purposes, virtually unlimited. Thus, cognitive systems have to explore a huge combinatorial space when searching for the consistent relations among features that define a perceptual object. Combinatorial problems of similar nature have to be solved for the programming and execution of movements. Although the elementary components of motor acts - the movements of individual muscle fibres - are limited in number, the diversity of movements that can be composed by combining the elementary components in ever changing constellations is again virtually infinite. The extraction of features in sensory processing is equivalent to the analysis and encoding of relations. One way of analyzing and representing

* Correspondence to: Prof. Wolf Singer, Max Planck Institute for Brain Research, Deutschordenstrasse 46, D-60528 Frankfurt/Main, Germany. Fax: +46 (69) 96769327. E-mail: [email protected]

relations is to recombine signals selectively by having subsets of input fibres converge onto target cells at subsequent processing stages (see Fig. I). This strategy of representing features and their constellations by the tuned responses of individual cells (labelled line coding) is rapid and reliable because it can be realized in simple feed-forward architectures. However, ifused as the only representational strategy, it requires astronomical numbers of neurons in order to cope with the virtually infinite diversity of possible feature conjunctions (Sejnowski 1986; Engel et a1. 1992). Moreover, it is not easy to see how such a strategy deals with the representation of novel objects at first encounter and how it can cope with the representation of composite objects, categories and semantic relations. A complementary strategy is needed, therefore, that permits sharing of neurons for the representation of different contents. One such strategy is population coding, known also as coarse coding. Here the information about a specific stimulus feature is distributed across large numbers of neurons and encoded in the graded responses of the respective cells. The great advantages of this coding strategy is that a given cell can be recruited into different assemblies and, thus, can participate in the encoding ofmany different contents (Fig. 2). Accordingly neurons participating in population codes need to be broadly tuned and this has as a consequence that

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Binding by convergence and smart neurons

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Fig. 1. Schematic wiring diagram of a hierarchically organized feed-forward network that generates smart neurons which respond selectively to different perceptual objects. Note that the smart neurons representing the faces and the vase, respectively, receive input from partially the same feature-specific neurons.

they react with graded responses to variations of features along several dimensions, e.g. the orientation, the location, the contrast and the length of a contour border. Hence, a particular stimulus always drives a large number ofcells with overlapping preferences and the precise nature and configuration

of features needs to be assessed by interpolation from the population response. This coding strategy reduces effectively the number of neurons required for the encoding of different features and appears to be applied at all levels ofcortical processing. However, it does have a price.

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Assembly coding

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Fig. 2. Schematic wiring diagram of neuronal architectures serving the representation of perceptual objects by assemblies. Note that the assembly representing the vase shares neurons with the assemblies representing the faces. In order to assure stability of the respective assemblies, additional reciprocal connections among neurons constituting an assembly are required (shaded regions) that bind responses of neurons belonging to the same assembly.

A need for dynamic response selection and binding As long as the populations activated by simultaneously presented stimuli do not overlap, population coding poses no special problems and can be realized in simple feed-forward architectures. De-

coding problems arise, however, when different, simultaneously present objects recruit partially overlapping populations. In this case the subsets of responses related to a single object need to be identified, selected and bound together for further joint processing. Otherwise ambiguities arise as to which neurons participate in which of the overlapping

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population codes. Such superposition problems are expected to occur whenever stimuli overlap either in euclidian or feature space or both and need to be represented simultaneously (Gray 1999; Von der Malsburg 1999). As coarse coding is applied at all levels ofprocessing, this superposition problem is ofa very general nature. Strategies to solve it can therefore be expected to be similar across the various stages of the cortical processing hierarchy. A classical assumption is that there are neurons at subsequent processing levels which receive convergent input in various constellations from subsets ofbroadly tuned low level neurons and thereby acquire selectivity for only the particular constellation offeatures that characterizes one of the objects (binding by convergence). In principle, population codes could be fully disambiguated by such conjunction specific binding units; but this solution is again very expensive in terms of the number of required binding units if all possible conjunctions were represented in this explicit way. One would require as many binding units as there are distinguishable population states. This is clearly not an attractive strategy because it sacrifices the main advantage of coarse coding: the parsimonious use of neurons (Fig. 3). Therefore, I am proposing that the cerebral cortex uses two strategies for the encoding of relations in parallel: First, the implementation ofconjunction specific units and, second, the formulation of dynamically bound cell assemblies. The latter coding strategy cannot be realized in simple feed-forward architectures but requires a complex network of highly specific reentry connections that link reciprocally cells distributed both within as well as across cortical areas. The role of these connections is to assure dynamic and context dependent association of neurons into functionally coherent assemblies. In order to distinguish between simple population codes that require only feed-forward architectures and dynamic grouping that requires cooperative interactions through reentrant networks, the neuronal populations selected by dynamic grouping will henceforth be addressed as assemblies (for a detailed discussion of this distinction see Phillips and Singer 1997; Singer et al. 1997; Singer 1999).

