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Computation and Neuroscience
(69 References)
Raizada, R. D. and S. Grossberg (2003). "Towards a theory of the laminar architecture of cerebral cortex: computational clues from the visual system." Cereb Cortex 13(1): 100-13.
One of the most exciting and open research frontiers in neuroscience is that of seeking to understand the functional roles of the layers of cerebral cortex. New experimental techniques for probing the laminar circuitry of cortex have recently been developed, opening up novel opportunities for investigating how its six-layered architecture contributes to perception and cognition. The task of trying to interpret this complex structure can be facilitated by theoretical analyses of the types of computations that cortex is carrying out, and of how these might be implemented in specific cortical circuits. We have recently developed a detailed neural model of how the parvocellular stream of the visual cortex utilizes its feedforward, feedback and horizontal interactions for purposes of visual filtering, attention and perceptual grouping. This model, called LAMINART, shows how these perceptual processes relate to the mechanisms that ensure the stable development of cortical circuits in the infant, and to the continued stability of learning in the adult. The present article reviews this laminar theory of visual cortex, considers how it may be generalized towards a more comprehensive theory that encompasses other cortical areas and cognitive processes, and shows how its laminar framework generates a variety of testable predictions.
McCollum, G. (2003). "Mathematics reflecting sensorimotor organization." Biol Cybern 88(2): 108-28.
This review combines short presentations of several mathematical approaches that conceptualize issues in sensorimotor neuroscience from different perspectives and levels of analysis. The intricate organization of neural structures and sensorimotor performance calls for characterization using a variety of mathematical approaches. This review points out the prospects for mathematical neuroscience: in addition to computational approaches, there is a wide variety of mathematical approaches that provide insight into the organization of neural systems. By starting from the perspective that provides the greatest clarity, a mathematical approach avoids specificity that is inaccurate in characterizing the inherent biological organization. Approaches presented include the mathematics of ordered structures, motion-phase space, subject-coincident coordinates, equivalence classes, topological biodynamics, rhythm space metric, and conditional dynamics. Issues considered in this paper include unification of levels of analysis, response equivalence, convergence, relationship of physics to motor control, support of rhythms, state transitions, and focussing on low-dimensional subspaces of a high-dimensional sensorimotor space.
Den Dulk, P., B. T. Heerebout, et al. (2003). "A computational study into the evolution of dual-route dynamics for affective processing." J Cogn Neurosci 15(2): 194-208.
The evolutionary justification by LeDoux (1996) for his dual-route model of fear processing was analyzed computationally by applying genetic algorithms to artificial neural networks. The evolution was simulated of a neural network controlling an agent that gathered food in an artificial world and that was occasionally menaced by a predator. Connections could not change in the agent's "lifetime," so there was no learning in the simulations. Only if the smells of food and predator were hard to distinguish and the fitness reflected time pressures in escaping from the predator did the type of dual processing postulated by LeDoux emerge in the surviving agents. Processing in the "quick and dirty" pathway of the fear system ensured avoidance of both predators and food, but a distinction between food and predator was made only in the long pathway. Elaborate processing inhibited the avoidance reaction and reversed it into an approach reaction to food, but strengthened the avoidance reaction to predators (and more finely tuned the direction of escape). It is suggested that "computational neuroethology" (Beer, 1990) may help constrain reasoning in evolutionary psychology, particularly when applied to specific neurobiological models, and in the future may even generate new hypotheses for cognitive neuroscience.
Xie, X. and M. A. Giese (2002). "Nonlinear dynamics of direction-selective recurrent neural media." Phys Rev E Stat Nonlin Soft Matter Phys 65(5 Pt 1): 051904.
The direction selectivity of cortical neurons can be accounted for by asymmetric lateral connections. Such lateral connectivity leads to a network dynamics with characteristic properties that can be exploited for distinguishing in neurophysiological experiments this mechanism for direction selectivity from other possible mechanisms. We present a mathematical analysis for a class of direction-selective neural models with asymmetric lateral connections. Contrasting with earlier theoretical studies that have analyzed approximations of the network dynamics by neglecting nonlinearities using methods from linear systems theory, we study the network dynamics with nonlinearity taken into consideration. We show that asymmetrically coupled networks can stabilize stimulus-locked traveling pulse solutions that are appropriate for the modeling of the responses of direction-selective neurons. In addition, our analysis shows that outside a certain regime of stimulus speeds the stability of these solutions breaks down, giving rise to lurching activity waves with specific spatiotemporal periodicity. These solutions, and the bifurcation by which they arise, cannot be easily accounted for by classical models for direction selectivity.
Webb, B. (2002). "Robots in invertebrate neuroscience." Nature 417(6886): 359-63.
Can we now build artificial animals? A combination of robot technology and neuroethological knowledge is enabling the development of realistic physical models of biological systems. And such systems are not only of interest to engineers. By exploring identified neural control circuits in the appropriate functional and environmental context, new insights are also provided to biologists.
Tsuzuki, T., T. Kawahara, et al. (2002). "[Connectionist modeling of higher-level cognitive processes]." Shinrigaku Kenkyu 72(6): 541-55.
Connectionist modeling is one approach to understanding human intelligence using simulated networks of neuron-like processing units. In this article, we report on recent progress in connectionist models that simulate empirical data of higher-level cognitive processes, these being memory, learning, language, thinking, cognitive development, and social cognition. We also review and summarize the advantages and disadvantages of these connectionist models. The computational framework of connectionist modeling has the potential to integrate specialized psychological findings of different areas using the same architectures and local functions of units and connections, inspired from neuroscience. In particular, the problems of dealing with structured information in distributed form, and doing tasks that require variable binding in connectionist networks are discussed from several different perspectives. As one possible solution to treat systematic mental representations properly, the symbolic connectionist model, which is a hybrid approach using symbolic representations and connectionist architectures, is explained. We argue that connectionist computer simulation offers significant benefits for today's psychological researches, and that connectionist modeling is likely to have an important influence on future studies.
Shepherd, G. M. (2002). "Supporting databases for neuroscience research." J Neurosci 22(5): 1497.
Migliore, M. and G. M. Shepherd (2002). "Emerging rules for the distributions of active dendritic conductances." Nat Rev Neurosci 3(5): 362-70.
A key goal in neuroscience is to explain how the operations of a neuron emerge from sets of active channels with specific dendritic distributions. If general principles can be identified for these distributions, dendritic channels should reflect the computational role of a given cell type within its functional neural circuit. Here, we discuss insights from experimental and computational data on the distribution of voltage-gated channels in dendrites, and attempt to derive rules for how their interactions implement different dendritic functions. We propose that this type of analysis will be important for understanding behavioural processes in terms of single-neuron properties, and that it constitutes a step towards a 'functional proteomics' of nerve cells, which will be essential for defining neuronal phenotypes.
Krichmar, J. L. and G. M. Edelman (2002). "Machine psychology: autonomous behavior, perceptual categorization and conditioning in a brain-based device." Cereb Cortex 12(8): 818-30.
