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This article investigates the relationship between the interconnectivity and simulated dynamics of the thalamocortical system from the specific perspective of attempting to maximize the diversity of cortical states. This is achieved by designing the dynamics such that they favor opposing activity between adjacent regions, thus promoting dynamic diversity while avoiding widespread activation or de-activation. The anti-ferromagnetic Ising model with Metropolis dynamics is adopted and applied to four variations of the large-scale connectivity of the cat thalamocortical system: (a) considering only cortical regions and connections; (b) considering the entire thalamocortical system; (c) the same as in (b) but with the thalamic connections rewired so as to maintain the statistics of node degree and node degree correlations; and (d) as in (b) but with attenuated weights of the connections between cortical and thalamic nodes. A series of interesting findings are obtained, including the identification of specific substructures revealed by correlations between the activity of adjacent regions in case (a) and a pronounced effect of the thalamic connections in splitting the thalamocortical regions into two large groups of nearly homogenous opposite activation (i.e. cortical regions and thalamic nuclei, respectively) in cases (b) and (c). The latter effect is due to the existence of dense connections between cortical and thalamic regions and the lack of interconnectivity between the latter. Another interesting result regarding case (d) is the fact that the pattern of thalamic correlations tend to mirror that of the cortical regions. The possibility to control the level of correlation between the cortical regions by varying the strength of thalamocortical connections is also identified and discussed.

Item does not contain fulltext Understanding the amazingly complex human cerebral cortex requires a map (or parcellation) of its major subdivisions, known as cortical areas. Making an accurate areal map has been a century-old objective in neuroscience. Using multi-modal magnetic resonance images from the Human Connectome Project (HCP) and an objective semi-automated neuroanatomical approach, we delineated 180 areas per hemisphere bounded by sharp changes in cortical architecture, function, connectivity, and/or topography in a precisely aligned group average of 210 healthy young adults. We characterized 97 new areas and 83 areas previously reported using post-mortem microscopy or other specialized study-specific approaches. To enable automated delineation and identification of these areas in new HCP subjects and in future studies, we trained a machine-learning classifier to recognize the multi-modal 'fingerprint' of each cortical area. This classifier detected the presence of 96.6% of the cortical areas in new subjects, replicated the group parcellation, and could correctly locate areas in individuals with atypical parcellations. The freely available parcellation and classifier will enable substantially improved neuroanatomical precision for studies of the structural and functional organization of human cerebral cortex and its variation across individuals and in development, aging, and disease.

The mature adrenal cortex is dependent upon proopiomelanocortin (POMC)-derived peptides for the maintenance of its size, structure, and endocrine function. Recent studies in mice genetically deficient in POMC have suggested that early exposure to POMC-derived peptides might also be necessary for the development of a functionally competent adrenal. We examined adrenal morphology and function in an independent line of mice lacking all POMC-derived peptides (Pomc-/-). Adrenal glands were found in all mice, although the glands of Pomc-/- mice had markedly reduced weight compared with control animals (0.5 +/- 0.1 vs. 2.1 +/- 0.1 mg, respectively; P0.05) and had disrupted cortical architecture. In Pomc-/- mice, plasma corticosterone was undetectable, and plasma aldosterone was significantly reduced compared with wild-type mice (498 +/- 88 vs. 1845 +/- 168 nmol/liter, respectively; P0.001). Heterozygous mice (Pomc+/-) had smaller adrenal glands with significantly lower levels of corticosterone both basally and in response to CRH and ACTH than wild-type mice, indicating that two functional copies of the Pomc gene are necessary to support the fully normal function of the hypothalamic-pituitary-adrenal axis. Three-month-old Pomc-/- mice were treated for 10 d with a highly specific ACTH analog. This treatment restored adrenal weight, cortical morphology, and plasma corticosterone to the levels seen in wild-type littermates. In conclusion, murine adrenal glands can develop without exposure to endogenous POMC-derived peptides during fetal and neonatal life. Although such glands are atrophic and hypofunctional, exposure to ACTH alone can restore their size, morphology, and corticosterone secretion.

Female Tg rasH2 (CB6F1/Jic-TgrasH2@Tac) mice were administered water once daily, water twice daily with 8 or 12 hours between doses, 1% sodium dodecyl sulfate in water (1% SDS) once daily, or 1% SDS twice daily with 12 hours between doses by oral gavage at volumes of 10 ml/kg/day for 28 or 29 consecutive days. A control group of mice received no treatment and no sham manipulation. There were no significant differences in body weight or food consumption between treated groups and untreated control mice. Mean weights of spleens, livers, and thymuses were lower than control values in most groups of mice subjected to gavage. Focal or multifocal loss of thymic cortical architecture was observed in 13 of 50 mice distributed among all groups (including naïve controls), however only in one instance was this finding suggestive of a precursor to neoplasia. This study demonstrated that Tg rasH2 mice can tolerate once or twice daily gavage dosing with water or vehicle containing 1% SDS. Loss of thymic cortical architecture was a common incidental finding in female Tg rasH2 mice.