In conclusion, if relations are encoded both by conjunction specific neurons and dynamically associated assemblies ofsuch neurons, rapid and flexible grouping operations have to be accomplished at all levels of cortical processing. Related neuronal responses have to be labelled in a way which assures that they are processed jointly and do not become confounded with other, simultaneously occurring but unrelated responses. As a matter of principle, assemblies that share common neurons but describe different objects cannot overlap in time (Fig. 4). They have to be generated successively to avoid their merging. Processing speed is thus critically limited by the rate at which different assemblies can be formed and dissolved. At peripheral levels of processing where conjunctions need to be defined for many different, often spatially contiguous features, the alternation rate between assemblies coding for different conjunctions of features has to be considerably faster than the rate at which different objects can be perceived and represented. The reason is that the results of the various grouping operations need to be interpreted jointly by higher processing stages for the evaluation of relations of higher order. Hence, the multiplexed results of low level grouping must alternate fast enough to permit their association at higher processing stages even though they are transmitted as a sequence. Any process that permits joint enhancement of the saliency of distributed responses can serve as a mechanism to dynamically bind subsets of selected responses for further joint processing. In vision it is assumed, e.g., that object centred attention enhances via top-down projections selectively the discharge rate of neurons responding to features of the same contour or object and that this joint increase of saliency leads to joint processing of the selected responses at subsequent stages (see, e.g., Treisman 1996; Lamme and Spekreijse 1998; Roelfsema et al. 1998; Ghose and Maunsell 1999; Reynolds and Desimone 1999; Shadlen and Movshon 1999; Wolfe and Cave 1999). However, grouping of responses solely by joint rate enhancement may encounter problems. First, it can lead to ambiguities. It may not always be easy for other processing stages to distinguish whether

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The read out of assemblies By smart neurons

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Fig. 3. Schematic representation of solutions for the read-out of assemblies. The distributed responses of assemblies A---e could be read out (bound together) by smart neurons which receive convergent input from the members ofeach ofthe three assemblies (upper box). This solution is not attractive because it leads to a combinatorial explosion of the number ofrequired read-out units. Moreover, it generates a bottle-neck problem (middle box) because the activity of individual read-out neurons needs to be redistributed onto large assemblies of neurons in order to orchestrate executive functions. The alternative option is the read-out of assemblies by assemblies (lower box). This requires parallel re-entry connections between the respective cortical areas in which 'sending' and 'receiving' assemblies are configurated.

rate increases are due to grouping or to variations in stimulus properties such as, e.g., position, orientation or contrast. Second, when objects overlap in euclid ian or feature space, only responses to a

single object can be grouped at anyone moment. Otherwise it would again be unclear which of the selected responses belong to which population code. Because evaluation of non-synchronized

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Fig. 4. Options for the solution of the superposition problem. Superposition problems arise if perceptual objects are present whose corresponding assemblies share partly the same neurons (upper box). In this case, different assemblies need to be segregated in time in order to avoid false conjunctions. One option is to raise successively the saliency of responses belonging to the respective assemblies by enhancing the discharge rate of the corresponding responses (lower box, option I). An alternative solution is to enhance the saliency of responses belonging to a particular assembly by making the discharges of the respective neurons coincident in time (option 2). This permits rapid multiplexing of the different assemblies because coincidence can be evaluated within short time intervals as it does not require temporal summation. Here it is assumed that the different assemblies alternate at intervals of approximately 25 ms. Note that this temporal structure can but does not have to be obvious in the discharge sequences of individual neurons (channels I - 12) but that the spike density function of the population response shows an oscillatory modulat:on in the 40 Hz range. Note also that the constellation of neurons contributing spikes to the oscillatory population response changes from cycle to cycle.

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rate changes requires integration of a minimal number of EPSPs arriving successively from the selected cells, the pace at which different populations can be defined by rate enhancement is slow. Both problems could be alleviated by introducing internal synchronization as an additional grouping mechanism. First, synchronization can bias the saliency of responses independently of rate fluctuations. Second, because it relies on coincidence detection and spatial summation rather than temporal summation, synchronization can define relations with sufficiently high temporal precision to permit rapid multiplexing (Fig. 4).

Response synchronization in the cerebral cortex Evidence for the existence of internally generated, context dependent response synchronization has first been obtained in the cat striate cortex (Gray and Singer 1987a,b) and since then similar observations have been made in numerous structures of the brains of different species (for a review see Singer 1999). Neurons in the visual cortex tend to synchronize their discharges with a precision in the millisecond range when activated with a single contour, whereas they fail to do so when activated by different contours moving in different directions (Gray et al. 1989; Engel et a1. 1991c). In addition, these stimulus induced, context dependent synchronization phenomena were found to be associated with a conspicuous oscillatory modulation of cell firing in a frequency range between 30 and 50 Hz, the socalled gamma frequency range. Two aspects make this synchronization interesting in the context of response selection and binding. First, it results from internal coordination of spike timing and is not simply caused by stimulus-locked changes in discharge rate. Second, synchronization probability changes in a systematic way when the perceptual coherence of stimulus constellations is modified. Thus, this type of synchrony is not a trivial reflection of anatomical connectivity such as shared input through bifurcating axons but does result from context dependent, dynamic interactions within the cortical network.