In studying brain activity during the behavior of living animals, it is not possible simultaneously to analyze all levels of control from molecular events to motor responses. To provide insights into how levels of control interact, we have carried out synthetic neural modeling using a brain-based real-world device. We describe here the design and performance of such a device, designated Darwin VII, which is guided by computer-simulated analogues of cortical and subcortical structures. All levels of Darwin VII's neural architecture can be examined simultaneously as the device behaves in a real environment. Analysis of its neural activity during perceptual categorization and conditioned behavior suggests neural mechanisms for invariant object recognition, experience-dependent perceptual categorization, first-order and second-order conditioning, and the effects of different learning rates on responses to appetitive and aversive events. While individual Darwin VII exemplars developed similar categorical responses that depended on exploration of the environment and sensorimotor adaptation, each showed highly individual patterns of changes in synaptic strengths. By allowing exhaustive analysis and manipulation of neuroanatomy and large-scale neural dynamics, such brain-based devices provide valuable heuristics for understanding cortical interactions. These devices also provide the groundwork for the development of intelligent machines that follow neurobiological rather than computational principles in their construction.
Ito, M. (2002). "Historical review of the significance of the cerebellum and the role of Purkinje cells in motor learning." Ann N Y Acad Sci 978: 273-88.
Classic studies of the cerebellum before the middle of the twentieth century established the structural entity of the cerebellum and characterized its function as enabling animals and humans to carry out smooth and accurate movements, even at a high speed and without visual feedback. In the 1960s, neuronal circuit structures of the cerebellum were analyzed in detail, which promoted computational approaches toward the study of neuronal network principles of the cerebellum. In the 1970s and 1980s, vestibulo-ocular reflex adaptation, adaptive locomotion, eye blink conditioning, and learning in hand/arm movement were established as effective experimental paradigms for investigating neural mechanisms of cerebellar functions. In the 1980s, long-term depression (LTD) was discovered and considered as a memory process in the cerebellum; in the 1990s, complex signal transduction processes underlying LTD were revealed. It was also in the 1980s that computational approaches were advanced for modeling control system functions of the cerebellum. Currently, there are two alternative models proposed for VOR adaptation. In this decade, we envisage new developments toward the fusion of knowledge of the cerebellum at molecular and cellular levels and those in systems and computation. Studies of LTD will play a key role in pursuing this direction.
Hirsch, M. D. (2002). "A neuroscience knowledge management systems approach: application to functional proteomics/genomics of the chromaffin cell." Ann N Y Acad Sci 971: 615-6.
Hauser, M. D., N. Chomsky, et al. (2002). "The faculty of language: what is it, who has it, and how did it evolve?" Science 298(5598): 1569-79.
We argue that an understanding of the faculty of language requires substantial interdisciplinary cooperation. We suggest how current developments in linguistics can be profitably wedded to work in evolutionary biology, anthropology, psychology, and neuroscience. We submit that a distinction should be made between the faculty of language in the broad sense (FLB) and in the narrow sense (FLN). FLB includes a sensory-motor system, a conceptual-intentional system, and the computational mechanisms for recursion, providing the capacity to generate an infinite range of expressions from a finite set of elements. We hypothesize that FLN only includes recursion and is the only uniquely human component of the faculty of language. We further argue that FLN may have evolved for reasons other than language, hence comparative studies might look for evidence of such computations outside of the domain of communication (for example, number, navigation, and social relations).
Gerlai, R. (2002). "Phenomics: fiction or the future?" Trends Neurosci 25(10): 506-9.
The ease with which genetic mutations can be induced in or introduced into mammalian organisms, such as the mouse, has created a significant need for phenotypic analysis. Developments in computer technology, instrumentation and bioinformatics, as well as in numerous neuroscience disciplines, will help to meet the demands set by the molecular revolution. As a result, the field of 'phenomics' is being born. This will integrate multidisciplinary research, with the goal of understanding the complex phenotypic consequences of genetic mutations at the level of the organism. This paper focuses on one of the disciplines that show promising developments, behavioral science.
Corchs, S. and G. Deco (2002). "Large-scale neural model for visual attention: integration of experimental single-cell and fMRI data." Cereb Cortex 12(4): 339-48.
A computational neuroscience framework is proposed to better understand the role and the neuronal correlate of spatial attention modulation in visual perception. The model consists of several interconnected modules that can be related to the different areas of the dorsal and ventral paths of the visual cortex. Competitive neural interactions are implemented at both microscopic and interareal levels, according to the biased competition hypothesis. This hypothesis has been experimentally confirmed in studies in humans using functional magnetic resonance imaging (fMRI) techniques and also in single-cell recording studies in monkeys. Within this neuro-dynamical approach, numerical simulations are carried out that describe both the fMRI and the electrophysiological data. The proposed model draws together data of different spatial and temporal resolution, as are the above-mentioned imaging and single-cell results.
Casey, B. J. and Y. Munakata (2002). "Converging methods in developmental science: an introduction." Dev Psychobiol 40(3): 197-9.
This special issue of Developmental Psychobiology reflects a number of recent advances in the field of developmental neuroscience. The most evident are methodological advances in noninvasive neuroimaging such as those described in a parallel special issue of Developmental Science, Volume 5, 2002. While advances in imaging methods offer a new era in developmental research, other methods (e.g., animal, computational, lesion, and genetic) remain essential in constraining and informing theories of brain and behavioral development. The papers in this issue highlight the importance of a converging methods approach to the study of developmental science and illustrate how a variety of available tools allow insights into both new and classic developmental questions.
Cannon, R. C., F. W. Howell, et al. (2002). "Non-curated distributed databases for experimental data and models in neuroscience." Network 13(3): 415-28.
Neuroscience is generating vast amounts of highly diverse data which is of potential interest to researchers beyond the laboratories in which it is collected. In particular, quantitative neuroanatomical data is relevant to a wide variety of areas, including studies of development, aging, pathology and in biophysically oriented computational modelling. Moreover, the relatively discrete and well-defined nature of the data make it an ideal application for developing systems designed to facilitate data archiving, sharing and reuse. At present, the only widely used forms of dissemination are figures and tables in published papers which suffer from inaccessibility and the loss of machine readability. They may also present only an averaged or otherwise selected subset of the available data. Numerous database projects are in progress to address these shortcomings. They employ a variety of architectures and philosophies, each with its own merits and disadvantages. One axis on which they may be distinguished is the degree of top-down control, or curation, involved in data entry. Here we consider one extreme of this scale in which there is no curation, minimal standardization and a wide degree of freedom in the form of records used to document data. Such a scheme has advantages in the ease of database creation and in the equitable assignment of perceived intellectual property by keeping the control of data in the hands of the experts who collected it. It does, however, require a more sophisticated infrastructure than conventional databases since the software must be capable of organizing diverse and differently documented data sets in an effective way. Several components of a software system to provide this infrastructure are now in place. Examples are presented, showing how these tools can be used to archive and publish neuronal morphology data, and how they can give an integrated view of data stored at many different sites.
Ascoli, G. A. (2002). "Neuroanatomical algorithms for dendritic modelling." Network 13(3): 247-60.
The complexity and variability of dendritic morphology constitutes a fascinating challenge to the investigation of the structure-activity-function relationship in the nervous system. Computational modelling has recently emerged as a powerful approach for the quantitative anatomical characterization of dendrites. The key idea is to design a stochastic algorithm to generate digital structures that are statistically indistinguishable from those of real neurons of a given morphological class. The set of parameters used by this algorithm would then constitute a complete and accurate description of that morphological class. We review the strengths and weaknesses of the major types of algorithms used to model dendrogram properties, including those based on branch diameter and on distance from the soma. We also describe some approaches to the simulation of dendritic orientation and three-dimensional geometry. Finally, we discuss the environmental influences on dendritic morphology (especially the presence of axons, other neurons, and anatomical boundaries) and thus the need to include models of the tissue volume in the algorithmic description of dendrites.