The layout of functional cortical maps exhibits a high degree of interindividual variability that may account for individual differences in sensory and cognitive abilities. By quantitatively assessing the interindividual variability of orientation preference columns in the primary visual cortex, we demonstrate that column sizes and shapes as well as a measure of the homogeneity of column sizes across the visual cortex are significantly clustered in genetically related animals and in the two hemispheres of individual brains. Taking the developmental timetable of column formation into account, our data indicate a substantial genetic influence on the developmental specification of visual cortical architecture and suggest ways in which genetic information may influence an individual's visual abilities.

According to the structural model of prefrontal cortex, the pattern of corticocortical connections is intricately linked to cortical architecture (Cereb. Cortex 7 (1997) 635). We further explored this model by using quantitative methods to describe the structure and connections of prefrontal cortices. Multi-parameter analyses distinguished different cortical types, positioning at one extreme medial and orbitofrontal (limbic) cortices, and at the other extreme, lateral (eulaminate) cortices. The structural model accurately predicted the laminar pattern of connections, and the relative distribution of connections within cortical layers, based on cortical type. This model may provide the foundation to predict the nature of corticocortical processing and its disruption in psychiatric and neurologic diseases, where neuropathology affects specific types of neurons and layers.

We present a model of a cortical-type architecture that uses a latency coding scheme to process inputs as fast as possible given certain neuronal constraints while at the same time keeping the potential to perform a detailed analysis. The basic processing elements of our network are columnar structures, called minicolumns. We propose that these minicolumns are the prime constituents of cortical architecture.

The purpose of the present study was to test the hypothesis that superficial and deep layers within a single cerebral region influence cerebral functions and behaviors in different ways. For this test, we selected posterior middle suprasylvian (pMS) sulcal cortex of the cat, a suspected homolog of the area V5 complex of primates, because the region has been implicated in several visually guided behaviors. Cats were trained on three tasks: (1) discrimination of direction of motion; (2) discrimination of static patterns partially obscured by static or moving masks; and (3) visual detection and orienting. Cooling of cryoloops in contact with pMS sulcal cortex to 8+/-1 degrees C selectively and completely impaired performance on the two motion discrimination tasks (1 and 2), while leaving the detection and orienting task (task 3) unimpaired. Further cooling to 3 degrees C resulted in an additional complete impairment of task 3. The 8 degrees C temperature resulted in silencing of neuronal activity in the supragranular layers (I-III) and the 3 degrees C temperature silenced activity throughout the thickness of pMS sulcal cortex. The variation in behavioral performance with covariation of cryoloop temperature and vertical, but not lateral, spread of deactivation shows that deactivation of superficial cerebral layers alone was sufficient to completely impair performance on the two motion discrimination tasks, whereas additional deactivation of the deep layers was essential to block performance on the detection and orienting task. Thus, these results show a functional bipartite division of labor between upper and lower cortical layers that is supported by efferent connectional anatomy. Similar bipartite division into upper and lower layers may be a general feature of cerebral cortical architecture, signal processing and guidance of behavior.

Mosaicism is the phenomenon in which a fraction of, rather than all, germ-line and somatic cells contain a mutation or chromosomal abnormality. It occurs in all genetic disorders in which spontaneous mutations occur and has important clinical consequences for the assessment of patients with localized expression of multisystem disorders, for genetic counseling, and for molecular diagnostic testing.1,2 Tuberous sclerosis is an autosomal dominant disorder characterized by the development of unusual tumor-like growths (hamartomas) in multiple organs.3,4 Arguably the most important hamartomas are cerebral cortical tubers, which are regions of abnormal cortical architecture with distinctive large neuronal cells. Cortical . . .

This paper presents an approach for mapping the human cortical architecture in vivo based on quantitative MRI indices of myelin. We automatically construct laminar profiles in several primary cortical areas and investigate different sampling strategies. The results demonstrate that our method is able to distinguish these areas at specific cortical depths.

