Dynamic systems theory offers conceptual andmathematical tools for describing the performance ofneural systems at very different levels oforganization. Three aspects of the dynamic paradigmare discussed, namely neural rhythms, neural andmental development, and macroscopic brain theories andmodels.

Cortical reverberations may induce synaptic changes that underlie developmental plasticity as well as long-term memory. They may be especially important for the consolidation of synaptic changes. Reverberations in cortical networks should have particular significance during development, when large numbers of new representations are formed. This includes the formation of representations across different sensory modalities.

A distinction should be made between the formation of stimulus-driven associations and cognitive concepts. To test the learning mode of a neural network, we propose a simple and classic input-output test: the discrimination shift task. Feed-forward PDP models appear to form stimulus-driven associations. A Hopfield network should be extended to apply the test.

Amit defines cell assemblies aslocal cortical neuron populationswith strong internal connections. However, Hebb himself proposed that cell assemblies are distributed over different cortical areas (nonlocal ortranscortical assemblies). We review evidence from cognitive neuroscience and neuropsychology supporting the assumption that cell assemblies are transcortical.

Wright & Liley contrast their theory that the global dynamics of the EEG are linear with that of Freeman, who hypothesizes an EEG governed by (nonlinear) deterministic-chaotic dynamics. A “call for further discussion” on the part of the authors is made as to how either theory fits with experimental findings indicating that EEG dynamics are non-linear but stochastic.

For some years there has been a controversy about whether brain state variables such as EEG or neuronal spike trains exhibit chaotic behaviour. Wright & Liley claim that the local dynamics measured by spike trains or local field potentials exhibit chaotic behaviour, but global measures like EEG should be governed by linear dynamics. We propose a different scheme. Based on simulation studies and various experiments, we suggest that the pointwise dimension of EEG time series may provide some valuable information about (...) underlying neuronal generators. (shrink)

This volume explores how functional brain imaging techniques like positron emission tomography have influenced cognitive studies. The first chapter outlines efforts to relate human thought and cognition in terms of great books from the late 1800s through the present. Chapter 2 describes mental operations as they are measured in cognitive science studies. It develops a framework for relating mental operations to activity in nerve cells. In Chapter 3, the PET method is reviewed and studies are presented that use PET to (...) map the striate cortex and to activate extrastriate motion, color, and form areas. Chapter 4 shows how top down processes involving attention can lead to activation of these same areas in the detection of targets, visual search, and visual imagery. This chapter reveals complex networks of activations. Chapters 5 and 6 deal with the presentation of words. Chapter 5 illustrates PET studies of the anatomy of visual word processing and shows how the circuitry used for generating novel uses of words changes as the task becomes automated. Chapter 6 applies high density electrical recording to explore these activations in real time and to show how a constant anatomy can be reprogrammed by task instructions to produce and perform different cognitive tasks. Chapter 7 shows how studies of brain lesions and PET converge on common networks underlying attentional functions such as visual orienting, target detection, and maintenance of the alert state. Chapters 8 and 9 apply the network approach to examine normal development of attention in infants and pathological conditions resulting from brain damage, and psychiatric pathologies of depression, schizophrenia, and attention deficit disorder. In Chapter 10, new developments such as functional MRI are discussed in terms of future developments and integration of cognitive neuroscience. (shrink)

We divided the many diverse comments on our book into categories. These are: theory, scope and goals of our project, methods, comments on specific anatomical areas, the concept of attention, consciousness and cognitive control, and finally other issues. Although many of the points of the critics are certainly well taken, we believe studies that have emerged since our book provide strong evidence that the general approach taken in our book is now yielding important new data on the relation of cognitive (...) processes to underlying brain activity. (shrink)

PET studies in domains like vision and attention have been successful because the experiments are the product of highly articulated theories. In contrast, the results of PET studies investigating language processing are difficult to interpret. We suggest that this difficulty is due to the more tentative connection of these experiments with the insights of psycholinguistics and linguistic theory.

Amit's “Attractor Neural Network” perspective on cognition raises difficult technical problems already met by prior dynamical models. This commentary sketches briefly some of them concerning the internal topological structure of attractors, the constituency problem, the possibility of activating simultaneously several attractors, and the different kinds of dynamical structures one can use to model brain activity: point attractors, strange attractors, synchronized arrays of oscillators, synfire chains, and so forth.

In Posner & Raichle's (1994) book, two essential and strictly related limitations of cognitive neurophysiology are not sufficiently enhanced: (1) The problem of “coding,” namely the capability of a natural brain to redefine its own “basic symbols” as a function of a changing environment; (2) the inadequacy of a Hebbian rule to reckon with complex computational problems such as those solved by real brains.

Pictures of normal brain activity during human thought can be worth a great deal. Electrophysiology and functional neuroimaging together allow both temporal and spatial dimensions of neurocognitive functions to be explored. Although these techniqueshave their limitations, the Cognitive Neuroscience approach is well-suited to pursuing questions about how words are perceived, understood, and remembered.