(2002). "Dedication to Carlo A. Terzuolo. Proceedings of a conference. Brainerd, Minnesota, USA. September 26-30, 2001." Arch Ital Biol 140(3): 159-262.
van Pelt, J., A. van Ooyen, et al. (2001). "The need for integrating neuronal morphology databases and computational environments in exploring neuronal structure and function." Anat Embryol (Berl) 204(4): 255-65.
Neurons connect to each other through a myriad of dendritic and axonal arborisations. Dendritic structures provide the substrate for integration of postsynaptic potentials and control of action potential generation. Axonal structures provide the substrate for action potential dissemination and signalling to target neurons. The morphological complexity of dendritic arborisations is assumed to play a critical role in the transformation of spatio-temporal patterns of postsynaptic potentials into time-structured series of action potentials. Although these transformations lie at the basis of information processing in the brain, it is still far from understood how their details are influenced by dendritic shape. To facilitate research in this area, it is necessary that data on both the morphology and electrical properties of neurons, as well as computational tools for analysis, become available in an integrated way. This requires a combined effort from the fields of informatics and neurosciences (together called neuroinformatics) in order to create data acquisition, databasing and computational tools. Focusing on neuronal morphology, this chapter will give a brief review of the current neuroinformatics developments in both reconstruction techniques, morphological quantification, modeling of morphological complexity, modeling of function and the need for databasing neuronal morphologies. Additionally, one of the dendritic modeling approaches is described in more detail in the Appendix.
Tiesinga, P. H. (2001). "Information transmission and recovery in neural communication channels revisited." Phys Rev E Stat Nonlin Soft Matter Phys 64(1-1): 012901.
Nerve cells in the brain generate all-or-none electric events-spikes-that are transmitted to other nerve cells via chemical synapses. An important issue in neuroscience is how neurons encode and transmit information using spike trains. Recently, signal transduction through two neurons connected by an excitatory chemical synapse was studied by Eguia et al. [Phys. Rev. E 62, 7111 (2000)]. They reported an apparent violation of the data processing inequality: The mutual information between the input signal and the output of the first neuron can be lower than the mutual information between the input signal and the output of the second neuron, that only receives input from the first neuron. We investigate whether it is possible, using a different method, to retrieve, from the first neuron's spike train, all the information about the input that is present in the second neuron's output. We find that single interspike intervals (ISI's) from the first neuron, at a resolution of 0.5 time units, contain more information about the input signal than those of the second neuron. Using a classification procedure based on the ISI return map, we recover 71% of the input entropy using the first neuron's spike train, and only 42% using the second neuron's spike train. Hence for these spike-train observables the data processing inequality is not violated.
Stephan, K. E., L. Kamper, et al. (2001). "Advanced database methodology for the Collation of Connectivity data on the Macaque brain (CoCoMac)." Philos Trans R Soc Lond B Biol Sci 356(1412): 1159-86.
The need to integrate massively increasing amounts of data on the mammalian brain has driven several ambitious neuroscientific database projects that were started during the last decade. Databasing the brain's anatomical connectivity as delivered by tracing studies is of particular importance as these data characterize fundamental structural constraints of the complex and poorly understood functional interactions between the components of real neural systems. Previous connectivity databases have been crucial for analysing anatomical brain circuitry in various species and have opened exciting new ways to interpret functional data, both from electrophysiological and from functional imaging studies. The eventual impact and success of connectivity databases, however, will require the resolution of several methodological problems that currently limit their use. These problems comprise four main points: (i) objective representation of coordinate-free, parcellation-based data, (ii) assessment of the reliability and precision of individual data, especially in the presence of contradictory reports, (iii) data mining and integration of large sets of partially redundant and contradictory data, and (iv) automatic and reproducible transformation of data between incongruent brain maps. Here, we present the specific implementation of the 'collation of connectivity data on the macaque brain' (CoCoMac) database (http://www.cocomac.org). The design of this database addresses the methodological challenges listed above, and focuses on experimental and computational neuroscientists' needs to flexibly analyse and process the large amount of published experimental data from tracing studies. In this article, we explain step-by-step the conceptual rationale and methodology of CoCoMac and demonstrate its practical use by an analysis of connectivity in the prefrontal cortex.
Sommer, F. T. and T. Wennekers (2001). "Associative memory in networks of spiking neurons." Neural Netw 14(6-7): 825-34.
Here, we develop and investigate a computational model of a network of cortical neurons on the base of biophysically well constrained and tested two-compartmental neurons developed by Pinsky and Rinzel [Pinsky, P. F., & Rinzel, J. (1994). Intrinsic and network rhythmogenesis in a reduced Traub model for CA3 neurons. Journal of Computational Neuroscience, 1, 39-60]. To study associative memory, we connect a pool of cells by a structured connectivity matrix. The connection weights are shaped by simple Hebbian coincidence learning using a set of spatially sparse patterns. We study the neuronal activity processes following an external stimulation of a stored memory. In two series of simulation experiments, we explore the effect of different classes of external input, tonic and flashed stimulation. With tonic stimulation, the addressed memory is an attractor of the network dynamics. The memory is displayed rhythmically, coded by phase-locked bursts or regular spikes. The participating neurons have rhythmic activity in the gamma-frequency range (30-80 Hz). If the input is switched from one memory to another, the network activity can follow this change within one or two gamma cycles. Unlike similar models in the literature, we studied the range of high memory capacity (in the order of 0.1 bit/synapse), comparable to optimally tuned formal associative networks. We explored the robustness of efficient retrieval varying the memory load, the excitation/inhibition parameters, and background activity. A stimulation pulse applied to the identical simulation network can push away ongoing network activity and trigger a phase-locked association event within one gamma period. Unlike as under tonic stimulation, the memories are not attractors. After one association process, the network activity moves to other states. Applying in close succession pulses addressing different memories, one can switch through the space of memory patterns. The readout speed can be increased up to the point where in every gamma cycle another pattern is displayed. With pulsed stimulation. bursts become relevant for coding, their occurrence can be used to discriminate relevant processes from background activity.
O'Reilly, R. C. (2001). "Generalization in interactive networks: the benefits of inhibitory competition and Hebbian learning." Neural Comput 13(6): 1199-241.
Computational models in cognitive neuroscience should ideally use biological properties and powerful computational principles to produce behavior consistent with psychological findings. Error-driven backpropagation is computationally powerful and has proven useful for modeling a range of psychological data but is not biologically plausible. Several approaches to implementing backpropagation in a biologically plausible fashion converge on the idea of using bidirectional activation propagation in interactive networks to convey error signals. This article demonstrates two main points about these error-driven interactive networks: (1) they generalize poorly due to attractor dynamics that interfere with the network's ability to produce novel combinatorial representations systematically in response to novel inputs, and (2) this generalization problem can be remedied by adding two widely used mechanistic principles, inhibitory competition and Hebbian learning, that can be independently motivated for a variety of biological, psychological, and computational reasons. Simulations using the Leabra algorithm, which combines the generalized recirculation (GeneRec), biologically plausible, error-driven learning algorithm with inhibitory competition and Hebbian learning, show that these mechanisms can result in good generalization in interactive networks. These results support the general conclusion that cognitive neuroscience models that incorporate the core mechanistic principles of interactivity, inhibitory competition, and error-driven and Hebbian learning satisfy a wider range of biological, psychological, and computational constraints than models employing a subset of these principles.