While many models of the dynamics and interactions of single neurons are extant, analogous constructs which attempt to describe large-scale (≥O(108)) neuronal activity are few and far between. Optical imaging of the visual cortex makes such macroscopic neuronal activity accessible. Symmetries latent in the cortical architecture are used here to develop a scheme for analyzing such images. In this way, intrinsic modes of cortical response can be uncovered, using minimal assumptions. Some of these modes correspond to already-familiar features of the functional architecture, and it is highly likely that others hold physiological relevance as well. Finally, the number of such modes that would be required in a more fully developed model (incorporating cortical dynamics) is approximated.

Literature studies showed that the fibers connected to gyri are significantly denser than those connected to sulci. Therefore, we hypothesize that gyral, sulcal and cortical brain networks might exhibit different graph properties and functional interactions that reflect the organizational principles of cortical architecture. In this way, we evaluated the graphical properties of the structural brain networks and the functional connectivities among brain networks which are composed of gyral regions of interest (ROI) (G-networks), sulcal ROIs (S-networks) and mixed gyral and sulcal ROIs (C-networks). The results demonstrated that G-networks have the highest global and local economical properties and the strongest small-worldness. In contrast, S-networks have the lowest global and local economical properties and the weakest small-worldness. Meanwhile, the overall functional connectivity strength among G-networks is stronger than those in S-networks, and those in C-networks are in between. The results indicate that gyri may play a hub role in human brains.

Even in absence of sensory stimuli cortical networks exhibit complex, self-organized activity patterns. While the function of those spontaneous patterns of activation remains poorly understood, recent studies both in vivo and in vitro have demonstrated that neocortical neurons activate in a surprisingly similar sequential order both spontaneously and following input into cortex. For example, neurons that tend to fire earlier within spontaneous bursts of activity also fire earlier than other neurons in response to sensory stimuli. These “default patterns” can last hundreds of milliseconds and are strongly conserved under a variety of conditions. In this paper, we will review recent evidence for these default patterns at the local cortical level. We speculate that cortical architecture imposes common constraints on spontaneous and evoked activity flow, which result in the similarity of the patterns.

Background/Aims: Ultrasound is the preferred imaging modality in nephrology. In many kidney diseases, however, more accurate methods are needed to distinguish between relevant intrarenal structures. MRI could be a solution, although the use of MRI contrast has caused severe complications in some cases, and invasive kidney biopsies may follow, even though such small specimens traditionally provide inaccurate quantitative data. We evaluated the usefulness of MRI volumetry and quantitative kidney biopsies to assess glomerular number and volume as well as cortical volume. Methods: We specifically highlight an experimental study in which different MRI scans were performed in healthy pigs as well as pigs with unilateral ureteral obstruction to assess intrarenal volume. Single-kidney glomerular filtration rate (GFR) was measured using ureteral cannulation and 51Cr-EDTA. Kidney biopsies were taken and evaluated employing stereological techniques to measure number and volume of glomeruli. Pigs were sacrificed and kidneys were removed for stereological analysis. Non-contrast-based MRI intrarenal volumes were – without significant difference to intrarenal volumes – obtained using contrast-enhanced MRI and ex vivo techniques. Results: Kidney biopsies gave valid estimates regarding quantitative parameters, such as mean number and volume of glomeruli in the cortex. Different structural parameters correlated with kidney GFR with high, although varying, correlation coefficients. Conclusion: Non-contrast MRI is suitable for estimating intrarenal volumes in healthy and diseased kidneys. We advocate further research in diagnostic modalities combining MRI and biopsies. Major challenges are the cortical architecture and heterogeneous distribution of glomeruli within the kidney.

The cerebral cortex is a vastly complex structure consisting of multiple distinct populations of neurons residing in functionally specialized cortical compartments. A fundamental goal in systems neuroscience is to understand the interactions among cortical neurons and their relationship to behavior. It is hypothesized that dynamic activity patterns, such as oscillations in global neuronal activity, could span large, heterogeneous populations of cortical neurons in such a manner as to bind together the activity of otherwise disparate cortical networks. Little is know about the mechanisms by which such global oscillatory patterns entrain cortical networks or the contribution of such activity patterns to cortical function. An important step toward elucidating the role of such patterned activity in cortical information processing is understanding these interactions at the local circuit level. Here, we highlight recent findings that provide insight into how dynamic activity patterns affect specific neuronal populations and circuits.

