Abstract
This article provides an overview of recent developments in solving the timing problem (discreteness vs. continuity) in cognitive neuroscience. Both theoretical and empirical studies have been considered, with an emphasis on the framework of operational architectonics (OA) of brain functioning (Fingelkurts and Fingelkurts in Brain Mind 2:291–29, 2001; Neurosci Biobehav Rev 28:827–836, 2005). This framework explores the temporal structure of information flow and interarea interactions within the network of functional neuronal populations by examining topographic sharp transition processes in the scalp EEG, on the millisecond scale. We conclude, based on the OA framework, that brain functioning is best conceptualized in terms of continuity–discreteness unity which is also the characteristic property of cognition. At the end we emphasize where one might productively proceed for the future research.
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Notes
This means that there exists a particular minimal inter-stimulus interval for which two successive events are consistently perceived as simultaneous.
Consider a typical transistor. The processes in it are indeed continuous: That is its transfer function traces out a (nonlinear, continuous) S-shaped curve—that is why they are used as both switches and amplifiers (Eliasmith 2002). Nevertheless, we treat them as if they are only ever in one of the two possible states. Consistently with this analogy, van Gelder (1995) claims that even though neurons and neuronal assemblies may be considered as discrete in state, this does not mean that they are discrete in time.
Figure 1 also illustrates the continuity and discreteness for the spatial dimension (see B). For the case of continuity, the change of neuronal activity appeared in a gradual fashion, whereas for the discrete condition, the change of neuronal activity appeared abruptly.
To illustrate this point, consider only one example, which we borrow form Llinas et al. (1998, p 1841): “We know full well that if we are tired we can fall asleep extraordinarily quickly and that if we are asleep and a strong stimulus is given to us (e.g. the havoc played by an alarm clock) we can awaken also extraordinarily fast. It is so fast, indeed, that the only substrate capable of supporting the speed of these two events must be electrical in nature given the large number of elements involved...” EEG is mainly the “product” of the cortex. With this respect, there is interesting finding: Sahraie et al. (1997) compared brain activity in a single blindsight subject (G.Y.) generated by stimuli which give rise to awareness with activity generated by stimuli (permitting similar levels of discrimination) without awareness. They found that the shift between “aware” and “unaware” modes was associated with a shift in the pattern of activity from cortical to subcortical levels. Nunez (2000) also stated that subcortical activity is only weakly correlated with cognition and behavior. However, one should acknowledge the importance of such factors as the effects of some definite neurotransmitters controlled by thalamus (Newman 1995) and the establishment of reentrant thalamocortical loops for oscillatory EEG synchronization in order to bind specific features for consciously cognitive representation of the objects that are integrated from these features (Bachmann 1984; Llinas et al. 2002).
The OA framework has its origin in the work of Kaplan and coworkers (Kaplan 1995, 1998, 1999; Kaplan et al. 1997; Kaplan and Shishkin 2000). We thoroughly endorse what they state, since our own particular perspective on the problem of brain-mind functioning does not differ substantially, although our choice of emphasis is very different in places.
This approach goes back to Hebb (1949); however, the classical neural assemblies are too slow and may be not suitable for cognitive operations (Kaplan and Borisov 2003). Modern understanding of neural assemblies stresses its functional nature, which is at scales both coarser and finer than that of the classical ones (von der Malsburg 1999). The idea is that large neuronal populations can quickly become associated or disassociated, thus giving rise to transient assemblies (Frison 2000; Triesch and von der Malsburg 2001), which thought to execute the basic operations of informational processing (Averbeck and Lee 2004). For definition of “brain operation,” see Fingelkurts and Fingelkurts (2003, 2005). It is important to note here that the cell assembly’s concept is difficult to falsify (see Appendix 2 for details).
If the data is stationary, its dynamics does not change significantly during the acquisition period, thus been stable. Therefore, quasi-stationary means almost (or near) stable.