O'Neill, M. A. and C. C. Hilgetag (2001). "The portable UNIX programming system (PUPS) and CANTOR: a computational environment for dynamical representation and analysis of complex neurobiological data." Philos Trans R Soc Lond B Biol Sci 356(1412): 1259-76.
Many problems in analytical biology, such as the classification of organisms, the modelling of macromolecules, or the structural analysis of metabolic or neural networks, involve complex relational data. Here, we describe a software environment, the portable UNIX programming system (PUPS), which has been developed to allow efficient computational representation and analysis of such data. The system can also be used as a general development tool for database and classification applications. As the complexity of analytical biology problems may lead to computation times of several days or weeks even on powerful computer hardware, the PUPS environment gives support for persistent computations by providing mechanisms for dynamic interaction and homeostatic protection of processes. Biological objects and their interrelations are also represented in a homeostatic way in PUPS. Object relationships are maintained and updated by the objects themselves, thus providing a flexible, scalable and current data representation. Based on the PUPS environment, we have developed an optimization package, CANTOR, which can be applied to a wide range of relational data and which has been employed in different analyses of neuroanatomical connectivity. The CANTOR package makes use of the PUPS system features by modifying candidate arrangements of objects within the system's database. This restructuring is carried out via optimization algorithms that are based on user-defined cost functions, thus providing flexible and powerful tools for the structural analysis of the database content. The use of stochastic optimization also enables the CANTOR system to deal effectively with incomplete and inconsistent data. Prototypical forms of PUPS and CANTOR have been coded and used successfully in the analysis of anatomical and functional mammalian brain connectivity, involving complex and inconsistent experimental data. In addition, PUPS has been used for solving multivariate engineering optimization problems and to implement the digital identification system (DAISY), a system for the automated classification of biological objects. PUPS is implemented in ANSI-C under the POSIX.1 standard and is to a great extent architecture- and operating-system independent. The software is supported by systems libraries that allow multi-threading (the concurrent processing of several database operations), as well as the distribution of the dynamic data objects and library operations over clusters of computers. These attributes make the system easily scalable, and in principle allow the representation and analysis of arbitrarily large sets of relational data. PUPS and CANTOR are freely distributed (http://www.pups.org.uk) as open-source software under the GNU license agreement.
Menon, R. S. (2001). "Imaging function in the working brain with fMRI." Curr Opin Neurobiol 11(5): 630-6.
The intrinsic flexibility of functional magnetic resonance imaging has allowed ever more innovative neuroscience applications. New acquisition and analysis techniques have contributed to improvements in detection sensitivity, as well as spatial and temporal resolution. Furthermore, by considering the dynamic evolution of the active brain areas in a network, computational models are making the first steps towards linking brain and mind.
Luo, Z. and D. H. Geschwind (2001). "Microarray applications in neuroscience." Neurobiol Dis 8(2): 183-93.
Advances in all facets of technology from molecular biology to imaging and computational biology offer unprecedented opportunities for improving our understanding of the brain in health and disease. Oligonucleotide and cDNA microarray analysis, using a variety of "DNA chips," is a recently developed high-throughput technique that allows for tour-de-force analysis of gene expression. We review this powerful technique, developed in genetics laboratories, with reference to applications in neurologic diseases in humans and the use of animal models. The typical microarray experiment is multistaged and includes preparation or purchase of arrays, preparation of target DNA and probe, target DNA hybridization, microarray scanning, and image analysis. The power and pitfalls of this technology are discussed in the context of neuroscience paradigms. Since unprecedented amounts of data are produced from microarray experiments, bioinformatics and modeling expertise are increasingly becoming critical components of this approach.
Inchiosa, M. E., V. In, et al. (2001). "Stochastic dynamics in a two-dimensional oscillator near a saddle-node bifurcation." Phys Rev E Stat Nonlin Soft Matter Phys 63(6 Pt 2): 066114.
We study the oscillator equations describing a particular class of nonlinear amplifier, exemplified in this work by a two-junction superconducting quantum interference device. This class of dynamic system is described by a potential energy function that can admit minima (corresponding to stable solutions of the dynamic equations), or "running states" wherein the system is biased so that the potential minima disappear and the solutions display spontaneous oscillations. Just beyond the onset of the spontaneous oscillations, the system is known to show significantly enhanced sensitivity to very weak magnetic signals. The global phase space structure allows us to apply a center manifold technique to approximate analytically the oscillatory behavior just past the (saddle-node) bifurcation and compute the oscillation period, which obeys standard scaling laws. In this regime, the dynamics can be represented by an "integrate-fire" model drawn from the computational neuroscience repertoire; in fact, we obtain an "interspike interval" probability density function and an associated power spectral density (computed via Renewal theory) that agree very well with the results obtained via numerical simulations. Notably, driving the system with one or more time sinusoids produces a noise-lowering injection locking effect and/or heterodyning.
Hines, M. L. and N. T. Carnevale (2001). "NEURON: a tool for neuroscientists." Neuroscientist 7(2): 123-35.
NEURON is a simulation environment for models of individual neurons and networks of neurons that are closely linked to experimental data. NEURON provides tools for conveniently constructing, exercising, and managing models, so that special expertise in numerical methods or programming is not required for its productive use. This article describes two tools that address the problem of how to achieve computational efficiency and accuracy.
Grant, S. G. and W. P. Blackstock (2001). "Proteomics in neuroscience: from protein to network." J Neurosci 21(21): 8315-8.
Proteomic tools offer a new platform for studies of complex biological functions involving large numbers and networks of proteins. Intracellular networks of proteins perform key functions in neurons and glia. The unicellular eukaryote Saccharomyces cerevisiae has been the prototype for eukaryotic proteomic studies, and when combined with genomics, microarrays, genetics, and pharmacology, new insights into the integrated function of the cell emerge. The anatomical complexity of the nervous system both in cell types and in the vast number of synapses introduces novel technical and biological issues regarding the subcellular organization of protein networks. Here we will discuss the technology of proteomics and its applications to the nervous system.
Gossl, C., D. P. Auer, et al. (2001). "Bayesian spatiotemporal inference in functional magnetic resonance imaging." Biometrics 57(2): 554-62.
Mapping of the human brain by means of functional magnetic resonance imaging (fMRI) is an emerging field in cognitive and clinical neuroscience. Current techniques to detect activated areas of the brain mostly proceed in two steps. First, conventional methods of correlation, regression, and time series analysis are used to assess activation by a separate, pixelwise comparison of the fMRI signal time courses to the reference function of a presented stimulus. Spatial aspects caused by correlations between neighboring pixels are considered in a separate second step, if at all. The aim of this article is to present hierarchical Bayesian approaches that allow one to simultaneously incorporate temporal and spatial dependencies between pixels directly in the model formulation. For reasons of computational feasibility, models have to be comparatively parsimonious, without oversimplifying. We introduce parametric and semiparametric spatial and spatiotemporal models that proved appropriate and illustrate their performance applied to visual fMRI data.
Goddard, N. H., M. Hucka, et al. (2001). "Towards NeuroML: model description methods for collaborative modelling in neuroscience." Philos Trans R Soc Lond B Biol Sci 356(1412): 1209-28.