The degree of cortical folding (GI) and the relation between sulci and borders of cyto- and receptorarchitectonically defined areas were analyzed in postmortem human brains. The GI reaches adult levels (with highest values in the association cortices) around birth and does not decrease during aging. It shows a sex-dependent left-over-right asymmetry. Sulci and borders of architectonical areas coincide only in a few examples; thus, sulci are not generally valid landmarks of the microstructural organization of the cortex. Individual sulci were studied in 3D-reconstructed MRI sequences of living brains. A considerable intersubject variability of the distances between the sulcal surfaces of individual brains and their mean sulcal surfaces is apparent. The depth of the central sulcus varies with manual skill and handedness.

As the number of devices available per chip continues to increase, the computational potential of future computer architectures grows likewise. While this is a clear benefit for future computing devices, future chips will also likely suffer from more faulty devices and increased power consumption. It is also likely that these chips will be difficult to program if the current trend of adding more parallel cores continues to follow in the future. However, recent advances in neuroscientific understanding make parallel computing devices modeled after the human neocortex a plausible, attractive, fault-tolerant, and energy-efficient possibility.In this paper we describe a GPGPU extension to an intelligent model based on the mammalian neocortex. The GPGPU is a readily-available architecture that fits well with the parallel cortical architecture inspired by the basic building blocks of the human brain. Using NVIDIA's CUDA framework, we have achieved up to 273x speedup over our unoptimized C++ serial implementation. We also consider two inefficiencies inherent to our initial design: multiple kernel-launch overhead and poor utilization of GPGPU resources. We propose using a software work-queue structure to solve the former, and pipelining the cortical architecture during training phase for the latter. Additionally, from our success in extending our model to the GPU, we speculate the necessary hardware requirements for simulating the computational abilities of mammalian brains.

In modern computer design considerable care is taken to arrange the components in such a way that wiring is kept to a minimum. Certain features of cortical structure — the mappings, stripes and blobs within areas, and areas themselves — are somewhat reminiscent of the layout of computer components, and suggest that the cortex may also be organized so as to economize on neuronal ‘wiring'. One important difference between the brain and a computer is that the wiring in the brain takes the form of elaborate branched structures, namely axonal trees. In this article, it is argued that an assessment of the efficiency of cortical wiring must take account of the branching rules of these trees.

This chapter provides a comprehensive review of the existing imaging techniques for assessing trabecular and cortical architecture as well as emerging advances in these areas. A brief description of the physics behind X-ray computed tomography and magnetic resonance imaging is provided to lay the foundation for review of image acquisition techniques. Next, the authors review at length image analysis algorithms that are used to derive measurements of bone structure from these images. Finally, to present the diagnostic capabilities of the imaging methods, the authors discuss published in vitro and in vivo studies on the relationships of the image-derived bone measures with bone strength, biomechanics, fracture discrimination, and osteoporotic status.

Synaptic dysfunction is a key process in the evolution of many neurodegenerative diseases, with synaptic loss preceding that of neuronal cell bodies. In Alzheimer, Huntington, and prion diseases early synaptic changes correlate with cognitive and motor decline, and altered synaptic function may also underlie deficits in a number of psychiatric and neurodevelopmental conditions. The formation, remodelling and elimination of spines and synapses are continual physiological processes, moulding cortical architecture, underpinning the abilities to learn and remember. In disease, however, particularly in protein misfolding neurodegenerative disorders, lost synapses are not replaced and this loss is followed by neuronal death. These two processes are separately regulated, with mechanistic, spatial and temporal segregation of the death 'routines' of synapses and cell bodies. Recent insights into the reversibility of synaptic dysfunction in a mouse model of prion disease at neurophysiological, behavioral and morphological levels call for a deeper analysis of the mechanisms underlying neurotoxicity at the synapse, and have important implications for therapy of prion and other neurodegenerative disorders.

The study of architectonic differentiation in the cortex is advanced by the articulation of objective definitions based on clear features of the cortical architecture. We adopted areal density profiles as a means of sampling the cortex. On the profiles, we isolated and quantified the density of individual cortical layers. These features may serve as criteria in objective definitions in a way that builds on qualitative observations found in the classical literature. A preprocessing procedure was introduced to overcome artefacts in the density profiles caused by the partial overlap of neighboring neuronal layers and cortical folding. We applied this method to the medial half of the orbital frontal cortex in specimens drawn from 10 human postmortem brain hemispheres. The measurements successfully confirmed the existence of several qualitatively observed areas (architectonic areas 14c, 14r, 11m, 11 and 13). The selection of specific sampling parameters was justified on the basis of simultaneous measurements of the cortical morphology which demonstrate its influence on the appearance of the cortical layers. We also examined the robustness of the measuring procedure by analyzing the outcome of varying systematically the sampling parameters. We describe here a novel method of sampling the cortex for architectonic analysis and demonstrate its application on histological sections obtained from the medial half of the human orbitofrontal cortex.