Response synchronization, mechanisms and properties Evidence indicates that the precise synchronization of cortical responses that is associated with oscillations in the gamma frequency range results from intracortical interactions. This distinguishes them from the less precise synchronization phenomena that occur in association with oscillatory patterning of responses in the alpha (~1O Hz) or delta «4 Hz) frequency range and are due to intrathalamic or thalamocortical interactions (Steriade et al. 1996; Contreras and Steriade 1997a,b; Steriade 1999). The cortical origin of synchronization in the gamma frequency range is suggested by several observations. First, isolated slices of the visual cortex can produce gamma oscillations when appropriately stimulated pharmacologically (Whittington et a1. 1995; Buhl et a1. 1998; Draguhn et al. 1998; Fisahn et a1. 1998; Tennigkeit, personal communication). Second, cortical networks contain at least two cell types with putative pacemaker functions in the appropriate frequency range - non-pyramidal cells that exhibit an oscillatory fluctuation of their membrane potential in the gamma frequency range (Llinas et al. 1991) and pyramidal cells that engage in rhythmic firing in the 40 Hz range (chattering cells; Gray and McCormick 1996) when sufficiently depolarized. Third, synchronization is mediated by corti co-cortical connections. This has been shown both for the intrinsic tangential connections that reciprocally link cells distributed across different columns (Lowel and Singer 1992; Konig et al. 1993) and for the long range connections that mediate interactions between homologous areas in the two hemispheres via the corpus callosum (Engel et al. 1991a). Fourth, the network of reciprocally coupled inhibitory interneurons can maintain oscillatory activity in the gamma frequency range even after blockade ofionotropic glutamate receptors if the interneurons are activated via metabotropic glutamate receptors (Beierlein et a1. 2000; Tennigkeit, unpublished observations). Fifth, local intracortical application of cholinergic agonists facilitates oscillatory activity in the gamma frequency range and its synchronization while

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blockade of muscarinic receptors has the reverse effect (Rodriguez et al. 2001). Sixth, analysis of the spatio-temporal patterning of oscillatory activity with multielectrodes suggests that it results from an intracortical self-organizing process which leads to an entrainment of distributed oscillators and is not caused by oscillatory subcortical input (Prechtl et al. 2000). How exactly the various cell populations and pacemaker mechanisms interact to produce the oscillatory patterning of responses and their synchronization remains to be clarified. Unclear also is whether oscillations are always synchronous across laminae or whether supra- and infra-granular oscillations can dissociate. It is clear, however, that there is a close relation between the oscillatory patterning ofresponses in the gamma frequency range and the occurrence of precise synchronization of discharge patterns, especially if synchronization occurs over longer distances between cells located in different functional columns or cortical areas (Konig et al. 1995b). Synchronization ofdischarges with close to zero time lag and a precision in the range of <10 ms can occur in the absence of any obvious oscillatorypatterning, especially among closely spaced neurons with overlapping receptive fields (Konig et al. 1995a). However, strong correlations and correlations over larger distances are usually associated with an oscillatory patterning of the individual responses in the gamma or beta frequency range. This suggests that oscillatory patterning facilitates entrainment of neuronal groups into coherently discharging assemblies, a notion supported by early simulation studies (Konig and Schillen 1991; for a review see Singer 1993). In this context it is important to note that analysis of single cell responses often fails to reveal that a cell participates in a synchronously oscillating assembly. The reason is that individual cells usually do not fire with every cycle. Interspike interval distributions may appear Poissonian even though the cell's discharges are precisely time locked to an oscillatory process and synchronized with other cells in the population. Such cycle skipping is a well known phenomenon of oscillatory processes in the hippocampus (Buzsaki and Chrobak 1995; Buzsaki 1996) and may be of functional significance rather than a reflection of noise fluctuations.

If cycles are skipped in a systematic way, the constellations of cells discharging in synchrony can change from cycle to cycle. In case of 40 Hz oscillations different contents could then be encoded in time slices following one another at intervals of 25 ms (Luck and Vogel 1997; Jensen and Lisman 1998; see also Fig. 4). Indications that such a coding strategy may actually be used have been obtained in the olfactory system of insects (Wehr and Laurent 1996). There are several ways to determine whether a cell participates in a synchronously active assembly. One possibility is to record also the local field potential (LFP), which reflects the average synaptic currents produced in nearby cells, and to correlate the cell's spike discharges with these LFP fluctuations. If this leads to a modulated correlation function it indicates that the cell's discharges exhibit a consistent temporal relation to the discharges of cells contributing to the LFP. If the resulting function exhibits in addition an oscillatory patterning, it proves that the cell's discharges are synchronized to the activity of an assembly of synchronously oscillating cells. This is what one usually finds in the cerebral cortex. When responses synchronize in the gamma frequency range, cells typically fire with high temporal precision around the negative peaks of the LFP fluctuations (Gray and Singer 1989). Another possibility to disclose oscillatory firing patterns and synchronization is to record multi unit activity. This greatly increases the chances to detect oscillatory patterns due to denser sampling. If some of the simultaneously recorded cells participate in an oscillatory ensemble, autocorrelation functions are likely to exhibit an oscillatory modulation. In case some of the cells discharge in synchrony but not in an oscillatory mode this is also visible in autocorrelation functions as it gives rise to a center peak whose width corresponds to the precision of synchronization. Alternatively or in addition, the spikes from multiunit recordings can be sorted and subject to cross-correlation analysis. Finally, the optimal approach is to use multiple electrodes and to record both multiunit and field potential responses from many sites simultaneously and to subject these signals to auto- and cross-correlation analysis. This is also the only option for