The attributes are the following: (1) Average amplitude within each segment (μV)—as generally agreed, indicates mainly the volume or size of neuronal population: indeed, the more neurons recruited into assembly through local synchronization of their activity, the higher will be the amplitude of corresponding to this assembly oscillations in the EEG (Nunez 2000); (2) Average length of segments (ms)—illustrates the functional life-span of neuronal population or the duration of operations produced by this population: since the transient NA functions during a particular time interval, this period is reflected in EEG as a stabilized interval of quasi-stationary activity (Fell et al. 2000; Kaplan and Shishkin 2000); (3) Coefficient of amplitude variability within segments (%)—shows the stability of local neuronal synchronization within neuronal population or assembly (Truccolo et al. 2002); (4) Average amplitude relation among adjacent segments (%)—indicates the NA behavior—growth (recruiting of new neurons) or distraction (functional elimination of neurons) (Kaplan and Borisov 2003); (5) Average steepness among adjacent segments (estimated in the close area of RTP) (%)—reflects the speed of neuronal population growth or distraction (Kaplan and Borisov 2003).
Although the concept of metastability has been around in physics for a long time, the specific interpretation of metastability in the context of a theoretical model of the coordination dynamics in the brain has been developed by Kelso (1991). Metastability is a theory of how global integrative and local segregative tendencies in the brain coexist (Kelso 1995; Friston 1997; Kaplan 1998). In the metastable regime of brain functioning, the individual parts of the brain exhibit tendencies to function autonomously at the same time as they exhibit tendencies for coordinated activity (Bressler and Kelso 2001; see also Fingelkurts and Fingelkurts 2001, 2004). The synchronized operations of distributed neuronal assemblies are metastable spatial-temporal patterns because intrinsic differences in activity between the neuronal assemblies are sufficiently large that they do their own job, while still retaining a tendency to be coordinated together.
OM means that the set of the neuronal assemblies synchronously participated in the same cognitive act during the analyzed period. The criterion for defining an OM is a sequence of the same synchro-complexes (SC). Whereby, SC is a set of EEG channels in which each channel forms a paired combination (with high values of index of structural synchrony) with all other EEG channels in the same set (Fig. 3a); meaning that all pairs of channels in an SC have to have significant index of structural synchrony (Fingelkurts et al. 2004b). For the properties of OM see Fingelkurts and Fingelkurts (2005).
The index of structural synchrony (ISS) is estimated through synchronization of rapid transition processes (RTP)—boundaries between quasi-stationary segments—between different EEG channels. This procedure reveals the functional (operational) interrelationships between cortical sites as distinct from those measured using correlation, coherence and phase analysis (Kaplan et al. 2005).
These values coincide precisely with the mean microstate duration of entire neocortex (82 ± 4 ms) obtained for healthy young adults using Lehmann approach (Koenig et al. 2002), which is essentially different from our method. Thus, these data cross-validate each other.
It has been demonstrated that if two areas of cortex are operationally synchronized, then they tend to be also synchronized with some other areas (Fingelkurts 1998). Calculations showed that the power-law statistics governs the probability that a number of cortical areas are recruited into an OM. This ubiquitous dependency is characterized by a fractal relation between different levels of resolution of the data, a property also called self-organized criticality (Bak et al. 1987).
Isomorphism is generally defined as a mapping of one entity into another having the same elemental structure, whereby the behaviors of the two entities are identically describable (Warfield 1977). A functional isomorphism on the other hand requires the functional connectivity between its component entities (Lehar 2003). It is an extension to Müller's psychophysical postulate (Müller 1896), and Chalmers' principle of structural coherence (Chalmers 1995).
Indeed, experimental evidence suggests that the behavioral or cognitive continuum is a succession of discrete behavioral/cognitive acts performed by an individual (Alexandrov 1999; Madison 2001). Each separate act is the integration of a certain number of operations, which are important and appropriate for the realization of this act. The change from one behavioral/cognitive act to another is embedded in a rapid “transitional process” (Alexandrov 1999). The same is true for the phenomenological structure of human consciousness which consists of stable nuclei (or thoughts) and transitive fringes (or periods)—as it is described by James’ metaphor of “Stream of Thoughts” (James 1890). It seems that metastability provides a mechanism of the functional isomorphism realization (Fingelkurts and Fingelkurts 2004).