Visual cortex of primates consists of a large number of discrete cortical areas and less well-defined cortical zones that are organized according to a number of principles including (1) topographic organization, (2) horizontal segregation of functionally distinct cortical connections, (3) horizontal segregation of functional properties, and (4) columnar homogeneity of function. Overall, these organizing principles of visual cortical areas are expressed as multiplexed functional maps within each cortical subdivision. An understanding of the types of information that are systematically mapped within a cortical area provides insight into the local computations performed within a given area and provides insight into how these local computations are communicated to functionally higher and lower cortical areas.

The ability to detect brain anatomy and pathophysiology with MRI is limited by the contrast-to-noise ratio (CNR), which depends on the contrast mechanism used and the spatial resolution. In this work, we show that in MRI of the human brain, large improvements in contrast to noise in high-resolution images are possible by exploiting the MRI signal phase at high magnetic field strength. Using gradient-echo MRI at 7.0 tesla and a multichannel detector, a nominal voxel size of 0.24 x 0.24 x 1.0 mm3 (58 nl) was achieved. At this resolution, a strong phase contrast was observed both between as well as within gray matter (GM) and white matter (WM). In gradient-echo phase images obtained on normal volunteers at this high resolution, the CNR between GM and WM ranged from 3:1 to 20:1 over the cortex. This CNR is an almost 10-fold improvement over conventional MRI techniques that do not use image phase, and it is an approximately 100-fold improvement when including the gains in resolution from high-field and multichannel detection. Within WM, phase contrast appeared to be associated with the major fiber bundles, whereas contrast within GM was suggestive of the underlying layer structure. The observed phase contrast is attributed to local variations in magnetic susceptibility, which, at least in part, appeared to originate from iron stores. The ability to detect cortical substructure from MRI phase contrast at high field is expected to greatly enhance the study of human brain anatomy in vivo.

The uniformity of the cortical architecture and the ability of functions to move to different areas of cortex following early damage strongly suggest that there is a single basic learning algorithm for extracting underlying structure from richly structured, high-dimensional sensory data. There have been many attempts to design such an algorithm, but until recently they all suffered from serious computational weaknesses. This chapter describes several of the proposed algorithms and shows how they can be combined to produce hybrid methods that work efficiently in networks with many layers and millions of adaptive connections.

Lamination of neurons and fibre projections is a fundamental organizational principle of the mammalian cerebral cortex. A laminated organization is likely to be essential for cortical function, as studies in mutant mice have revealed causal relationships between lamination defects and functional deficits. Unveiling the determinants of the laminated cortical architecture will contribute to our understanding of how cortical functions have evolved in phylogenetic and ontogenetic development. Recently, the hippocampus, with its clearly segregated cell and fibre layers, has become a major subject of studies on cortical lamination.

The analysis of complex networks has revealed patterns of organization in a variety of natural and artificial systems, including neuronal networks of the brain at multiple scales. In this paper, we describe a novel analysis of the large-scale connectivity between regions of the mammalian cerebral cortex, utilizing a set of hierarchical measurements proposed recently. We examine previously identified functional clusters of brain regions in macaque visual cortex and cat cortex and find significant differences between such clusters in terms of several hierarchical measures, revealing differences in how these clusters are embedded in the overall cortical architecture. For example, the ventral cluster of visual cortex maintains structurally more segregated, less divergent connections than the dorsal cluster, which may point to functionally different roles of their constituent brain regions., 17 pages, 6 figures

The evolution of the structure of the neocortex is one of the most important events in the chain that led to the human brain. The paleontological evidence shows that the human brain expanded two-fold in size over three million years, while modern chimpanzees still have brains about the size of the earliest hominids. The brains of chimpanzees and modern humans have a similar anatomy, so the vast difference in their size (400ml vs 1400ml) is due to an expansion of the cerebral cortex, rather than the development of entirely novel brain structures. Here we explore in what way the neocortical circuits are common to all mammalian species. We define a canonical structure that can be identified in all cortical areas and in all land-based mammalian species where data are available. This structure has recurrent excitatory and inhibitory loops formed by local neurons as a feature of its design. Quantitative studies from our laboratory show that the input from the sensory periphery forms less than one percent of the total input to the primary visual cortex in the cat. Thus the major synaptic input to a cortical neuron comes from its neighbors. We provide a conceptual model that offers an operational view of how the canonical circuit of the neocortex might operate.

