The modular architectonic principle of neural centers

On human nanoscale synaptome: Morphology modeling and storage estimation

One of the key challenges in neuroscience is to generate the human nanoscale connectome which requires comprehensive knowledge of synaptome forming the neural microcircuits. The synaptic architecture determines limits of individual mental capacity and provides the framework for understanding neurologic disorders. Here, I address morphology modeling and storage estimation for the human synaptome at the nanoscale. A synapse is defined as a pair of pairs [(presynaptic_neuron),(presynaptic_axonal_terminal);(postsynaptic_neuron),(postsynaptic_dendritic_terminal)]. Center coordinates, radius, and identifier characterize a dendritic or axonal terminal. A synapse comprises topology with the paired neuron and terminal identifiers, location with terminal coordinates, and geometry with terminal radii. The storage required for the synaptome depends on the number of synapses and storage necessary for a single synapse determined by a synaptic model. I introduce three synaptic models: topologic with topology, point with topology and location, and geometric with topology, location, and geometry. To accommodate for a wide range of variations in the numbers of neurons and synapses reported in the literature, four cases of neurons (30;86;100;138 billion) and three cases of synapses per neuron (1,000;10,000;30,000) are considered with three full and simplified (to reduce storage) synaptic models resulting in total 72 cases of storage estimation. The full(simplified) synaptic model of the entire human brain requires from 0.21(0.14) petabytes (PB) to 28.98(18.63) PB for the topologic model, from 0.57(0.32) PB to 78.66(43.47) PB for the point model, and from 0.69(0.38) PB to 95.22(51.75) PB for the geometric model. The full(simplified) synaptic model of the cortex needs from 86.80(55.80) TB to 2.60(1.67) PB for the topologic model, from 235.60(130.02) TB to 7.07(3.91) PB for the point model, and from 285.20(155.00) TB to 8.56(4.65) PB for the geometric model. The topologic model is sufficient to compute the connectome's topology, but it is still too big to be stored on today's top supercomputers related to neuroscience. Frontier, the world's most powerful supercomputer for 86 billion neurons can handle the nanoscale synaptome in the range of 1,000-10,000 synapses per neuron. To my best knowledge, this is the first big data work attempting to provide storage estimation for the human nanoscale synaptome.

A mean-field model of glutamate and GABA synaptic dynamics for functional MRS

Advances in functional magnetic resonance spectroscopy (fMRS) have enabled the quantification of activity-dependent changes in neurotransmitter concentrations in vivo. However, the physiological basis of the large changes in GABA and glutamate observed by fMRS (>10%) over short time scales of less than a minute remain unclear as such changes cannot be accounted for by known synthesis or degradation metabolic pathways. Instead, it has been hypothesized that fMRS detects shifts in neurotransmitter concentrations as they cycle from presynaptic vesicles, where they are largely invisible, to extracellular and cytosolic pools, where they are detectable. The present paper uses a computational modelling approach to demonstrate the viability of this hypothesis. A new mean-field model of the neural mechanisms generating the fMRS signal in a cortical voxel is derived. The proposed macroscopic mean-field model is based on a microscopic description of the neurotransmitter dynamics at the level of the synapse. Specifically, GABA and glutamate are assumed to cycle between three metabolic pools: packaged in the vesicles; active in the synaptic cleft; and undergoing recycling and repackaging in the astrocytic or neuronal cytosol. Computational simulations from the model are used to generate predicted changes in GABA and glutamate concentrations in response to different types of stimuli including pain, vision, and electric current stimulation. The predicted changes in the extracellular and cytosolic pools corresponded to those reported in empirical fMRS data. Furthermore, the model predicts a selective control mechanism of the GABA/glutamate relationship, whereby inhibitory stimulation reduces both neurotransmitters, whereas excitatory stimulation increases glutamate and decreases GABA. The proposed model bridges between neural dynamics and fMRS and provides a mechanistic account for the activity-dependent changes in the glutamate and GABA fMRS signals. Lastly, these results indicate that echo-time may be an important timing parameter that can be leveraged to maximise fMRS experimental outcomes.

Occlusal Splints and Exercise Performance: A Systematic Review of Current Evidence

The role of the dento-mandibular apparatus and, in particular, occlusion and jaw position, received increased attention during last years. In the present study, we aimed to systematically review, on the light of the new potential insights, the published literature covering the occlusal splint (OS) applications, and its impact on exercise performance. A structured search was carried out including MEDLINE^®/PubMed and Scopus databases with additional integration from external sources, between March and June 2021. To meet the inclusion criteria, studies published in the English language, involving humans in vivo, published from 2000 to 2021 and that investigated the role of occlusal splints on athletes' performance were selected. Starting from the 587 identified records, 17 items were finally included for the review. Four main aspects were considered and analyzed: (1) occlusal splint characteristics and occlusion experimental conditions, (2) jump performance, (3) maximal and explosive strength, and (4) exercise technique and biomechanics. The results of the systematic literature analysis depicted a wide heterogenicity in the experimental conditions and suggested the application of the OS as a way to improve athletes' or individuals' oral health, and as a potential tool to optimize marginal aspects of exercise performance.