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the detection ofnon-local synchronization phenomena. All these analyses need to be applied to nonaveraged time series obtained from the same trials because averaging or trial scrambling renders detection of oscillatory patterning and/or synchronization impossible. The reason is that the internally generated episodes of synchronized firing are usually not time locked to the temporal structure of stimuli. Moreover, oscillation frequencies may vary from trial to trial (Gray et al. 1992). Hence, both oscillatory patterning and synchronization of responses, if due to internal interactions, can only be detected by analysing single trials. As detailed below, there are also instances where responses get synchronized by phase locking to stimuli. Although this type of synchronization is of great relevance for functions such as perceptual grouping, it will be discussed only after having explored the putative functions of internally generated synchrony.

Relation between response synchronization and perceptual phenomena Perceptual grouping Based on the hypothesis that internal synchronization of discharges could serve to group responses for joint processing, a series ofexperiments has been performed in search for a correlation between dynamic changes of response synchronization and particular stimulus configurations. One prediction to be tested was that in early visual areas synchronization probability should reflect some of the basic Gestalt criteria according to which the visual system groups related features during scene segmentation. A consistent finding was that neurons distributed across different columns within the same area or across different visual areas and even different hemispheres synchronized their responses with close to zero phase lag when activated with a single contour but fired independently when stimulated simultaneously with two different contours (Gray et a1. 1989; Engel et al. 1991a,b,c; Freiwald et al. 1995; Kreiter and Singer 1996). This suggested that synchronization was the result of a context-dependent selection and grouping process.

Analysis of the dependence of synchrony on receptive field and stimulus configurations revealed that the probability and strength of response synchronization reflected elementary Gestalt criteria for perceptual grouping such as continuity, proximity, similarity in the orientation domain, colinearity, and common fate (for a review see Singer 1993; Singer et a1. 1997; Gray 1999). These early experiments were performed in anaesthetised animals but more recent multielectrode recordings from awake cats and monkeys indicate that these synchronization phenomena are not artefacts of anaesthesia but are even more pronounced when the animals are awake and attentive (Kreiter and Singer 1992, 1996; Frien et a1. 1994; Fries et a1. 1997; Gray and Viana Oi Prisco 1997; FriedmanHill et a1. 2000; Maldonado et a1. 2000). It is noteworthy that none of these systematic changes in synchronization probability have been associated with systematic changes of the neurons' discharge rate. A particularly close correlation between neuronal synchrony and perceptual grouping has recently been observed in experiments with plaid stimuli. These stimuli are well suited for the study of dynamic binding mechanisms because minor changes of the stimulus cause a binary switch in perceptual grouping. Two superimposed gratings moving in different directions (plaid stimuli) may be perceived either as two surfaces, one being transparent and sliding on top of the other (component motion), or as a single surface, consisting ofcrossed bars, that moves in a direction intermediate to the component vectors (pattern motion) (Adelson and Movshon 1982; Stoner et a1. 1990). Which percept dominates depends on the luminance of grating intersections because this variable defines the degree of transparency or on the duty cycle of the gratings because this variable determines changes in foreground-background assignments (Albright and Stoner 1995). Component (pattern) motion is perceived when luminance conditions are compatible (incompatible) with transparency (Fig. SA). Here is a case where local changes in stimulus properties cause global changes in perceptual grouping. In the case of component motion, responses evoked by the two gratings must be segregated and

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Fig. 5. (A) Two superimposed gratings that differ in orientation and drift in different directions are perceived either as two independently moving gratings (component motion) or as a single pattern drifting in the intermediate direction (pattern motion), depending on whether the luminance conditions at the intersections are compatible with transparency. (B) Predictions on the synchronization behav.our ofneurons as a function of their receptive field configuration (left) and stimulation conditions (right). (C) Changes in synchronization behaviour of two neurons recorded simultaneously from areas 18 and PMLS that were activated with a plaid stimulus under component (upper graph) and pattern motion conditions (lower graph). The two neurons preferred gratings with orthogonal orientation (see receptive field configuration, top, and tuning curves obtained with component and pattern, respectively) and synchronized their responses only when activated with the pattern stimulus (compare cross-correlograms on the right) (adapted from Castelo-Branco et al. 2000).