We describe human area MT histologically based on the observer independent analysis of cortical myeloarchiteture, multiple complementary staining techniques and 3-D reconstruction. The topography of an architectonic field that presented constant structural characteristics across specimens was studied in relation to the sulcal geography of the occipito-temporal region. Objective and semi-automated analysis of local microstructure revealed a distinct cortical architecture and matched topographically the localization of MT derived from functional imaging. MT was localized by the histotopographic method in relation to definite macroscopic landmarks. This study demonstrates a new set of distinguishing architectonic features of human MT that permit localization on structural grounds and suggests that the characteristic laminar structure of this area may be related to its unique pattern of connections and to its role in visual perception.

A large-scale computational model of a local patch of input layer 4 [Formula: see text] of the primary visual cortex (V1) of the macaque monkey, together with a coarse-grained reduction of the model, are used to understand potential effects of cortical architecture upon neuronal performance. Both the large-scale point neuron model and its asymptotic reduction are described. The work focuses upon orientation preference and selectivity, and upon the spatial distribution of neuronal responses across the cortical layer. Emphasis is given to the role of cortical architecture (the geometry of synaptic connectivity, of the ordered and disordered structure of input feature maps, and of their interplay) as mechanisms underlying cortical responses within the model. Specifically: (i) Distinct characteristics of model neuronal responses (firing rates and orientation selectivity) as they depend upon the neuron's location within the cortical layer relative to the pinwheel centers of the map of orientation preference; (ii) A time independent (DC) elevation in cortico-cortical conductances within the model, in contrast to a "push-pull" antagonism between excitation and inhibition; (iii) The use of asymptotic analysis to unveil mechanisms which underly these performances of the model; (iv) A discussion of emerging experimental data. The work illustrates that large-scale scientific computation--coupled together with analytical reduction, mathematical analysis, and experimental data, can provide significant understanding and intuition about the possible mechanisms of cortical response. It also illustrates that the idealization which is a necessary part of theoretical modeling can outline in sharp relief the consequences of differing alternative interpretations and mechanisms--with final arbiter being a body of experimental evidence whose measurements address the consequences of these analyses.

Neural associative memories provide efficient methods for information retrieval which can be superior to classical algorithms such as look-up-tables or hashing. The first part of this work extends the classical theory of binary associative networks. It is shown that matrix compression enables the full exploitation of the physical memory, while previously the storage capacity was assumed to be about 70 percent. Further analysis showed that using spiking neurons can improve drastically the fault tolerance against false one-entries and enable fast separation of superimposed patterns. The classical analysis relies on a binomial approximation of the neuron potential distribution. This is shown to underestimate massively the variance leading to overestimation of storage capacity and fault tolerance. A better approximation reveals that the variance grows with the square of the address pattern activity, not just linearly. The second part of this work contributes to brain theory. Neural associative memory can be interpreted as an abstract model for cortical networks, where the stored patterns correspond to assemblies of strongly coupled neurons. From the spike counter model, a biological version was derived showing a similar performance as the technical model. In order to investigate the binding problem, two reciprocally connected cortical areas were implemented. The lower area was modeled similar to the primary visual cortex, the higher area as a spiking associative memory. When presenting several superimposed shapes, scene segmentation happens on a time scale of hundreds of milliseconds. This is one order of magnitude slower than in earlier proposed models. However, the model is consistent with various neurophysiological observations such as synchronization on different time scales, fast oscillations, ongoing neural activity, and attentional or biased competition. Moreover, the model suggests new solutions to some of the harder binding problems such as role assignment.

The brain can be parcellated into numerous anatomical and functional subunits. The classic work by Brodmann (Vergleichende Lokalisationslehre der Grosshirnrinde in ihren Prinzipien dargestellt auf Grund des Zellenbaues. Leipzig: Barth; 1909) identified areas of the cerebral cortex based on histological differences. An alternative to his cytoarchitectonic approach is the myeloarchitectonic approach. MRI has excellent white/gray matter contrast in the brain due to the presence of myelin, and thus seems uniquely suited for in vivo studies of cortical myeloarchitecture. Here it is demonstrated that the stripe or stria of Gennari can be consistently detected in human occipital cortex. T(1)-weighted images obtained at 3T from six of 10 normal volunteers, with resolutions of 350 x 350 x 600 mu clearly demonstrate this myelin-rich intracortical layer. It is concluded that the striate cortex (area 17 of Brodmann) of the human brain can be delineated in vivo on T(1)-weighted images, potentially enabling detection of specific cortical boundaries within individual brains.