Distinct Temporal Coordination of Spontaneous Population Activity between Basal Forebrain and Auditory Cortex

The basal forebrain (BF) has long been implicated in attention, learning and memory, and recent studies have established a causal relationship between artificial BF activation and arousal. However, neural ensemble dynamics in the BF still remains unclear. Here, recording neural population activity in the BF and comparing it with simultaneously recorded cortical population under both anesthetized and unanesthetized conditions, we investigate the difference in the structure of spontaneous population activity between the BF and the auditory cortex (AC) in mice. The AC neuronal population show a skewed spike rate distribution, a higher proportion of short (≤80 ms) inter-spike intervals (ISIs) and a rich repertoire of rhythmic firing across frequencies. Although the distribution of spontaneous firing rate in the BF is also skewed, a proportion of short ISIs can be explained by a Poisson model at short time scales (≤20 ms) and spike count correlations are lower compared to AC cells, with optogenetically identified cholinergic cell pairs showing exceptionally higher correlations. Furthermore, a smaller fraction of BF neurons shows spike-field entrainment across frequencies: a subset of BF neurons fire rhythmically at slow (≤6 Hz) frequencies, with varied phase preferences to ongoing field potentials, in contrast to a consistent phase preference of AC populations. Firing of these slow rhythmic BF cells is correlated to a greater degree than other rhythmic BF cell pairs. Overall, the fundamental difference in the structure of population activity between the AC and BF is their temporal coordination, in particular their operational timescales. These results suggest that BF neurons slowly modulate downstream populations whereas cortical circuits transmit signals on multiple timescales. Thus, the characterization of the neural ensemble dynamics in the BF provides further insight into the neural mechanisms, by which brain states are regulated.

The search for true numbers of neurons and glial cells in the human brain: A review of 150 years of cell counting

For half a century, the human brain was believed to contain about 100 billion neurons and one trillion glial cells, with a glia:neuron ratio of 10:1. A new counting method, the isotropic fractionator, has challenged the notion that glia outnumber neurons and revived a question that was widely thought to have been resolved. The recently validated isotropic fractionator demonstrates a glia:neuron ratio of less than 1:1 and a total number of less than 100 billion glial cells in the human brain. A survey of original evidence shows that histological data always supported a 1:1 ratio of glia to neurons in the entire human brain, and a range of 40-130 billion glial cells. We review how the claim of one trillion glial cells originated, was perpetuated, and eventually refuted. We compile how numbers of neurons and glial cells in the adult human brain were reported and we examine the reasons for an erroneous consensus about the relative abundance of glial cells in human brains that persisted for half a century. Our review includes a brief history of cell counting in human brains, types of counting methods that were and are employed, ranges of previous estimates, and the current status of knowledge about the number of cells. We also discuss implications and consequences of the new insights into true numbers of glial cells in the human brain, and the promise and potential impact of the newly validated isotropic fractionator for reliable quantification of glia and neurons in neurological and psychiatric diseases. J. Comp. Neurol. 524:3865-3895, 2016. © 2016 Wiley Periodicals, Inc.

Large-scale functional network overlap is a general property of brain functional organization: Reconciling inconsistent fMRI findings from general-linear-model-based analyses

Functional magnetic resonance imaging (fMRI) studies regularly use univariate general-linear-model-based analyses (GLM). Their findings are often inconsistent across different studies, perhaps because of several fundamental brain properties including functional heterogeneity, balanced excitation and inhibition (E/I), and sparseness of neuronal activities. These properties stipulate heterogeneous neuronal activities in the same voxels and likely limit the sensitivity and specificity of GLM. This paper selectively reviews findings of histological and electrophysiological studies and fMRI spatial independent component analysis (sICA) and reports new findings by applying sICA to two existing datasets. The extant and new findings consistently demonstrate several novel features of brain functional organization not revealed by GLM. They include overlap of large-scale functional networks (FNs) and their concurrent opposite modulations, and no significant modulations in activity of most FNs across the whole brain during any task conditions. These novel features of brain functional organization are highly consistent with the brain's properties of functional heterogeneity, balanced E/I, and sparseness of neuronal activity, and may help reconcile inconsistent GLM findings.

Depth-specific optogenetic control in vivo with a scalable, high-density μLED neural probe

Controlling neural circuits is a powerful approach to uncover a causal link between neural activity and behaviour. Optogenetics has been widely adopted by the neuroscience community as it offers cell-type-specific perturbation with millisecond precision. However, these studies require light delivery in complex patterns with cellular-scale resolution, while covering a large volume of tissue at depth in vivo. Here we describe a novel high-density silicon-based microscale light-emitting diode (μLED) array, consisting of up to ninety-six 25 μm-diameter μLEDs emitting at a wavelength of 450 nm with a peak irradiance of 400 mW/mm². A width of 100 μm, tapering to a 1 μm point, and a 40 μm thickness help minimise tissue damage during insertion. Thermal properties permit a set of optogenetic operating regimes, with ~0.5 °C average temperature increase. We demonstrate depth-dependent activation of mouse neocortical neurons in vivo, offering an inexpensive novel tool for the precise manipulation of neural activity.

Influence of the Lower Jaw Position on the Running Pattern

Introduction

The effects of manipulated dental occlusion on body posture has been investigated quite often and discussed controversially in the literature. Far less attention has been paid to the influence of dental occlusion position on human movement. If human movement was analysed, it was mostly while walking and not while running. This study was therefore designed to identify the effect of lower jaw positions on running behaviour according to different dental occlusion positions.

Methods

Twenty healthy young recreational runners (mean age = 33.9±5.8 years) participated in this study. Kinematic data were collected using an eight-camera Vicon motion capture system (VICON Motion Systems, Oxford, UK). Subjects were consecutively prepared with four different dental occlusion conditions in random order and performed five running trials per test condition on a level walkway with their preferred running shoes. Vector based pattern recognition methods, in particular cluster analysis and support vector machines (SVM) were used for movement pattern identification.

Results

Subjects exhibited unique movement patterns leading to 18 clusters for the 20 subjects. No overall classification of the splint condition could be observed. Within individual subjects different running patterns could be identified for the four splint conditions. The splint conditions lead to a more symmetrical running pattern than the control condition.