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only responses evoked by the contours of the same grating must be grouped to represent the two surfaces; in the case of pattern motion, responses to all contours must be bound together to represent a single surface. If this grouping of responses is initiated by selective synchronization, three predictions must hold (see Fig. 5B): First, neurons that prefer the direction of motion of one of the two gratings and have colinearly aligned receptive fields should always synchronize their responses because they respond always to contours that belong to the same surface. Second, neurons that are tuned to the respective motion directions of the two gratings should synchronize their responses in case of pattern motion because they then respond to contours of the same surface but they should not synchronize in case ofcomponent motion because their responses are then evoked by contours belonging to different surfaces. Third, neurons preferring the direction of pattern motion should also synchronize only in the pattern and not in the component motion condition. An important aspect of these predictions is that the expected changes in synchrony differ for different cell pairs, depending on the configuration of their receptive fields. Thus, when searching for relations between synchrony and cognitive functions, it is not only crucial to identify the processing stage where one assumes a particular binding function to be accomplished but also to select the appropriate cell pairs. Averaging data across cell pairs with different receptive field configuration can mask dynamic changes in synchrony and is likely to reveal only the static anisotropies in the network of synchronizing connections. Such a problem may have contributed to the negative results of a recent study which failed to show a relation between perceptual grouping and internal synchronization in monkey striate cortex (Lamme and Spekreijse 1998). In the case ofthe plaid stimuli, predictions were tested with multielectrode recordings from area 18 and the posterior medio-lateral suprasylvian sulcus (PMLS) of the visual cortex of lightly anaesthetised cats after it had been confirmed with eye movement recordings in awake cats that the animals distinguished between component and pat-

tern motion. Cross-correlation analysis ofresponses from cell pairs distributed either within or across area 18 and PMLS confirmed all three predictions. Cells synchronized their activity if they responded to contours that are perceived as belonging to the same surface (Castelo-Branco et al. 2000) (Fig. 5C). Analysis of the neurons' discharge rate confirmed that most of the cells in these visual areas respond preferentially to the component gratings of the plaids (component specific cells, Gizzi et al. 1990) and not to the pattern as a whole. However, in contrast to synchrony, variations in response amplitude failed to reflect the transition from component to pattern motion induced by transparency manipulation. Dynamic changes in synchronization could, thus, serve to encode in a context dependent way the relations among the simultaneous responses to spatially superimposed contours and thereby bias their association with distinct surfaces. Perceptual selection While the experiments reviewed above examined relations between synchronization and feature binding in the context of perceptual grouping, the studies described in this paragraph deal with more global but still preattentive processes of stimulus selection. A close relation between response synchronization and stimulus selection has been found in cats who suffered from strabismic amblyopia, a developmental impairment of vision associated with suppression of the amblyopic eye, reduced visual acuity and crowding. Crowding refers to the inability of amblyopic subjects to identify target stimuli if these are surrounded by nearby contours and is thought to result from false binding of responses evoked by the target and the embedding background. Quite unexpectedly, the light responses of individual neurons in the primary visual cortex were normal and the neurons continued to respond vigorously to stimuli which the animals were unable to perceive through the amblyopic eye. The only significant correlate of amblyopia detected in this study was a reduction of response synchronization among neurons driven by the amblyopic eye. This reduction in synchrony became particularly

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pronounced when responses were evoked with gratings that had been identified in previous behavioural experiments as too fine to be resolvable by the animal through the amblyopic eye (Roelfsema et al. 1994). Impaired synchrony is likely to account for at least some of the perceptual deficits: By reducing the saliency of responses, it could explain why signals from the amblyopic eye cannot compete successfully with the well synchronized responses from the normal eye and are excluded from supporting perception when both eyes are open. Recent recordings from area 21, a higher visual area, have indeed shown that the poorly synchronized responses evoked from the amblyopic eye drive neurons in area 21 less efficiently than the well synchronized responses from the normal eye (Schroder et a]. 1998). Poor synchronization is also likely to be responsible for the reduction in visual acuity and for crowding because it is expected to impair disambiguation of responses evoked by closely spaced stimuli. A correlation between response synchronization and stimulus selection has also been documented in experiments on binocular rivalry that were again performed in strabismic animals (Fries et al. 1997). Perception in strabismic subjects always alternates between the two eyes. This can be exploited to investigate how neuronal responses to constant stimuli change if they pass from being selected and perceived to being suppressed and excluded from perception and vice versa (Fig. 6). The outcome of these experiments was surprising because the responses of neurons in areas 17 and 18 were not attenuated when they were excluded from supporting perception. A close and highly significant correlation existed, however, between changes in the strength of response synchronization and the outcome of rivalry. Cells mediating responses of the eye that won in interocular competition increased the synchronicity of their responses upon presentation of the rivalrous stimulus to the other, losing eye while the reverse was true for cells driven by the eye that became suppressed. In addition there was a marked increase in the oscillatory modulation of the synchronized discharges that supported perception. Thus, in primary visual cortex there are instances where internal selection of responses