Biological nervous systems and the mechanisms underlying their operation exhibit astonishing complexity. Computational models of these systems have been correspondingly complex. As these models become ever more sophisticated, they become increasingly difficult to define, comprehend, manage and communicate. Consequently, for scientific understanding of biological nervous systems to progress, it is crucial for modellers to have software tools that support discussion, development and exchange of computational models. We describe methodologies that focus on these tasks, improving the ability of neuroscientists to engage in the modelling process. We report our findings on the requirements for these tools and discuss the use of declarative forms of model description--equivalent to object-oriented classes and database schema--which we call templates. We introduce NeuroML, a mark-up language for the neurosciences which is defined syntactically using templates, and its specific component intended as a common format for communication between modelling-related tools. Finally, we propose a template hierarchy for this modelling component of NeuroML, sufficient for describing models ranging in structural levels from neuron cell membranes to neural networks. These templates support both a framework for user-level interaction with models, and a high-performance framework for efficient simulation of the models.
Dimitrov, A. G. and J. P. Miller (2001). "Analyzing sensory systems with the information distortion function." Pac Symp Biocomput: 251-62.
The nature and information content of neural signals have been discussed extensively in the neuroscience community. They are important ingredients in many theories on neural function, yet there is still no agreement on the details of neural coding. There have been various suggestions about how information is encoded in neural spike trains: by the number of spikes, by temporal correlations, through single spikes, or by spike patterns in one, or across many neurons. The latter scheme is most general and encompasses many others. We present an algorithm which can recover a coarse representation of a pattern coding scheme, through quantization to a reproduction set of smaller size. Among many possible quantizations, we choose one which preserves as much of the informativeness of the original stimulus/response relation as possible, through the use of an information-based distortion function. This method allows us to study coarse but highly informative models of a coding scheme, and then to refine them when more data becomes available. We shall describe a model in which full recovery is possible and present example for cases with partial recovery.
Deco, G. and J. Zihl (2001). "A neurodynamical model of visual attention: feedback enhancement of spatial resolution in a hierarchical system." J Comput Neurosci 10(3): 231-53.
Human beings have the capacity to recognize objects in natural visual scenes with high efficiency despite the complexity of such scenes, which usually contain multiple objects. One possible mechanism for dealing with this problem is selective attention. Psychophysical evidence strongly suggests that selective attention can enhance the spatial resolution in the input region corresponding to the focus of attention. In this work we adopt a computational neuroscience perspective to analyze the attentional enhancement of spatial resolution in the area containing the objects of interest. We extend and apply the computational model of Deco and Schurmann (2000), which consists of several modules with feedforward and feedback interconnections describing the mutual links between different areas of the visual cortex. Each module analyses the visual input with different spatial resolution and can be thought of as a hierarchical predictor at a given level of resolution. Moreover, each hierarchical predictor has a submodule that consists of a group of neurons performing a biologically based 2D Gabor wavelet transformation at a given resolution level. The attention control decides in which local regions the spatial resolution should be enhanced in a serial fashion. In this sense, the scene is first analyzed at a coarse resolution level, and the focus of attention enhances iteratively the resolution at the location of an object until the object is identified. We propose and simulate new psychophysical experiments where the effect of the attentional enhancement of spatial resolution can be demonstrated by predicting different reaction time profiles in visual search experiments where the target and distractors are defined at different levels of resolution.
Burns, G. A. (2001). "Knowledge management of the neuroscientific literature: the data model and underlying strategy of the NeuroScholar system." Philos Trans R Soc Lond B Biol Sci 356(1412): 1187-208.
This paper describes the underlying strategy and system's design of a knowledge management system for the neuroscientific literature called 'NeuroScholar'. The problem that the system is designed to address is to delineate fully the neural circuitry involved in a specific behaviour. The use of this system provides experimental neuroscientists with a new method of building computational models ('knowledge models') of the contents of the published literature. These models may provide input for analysis (conceptual or computational), or be used as constraint sets for conventional neural modelling work. The underlying problems inherent in this approach, the general framework for the proposed solution, the practical issues concerning usage of the system and a detailed, technical account of the system are described. The author uses a widely used software specification language (the Universal Modelling Language) to describe the design of the system and present examples from published work concerned with classical eyeblink conditioning in the rabbit.
Brown, G. D., S. Yamada, et al. (2001). "Independent component analysis at the neural cocktail party." Trends Neurosci 24(1): 54-63.
'Independent component analysis' is a technique of data transformation that finds independent sources of activity in recorded mixtures of sources. It can be used to recover fluctuations of membrane potential from individual neurons in multiple-detector optical recordings. There are some examples in which more than 100 neurons can be separated simultaneously. Independent component analysis automatically separates overlapping action potentials, recovers action potentials of different sizes from the same neuron, removes artifacts and finds the position of each neuron on the detector array. One limitation is that the number of sources--neurons and artifacts--must be equal to or less than the number of simultaneous recordings. Independent component analysis also has many other applications in neuroscience including, removal of artifacts from EEG data, identification of spatially independent brain regions in fMRI recordings and determination of population codes in multi-unit recordings.
Breslin, C. and A. O'Lenskie (2001). "Neuromorphic hardware databases for exploring structure-function relationships in the brain." Philos Trans R Soc Lond B Biol Sci 356(1412): 1249-58.
Neuromorphic hardware is the term used to describe full custom-designed integrated circuits, or silicon 'chips', that are the product of neuromorphic engineering--a methodology for the synthesis of biologically inspired elements and systems, such as individual neurons, retinae, cochleas, oculomotor systems and central pattern generators. We focus on the implementation of neurons and networks of neurons, designed to illuminate structure-function relationships. Neuromorphic hardware can be constructed with either digital or analogue circuitry or with mixed-signal circuitry--a hybrid of the two. Currently, most examples of this type of hardware are constructed using analogue circuits, in complementary metal-oxide-semiconductor technology. The correspondence between these circuits and neurons, or networks of neurons, can exist at a number of levels. At the lowest level, this correspondence is between membrane ion channels and field-effect transistors. At higher levels, the correspondence is between whole conductances and firing behaviour, and filters and amplifiers, devices found in conventional integrated circuit design. Similarly, neuromorphic engineers can choose to design Hodgkin-Huxley model neurons, or reduced models, such as integrate-and-fire neurons. In addition to the choice of level, there is also choice within the design technique itself; for example, resistive and capacitive properties of the neuronal membrane can be constructed with extrinsic devices, or using the intrinsic properties of the materials from which the transistors themselves are composed. So, silicon neurons can be built, with dendritic, somatic and axonal structures, and endowed with ionic, synaptic and morphological properties. Examples of the structure-function relationships already explored using neuromorphic hardware include correlation detection and direction selectivity. Establishing a database for this hardware is valuable for two reasons: first, independently of neuroscientific motivations, the field of neuromorphic engineering would benefit greatly from a resource in which circuit designs could be stored in a form appropriate for reuse and re-fabrication. Analogue designers would benefit particularly from such a database, as there are no equivalents to the algorithmic design methods available to designers of digital circuits. Second, and more importantly for the purpose of this theme issue, is the possibility of a database of silicon neuron designs replicating specific neuronal types and morphologies. In the future, it may be possible to use an automated process to translate morphometric data directly into circuit design compatible formats. The question that needs to be addressed is: what could a neuromorphic hardware database contribute to the wider neuroscientific community that a conventional database could not? One answer is that neuromorphic hardware is expected to provide analogue sensory-motor systems for interfacing the computational power of symbolic, digital systems with the external, analogue environment. It is also expected to contribute to ongoing work in neural-silicon interfaces and prosthetics. Finally, there is a possibility that the use of evolving circuits, using reconfigurable hardware and genetic algorithms, will create an explosion in the number of designs available to the neuroscience community. All this creates the need for a database to be established, and it would be advantageous to set about this while the field is relatively young. This paper outlines a framework for the construction of a neuromorphic hardware database, for use in the biological exploration of structure-function relationships.