Discussion

The influence of an occlusal splint on running pattern can be confirmed in this study. Wearing a splint increases the symmetry of the running pattern. A more symmetrical running pattern might help to reduce the risk of injuries or help in performance. The change of the movement pattern between the neutral condition and any of the three splint conditions was significant within subjects but not across subjects. Therefore the dental splint has a measureable influence on the running pattern of subjects, however subjects individuality has to be considered when choosing the optimal splint condition for a specific subject.

Möbius-strip-like columnar functional connections are revealed in somato-sensory receptive field centroids

Receptive fields of neurons in the forelimb region of areas 3b and 1 of primary somatosensory cortex, in cats and monkeys, were mapped using extracellular recordings obtained sequentially from nearly radial penetrations. Locations of the field centroids indicated the presence of a functional system in which cortical homotypic representations of the limb surfaces are entwined in three-dimensional Möbius-strip-like patterns of synaptic connections. Boundaries of somatosensory receptive field in nested groups irregularly overlie the centroid order, and are interpreted as arising from the superposition of learned connections upon the embryonic order. Since the theory of embryonic synaptic self-organization used to model these results was devised and earlier used to explain findings in primary visual cortex, the present findings suggest the theory may be of general application throughout cortex and may reveal a modular functional synaptic system, which, only in some parts of the cortex, and in some species, is manifest as anatomical ordering into columns.

Mathematical modeling for evolution of heterogeneous modules in the brain

Modular architecture has been found in most cortical areas of mammalian brains, but little is known about its evolutionary origin. It has been proposed by several researchers that maximizing information transmission among subsystems can be used as a principle for understanding the development of complex brain networks. In this paper, we study how heterogeneous modules develop in coupled-map networks via a genetic algorithm, where selection is based on maximizing bidirectional information transmission. Two functionally differentiated modules evolved from two homogeneous systems with random couplings, which are associated with symmetry breaking of intrasystem and intersystem couplings. By exploring the parameter space of the network around the optimal parameter values, it was found that the optimum network exists near transition points, at which the incoherent state loses its stability and an extremely slow oscillatory motion emerges.

The dual loop model: its relation to language and other modalities

The current neurobiological consensus of a general dual loop system scaffolding human and primate brains gives evidence that the dorsal and ventral connections subserve similar functions, independent of the modality and species. However, most current commentators agree that although bees dance and chimpanzees grunt, these systems of communication differ qualitatively from human language. So why is language unique to humans? We discuss anatomical differences between humans and other animals, the meaning of lesion studies in patients, the role of inner speech, and compare functional imaging studies in language with other modalities in respect to the dual loop model. These aspects might be helpful for understanding what kind of biological system the language faculty is, and how it relates to other systems in our own species and others.

Reverse engineering for biologically inspired cognitive architectures: a critical analysis

Research initiatives on both sides of the Atlantic try to utilize the operational principles of organisms and brains to develop biologically inspired, artificial cognitive systems. This paper describes the standard way bio-inspiration is gained, i.e. decompositional analysis or reverse engineering. The indisputable complexity of brain and mind raise the issue of whether they can be understood by applying the standard method. Using Robert Rosen's modeling relation, the scientific analysis method itself is made a subject of discussion. It is concluded that the fundamental assumption of cognitive science, i.e. complex cognitive systems are decomposable, must be abandoned. Implications for investigations of organisms and behavior as well as for engineering artificial cognitive systems are discussed.

Laminar structure of spontaneous and sensory-evoked population activity in auditory cortex

Spontaneous activity plays an important role in the function of neural circuits. Although many similarities between spontaneous and sensory-evoked neocortical activity have been reported, little is known about consistent differences between them. Here, using simultaneously recorded cortical populations and morphologically identified pyramidal cells, we compare the laminar structure of spontaneous and sensory-evoked population activity in rat auditory cortex. Spontaneous and evoked patterns both exhibited sparse, spatially localized activity in layer 2/3 pyramidal cells, with densely distributed activity in larger layer 5 pyramidal cells and putative interneurons. However, the propagation of spontaneous and evoked activity differed, with spontaneous activity spreading upward from deep layers and slowly across columns, but sensory responses initiating in presumptive thalamorecipient layers, spreading rapidly across columns. The similarity of sparseness patterns for both neural events and distinct spread of activity may reflect similarity of local processing and differences in the flow of information through cortical circuits, respectively.

In search of lost presynaptic inhibition

This chapter presents an historical review on the development of some of the main findings on presynaptic inhibition. Particular attention is given to recent studies pertaining the differential GABAa control of the synaptic effectiveness of muscle, cutaneous and articular afferents, to some of the problems arising with the identification of the interneurons mediating the GABAergic depolarization of primary afferents (PAD) of muscle afferents, on the influence of the spontaneous activity of discrete sets of dorsal horn neurons on the pathways mediating PAD of muscle and cutaneous afferents, and to the unmasking of the cutaneous-evoked responses in the lumbosacral spinal cord and associated changes in tonic PAD that follow acute and chronic section of cutaneous nerves. The concluding remarks are addressed to several issues that need to be considered to have a better understanding of the functional role of presynaptic inhibition and PAD on motor performance and sensory processing and on their possible contribution to the shaping of a higher coherence between the cortically programmed and the executed movements.

Telencephalon: Neocortex

The neocortex is an ultracomplex, six-layered structure that develops from the dorsal palliai sector of the telencephalic hemispheres (Figs. 2.24, 2.25, 11.1). All mammals, including monotremes and marsupials, possess a neocortex, but in reptiles, i.e. the ancestors of mammals, only a three-layered neocortical primordium is present [509, 511]. The term neocortex refers to its late phylogenetic appearance, in comparison to the “palaeocortical” olfactory cortex and the “archicortical” hippocampal cortex, both of which are present in all amniotes [509].