for further processing is associated with enhanced synchronization rather than increased firing. This agrees with rivalry experiments in awake, behaving monkeys which showed no systematic relation between the strength of visual responses and perception in early visual areas but a clear correlation between perceptual suppression and loss of neuronal responses in higher visual areas (Logothetis and Schall 1989; Leopold and Logothetis 1996; Sheinberg and Logothetis 1997). This is what one expects if the saliency of the responses from the two eyes is adjusted at early processing stages by modulating synchronization rather than discharge rates. These results do, of course, not exclude additional response selection by rate modulation. Thus, the outcome of rivalry can also be biased by changing the contrast ofthe patterns presented to the two eyes and this manifests itself in rate changes. Dependency on central states and attention A characteristic feature of response synchronization in the gamma frequency range is its marked state dependency. It is particularly prominent when the cortex is in an activated state, i.e. when the EEG is desynchronized and exhibits high power in the beta and gamma frequency range (Fig. 7; Munk et al. 1996; Herculano-Houzel et al. 1999). In particular, synchronization over long distances that requires oscillatory patterning of responses breaks down completely when the EEG gets 'synchronized' and exhibits high power in the low frequency range « 10 Hz). This close correlation between the occurrence of response synchronization on the one hand and EEG states characteristic for the aroused and performing brain on the other hand is strong support for a functional role of precise synchrony in cortical processing. Precise response synchronization is indeed a much more sensitive indicator of changes in central states that are associated with a loss of perceptual functions than the response amplitudes of individual neurons. Discharge rates also fluctuate in a state dependent way but these fluctuations are neither related to changes in synchrony nor do they exhibit a close relation to changes in EEG

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Fig. 6. Neuronal synchronization under conditions of binocular rivalry. (A) Using two mirrors, different patterns were presented to the two eyes of strabismic cats. Panels (B-E) show normalized cross-correlograms for two pairs of recording sites activated by the eye that won (B, C) and lost (D, E) in interocular competition, respectively. Insets above the correlograms indicate stimulation conditions. Under monocular stimulation (B), cells driven by the winning eye show a significant correlation which is enhanced after introduction of the rivalrous stimulus to the other eye (C). The reverse is the case for cells driven by the losing eye (compare conditions D and E). The white continuous line superimposed on the correlograms represents a damped cosine function fitted to the data. RMA, relative modulation amplitude of the center peak in the correlogram, computed as the ratio of peak amplitude over offset of correlogram modulation. This measure reflects the strength of synchrony (modified from Fries et al. 1997).

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Fig. 7. State dependence of response synchronization. (Upper row) Power distribution in the EEG recorded during three episodes from an anaesthetized cat. (Lower row) Averaged cross-correlograms of multi unit responses evoked by a drifting grating and recorded from two different sites in Al7 during corresponding episodes. Note that the synchronization of the responses and the oscillatory modulation in the y-frequency range increase with increasing y-activity in the EEG. Inserts in the power spectra give the relative power in the 8- and y-frequency range. Inserts in the cross-correlograms give the oscillation frequency (Hz), the relative modulation amplitude of the center peak (MAl and of the first side peak (MAS) (from Herculano-Houzel et al. 1999).

power. It is not uncommon to observe an increase in discharge rate while oscillatory patterning and synchronization in the gamma frequency range disappear and vice: versa. The magnitude and precision of synchronization in the gamma frequency range reflects even fluctuations of attention in the fully aroused brain. Attention and task dependent modulation ofsynchrony between areas 17 and 18 of the two hemispheres but also between visual cortex and more frontally located cortical areas has been observed in cats trained to perform a visually triggered motor response. The visual, association, somatosensory and motor areas involved in the execution of the task synchronised their activity in the gamma frequency range as soon as the animals focused their attention on the relevant stimulus whereby the strength ofsynchronization among areas reflected precisely the coupling of these areas by cortico-cortical connections. Immediately after the appearance of the visual stimulus, synchronization increased further, and these coordinated activation patterns were maintained until the task was completed. However, once the reward was available and the animals engaged

in consumatory behaviour, these coherent patterns collapsed and gave way to low frequency oscillatory activity that did not exhibit any consistent relations with regard to phase and areal topology (Roelfsema et a1. 1997). These results suggest that an attention related process had imposed a coherent temporal patterning on the activity of cortical areas required for the execution of the task. Here, attentional mechanisms, probably involving topdown projections and/or non-specific thalamic projections (Ribary et a1. 1991; Steriade 1999), seem to prepare cortical areas so that neurons can rapidly synchronize their responses both within as well as across areas once the stimulus appears, thereby accelerating selection and grouping of responses. These anticipatory synchronization patterns would be particularly effective if they exhibited columnar specificity, because they could then act as a read out mechanism of the grouping criteria expressed in the architecture of tangential intracortical connections. Recent evidence supports such a scenario. Measurements offluctuations in response latency revealed that self-generated gamma activ-