Bloom, F. E. (2001). "What does it all mean to you?" J Neurosci 21(21): 8304-5.
Bjaalie, J. G. (2001). "Advances in computational neuroanatomy." Anat Embryol (Berl) 204(4): 253-4.
Ascoli, G. A., J. L. Krichmar, et al. (2001). "Generation, description and storage of dendritic morphology data." Philos Trans R Soc Lond B Biol Sci 356(1412): 1131-45.
It is generally assumed that the variability of neuronal morphology has an important effect on both the connectivity and the activity of the nervous system, but this effect has not been thoroughly investigated. Neuroanatomical archives represent a crucial tool to explore structure-function relationships in the brain. We are developing computational tools to describe, generate, store and render large sets of three-dimensional neuronal structures in a format that is compact, quantitative, accurate and readily accessible to the neuroscientist. Single-cell neuroanatomy can be characterized quantitatively at several levels. In computer-aided neuronal tracing files, a dendritic tree is described as a series of cylinders, each represented by diameter, spatial coordinates and the connectivity to other cylinders in the tree. This 'Cartesian' description constitutes a completely accurate mapping of dendritic morphology but it bears little intuitive information for the neuroscientist. In contrast, a classical neuroanatomical analysis characterizes neuronal dendrites on the basis of the statistical distributions of morphological parameters, e.g. maximum branching order or bifurcation asymmetry. This description is intuitively more accessible, but it only yields information on the collective anatomy of a group of dendrites, i.e. it is not complete enough to provide a precise 'blueprint' of the original data. We are adopting a third, intermediate level of description, which consists of the algorithmic generation of neuronal structures within a certain morphological class based on a set of 'fundamental', measured parameters. This description is as intuitive as a classical neuroanatomical analysis (parameters have an intuitive interpretation), and as complete as a Cartesian file (the algorithms generate and display complete neurons). The advantages of the algorithmic description of neuronal structure are immense. If an algorithm can measure the values of a handful of parameters from an experimental database and generate virtual neurons whose anatomy is statistically indistinguishable from that of their real counterparts, a great deal of data compression and amplification can be achieved. Data compression results from the quantitative and complete description of thousands of neurons with a handful of statistical distributions of parameters. Data amplification is possible because, from a set of experimental neurons, many more virtual analogues can be generated. This approach could allow one, in principle, to create and store a neuroanatomical database containing data for an entire human brain in a personal computer. We are using two programs, L-NEURON and ARBORVITAE, to investigate systematically the potential of several different algorithms for the generation of virtual neurons. Using these programs, we have generated anatomically plausible virtual neurons for several morphological classes, including guinea pig cerebellar Purkinje cells and cat spinal cord motor neurons. These virtual neurons are stored in an online electronic archive of dendritic morphology. This process highlights the potential and the limitations of the 'computational neuroanatomy' strategy for neuroscience databases.
Ascoli, G. A., J. L. Krichmar, et al. (2001). "Computer generation and quantitative morphometric analysis of virtual neurons." Anat Embryol (Berl) 204(4): 283-301.
An important goal in computational neuroanatomy is the complete and accurate simulation of neuronal morphology. We are developing computational tools to model three-dimensional dendritic structures based on sets of stochastic rules. This paper reports an extensive, quantitative anatomical characterization of simulated motoneurons and Purkinje cells. We used several local and global algorithms implemented in the L-Neuron and ArborVitae programs to generate sets of virtual neurons. Parameters statistics for all algorithms were measured from experimental data, thus providing a compact and consistent description of these morphological classes. We compared the emergent anatomical features of each group of virtual neurons with those of the experimental database in order to gain insights on the plausibility of the model assumptions, potential improvements to the algorithms, and non-trivial relations among morphological parameters. Algorithms mainly based on local constraints (e.g., branch diameter) were successful in reproducing many morphological properties of both motoneurons and Purkinje cells (e.g. total length, asymmetry, number of bifurcations). The addition of global constraints (e.g., trophic factors) improved the angle-dependent emergent characteristics (average Euclidean distance from the soma to the dendritic terminations, dendritic spread). Virtual neurons systematically displayed greater anatomical variability than real cells, suggesting the need for additional constraints in the models. For several emergent anatomical properties, a specific algorithm reproduced the experimental statistics better than the others did. However, relative performances were often reversed for different anatomical properties and/or morphological classes. Thus, combining the strengths of alternative generative models could lead to comprehensive algorithms for the complete and accurate simulation of dendritic morphology.
Wolpert, D. M. and Z. Ghahramani (2000). "Computational principles of movement neuroscience." Nat Neurosci 3 Suppl: 1212-7.
Unifying principles of movement have emerged from the computational study of motor control. We review several of these principles and show how they apply to processes such as motor planning, control, estimation, prediction and learning. Our goal is to demonstrate how specific models emerging from the computational approach provide a theoretical framework for movement neuroscience.
Stephan, K. E., K. Zilles, et al. (2000). "Coordinate-independent mapping of structural and functional data by objective relational transformation (ORT)." Philos Trans R Soc Lond B Biol Sci 355(1393): 37-54.
Neuroscience has produced an enormous amount of structural and functional data. Powerful database systems are required to make these data accessible for computational approaches such as higher-order analyses and simulations. Available databases for key data such as anatomical and functional connectivity between cortical areas, however, are still hampered by methodological problems. These problems arise predominantly from the parcellation problem, the use of incongruent parcellation schemes by different authors. We here present a coordinate-independent mathematical method to overcome this problem: objective relational transformation (ORT). Based on new classifications for brain data and on methods from theoretical computer science, ORT represents a formally defined, transparent transformation method for reproducible, coordinate-independent mapping of brain data to freely chosen parcellation schemes. We describe the methodology of ORT and discuss its strengths and limitations. Using two practical examples, we show that ORT in conjunction with connectivity databases like CoCoMac (http://www.cocomac.org) is an important tool for analyses of cortical organization and structure-function relationships.
Smaglik, P. (2000). "Internet gateway planned for neuroinformatics data." Nature 405(6787): 603.
Schmidt, L. A. and J. Schulkin (2000). "Toward a computational affective neuroscience." Brain Cogn 42(1): 95-8.
Schalow, G. and G. A. Zach (2000). "Reorganization of the human central nervous system." Gen Physiol Biophys 19 Suppl 1: 11-240.