Eccles' perspective of the forebrain, its role in skilled movements, and the mind-brain problem

Sir John Eccles' experimental life evolved from the "bottom" up: the synapse to the modular circuitry of the spinal cord, later the cerebellum and, less extensively, also the thalamus and hippocampus. He experimented quantitatively on basic properties of cell membranes, synapses, transmitters, cellular modules, reflexes, and plasticity. In parallel, he was also motivated to consider philosophical problems of mind-brain interactions. It was mostly during Eccles' "Swiss period" (1976-1997) that new experimental work advanced understanding of intentional motor actions and their preparation. For example, early brain imaging work suggested that the so-called "supplementary" motor area was rather a "supramotor" area, concerned with intentional preparation to move. Eccles also closely followed work on cortico-cerebellar integration and learning. His final contribution, in collaboration with the quantum physicist, Friedrich Beck, was a model of how specific neuronal modules interact with the mind. Being a declared dualist, Eccles encountered considerable resistance and skepticism among neuroscientists in accepting his experimentally untestable mind-brain theories. But one can only admire the remarkable continuity of effort in his search for modular operations of identified neurons in the central nervous system and their synaptic actions. This effort was facilitated by collaboration with the eminent anatomist, János Szentágothai, who had previously helped Eccles advance understanding of spinal and cerebellar circuitry. This review also includes some personal views on current understanding of the forebrain, with an emphasis on the multiplicity of cortical modules, all of which contribute in the mental preparation for forthcoming intentional actions.

The multiscale character of evoked cortical activity

Both the architecture and the dynamics of the brain have characteristic features at different spatial scales. However, the existence, nature and function of dynamical interdependencies between such scales have not been investigated. We studied the multiscale properties of functional magnetic resonance imaging (fMRI) data acquired while human subjects viewed a visual image. Traditional "region of interest" analysis of this data set revealed evoked activity in primary and extrastriate visual cortex. Wavelet transform in the spatial domain provides a multiscale representation of this evoked brain activity. Studying the correlation structure of this representation revealed strong and novel interdependencies in these data within and between different spatial scales. We found that such correlations are stronger than those evident in the original data and comparable in magnitude to those obtained after Gaussian smoothing. However, analysis of the data in the wavelet domain revealed additional structure such as positive correlations, strong anti-correlations and phase-lagged interdependencies. Statistical significance of these effects was inferred through nonparametric bootstrap techniques. We conclude that the spatial analysis of functional neuroimaging data in the wavelet domain provides novel information which may reflect complex spatiotemporal neuronal activity and information encoding. It also affords a quantitative means of testing hierarchical and multiscale models of cortical activity.

Global rhythmic activities in hippocampal neural fields and neural coding

Global oscillations of the neural field represent some of the most interesting expressions of the hippocampal activity, being related also to learning and memory. To study oscillatory activities of the CA3 field in theta range, a model of this sub-field of Hippocampus has been formulated. The model describes the firing activity of CA3 neuronal populations within the frame of a kinetic theory of neural systems and it has been used for computer simulations. The results show that the propagation of activities induced in the neural field by hippocampal afferents occurs only in narrow time windows confined by inhibitory barrages, whose time-course follows the theta rhythm. Moreover, during each period of a theta wave, the entire CA3 field bears a firing activity with peculiar space-time patterns, a sort of specific imprint, which can induce effects with similar patterns on brain regions driven by the hippocampal formation. The simulation has also demonstrated the ability of medial septum to influence the global activity of the CA3 pyramidal population through the control of the population of inhibitory interneurons. At last, the possible involvement of global population oscillations in neural coding has been discussed.

The nonlinear theory of schizophrenia

OBJECTIVE

Nonlinear properties exist within the brain across a hierarchy of scales and within a variety of critical neural processes. Only a few studies of brain activity in schizophrenia, however, have used nonlinear methods. This review paper evaluates the contribution of the nonlinear sciences towards understanding schizophrenia.

METHOD

Applications of nonlinear methods to the study of schizophrenia symptoms and to healthy and schizophrenia functional neuroscience data are reviewed. The main flaws of nonlinear algorithms and recent methods to correct these are also appraised.

RESULTS

Initial research methods utilized in the study of nonlinearity in schizophrenia have fundamental methodological limitations. In the last decade, many of these problems have been addressed, facilitating future progress. Research incorporating these improvements has been applied to normal electroencephalogram (EEG) data and to the symptoms of schizophrenia, but not systematically to brain imaging data collected from patients with schizophrenia.

CONCLUSION

There is strong statistical evidence for weak nonlinearity in normal EEG and in the fluctuations of the symptoms of schizophrenia. However, the contribution of nonlinear processes to brain dysfunction in schizophrenia is yet to be properly established or accurately quantified. Despite this, recent methodological advances suggest that a 'nonlinear theory' of schizophrenia may be helpful in understanding this disorder.

Dynamics of a neural system with a multiscale architecture

The architecture of the brain is characterized by a modular organization repeated across a hierarchy of spatial scales-neurons, minicolumns, cortical columns, functional brain regions, and so on. It is important to consider that the processes governing neural dynamics at any given scale are not only determined by the behaviour of other neural structures at that scale, but also by the emergent behaviour of smaller scales, and the constraining influence of activity at larger scales. In this paper, we introduce a theoretical framework for neural systems in which the dynamics are nested within a multiscale architecture. In essence, the dynamics at each scale are determined by a coupled ensemble of nonlinear oscillators, which embody the principle scale-specific neurobiological processes. The dynamics at larger scales are 'slaved' to the emergent behaviour of smaller scales through a coupling function that depends on a multiscale wavelet decomposition. The approach is first explicated mathematically. Numerical examples are then given to illustrate phenomena such as between-scale bifurcations, and how synchronization in small-scale structures influences the dynamics in larger structures in an intuitive manner that cannot be captured by existing modelling approaches. A framework for relating the dynamical behaviour of the system to measured observables is presented and further extensions to capture wave phenomena and mode coupling are suggested.