17

ity exhibits a specific patterning that reflects the grouping criteria of vicinity and colinearity. Response latencies of neurons in striate cortex fluctuate in the range of about 20 ms for identical, repeatedly presented stimuli. The new finding is that these fluctuations of response latency are often correlated across cortical columns (Fries et al. 1997). In this case, synchronization of the early response phase is better than expected from mere stimulus locking. Correlating response latencies of cells recorded from one site with the LFP fluctuations recorded from a site in the opposite hemisphere that exhibJited latency covariations revealed that response latencies could be predicted by the polarity of the ongoing LFP oscillations, but only when these oscillations were in the gamma frequency range. This suggests that the rapid synchronization of the very first spikes of a response is due to ongoing, oscillatory fluctuations ofthe cells' membrane potential and that these fluctuations can be coherent over large distances, in this case across hemispheres. These oscillations delay or advance responses to light stimuli depending on the timing of the stimulus relative to the phase ofthe ongoing oscillation, leading to synchronization of onset latencies in cells located in coherently oscillating columns. In this case, then, synchronization is achieved by latency adjustment, probably via the delay mechanism identified in the in vitro experiments (Volgushev et al. 1997). In oscillating cells the first discharges evoked by a barrage of EPSPs are likely to occur shortly after the peak of the respective next depolarizing cycle, and in cells that are already engaged in a synchronously oscillating network these first responses will thus be synchronous. Analysis of the receptive field properties of cell pairs exhibiting correlated latency fluctuations revealed that the ongoing fluctuations of cortical activity are not random and not simply noise but exhibit columnar specificity: Fluctuations are coherent among cells with overlapping receptive fields and for cells with non-overlapping fields they are coherent only if these have similar orientation preference. Thus, the latency adjustments exhibit feature selectivity and could be exploited for rapid perceptual grouping. The grouping criteria which

reside in the functional architecture of the intracortical association connections thus seem to be translated into specific spatio-temporal patterns of membrane potential fluctuations. Columns encoding group able features oscillate in phase and this enhances the temporal coherence of responses to nearby or colinear contours. Anatomical evidence indicates that the cortico-cortical association connections link preferentially columns coding for related features that tend to be grouped perceptually (Ts'o and Gilbert 1988; Gilbert and Wiesel 1989; Malach et al. 1993; Schmidt et al. 1997) and physiological data suggest that these cortico-cortical connections contribute to the synchronization of spatially segregated groups of neurons (Engel et al. 1991a; Lowel and Singer 1992; Konig et al. 1993). It appears likely then that the functional architecture of these association connections is continuously translated into coordinated fluctuations of neuronal excitability which reflect dynamically the system's 'knowledge' of grouping rules. Inevitably the pattern of these fluctuations is in addition modified by top-down influences from higher cortical areas and by immediately preceding changes of sensory input. Both effects would be equivalent to the functions commonly attributed to attentional mechanisms, the selection and binding of expected events, either as a consequence of bottom up priming or of intentional top down selection. The observed fluctuations could thus be equivalent with the system's updated expectancy that is determined by the fixed, locally installed grouping rules, by top-down influences and preceding sensory input that influences subsequent selection, grouping and binding of actual input patterns. Seen in this context, ongoing activity assumes the function ofa predictor against which incoming activity is matched. One ofthe effects of matching these predictions with incoming signals is a rapid temporal regrouping of output activity (see Fig. 8). The impact ofsynchronized responses If response synchronization served response selection and grouping for further joint processing, the respective target networks must be able to differentiate between synchronized and non-synchro-

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:! Fig. 8. A model of rapid feature selective neuronal synchronization through correlated latency shifting. (a) Three groups of neurons are shown schematically as circles (middle column). The three groups have RFs at different locations in the visual field (left column) and each group contains neurons with three different orientation selectivities (small black bars inside circles). The graphs in the right column depict LFPs dominated by gamma frequency oscillations that reflect the neurons' membrane potential fluctuations. The model assumes that fluctuations are coherent for neurons of the same orientation preference, but incoherent for neurons preferring dissimilar orientation. When a stimulus array of three vertical bars (black bars over the RFs) is presented, only the neurons selective for vertical orientations respond. Arrow and broken vertical line over LFPs indicate the time when stimulus driven synaptic input reaches the recorded neurons. As their response onsets are shifted coherently by the coherent membrane potential fluctuations, their first spikes are correlated and add up to a strong population response, shown as spike density function in the lowest trace. Note that for the sake of clarity we have focused on the effect of similarity of orientation selectivity and ignored RF overlap as a factor influencing the strength ofiatency correlations. (b) The same conventions as in (a), but stimulation with bars of different orientations. Neurons of different orientation selectivities are activated by the three bars. As in this case the neuronal membrane potentials fluctuate incoherently, the response onsets are not aligned and the population response is spread out in time. (c) The same conventions as in (a) but the LFP is dominated by low frequency oscillations. These low frequency oscillations are globally synchronous (coherence is not dependent on orientation selectivity), but due to their low frequency they have no latency shifting effect. Upon stimulation with three aligned bars, the neurons fire more spikes than with gamma dominated LFP (a), but the latency is long, the spikes are not synchronized and the responses to non-aligned bars would exhibit the same temporal dispersion as the responses to aligned bars (courtesy of Pascal Fries).

