According to the dominant computational approach in cognitive science, cognitive agents are digital computers; according to the alternative approach, they are dynamical systems. This target article attempts to articulate and support the dynamical hypothesis. The dynamical hypothesis has two major components: the nature hypothesis (cognitive agents are dynamical systems) and the knowledge hypothesis (cognitive agents can be understood dynamically). A wide range of objections to this hypothesis can be rebutted. The conclusion is that cognitive systems may well be dynamical systems, (...) and only sustained empirical research in cognitive science will determine the extent to which that is true. (shrink)

Some dichotomies related to modeling electrocortical activities are analyzed. Attractor neural networks versus biologically motivated models, near-equilibrium versus nonequilibrium processes, linear and nonlinear dynamics, stochastic and chaotic patterns, local and global scale simulation of cortical activities are discussed.

How do minds emerge from developing brains? According to the representational features of cortex are built from the dynamic interaction between neural growth mechanisms and environmentally derived neural activity. Contrary to popular selectionist models that emphasize regressive mechanisms, the neurobiological evidence suggests that this growth is a progressive increase in the representational properties of cortex. The interaction between the environment and neural growth results in a flexible type of learning: minimizes the need for prespecification in accordance with recent neurobiological evidence (...) that the developing cerebral cortex is largely free of domain-specific structure. Instead, the representational properties of cortex are built by the nature of the problem domain confronting it. This uniquely powerful and general learning strategy undermines the central assumption of classical learnability theory, that the learning properties of a system can be deduced from a fixed computational architecture. Neural constructivism suggests that the evolutionary emergence of neocortex in mammals is a progression toward more flexible representational structures, in contrast to the popular view of cortical evolution as an increase in innate, specialized circuits. Human cortical postnatal development is also more extensive and protracted than generally supposed, suggesting that cortex has evolved so as to maximize the capacity of environmental structure to shape its structure and function through constructive learning. (shrink)

A unifing theory of spatiotemporal brain dynamics should incorporate multiple spatial and temporal scales. Between the microscopic (local) and macroscopic (global) components proposed by Nunez, mesoscopic (intermediate-range) elements should be integral parts of models. The corresponding mathematical formalism requires tools of nonlinear dynamics and the use of aperiodic (chaotic) attractors. Some relations between local-mesoscopic and mesoscopic-global components are outlined.

The metaphorical nature of biological language is examined and the use of metaphors for providing the linguistic context in which similarities and differences are made is described. Certain pervasive metaphors which are characterised by systemic properties are noted, and in order to provide some focus to the study, systemic metaphors associated with machine, text and organism are discussed. Other systemic metaphors such as society and circuit are also reported. Some details concerning interrelations between automaton and organism are presented in the (...) light of the previous discussion.An approach towards the analysis of biosystem metaphors is outlined which relates part-whole, organisational level and systemic metaphors in a single model. Examples are provided throughout the discussion and mainly come from computing. The potential for metaphorical transfers between these domains is considered. (shrink)

There is some complementarity of models for the origin of the electroencephalogram (EEG) and neural network models for information storage in brainlike systems. From the EEG models of Freeman, of Nunez, and of the authors' group we argue that the wavelike processes revealed in the EEG exhibit linear and near-equilibrium dynamics at macroscopic scale, despite extremely nonlinear – probably chaotic – dynamics at microscopic scale. Simulations of cortical neuronal interactions at global and microscopic scales are then presented. The simulations depend (...) on anatomical and physiological estimates of synaptic densities, coupling symmetries, synaptic gain, dendritic time constants, and axonal delays. It is shown that the frequency content, wave velocities, frequency/wavenumber spectra and response to cortical activation of the electrocorticogram (ECoG) can be reproduced by a “lumped” simulation treating small cortical areas as single-function units. The corresponding cellular neural network simulation has properties that include those of attractor neural networks proposed by Amit and by Parisi. Within the simulations at both scales, sharp transitions occur between low and high cell firing rates. These transitions may form a basis for neural interactions across scale. To maintain overall cortical dynamics in the normal low firing-rate range, interactions between the cortex and the subcortical systems are required to prevent runaway global excitation. Thus, the interaction of cortex and subcortex via corticostriatal and related pathways may partly regulate global dynamics by a principle analogous to adiabatic control of artificial neural networks. (shrink)

Neural organization: Structure, function, and dynamics shows how theory and experiment can supplement each other in an integrated, evolving account of the brain's structure, function, and dynamics. Structure: Studies of brain function and dynamics build on and contribute to an understanding of many brain regions, the neural circuits that constitute them, and their spatial relations. We emphasize Szentágothai's modular architectonics principle, but also stress the importance of the microcomplexes of cerebellar circuitry and the lamellae of hippocampus. Function: Control of eye (...) movements, reaching and grasping, cognitive maps, and the roles of vision receive a functional decomposition in terms of schemas. Hypotheses as to how each schema is implemented through the interaction of specific brain regions provide the basis for modeling the overall function by neural networks constrained by neural data. Synthetic PET integrates modeling of primate circuitry with data from human brain imaging. Dynamics: Dynamic system theory analyzes spatiotemporal neural phenomena, such as oscillatory and chaotic activity in both single neurons and neural networks, the self-organizing development and plasticity of ordered neural structures, and learning and memory phenomena associated with synaptic modification. Rhythm generation involves multiple levels of analysis, from intrinsic cellular processes to loops involving multiple brain regions. A variety of rhythms are related to memory functions. The Précis presents a multifaceted case study of the hippocampus. We conclude with the claim that language and other cognitive processes can be fruitfully studied within the framework of neural organization that the authors have charted with John Szentágothai. (shrink)

This paper proposes that neurodynamic system theory may be used to connect structural and functional aspects of neural organization. The paper claims that generalized causal dynamic models are proper tools for describing the self-organizing mechanism of the nervous system. In particular, it is pointed out that ontogeny, development, normal performance, learning, and plasticity, can be treated by coherent concepts and formalism. Taking into account the self-referential character of the brain, autopoiesis, endophysics and hermeneutics are offered as elements of a poststructuralist (...) brain (-mind-computer) theory. (shrink)