Plasticity in single neuron and circuit computations

Plasticity in neural circuits can result from alterations in synaptic strength or connectivity, as well as from changes in the excitability of the neurons themselves. To better understand the role of plasticity in the brain, we need to establish how brain circuits work and the kinds of computations that different circuit structures achieve. By linking theoretical and experimental studies, we are beginning to reveal the consequences of plasticity mechanisms for network dynamics, in both simple invertebrate circuits and the complex circuits of mammalian cerebral cortex.

"Dynamic" connectivity in neural systems: theoretical and empirical considerations

The study of functional interdependences between brain regions is a rapidly growing focus of neuroscience research. This endeavor has been greatly facilitated by the appearance of a number of innovative methodologies for the examination of neurophysiological and neuroimaging data. The aim of this article is to present an overview of dynamical measures of interdependence and contrast these with statistical measures that have been more widely employed. We first review the motivation, conceptual basis, and experimental approach of dynamical measures of interdependence and their application to the study of neural systems. A consideration of boot-strap "surrogate data" techniques, which facilitate hypothesis testing of dynamical measures, is then used to clarify the difference between dynamical and statistical measures of interdependence. An overview of some of the most active research areas such as the study of the "synchronization manifold," dynamical interdependence in neurophysiology data and the putative role of nonlinear desynchronization is then given. We conclude by suggesting that techniques based on dynamical interdependence--or "dynamical connectivity"--show significant potential for extracting meaningful information from functional neuroimaging data.

Laminar analysis of human neocortical interictal spike generation and propagation: current source density and multiunit analysis in vivo

Multicontact microelectrodes were chronically implanted in epilepsy patients undergoing subdural grid implantation for seizure localization. Current source density and multiple unit activity of interictal spikes (IISs) were sampled every approximately 150 microm in a line traversing all layers of a cortical column. Our data suggest that interictal epileptiform events in humans are initiated by large postsynaptic depolarizations, consistent with the hypothesis that human IISs correspond to animal paroxysmal depolarization shifts. Furthermore, the cortical layer where the initial depolarization occurs may differ according to whether the IIS is locally generated or propagated from a distant location, and among the propagated IISs, whether the IIS is in the direct path of propagation or on the periphery of that path.

An information-processing analysis of the functional architecture of the primate neocortex

Working at the systems level of analysis, we will use the term functional architecture to concern what processing components exist, how they are interconnected, and what information-processing functions each is involved in. In this paper, experimental evidence for the primate neocortex is analysed for conclusions concerning the existence of neural areas, for corticocortical connectivity among neural areas, and for the involvement of each cortical neural area in the functioning of the brain. We characterize the information-processing function for each neural area in terms of the types of information it is associated with, and conceive of its activity as processing, storage and transmission of data of the corresponding types for that area. We also adapt concepts of goal, plan, sequence, event and context for the description of information processing in the neocortex. This analysis shows that the primate neocortex consists in the main of a perception hierarchy, an action hierarchy and connections between them. In other words, from an information-processing point of view, the primate neocortex has a hierarchical perception-action architecture.

A theory of sulcal-gap signalization

Two sulcal-gap signalization systems are hypothesized to have evolved as emergent functional "exaptations" having the capacity to transmit two-way cortex-to-cortex signals across tissues embedded in opposing sulcal banks. Hypothesis 1 posits that a primary sulcal-gap signalization system evolved the capacity to transmit nonlanguage signals within hierarchically lower-order sensory, motor, and perceptual functional areas of the neocortex having elemental functional units consisting of columns of about 110 neurons. Hypothesis 2 posits that a secondary sulcal-gap signalization system evolved the capacity to transmit language signals within hierarchically higher-order cognitive functional areas of the "neo-neocortex" having elemental functional units consisting of modules of about 4,000 neurons. Neuroanatomical, neurophysiological neuroevolutionary, and neurodevelopmental evidence is presented in support of these two sulcal-gap hypotheses. It is speculated that the combined cognitive capacities of these two sulcal-gap signalization systems may contribute to the transduction of physiological brain into psychological mind.

The minicolumn hypothesis in neuroscience

The minicolumn is a continuing source of research and debate more than half a century after it was identified as a component of brain organization. The minicolumn is a sophisticated local network that contains within it the elements for redundancy and plasticity. Although it is sometimes compared to subcortical nuclei, the design of the minicolumn is a distinctive form of module that has evolved specifically in the neocortex. It unites the horizontal and vertical components of cortex within the same cortical space. Minicolumns are often considered highly repetitive, even clone-like, units. However, they display considerable heterogeneity between areas and species, perhaps even within a given macrocolumn. Despite a growing recognition of the anatomical basis of the cortical minicolumn, as well as its physiological properties, the potential of the minicolumn has not been exploited in fields such as comparative neuroanatomy, abnormalities of the brain and mind, and evolution.

Morphological differences between minicolumns in human and nonhuman primate cortex

Our study performed a quantitative investigation of minicolumns in the planum temporale (PT) of human, chimpanzee, and rhesus monkey brains. This analysis distinguished minicolumns in the human cortex from those of the other nonhuman primates. Human cell columns are larger, contain more neuropil space, and pack more cells into the core area of the column than those of the other primates tested. Because the minicolumn is a basic anatomical and functional unit of the cortex, this strong evidence showed reorganization in this area of the human brain. The relationship between the minicolumn and cortical volume is also discussed.