The key strategies on which the discovery of the functional organization of the central nervous system (CNS) under physiologic and pathophysiologic conditions have been based included (1) our measurements of phase and frequency coordination between the firings of alpha- and gamma-motoneurons and secondary muscle spindle afferents in the human spinal cord, (2) knowledge on CNS reorganization derived upon the improvement of the functions of the lesioned CNS in our patients in the short-term memory and the long-term memory (reorganization), and (3) the dynamic pattern approach for re-learning rhythmic coordinated behavior. The theory of self-organization and pattern formation in nonequilibrium systems is explicitly related to our measurements of the natural firing patterns of sets of identified single neurons in the human spinal premotor network and re-learned coordinated movements following spinal cord and brain lesions. Therapy induced cell proliferation, and maybe, neurogenesis seem to contribute to the host of structural changes during the process of re-learning of the lesioned CNS. So far, coordinated functions like movements could substantially be improved in every of the more than 100 patients with a CNS lesion by applying coordination dynamic therapy. As suggested by the data of our patients on re-learning, the human CNS seems to have a second integrative strategy for learning, re-learning, storing and recalling, which makes an essential contribution of the functional plasticity following a CNS lesion. A method has been developed by us for the simultaneous recording with wire electrodes of extracellular action potentials from single human afferent and efferent nerve fibres of undamaged sacral nerve roots. A classification scheme of the nerve fibres in the human peripheral nervous system (PNS) could be set up in which the individual classes of nerve fibres are characterized by group conduction velocities and group nerve fibre diameters. Natural impulse patterns of several identified single afferent and efferent nerve fibres (motoneuron axons) were extracted from multi-unit impulse patterns, and human CNS functions could be analyzed under physiologic and pathophysiologic conditions. With our discovery of premotor spinal oscillators it became possible to judge upon CNS neuronal network organization based on the firing patterns of these spinal oscillators and their driving afferents. Since motoneurons fire occasionally for low activation and oscillatory for high activation, the coherent organization of subnetworks to generate macroscopic function is very complex and for the time being, may be best described by the theory of coordination dynamics. Since oscillatory firing has also been observed by us in single motor unit firing patterns measured electromyographically, it seems possible to follow up therapeutic intervention in patients with spinal cord and brain lesions not only based on the activity levels and phases of motor programs during locomotion but also based on the physiologic and pathophysiologic firing patterns and recruitment of spinal oscillators. The improvement of the coordination dynamics of the CNS can be partly measured directly by rhythmicity upon the patient performing rhythmic movements coordinated up to milliseconds. Since rhythmic dynamic, coordinated, stereotyped movements are mainly located in the spinal cord and only little supraspinal drive is necessary to initiate, maintain, and terminate them, rhythmic, dynamic, coordinated movements were used in therapy to enforce reorganization of the lesioned CNS by improving the self-organization and relative coordination of spinal oscillators (and their interactions with occasionally firing motoneurons) which became pathologic in their firing following CNS lesion. Paraparetic, tetraparetic spinal cord and brain-lesioned patients re-learned running and other movements by an oscillator formation and coordination dynamic therapy. Our development in neurorehabilitation is in accordance with those of theoretical and computational neurosciences which deal with the self-organization of neuronal networks. In particular, jumping on a springboard 'in-phase' and in 'anti-phase' to re-learn phase relations of oscillator coupling can be understood in the framework of the Haken-Kelso-Bunz coordination dynamic model. By introducing broken symmetry, intention, learning and spasticity in the landscape of the potential function of the integrated CNS activity, the change in self-organization becomes understandable. Movement patterns re-learned by oscillator formation and coordination dynamic therapy evolve from reorganization and regeneration of the lesioned CNS by cooperative and competitive interplay between intrinsic coordination dynamics, extrinsic therapy related inputs with physiologic re-afferent input, including intention, motivation, supervised learning, interpersonal coordination, and genetic constraints including neurogenesis. (ABSTRACT TRUNCATED)
Riesenhuber, M. and T. Poggio (2000). "Models of object recognition." Nat Neurosci 3 Suppl: 1199-204.
Understanding how biological visual systems recognize objects is one of the ultimate goals in computational neuroscience. From the computational viewpoint of learning, different recognition tasks, such as categorization and identification, are similar, representing different trade-offs between specificity and invariance. Thus, the different tasks do not require different classes of models. We briefly review some recent trends in computational vision and then focus on feedforward, view-based models that are supported by psychophysical and physiological data.
Poggio, T. and C. R. Shelton (2000). "Learning in brains and machines." Spat Vis 13(2-3): 287-96.
The problem of learning is arguably at the very core of the problem of intelligence, both biological and artificial. In this paper we sketch some of our work over the last ten years in the area of supervised learning, focusing on three interlinked directions of research: theory, engineering applications (that is, making intelligent software) and neuroscience (that is, understanding the brain's mechanisms of learning).
Miller, P. L. (2000). "Opportunities at the intersection of bioinformatics and health informatics: a case study." J Am Med Inform Assoc 7(5): 431-8.
This paper provides a "viewpoint discussion" based on a presentation made to the 2000 Symposium of the American College of Medical Informatics. It discusses potential opportunities for researchers in health informatics to become involved in the rapidly growing field of bioinformatics, using the activities of the Yale Center for Medical Informatics as a case study. One set of opportunities occurs where bioinformatics research itself intersects with the clinical world. Examples include the correlations between individual genetic variation with clinical risk factors, disease presentation, and differential response to treatment; and the implications of including genetic test results in the patient record, which raises clinical decision support issues as well as legal and ethical issues. A second set of opportunities occurs where bioinformatics research can benefit from the technologic expertise and approaches that informaticians have used extensively in the clinical arena. Examples include database organization and knowledge representation, data mining, and modeling and simulation. Microarray technology is discussed as a specific potential area for collaboration. Related questions concern how best to establish collaborations with bioscientists so that the interests and needs of both sets of researchers can be met in a synergistic fashion, and the most appropriate home for bioinformatics in an academic medical center.
McCollum, G. (2000). "Social barriers to a theoretical neuroscience." Trends Neurosci 23(8): 334-6.
Social rather than scientific barriers are impeding neuroscience theory. There are plenty of experimental data and mathematical methods to develop a rigorous, mathematical theory in neuroscience. However, structural mathematical efforts are being suffocated by the requirement to produce numbers immediately. Also theoretical development is tied too closely to one experimental group. The social barriers can be addressed by: (1) judging theory by structural accuracy rather than numerical output; (2) recognizing mathematical theory (not just computational modeling) as a method for producing insight into neurobiological phenomena; (3) funding fundamental theoretical neuroscience and (4) recognizing theoretical neuroscientists as neuroscientists.
Koch, P. and G. Leisman (2000). "Numbers, models, and understanding of natural intelligence: computational neuroscience in the service of clinical neuropsychology." J Int Neuropsychol Soc 6(5): 580-2.
Ghahramani, Z. (2000). "Computational neuroscience. Building blocks of movement." Nature 407(6805): 682-3.
Di Lollo, V., J. T. Enns, et al. (2000). "Competition for consciousness among visual events: the|| psychophysics of reentrant visual processes." J Exp Psychol Gen 129(4): 481-507.
Advances in neuroscience implicate reentrant signaling as||| the predominant form of communication between brain areas. This principle was||| used in a series of masking experiments that defy explanation by feed-forward||| theories. The masking occurs when a brief display of target plus mask is||| continued with the mask alone. Two masking processes were found: an early||| process affected by physical factors such as adapting luminance and a later||| process affected by attentional factors such as set size. This later process is||| called masking by object substitution, because it occurs whenever there is a||| mismatch between the reentrant visual representation and the ongoing lower||| level activity. Iterative reentrant processing was formalized in a computational model that provides an excellent fit to the data. The model||| provides a more comprehensive account of all forms of visual masking than do||| the long-held feed-forward views based on inhibitory contour||| interactions.