Interest in ventral and dorsal streams is not limited to vision, and the functionality of similar pathways in other domains has also been considered. Auditory dual pathway models share many conceptual and empirical concerns with those put forward for vision, including the absolute vs. relative, localized vs. distributed, and exact nature of functionality of the two streams. Despite their problems, dual pathway hypotheses provide broad frameworks with which to consider cortical architecture across the senses.

The basic idea of the authors' anticipatory approach to perception is to avoid the common separation of perception and generation of behavior and to fuse both aspects into a consistent neural process. The authors' approach is based on the prediction of the consequences of hypothetically executed actions. In this sense, perception of space and shape is assumed to be a generative process of anticipating the course of events resulting from different sequences of actions. The authors present a biologically motivated computational model that is able to anticipate and evaluate hypothetical sensorimotor sequences. Their model for anticipation based on cortical representations (MACOR) allows a completely parallel search at the neocortical level using assemblies of rate coded neurons for grouping, separation, and selection of sensorimotor sequences.

A number of new imaging techniques are available to scientists to visualize the functioning brain directly, revealing unprecedented details. These imaging techniques have provided a new level of understanding of the principles underlying cortical development, organization and function. In this chapter we will focus on optical imaging in the living mammalian brain, using two complementary imaging techniques. The first technique is based on intrinsic signals. The second technique is based on voltage-sensitive dyes. Currently, these two optical imaging techniques offer the best spatial and temporal resolution, but also have inherent limitations. We shall provide a few examples of new findings obtained mostly in work done in our laboratory. The focus will be upon the understanding of methodological aspects which in turn should contribute to optimal use of these imaging techniques. General reviews describing earlier work done on simpler preparations have been published elsewhere (Cohen, 1973; Tasaki and Warashina, 1976; Waggoner and Grinvald, 1977; Waggoner, 1979; Salzberg, 1983; Grinvald, 1984; Grinvald et al., 1985; De Weer and Salzberg, 1986; Cohen and Lesher, 1986; Salzberg et al., 1986; Loew, 1987; Orbach, 1987; Blasdel, 1988, 1989; Grinvald et al., 1988; Kamino, 1991; Cinelli and Kauer, 1992; Frostig, 1994).

The present review outlines studies of electrophsyiological organization, cortical architecture and thalmocortical and corticocortical connections in monotremes. Results of these studies indicate that the neocortex of monotremes has many features in common with other mammals. In particular, monotremes have at least two, and in some instances three, sensory fields for each modality, as well as regions of bimodal cortex. The internal organization of cortical fields and thalamocortical projection patterns are also similar to those described for other mammals. However, unlike most mammals investigated, the monotreme neocortex has cortical connections between primary sensory fields, such as SI and VI. The results of this analysis lead us to pose the question of what monotremes can tell us about brain evolution. Monotremes alone can tell us very little about the evolutionary process, or the construction of complex neural networks, as an individual species represents only a single example of what the process is capable of generating. Perhaps a better question is: what can comparative studies tell us about brain evolution? Monotreme brains, when compared with the brains of other animals, can provide some answers to questions about the evolution of the neocortex, the historical precedence of some features over others, and how basic circuits were modified in different lineages. This, in turn, allows us to appreciate how normal circuits function, and to pose very specific questions regarding the development of the neocortex.

The mammalian isocortex is an intriguing mix of structural consistency and functional diversity. Across mammals, isocortex configuration is conserved to a very high degree, with visual cortex posterior, somatosensory cortex medial, and the associated secondary areas proximal to these primary areas. The classic, six- layered internal cortical architecture is conserved. Yet, cortical areas are also structurally and functionally distinct and can be distinguished on the basis of architecture, biochemistry, physiology and connectivity. Across species, the differences between areas are remarkably consistent. Is there something special about each cortical area that makes it adept at handling its particular input, or is it just a piece of a calculating machine that is indifferent to the modality of its information and simply performs routine transformations? If there are true differences in the cortical mosaic, when in development do they arise, and what feature of gene expression, connectivity or function carries the information that diversifies cortical areas?

Extrastriate visual cortex consists of multiple areas. As reviewed elsewhere (Kaas, 1989; Colby and Duhamel, 1991; and several chapters in this volume), there are still many questions concerning specific boundaries and subdivisions, and the criteria for area identification themselves remain under discussion. How areas interact is even less well known and is very much a topic of active research. Is there an overall architecture? Are there patterns of sequential or synchronous coactivation?

We present a schematic cortical architecture, constructed from recurrently connected oscillatory associative memory modules that model sensory and motor “patches” of cortex. We show how it can employ selective “attentional” control of synchronization to direct the flow of communication and computation within itself to learn to generate sequential outputs.