An approach to the complexity of the brain

The establishment of ordered neuronal connections is supposed to take place under the control of specific cell adhesion molecules (CAM) which guide neuroblasts and axons to their appropriate destination. The extreme complexity of the nervous system does not provide a favorable medium for the development of deterministic connections. Simon's [112] theorems offer a mean to approach the high level of complexity of the nervous system. The basic tenet is that complex systems are hierarchically organized and decomposable. Such systems can arise by selective trial and error mechanisms. Subsystems in complex systems only interact in an aggregate manner, and no significant information is lost if the detail of aggregate interactions is ignored. A number of nervous activities, which qualify for these requirements, are shown. The following sources of selection are considered: internal and external feedbacks, previous experience, plasticity in simple structures, and the characteristic geometry of dendrites. The role played by CAMs and other membrane-associated molecules is discussed in the sense that they are either inductor molecules that turn on different homeobox genes, or downstream products of genes, or both. These molecules control cellular and tissular differentiation in the developing brain creating sources of selection required for the trial and error process in the organization of the nervous tissue.

Comparative lateralisation patterns in the language area of human, chimpanzee, and rhesus monkey brains

Lateralised architectural differences in radial cell column structure were detected in the planum temporale of humans but were not found in homologous regions of ape or monkey brains. This study used a new computer imaging method to quantify the architecture of thousands of cortical minicolumns. A study of Lamina III in the left hemisphere of human brains revealed a wider separation between cell columns and more non-neuronal (empty) space within cell columns compared to the right hemisphere. This asymmetry was absent in the chimpanzee brains and weakly reversed in the rhesus monkey brains. The results imply an evolution towards more clearly defined columnar structures in the left hemisphere of human brains compared to those of monkeys.

Quantitative analysis of cell columns in the cerebral cortex

We present a quantified imaging method that describes the cell column in mammalian cortex. The minicolumn is an ideal template with which to examine cortical organization because it is a basic unit of function, complete in itself, which interacts with adjacent and distance columns to form more complex levels of organization. The subtle details of columnar anatomy should reflect physiological changes that have occurred in evolution as well as those that might be caused by pathologies in the brain. In this semiautomatic method, images of Nissl-stained tissue are digitized or scanned into a computer imaging system. The software detects the presence of cell columns and describes details of their morphology and of the surrounding space. Columns are detected automatically on the basis of cell-poor and cell-rich areas using a Gaussian distribution. A line is fit to the cell centers by least squares analysis. The line becomes the center of the column from which the precise location of every cell can be measured. On this basis several algorithms describe the distribution of cells from the center line and in relation to the available surrounding space. Other algorithms use cluster analyses to determine the spatial orientation of every column.

A model of computation in neocortical architecture

We propose that the specific architecture of the neocortex reflects the organization principles of neocortical computation. In this paper, we place the anatomically defined concept of columns into a functional context. It is provided by a large-scale computational hypothesis on visual recognition, which includes both, rapid parallel forward recognition, independent of any feedback prediction, and a feedback controlled refinement system. Short epochs of periodic clocking define a global reference time and introduce a discrete time for cortical processing which enables the combination of parallel categorization and sequential refinement. The presented model differs significantly from conventional neural network architectures and suggests a novel interpretation of the role of gamma oscillations and cognitive binding.

Essential functions of the human self model are implemented in the prefrontal cortex

The human self model comprises essential features such as the experiences of ownership, of body-centered spatial perspectivity, and of a long-term unity of beliefs and attitudes. In the pathophysiology of schizophrenia, it is suggested that clinical subsyndromes like cognitive disorganization and derealization syndromes reflect disorders of this self model. These features are neurobiologically instantiated as an episodically active complex neural activation pattern and can be mapped to the brain, given adequate operationalizations of self model features. In its unique capability of integrating external and internal data, the prefrontal cortex (PFC) appears to be an essential component of the neuronal implementation of the self model. With close connections to other unimodal association cortices and to the limbic system, the PFC provides an internally represented world model and internal milieu data of the organism, both serving world orientation. In the pathophysiology of schizophrenia, it is the dysfunction of the PFC that is suggested to be the neural correlate for the different clinical schizophrenic subsyndromes. The pathophysiological study of psychiatric disorders may contribute to the theoretical debate on the neuronal basis of the self model.

Ascending granule cell axon: an important component of cerebellar cortical circuitry

Physiologic evidence suggests that local activation of the cerebellar granule cell layer produces a much more restricted spatial activation of overlying Purkinje cells than would be expected from the parallel fiber system. These results have led to the suggestion that synapses associated with the ascending granule cell axon may provide a large, direct, excitatory input to Purkinje cells, whereas parallel fiber synapses may be more modulatory in nature. In the current experiments, serial electron microscopy was used to reconstruct synapses associated with these two segments of the granule cell axons in the cerebellar cortex of albino rats. The results indicate that there are significantly more presynaptic vesicles in ascending segment synapses than in parallel fiber synapses. Furthermore, a first-order linear regression analysis revealed positive correlations between all measures of pre- and postsynaptic morphology for parallel fibers, but not for ascending segment synapses. Perhaps most surprisingly, serial reconstructions of postsynaptic spines and their associated dendrites demonstrated that spines contacted by ascending segment synapses are located exclusively on the smallest diameter distal regions of the Purkinje cell dendrites, whereas parallel fiber synapses are found exclusively on intermediate- and large-diameter regions of the spiny branchlets. Based on two independent calculations, we estimate that 20% of the granule cell synapses onto a Purkinje cell are actually made by the ascending segment. By using computer simulations of a single Purkinje cell dendrite, we have also demonstrated that synchronous activation of these distal ascending segment inputs could produce a substantial somatic response. Taken together, these results suggest that the two different regions of granule cell axons may play very different physiologic roles in cerebellar cortex.