In this effect normal listeners report hearing audio-visually fusion syllables as some combination of the auditory and visual syllables (e.g., auditory /ba/ + visual /ga/ are perceived as /va/) or as a syllable dominated by the visual syllable (e.g., auditory /ba/ + visual /va/ are perceived as /va/). The vast majority of people (but not all) experience the McGurk illusion. It was also shown that the McGurk illusion exists between other sensory modalities.
Here there are no restrictions for the relations between frequency bands, because the method we used for assessing the OMs is not associated with the phase relation as the usual techniques estimating synchrony (Kaplan et al. 2005).
Complexity hierarchy enables the system to build complex representations from primitive ones so that the semantic value of the complex representation is determined by, and dependent on, the semantic values of the primitives (Fingelkurts and Fingelkurts 2003).
It should be stressed that the concepts “state” and “contents” of consciousness should be differentiated from each other. The “contents” of consciousness refer to the patterns of subjective experience at the phenomenal level: percepts, emotions, sensations, mental images, etc. (Block 1995), while the term “state” of consciousness refers to the underlying context in the brain in which the phenomenal contents of consciousness are realized. Thus, the “state” does not refer to the subjective experiences themselves (Kallio and Revonsuo 2003).
The use of a posthypnotic suggestion would minimize the need for suggestions of relaxation, drowsiness, etc. which are typically used in a hypnotic induction (Kallio and Revonsuo 2003).
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Acknowledgments
The authors are grateful for stimulating discussions on related questions to Prof. Alexander Kaplan, Prof. Walter Freeman, Prof. Erol Basar, Prof. Hermann Haken, Prof. Steve Bressler, and Mr. Carlos Neves (Computer Science specialist). Conversations with Prof. Antti Revonsuo about neurophysiology of consciousness have had a significant influence on the ideas in this paper. We wish also to thank Prof. William Banks and Prof. Max Velmans for their very useful comments on the earlier version of this paper. The writing of this paper has been supported by the BM-SCIENCE. Special thanks to Prof. Richard Lippa for skilful text editing.
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Appendices
Appendix 1: OA and other related theoretical frameworks
The OA theoretical framework has a marked resemblance to other theoretical accounts currently dominating the field of research. This is not the place for a detailed discussion of the similarities and differences between various theoretical frameworks, since the scope of the present paper is limited to the “continuity–discreteness” problem. Therefore, we will touch this subject very briefly. The first theory which we want to address is the “Global Workspace (GW)” framework (Baars 1988). According to this theory, the brain seems to show a distributed style of functioning, in which the detailed work is done by millions of specialized neural groupings without specific instructions from some command centre. Mostly these are unconscious processes; however, consciousness creates widespread access (free from interference) to complex and unconscious systems. Using “theater metaphor,” Baars argued that as theatre combines very limited events taking place on stage with a vast audience, consciousness involves limited information that creates access to a vast number of unconscious sources of knowledge (Baars 1997).
Generally, the OA framework is also consistent with the “Framework for Consciousness” suggested by Crick and Koch (2003). The main lines of correspondence are the following: a preamble on the cerebral cortex; the fact, that most cortical areas is sensitive to correlations among correlations being expressed by other cortical areas; the importance of neuronal assemblies; the claim that conscious awareness is a series of discrete snapshots and that the durations of successive snapshots are not constant (Crick and Koch 2003).
Further, our theoretical framework is also compatible with Chalmers’ “Philosophy of Mind” (2002) in the part that any distinction in experience should be mirrored by a distinction in neural activity, and the pattern of experience should be matched by the pattern of awareness (see “functional isomorphism” in Fingelkurts and Fingelkurts 2001, 2003, 2004).