Perceptual learning is accompanied by changes in the properties of individual neurons and in the functional cortical architecture; these are observed in a number of cortical areas, over short and long time scales.

A fundamental understanding of the mechanisms of cortical processing requires an examination of the relationships of cortical circuitry, cortical functional architecture and receptive field properties. Ultimately, this kind of analysis can be used to explore the neurobiological basis of psychophysics and perception. At the outset our studies were intended to account for the then known receptive field properties of cortical cells in terms of their underlying circuitry but, surprisingly, a good part of the cortical circuit appeared to be in violation of the principles of cortical architecture. This led us to explore the possibility of new, more complex properties of cortical cells. It has become increasingly possible to relate the responsive specificity of cortical cells and the circuitry underlying this specificity to the perceptual capabilities of the visual system by performing analogous experiments on single cells and in human psychophysics.

The convoluted form of the sheet-like mammalian cortex naturally raises the question whether there is a simple geometrical reason for the prevalence of cortical architecture in the brains of higher vertebrates. Addressing this question, we present a formal analysis of the volume occupied by a massively connected network or processors (neurons) and then consider the pertaining cortical data. Three gross macroscopic features of cortical organization are examined: the segregation of white and gray matter, the circumferential organization of the gray matter around the white matter, and the folded cortical structure. Our results testify to the efficiency of cortical architecture.

Since the work of Hartline in 1938 the concept of receptive fields (RFs) as one of the basic elements for information processing has been the guiding principle in the investigation of sensory systems. Although we perceive the visual world as a unitary whole, the neuronal machinery consists of elements that can analyse only small portions of the visual field. Knowing structure, organization and properties of RFs is indispensable for understanding their contribution for signal processing and, more generally, for brain function.

The developmental pathogenesis of cranium bifidum and exencephaly, as well as other forms of the dysraphic state, remains unresolved. The process of neuronal maturation under such conditions is the major definitive factor for functional prognosis and may be the key to the embryopathogenesis. The neuronal maturation processes in cranium bifidum and anencephaly were analyzed in both human neonatal autopsy and surgical specimens, utilizing the technique of immunohistochemical morphological analysis. The results suggest that anencephaly is a form of neuroschisis, a defect in which the neuronal developmental process arrests and results in dys- or rather aplastic cortical architecture. In contrast, although the cortical architecture is often distorted, neuronal maturation in cases of cranium bifidum cysticum is primarily complete, demonstrating normal cerebral cortical layers and NSE positive neural elements. In conclusion, it is strongly suggested that anencephaly is the most severe form of cranium bifidum, as is myeloschisis in spina bifida, without supportable neuronal maturation. Recognizing these points in the developmental stages with regard to the degree of neuronal maturation, a reclassification of cephalic dysraphism is proposed. The embryopathogenetic and epidemiologic aspects of this proposal of reclassification are discussed.

During the first 1 to 2 h of the classical migraine attack a hypoperfusion develops which starts in the posterior part of the brain and progresses anteriorly at a rate of 2-3mm/min. The hypoperfusion stops at primary sulci outlining major cortical macro-and microstructural changes, but seems not to be inhibited by other changes of the cortical architecture. The low flow regions are cortical and the low flow persists for 4–6 h, until the attack abates. Regions of hyperperfusion are either minor or non-existent. A similar behavior characterizes the velocity and mode of evolution of a cortical spreading depression, a transient perturbation of cortical neuronal function which has profound and long-lasting influence on the cortical blood flow. This paper briefly summarizes the arguments which have been put forward in recent years suggesting that spreading depression is a pathogenetic mechanism of migraine.

Scalp responses evoked by patterned visual stimuli and by changes in luminance were recorded both from human subjects and monkeys. Three models are proposed to explain the observed stimulus-response relations: 1. (1) Luminance detection by first order summing units. 2. (2) A centre-surround antagonistic mechanism which enhances responses to spatial patterning. 3. (3) Contour detection by ordered arrays of overlapping receptive fields. Implications of the three hypotheses are discussed. Experimental evidence is presented which shows that a definite contour-specific response component can be observed in most human subjects. Spatial frequency selectivity by centre-surround antagonism seems the most plausible explanation of our results in monkey. The differences between human and monkey stimulus-response relationships are tentatively explained by the differences in cortical architecture, assuming the origin of spatial frequency selectivity in striate cortex and, in accordance with Michael and Halliday (1971) and Jeffreys (1971), a mainly extrastriate origin of the specific contour response.