The linear organization of cell columns in human and nonhuman anthropoid Tpt cortex

Neurons in the cerebral cortex are organized horizontally into laminae and vertically into columns and modules. Little is known about the structural variation of neuronal organization in the vertical (pia to white matter) dimension. We describe here a new computer-assisted methodology that quantifies the linear arrangement of cells and shows how cortical columns in a homologous region differ by species and age. Perikarya in eulaminate temporal cortex, Tpt, were segmented from the background on the basis of their optical densities and sizes in human, rhesus (Macaca mulatta), and chimpanzee (Pantroglodytes) brains. Within each lamina, the two-dimensional arrays of neurons were divided into repetitive, objectively defined vertical clusters. Following this, ratios and indices quantified the displacement of perikaryal centroids from the central axis and from the center point in each cell cluster. The extremely linear and vertical arrangement of cells in the prelaminated fetal cortical plate served as the template to which the other arrays were compared. In all species, the linear arrangements of perikarya in lamina III, and to a lesser extent, in lamina V, closely resemble that of the early fetal template, whereas perikaryal arrangements in layers II and IV diverge from the template formation. Corroborating subjective visualization, each lamina had its own 'fingerprint'. As expected, cell density is less in the species with larger brains, with most of the differences in density coming from increased spacing between cellular columns rather than among the cells within columns. Not all aspects of perik-aryal organization alter when bigger brains are compared with smaller ones. Although chimpanzee brains are about four times bigger than those of rhesus monkeys and human brains are about three times larger than chimpanzee brains, absolute measures of cellular linearity in chimpanzees and rhesus monkeys resemble each other more closely than the same measures do in humans and chimpanzees. After accounting for differences in interval widths, the parameters of linearity sorted on the basis of brain weight in pyramidal cell layers III and V, but not in the stellate cell layers II and IV. Human perikarya have the widest horizontal dispersion and this displacement is most pronounced in layer II, least in layer III.

Correspondence between sites of NGFI-A induction and sites of morphological plasticity following exposure to environmental complexity

To determine if gene regulation may play a role in behaviorally-induced morphological plasticity in the brain, we used in situ hybridization to measure levels of mRNA for the immediate early gene transcription factor NGFI-A (also known as ZENK, zif/268, egr-1 and Krox 24). Brains of periadolescent male rats exposed to 2-4 days of the following behavioral treatments were compared: (1) group housing in a complex environment (EC); (2) individual housing with daily handling (HIC); and (3) individual handling (IC). Quantitative analysis of the autoradiograms revealed that EC rats had significantly higher levels of NGFI-A than IC rats in regions of cortex previously shown to exhibit morphological plasticity (most pronounced in visual cortex), but not in frontal cortex where no dendritic changes have been detected. HIC rats were intermediate between the two groups. These data support an association between structural plasticity and altered patterns of immediate early gene expression.

Quantitative analysis of the columnar arrangement of neurons in the human cingulate cortex

The spatial organization of human cingulate (areas 24b, 23b, and 31) and pericingulate (areas 7 and 19) cortex was examined by using an image analyzer to measure characteristics of vertically oriented, translaminar columns of neurons in the cerebral cortex. Columns of 30-50 microns in diameter are hypothesized to be a general feature of cortical organization, but no quantitative analysis of different human cortical areas has been performed. Our results prove for the first time that a columnar organization was detectable in every area examined. The average width of cell columns was approximately 40 microns separated by a neuropil-rich fascicle of the same dimension. Because differences in the expression of a columnar organization were seen, the degree of columnization was subsequently expressed by a verticality index (VI) revealing specific changes in its dimension depending on the architectonic area. The VI was calculated by a linear combination of three variables derived from the measurement of cell density profiles in Nissl-stained sections at right angles to vertically oriented cell columns. Variables included the amplitude of profile peaks, the standard deviation of the width of those profile peaks, and the standard deviation of the distances between profile peaks. The index of verticality describes the deviation of a distinct area and layer from the mean degree of vertical organization of all cortical areas and layers examined. Thus, different degrees of columnar organization can be quantitatively described by the verticality index and can be used as criteria to characterize architectonic areas.

Representation and identity - convergence of brain research and mind-brain philosophy

The localization or representation of mental abilities in the brain have always been considered as key questions for understanding the organization of the human nervous system. Particularly with the advent of modern electrophysiological and imaging techniques that provide maps of electromagnetic fields and metabolic processes on the living central nervous system, the representation theory is experiencing a scientific renaissance in neurology, but is only one theory, however, in the succession of a long philosophical tradition dealing with the possible identification of mental phenomena and brain processes. This dichotomy was formulated at the latest in the Cartesian dualism of res cogitans and res extensa of the mind-body problem. Nowadays philosophical discussion, on the contrary, is dominated by monistic concepts that attempt to explain the mental realm on an organic foundation in order not to succumb to the problem of a psychophysical dualism. Of these, the identity theory offers a philosophically plausible concept postulating that the identity of brain conditions and mental phenomena is based on organic foundations. In this theory, the efforts of brain research converge on the representations of mental phenomena in the human nervous system. In a comprehensive approach, both concepts could complement each other.