nized input; i.e. synchronized input must have a stronger impact on postsynaptic responses than non-synchronized input. Evidence indicates that this is indeed the case both for the visual cortex itself and its target structures. Simultaneous recordings from coupled neuron triplets along thalamocortical (Alonso et al. 1996; Usrey and Reid 1999) and intracortical pathways (Alonso and Martinez 1998) in the visual system have revealed that EPSPs synchronized within intervals below 2 ms are much more effective than EPSPs dispersed over longer intervals. A similar

conclusion is suggested by simulation studies (Niebur and Koch 1994) and in vitro experiments (Stevens and Zador 1998). Evidence for the enhanced saliency of synchronized activity has also been obtained at a more globallevel with simultaneous recordings from several sites (area 18 and PMLS) of the cat visual cortex and retinotopically corresponding sites in the superior colliculus (Brecht et al. 1998). The impact that a particular group of cortical cells has on target cells in the colliculus was found to increase dramatically whenever the cortical cells synchro-

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Fig, 9, Dependence of cortico-tectal synchronization on intracortical synchronization within (left plots) and across cortical areas (right plots). (Left) Percentage of significant cortico-tectal correlations if cortical cells recorded from two sites in the same cortical area synchronize (shaded column) or do not synchronize (black column) their responses. (Right) Percentage of corticotectal interactions between AI7 and the tectum if area 17 cells synchronize (shaded column) or do not synchronize (black column) their responses with those of cells in the latcral suprasylvian sulcus (from Brecht et al. 1998).

nized their discharges with other cortical cell groups projecting to the same site in the tectum (Fig. 9). Finally, enhanced saliency of synchronized responses can also be inferred from the tight correlation between the strength of neuronal response synchronization and perception that was observed in experiments on binocular rivalry in cats (Fries et al. 1997) and human subjects (Tononi and Edelman 1998; Tononi et al. 1998; see also below). Last but not least, simulation studies also indicate that neurons with conventional integrate and fire properties can be quite sensitive to the temporal dispersion of synaptic input. Large scale simulations of biologically inspired thalamocortical networks revealed that neurons exhibited a strong tendency to engage in oscillatory firing patterns and to synchronize their responses. When synchrony was artificially disrupted by introducing a jitter in spike timing, transmission across

polysynaptic pathways was drastically reduced (Lumer et al. 1997a,b). There are several mechanisms, some of which have been identified only recently, that make synchronously arriving EPSPs more efficient than temporally dispersed EPSPs. First, because of their exponential decay, simultaneous EPSPs summate more effectively than temporally dispersed EPSPs, and there is some evidence for supralinear summation due to voltage-gated dendritic conductances. Second, firing threshold is sensitive to the rising slope of the depolarization and lowers for fast rising depolarizations (Gray, personal communication). Third, the effect of EPSPs is dramatically enhanced when these coincide with a backpropagating dendritic spike and hence with the input that generated this spike (Larkum et al. 1999). All three mechanisms are sensitive to dispersions of EPSPs in the range of a few milliseconds.

20 Conclusions

The experimental evidence reviewed above supports the notion that cortical functions are not confined to feature extraction but are an integral part of the constructivistic processes that underlie perception. The analysis of precise temporal relations between simultaneously recorded responses of several neurons revealed the existence of highly dynamic, context sensitive synchronization phenomena. Their numerous and close relations to perceptual processes suggest that they are not epiphenomenal but play an essential role in cortical processing and coding. These dynamic phenomena and their systematic relation to cognitive processes cannot be adequately assessed by evaluating the rate changes of single cells, because the firing rates of individual cells exhibit no systematic relation with response synchronization. However, pairwise analysis of temporal relations among distributed responses as performed in most of the studies reviewed here is probably revealing only the surface structures of immensely complex and ever changing dynamic states that are characterized by higher order correlations. These will eventually have to be analyzed for a deeper understanding of the cortical functions that go beyond sequential extraction and recombination of features in feed forward architectures. Analysis ofthese dynamic states is difficult and therefore still at its very beginning. It requires massive parallel recording of spontaneous and evoked activity with high temporal resolution and the development of appropriate mathematical tools for the assessment of higher order correlations between distributed responses. Ultimately, relations have to be established between cognitive and motor functions of the human brain on the one hand and the complex patterns of distributed neuronal activity on the other hand. Among the available non-invasive recording techniques only the EEG and the MEG operate with the required temporal resolution. The fact that these techniques average over the activity oflarge populations of neurons has both advantages and disadvantages. It precludes analysis of interactions among individual neurons and therefore cannot substitute for invasive recording methods when fine grain inves-

tigation of neuronal mechanisms is required. However, because EEG and MEG signals reflect the joint activation of large neuron clusters they are ideally suited to the detection of synchronized activity. Actually, any fluctuation in EEG and MEG signals indicates a coherent change in neuronal mass activity. If these fluctuations exhibit an oscillatory patterning and/or are synchronous between widely spaced recording sites it can be inferred that large populations of neurons have engaged in oscillatory and/or synchronized activity. Thus, EEG and MEG techniques are ideally suited for the analysis of the dynamic selection and grouping operations discussed in this paper. Accordingly, there has recently been a surge of EEG and MEG studies which have established fascinating relations between cognitive functions and synchronous oscillatory activity in the beta and gamma frequency band in human subjects (for review see Singer 1999; Tallon-Baudry et al. 1999).

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