It is worth to note that OA framework is in vein with Revonsuo’ “Neuroconsciousness” conception (2001). According to this framework, consciousness is a real biological phenomenon that is physically located within the brain—it is the phenomenal level of brain organization (Revonsuo 2000). Further, it is suggested that physiologically neuroconsciousness consists of large-scale electrophysiological (or bioelectrical) activity spatio-temporal patterns; and synchrony in these patterns may be the mechanism by which the conscious state and its contents are realized in the brain. Further, it has been proposed that “neural synchrony seems to be capable of supporting higher-level electrophysiological entities that resemble the content of the phenomenal level of organization” (Revonsuo 2001, p 6).
Besides just mentioned theories, there are two related theoretical frameworks which have the closest similarities with the OA conceptualization. These are (a) the “resonant cell assemblies” framework developed by Varela (1995) and (b) the “dynamic core” (DC) theory proposed by Tononi and Edelman (1998). Because of limited space, we summarize the similarities and differences between these theoretical frameworks and our OA theory in Table 1. One can notice that besides similarities between all three theories, the OA framework has several notable differences. We will concentrate here only on the most relevant ones.
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1.
Neither the NA, nor the DC is flexible enough to allow for a representation of complex objects or for the execution of complex combinatorial cognitive operations, which are also the gist of their internal structure. This is so because NA and DC do not have internal hierarchical organizations. Here, it is essentially necessary to allow for hierarchical organization with the structured integration of subcomponents (von der Malsburg 1999). The components in question are often necessarily activated under the same overall conditions; hence without defined internal structure a NA/DC could not distinguish between the two (or more) types of events. In contrast, OMs, which are the main constituents of the OA framework, do have such internal organization (see Fingelkurts and Fingelkurts 2003, 2005): one OM may be a member of another more complex one, or it may be decomposed until simple neuronal assemblies, each of which would be responsible for simple brain/cognitive operations. Therefore, the recombination of subsets of neuronal assemblies into OMs, and of different OMs into larger structured OMs yields a vast number of potential combinations needed to represent the multivariability of cognition and eventually consciousness (Fingelkurts and Fingelkurts 2004). Such complex structure of OM is also important for the semantic representations of words with similar meanings, for example, for hyponyms and hyperonyms (Pulvermüller 1999). For instance, it can be shown that between-assembly connections and activity dynamics are a possible basis of semantic associations and/or grammatical phenomena (see Ivancich et al. 1999; Pulvermüller 1999; Fingelkurts and Fingelkurts 2001, 2003 for further discussion).
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2.
In contrast to NA/DC models, the OA framework supposes large number of coexisting OMs. Considering the composite polyphonic character of the electrical brain field (EEG), this field may be presented as a mixture of many time-scale processes (individual frequency components) (Nunez 2000; Basar et al. 2001; Basar 2004). Consequently, a large amount of functionally distinct OMs can co-exist simultaneously at different time-scales and even between them (Kaplan and Shishkin 2000; Fingelkurts and Fingelkurts 2001; here there are no restrictions for the relations between frequency bands, because the method we used for assessing the OMs is not associated with the phase relation as the usual techniques estimating synchrony). Simultaneous existence of these OMs subserves the numerous operational acts on the functioning of the brain/organism and on the interaction of the organism with its environment (Arbib 2001). Only subset of these OMs constitutes mental states, some of which are of conscious nature (see Fingelkurts and Fingelkurts 2001, 2003, 2005 for further discussion).
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3.
Even though all three theoretical frameworks stress the importance of functional connections, the concepts they used to define the values of functional connectivity differ significantly between them. This subject is discussed in a grate detail in our previous publications; therefore we address interested reader to them (Fingelkurts et al. 2005; Fingelkurts and Fingelkurts 2005). Here we should only mention that OA framework supposes “true” functional synchrony which not necessarily requires any anatomical connections. It is the stimuli (either external or internal), the task, and other functions which cause the synchronization; therefore, it is a function-based synchronization (Fingelkurts and Fingelkurts 2005).
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4.