The synaptic organization of the neocortex in Alzheimer's disease

Alzheimer's disease (AD) is characterized by a progressive deterioration of cognitive functions. Recent studies have shown that, in addition to the classically described lesions (plaques and tangles) found in AD, this neurodegenerative disorder is characterized by neuronal and synaptic loss and by synapto-axonal pathology. Stepwise regression analysis has shown that the major correlate of cognitive deficiency is the synapse loss in the prefrontal cortex, contributing about 70% of the strength of the correlation with global psychometric tests. We review evidence that supports the theory that most of the synaptic loss in the neocortex is derived from loss of cortico-cortical associational input into the modules. This hypothesis also predicts that neuritic plaque formation in the neocortical modules could represent an aberrant sprouting reaction of associational fibers responding to abnormal growth stimuli or to local damage. On these bases, it is also proposed that the cellular substrate of AD pathology is synapto-axonal, while in certain other forms of dementia such as Creutzfeldt-Jacob disease (CJD) and HIV encephalitis (HIVE) it is primarily dendritic.

Self-organization: the basic principle of neural functions

Recent neurophysiological observations are giving rise to the expectation that in the near future genuine biological experiments may contribute more than will premature speculations to the understanding of global and cognitive functions. The classical reflex principle--as the basis of neural functions--has to yield to new ideas, like autopoiesis and/or self-organization, as the basic paradigm in the framework of which the essence of the neural can be better understood. Neural activity starts in the very earliest stages of development well before receptors and afferent input become functional. Under suitable conditions, both in nervous tissue cultures and in embryonic tissue recombination experiments, the conditions of such initial autopoietic activity can be studied. This paper tries to generalize this elementary concept for various neural centers, notably for the spinal segmental apparatus and the cerebral cortex.

Neurodynamic system theory: scope and limits

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.

Neurotransmitter organization and connections of turtle cortex: implications for the evolution of mammalian isocortex

Telencephalic cortex in turtles is a simple three layered-structure. The dorsal most part of this structure is thought to resemble the reptilian forerunner of at least parts of mammalian isocortex. This dorsal part of turtle cortex contains several functionally distinct regions that show similarity in their connections and function to specific areas in mammalian isocortex. The types of neurons found in turtle dorsal cortex (as defined by their morphology and neurotransmitter content) also show great similarity to those observed in mammals, with the major exception that turtle cortex appears to lack the types of neurons found in granular and supragranular layers of mammalian isocortex. Similar results have also been observed in other living reptiles. Thus, one major step in the evolution of reptilian cortex into mammalian cortex must have been the addition of the types of neurons found in the granular and supragranular layers of mammalian isocortex. These observations for turtles also suggest that turtle cortex in particular and reptilian telencephalic cortex in general must differ functionally from mammalian isocortex with respect to those features associated with the laminar and columnar organization of isocortex. These issues are discussed in more detail below and in Reiner (1991).

Barrelettes--architectonic vibrissal representations in the brainstem trigeminal complex of the mouse. II. Normal post-natal development

Vibrissal representations in the brainstem trigeminal complex (BTC) of rodents are manifested as architectural sub-units called barrelettes. The development of barrelettes was studied by using Nissl staining, cytochrome oxidase histochemistry, and Golgi-impregnation methods. On the day of birth (PND-1), barrelettes are manifested as longitudinal, histochemical cylinders in sub-nuclei principalis, interpolaris and caudalis of the BTC. One day later (PND-2), fully formed histochemical barrelette formations are seen in the three sub-nuclei. The development of cytoarchitectural barrelettes lags behind histochemical barrelettes by about two days. Between PND-2 and PND-3, longitudinal cytoarchitectonic cylinders begin to appear. By PND-3, BTC neurons segregate into five rows of barrelettes in the coronal plane. Segmentation of rows into individual barrelettes begins on PND-4, and complete cytoarchitectonic barrelette formations are seen by PND-5. Golgi-impregnation shows that on the day of birth, primary afferent terminals and dendritic arbors of second-order trigeminal neurons within the BTC are short and poorly ramified. Over the next five post-natal days, lengthening of these processes as well as elaboration into secondary and tertiary branches take place. Growth of these processes continues for two additional weeks, contributing to the increase in barrelette neuropils (hollows). As the neuropils expand, neuronal somata are pushed toward barrelette sides. Morphometric measurements show that there is a relatively constant rate of growth of barrelettes over the first three post-natal weeks. The growth rate of the barrelette formations is identical to that of BTC as a whole. Thus, at the time of birth, the volume of neural tissue in the brainstem allotted to vibrissae is fixed relative to that allotted to other sensory receptors. Several features of the early development of barrelettes are identified: (1) Chemoarchitectural barrelettes appear before cytoarchitectural barrelettes, suggesting that terminal arbors of primary trigeminal afferents are organized before their target neurons form barrelettes. (2) Early cytoarchitecture is manifested in the form of unsegmented rows, suggesting that rough, row-based topological maps are first formed, which are then fine-tuned into individual sub-units. Recent evidence shows that other vibrissal representations--thalamic barreloids and cortical barrels--also follow these "afferent-before-target" and "row-before-individual units" sequences of development. This gradual, afferent-dependent fine-tuning of topological organization is analogous to similar events during the early development of the visual system, and may be a general feature of developing sensory systems.(ABSTRACT TRUNCATED AT 400 WORDS)

Oscillatory binocular system and temporal segmentation of stereoscopic depth surfaces

A dynamical neural network model of binocular stereopsis is proposed to solve the problem of segmentation which remains ambiguous even when the problem of binocular correspondence is solved. Being compatible with the recent neurophysiological findings (Engel et al. 1991), the model assumes that neural cells show oscillatory activities and that segmentation into a coherent depth surface is coded by synchronization of activities. Employing appropriate constraints for segmentation, the present model shows proper segmentation of depth surfaces and also solves segmentational ambiguity caused by a gap. It is newly shown that binocularly-unmatched monocular cells are discriminated in temporal segmentation of monocular cells caused by recurrent interactions between monocular and binocular cells. Integrative interactions with the other visual components through temporal segmentation are also discussed.