Brain damage may doubly dissociate cognitive modules, but the practice of revealing dissociations is predicated on modularity being true (T. Shallice, 1988). This article questions the utility of assuming modularity, as it examines a paradigmatic double dissociation of reading modules. Reading modules illustrate two general problems. First, modularity fails to converge on a fixed set of exclusionary criteria that define pure cases. As a consequence, competing modular theories force perennial quests for purer cases, which simply perpetuates growth in the list (...) of exclusionary criteria. The first problem leads, in part, to the second problem. Modularity fails to converge on a fixed set of pure cases. The second failure perpetuates unending fractionation into more modules. (shrink)

Posner & Raichle's book is a superbly presented and wellwritten overview of a fast-developing and important field in contemporary neuroscience. It suffers from being an overview, however, because it does not go into sufficient detail or depth in many of the issues that it raises. It also neglects many other important areas of current research, for example, technical advances in other areas, learning and memory, and lesion analysis of brain function.

The unification of microscopic and macroscopic models of brain behaviour is of paramount importance and Wright & Liley's target article provides some important groundwork. In this commentary, I propose that a useful approach for the future is to incorporate a developmental perspective into such models. This may be an important constraint, providing a key to understanding the nature of macroscopic measures of brain function such as functional measures like ERP.

Multiscale dynamics, linear approximations, global boundary conditions, experimental verification, and global influences on local cell assemblies are considered in the context of Wright & Liley's work. W&L provide a nice introduction to these issues and a reasonable simulation of intermediate scale dynamics, but the model does not adequately simulate combined local and global processes.

This paper argues that humans possess unique cognitive abilities due to the presence of a functional system that exists in the human brain that is absent in the non-human brain. This system, the frontal feedback system, was born in the hominin brain when the great phylogenetic expansion of the prefrontal cortex relative to posterior sensory regions surpassed a critical threshold. Surpassing that threshold effectively reversed the preferred direction of information flow in the highest association regions of the neocortex, producing the (...) frontal feedback system. This reversal was from the caudo-rostral bias characteristic of non-human, or pre-human, brain dynamics to a rostro-caudal bias characteristic of modern human brain dynamics. The frontal feedback system works through frontal motor routines, or action schemes, manipulating the release and reconstruction of stored sensory memories in posterior sensory areas. As an obligatory feature of frontal feedback, a central character, or self, emerges within this cortical network that manifests itself as agent in these reconstructions as well as in the experience of sensory perceptions. Dynamical-systems modeling of cortical interactions is combined in the paper with recent neuroimaging studies of “resting-state” brain activity to bridge the gap between microscopic and macroscopic levels of cortical behavior. This synthesis is used to support the proposal of an information flow reversal occurring in the hominin brain and also to explain how such a reversal generates the wide variety of cognitive and experiential phenomena that many consider to be uniquely human. (shrink)

This article pursues overlapping points about ontology, philosophical method, and our kinship with and difference from nonhuman animals. The ontological point is that being is determinately different in different places not because of differences, or even a space, already given in advance, but in virtue of a negative in being that is regional and rooted in place, which Mer-leau-Ponty calls the “hollow.” The methodological point is that we tend to miss this ontological point because we are inclined to what I (...) call transportable thinking, which conceives of things and spatial determinacy itself as being what they are independent of where they are. I argue that we are inclined this way because, in contrast to other animals, we have a weak sense of where we are. We are lost animals. To compensate for lostness, we abstract ourselves from place and conceptualize ourselves and things by way of a transportable, Cartesian “view from above.”. (shrink)

Studies that compare human and animal behaviour suspend prejudices about mind, body and their relation, by approaching thinking in terms of behaviour. Yet comparative approaches typically engage another prejudice, motivated by human social and bodily experience: taking the lone animal as the unit of comparison. This prejudice informs Heidegger’s and Merleau-Ponty’s comparative studies, and conceals something important: that animals moving as a group in an environment can develop new sorts of “sense.” The study of animal group-life suggests a new way (...) of thinking about the creation of sense, about the body, the brain, and the relation between thinking and nature. (shrink)

The Miyashita experiments are very interesting and the results should be examined from a viewpoint of attractor dynamics. Amit's target article shows a path toward realistic modeling by artificial neural networks (ANN), but it is not necessarily the only one. I introduce another model that can explain a substantial part of the empirical observations and makes an interesting prediction. This model consists of such units that have nonmonotonic input-output characteristics with local inhibition neurons.

Event-related potentials (ERPs) – neglected almost entirely by Wright & Liley – allow objective investigation of information processing in the brain. The application of chaos theory to such an analysis broadens this possibility. Through the use of the point correlation dimension (PD2) accurate dimensional analysis of different Event-Related Potential components such as the P3 wave is possible.

Simulation of brain dynamics requires the use of accurate empirical data. This commentary points out major errors in some of the empirical data used in Wright & Laley's simulation. The simulation is quantitatively very different from the real cortex, and may also have important qualitative differences.

Every immediate memory is unique; it is therefore unlikely to consist of an attractor or even a combination of attractors. In the present state of knowledge about the chemistry of synaptic transmission, there is no reason to look beyond neurons that directly receive sensory afferents for the afterdischarges that correspond to active memories.