The OMs in contrast to NA/DC are characterized by the metastable nature. Attention, we speak here not about dynamics of OM/NA/DC which is also metastable, but about the functional entity (OM/NA/DC) pre se. As we have already mentioned in the main body of the text, the OMs are inherently metastable since they constructed by separate neuronal assemblies which process and represent different types of information from relatively independent brain functional systems; however, at the same time they exhibit tendency for the coordinated activity (Fingelkurts and Fingelkurts 2004, 2005). Such simultaneous existence of autonomous and coordinated tendencies is the essence of the metastable regime of system (brain) functioning (Kelso 1991, 1995).
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5.
NA/DC are lacking of the time-dimensional information in each cortical area separately, while the OM is based on the detailed and known time-dimensional information in each cortical area.
Appendix 2: The methodological problems with cell assembly model
The claim held by many researches that cell assembly framework cannot be easily falsified may be the basis for a premature rejection of many cell assembly based theories (as the OA theory) that offer a neurobiologically plausible framework within which so much can actually be explained. The existence of cell assemblies could, in principle, be tested by recording, in parallel, multiple neurons whose activity is correlated with different cognitive functions or conscious experience. Multielectrode recordings have already indicated that rapid changes in the functional connectivity among distributed populations of neurons can occur independently of firing rate (Vaadia et al. 1995). Furthermore, recent studies in monkey frontal cortex show abrupt and simultaneous shifts among stationary activity states involving many, but not all recorded neurons (Seidemann et al. 1996), thus clearly indicating the functional clustering of neurons into neuronal assemblies.
However, such studies have several methodological limitations. For example, understanding of how the cooperated activity of neurons gives rise to collective assembly behavior requires improved methods for simultaneous recording with minimal damage to the neuronal tissue (Buzsáki 2004). Another important step in multielectrode recording analysis is the isolation of single neurons on the basis of extracellular features. Several methods exist, however they based on the assumptions which are difficult to justify in most cases (Llinas 1988; Gray et al. 1995). Yet another difficulty is that no independent criteria are available for the assessment of unit isolation (Buzsáki 2004) and therefore, inter-laboratory comparison is difficult and makes interpretation of the results controversial. Additionally, it is often not known what a given cell assembly is coding for: there is considerable evidence that the firing pattern of neurons even in primary sensory cortices reflects not just the physical nature of a stimulus, but also internal factors (Zhou and Fuster 2000).
Additionally, cell assemblies widely distributed over distant cortical regions are obviously difficult to observe through electrophysiological recordings from local neuron clusters or small areas. If large-scale neuronal theories of cognitive functions are correct, then fast, large-scale recording techniques are necessary to visualize activity changes in distributed assemblies. Fortunately, several such new techniques have been already emerged (see for example, Grinvald et al. 2003; Buzsáki 2004; Bennett and Zukin 2004) and therefore there is a hope that the question of whether there are specific cell assemblies for different cognitive operations will be soon answered experimentally (and therefore will give bases for possible falsification of cell assembly based theories).
We should stress also, that among already existed large-scale techniques, EEG and MEG approaches can be extremely useful when new methods of their analysis (as described in the present paper) are used for investigating the cortical topographies of neuronal assemblies. A coherent picture can be drawn already on the basis of a number of studies (see Fingelkurts and Fingelkurts 2004, 2005 for the resent review of research).
It is relevant to point out here that the proposed concept of cell assemblies is necessarily fuzzy. We agree with Pulvermüller (1999) that this is not a problem: “It is essential to see that fuzziness is intrinsic to the assembly concept and that this is only problematic in the way it is a problem to determine the boundaries of the sun or the Milky Way” (Pulvermüller 1999, p 310). What is important—it is functional discreteness of cell assemblies (Braitenberg 1980; Braitenberg and Pulvermüller 1992). Exactly this main property of neuronal assemblies is used in the OA framework (see Fingelkurts and Fingelkurts 2005).
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Fingelkurts, A.A., Fingelkurts, A.A. Timing in cognition and EEG brain dynamics: discreteness versus continuity. Cogn Process 7, 135–162 (2006). https://doi.org/10.1007/s10339-006-0035-0
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DOI: https://doi.org/10.1007/s10339-006-0035-0