In brains ofhigher vertebrates, the functional segregation of local areas that differ in their anatomy and physiology contrasts sharply with their global ination during perception and behavior. In this paper, we introduce a measure, called neural complexity (CN), that captures the interplay between these two dental aspects of brain organization. We express functional segregation within a neural system in terms of the relative statistical independence of small subsets of the system and functional integration in terms of signicant deviations from independence of large subsets. CN is then obtained from estimates of the average deviation from statistical independence for subsets of increasing size. CN is shown to be high when functional segregation coexists with integration and to be low when the components of a system are either completely independent (segregated) or completely dependent (integrated). We apply this complexity measure in computer simulations of cortical areas to examine how some basic principles of neuroanatomical organization constrain brain dynamics. We show that the connectivity patterns of the cerebral cortex, such as a high density of connections, strong local connectivity ornizing cells into neuronal groups, patchiness in the connectivity am neuronal groups, and prevalent reciprocal connections, are associated with hi values of CN. The approach outlined here may prove useful in analyzing complexity in other biological domains such as gene regulation and embryogenesis.A long-standing controversy in neuroscience has set localizationist views of brain function against holist views. The former emphasize the specificity and modularity of brain organization, whereas the latter stress global functions, mass action, and Gestalt phenomena (1). This controversy mirrors two contrasting properties that coexist in the brains of higher vertebrates: the functional segregation of different brain regions and their integration in perception and behavior. In this paper, we attempt to provide a measure that reflects their interaction. The understanding of these two aspects of brain organization is central to any theoretical description of brain function (2-4).Evidence that the brain is functionally segregated at multiple levels of organization is overwhelming. Developmental events and activity-dependent selection result in the formation of neuronal groups-local collectives of strongly interconnected cells sharing inputs, outputs, and response properties (2). Each group tends to be connected to a specific subset of other groups and, directly or indirectly, to specific sensory afferents. Different groups within a given brain area (e.g., a primary visual area) can show preferential responses for different stimulus orientations or retinotopic positions. Moreover, at the level of areas or subdivisions of areas, there is functional segregation for different stimulus attributes such as color, motion, and form (5-7). Further evidence for functional segregation in a variety of systems is provided by the analysis o...

Degeneracy, the ability of elements that are structurally different to perform the same function or yield the same output, is a well known characteristic of the genetic code and immune systems. Here, we point out that degeneracy is a ubiquitous biological property and argue that it is a feature of complexity at genetic, cellular, system, and population levels. Furthermore, it is both necessary for, and an inevitable outcome of, natural selection.

Conventional approaches to understanding consciousness are generally concerned with the contribution of specific brain areas or groups of neurons. By contrast, it is considered here what kinds of neural processes can account for key properties of conscious experience. Applying measures of neural integration and complexity, together with an analysis of extensive neurological data, leads to a testable proposal-the dynamic core hypothesis-about the properties of the neural substrate of consciousness.

The understanding of the structural and dynamic complexity of mammalian brains is greatly facilitated by computer simulations. We present here a detailed large-scale thalamocortical model based on experimental measures in several mammalian species. The model spans three anatomical scales. (i) It is based on global (white-matter) thalamocortical anatomy obtained by means of diffusion tensor imaging (DTI) of a human brain. (ii) It includes multiple thalamic nuclei and six-layered cortical microcircuitry based on in vitro labeling and three-dimensional reconstruction of single neurons of cat visual cortex. (iii) It has 22 basic types of neurons with appropriate laminar distribution of their branching dendritic trees. The model simulates one million multicompartmental spiking neurons calibrated to reproduce known types of responses recorded in vitro in rats. It has almost half a billion synapses with appropriate receptor kinetics, short-term plasticity, and long-term dendritic spike-timing-dependent synaptic plasticity (dendritic STDP). The model exhibits behavioral regimes of normal brain activity that were not explicitly built-in but emerged spontaneously as the result of interactions among anatomical and dynamic processes. We describe spontaneous activity, sensitivity to changes in individual neurons, emergence of waves and rhythms, and functional connectivity on different scales.brain models ͉ cerebral cortex ͉ diffusion tensor imaging ͉ oscillations ͉ spike-timing-dependent synaptic plasticity T he last decade has seen great progress in our understanding of brain dynamics and underlying neuronal mechanisms. Linking these mechanisms to behavior such as perception is facilitated by large-scale computer simulations of anatomically detailed models of the cerebral cortex (1-3). Although these models have stressed microcircuitry and local dynamics, they have not incorporated multiple cortical regions, corticocortical connections, and synaptic plasticity. In the present article, we describe a large-scale model of the mammalian thalamocortical system that includes these components.Spatiotemporal dynamics of the simulation show that some features of normal brain activity, although not explicitly built into the model, emerged spontaneously. The model exhibited selfsustained activity in the absence of any external sources of input. The behavior of the model was extremely sensitive to contributions of individual spikes: adding or removing one spike of one neuron completely changed the state of the entire cortex in Ͻ0.5 s. Regions of the model brain exhibited collective waves and oscillations of local field potentials in the delta, alpha, and beta ranges, similar to those recorded in humans (4). Simulated fMRI signals exhibited slow fronto-parietal anti-phase oscillations, as seen in humans (5).The shape and connectivity of the model were determined by diffusion tensor imaging (DTI) data for a human brain. Experimental data from three species, human, cat, and rat, were incorporated to build other details of the model.Model ...