Timing in cognition and EEG brain dynamics: discreteness versus continuity

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.

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.

Table 1 Comparison of OA framework with resonant cell assemblies and dynamical core frameworks

• 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).

• 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).

• 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).

• 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).

• 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).