Di Lollo, V., J. T. Enns, et al. (2000). "Competition for consciousness among visual events: the psychophysics of reentrant visual processes." J Exp Psychol Gen 129(4): 481-507.
Advances in neuroscience implicate reentrant signaling as the predominant form of communication between brain areas. This principle was used in a series of masking experiments that defy explanation by feed-forward theories. The masking occurs when a brief display of target plus mask is continued with the mask alone. Two masking processes were found: an early process affected by physical factors such as adapting luminance and a later process affected by attentional factors such as set size. This later process is called masking by object substitution, because it occurs whenever there is a mismatch between the reentrant visual representation and the ongoing lower level activity. Iterative reentrant processing was formalized in a computational model that provides an excellent fit to the data. The model provides a more comprehensive account of all forms of visual masking than do the long-held feed-forward views based on inhibitory contour interactions.
DeMarco, P. J., Jr., A. Hughes, et al. (2000). "Increment and decrement detection on temporally modulated fields." Vision Res 40(14): 1907-19.
Increment and decrement probe thresholds were measured during the presentation of two types of temporal masking stimuli. In Experiment 1, we measured thresholds for increment or decrement rectangular probes presented during the presentation of an increment or decrement Gaussian masking stimulus. We find that thresholds are higher when the probe and the Gaussian mask are of the same sign (e. g. both increments). However, both types of Gaussian mask raised increment and decrement probe thresholds above steady state conditions. In Experiment 2, we presented increment or decrement probes at one of eight possible phases of a 1 Hz luminance-modulated sine wave. For both increment and decrement probes, threshold variation with phase is non-sinusoidal in shape, but increment and decrement probe thresholds vary as a function of the sinusoid phase. These experiments show that increment and decrement thresholds vary as a function of the adaptation state of the visual system, and as a function of the direction of change in the adaptation state. Data from both experiments are discussed in terms of a recent neurophysiological model [Hood & Graham (1998). Threshold fluctuations on temporally modulated backgrounds: a possible physiological explanation based upon a recent computational model. Visual Neuroscience, 15 (5), 957-967]. We find that the predicted ON- and OFF-pathway responses do not correlate in a straightforward manner with the psychophysical thresholds, suggesting that detection of increment and decrement probes may not be performed exclusively by one pathway. Our data have implications for modeling visual performance under conditions where visual adaptation is dynamic, such as when scanning complex images or natural scenes.
Dell, G. S., M. F. Schwartz, et al. (2000). "The role of computational models in neuropsychological investigations of language: reply to Ruml and Caramazza (2000)." Psychol Rev 107(3): 635-45.
W. Ruml and A. Caramazza's (2000) analysis of the model of normal and aphasic lexical access proposed by G. S. Dell, M. F. Schwartz, N. Martin, E. M. Saffran, and D. A. Gagnon (1997) is completely at odds with current practice concerning the use of models in psychology. An evaluation of Dell et al.'s original claims using Ruml and Caramazza's model parameters sustains these claims in all respects.
Deco, G. and B. Schurmann (2000). "A hierarchical neural system with attentional top-down enhancement of the spatial resolution for object recognition." Vision Res 40(20): 2845-59.
We present a hierarchical neurodynamical system for object recognition based on attentional control of the spatial resolution with which an object is analyzed during an iterative hypothesis testing cycle. Psychophysical evidence strongly suggests that attentional processing results in the enhancement of the spatial resolution in the input region corresponding to the focus of attention. We adopt a computational neuroscience approach in order to analyze this attentional enhancement of the spatial resolution for object recognition. The system consists of a where- and a what-module which include networks with feedforward and feedback interconnections describing the mutual links between different areas of the visual cortex.
Chicurel, M. (2000). "Databasing the brain." Nature 406(6798): 822-5.
Carlacci, L. and A. S. Edison (2000). "Computational analysis of two similar neuropeptides yields distinct conformational ensembles." Proteins 40(3): 367-77.
Conformational states and thermodynamic properties for two similar neuropeptides, GDPFLRF-NH(2) and GYPFLRF-NH(2), have been computed by Monte Carlo simulated annealing (MCSA) conformational searches and Metropolis Monte Carlo (MMC) calculations. These peptides were recently shown to have dramatically different conformations in solution by NMR [Edison et al., J Neuroscience 1999;19:6318-6326]. Final conformations of multiple independent MCSA runs were the starting points for MMC calculations, and conformations saved at intervals during MMC runs were characterized in terms of total energy, configuration entropy, side-chain fraction population, and ensemble average inter-nuclear distances. Without the use of any NMR data-generated pseudo-potentials, the present calculations were in excellent qualitative agreement with all previous NMR experimental data and provided a foundation by which to more quantitatively interpret the experimental NMR results. Proteins 2000;40:367-377.
Burke, R. E. (2000). "Comparison of alternative designs for reducing complex neurons to equivalent cables." J Comput Neurosci 9(1): 31-47.
Reduction of the morphological complexity of actual neurons into accurate, computationally efficient surrogate models is an important problem in computational neuroscience. The present work explores the use of two morphoelectrotonic transformations, somatofugal voltage attenuation (AT cables) and signal propagation delay (DL cables), as bases for construction of electrotonically equivalent cable models of neurons. In theory, the AT and DL cables should provide more accurate lumping of membrane regions that have the same transmembrane potential than the familiar equivalent cables that are based only on somatofugal electrotonic distance (LM cables). In practice, AT and DL cables indeed provided more accurate simulations of the somatic transient responses produced by fully branched neuron models than LM cables. This was the case in the presence of a somatic shunt as well as when membrane resistivity was uniform.
Braun, J. (2000). "Computational neuroscience. Intimate attention." Nature 408(6809): 154-5.
Bickle, J., C. Worley, et al. (2000). "Vector subtraction implemented neurally: a neurocomputational model of some sequential cognitive and conscious processes." Conscious Cogn 9(1): 117-44.
Although great progress in neuroanatomy and physiology has occurred lately, we still cannot go directly to those levels to discover the neural mechanisms of higher cognition and consciousness. But we can use neurocomputational methods based on these details to push this project forward. Here we describe vector subtraction as an operation that computes sequential paths through high-dimensional vector spaces. Vector-space interpretations of network activity patterns are a fruitful resource in recent computational neuroscience. Vector subtraction also appears to be implemented neurally in primate frontal eye field activity, which computes dimensions of saccadic eye movements. We use this apparent neural implementation as a model and construct from it a general neurocomputational account of an important type of sequential cognitive and conscious process. We defend the biological plausibility of all components of the general model and show that it yields testable anatomical and physiological predictions. We close by suggesting some interesting consequences for consciousness if our model characterizes correctly the neural mechanisms producing a common type of episode in our conscious streams.
Arbib, M. A., A. Billard, et al. (2000). "Synthetic brain imaging: grasping, mirror neurons and imitation." Neural Netw 13(8-9): 975-97.
The article contributes to the quest to relate global data on brain and behavior (e.g. from PET, Positron Emission Tomography, and fMRI. functional Magnetic Resonance Imaging) to the underpinning neural networks. Models tied to human brain imaging data often focus on a few "boxes" based on brain regions associated with exceptionally high blood flow, rather than analyzing the cooperative computation of multiple brain regions. For analysis directly at the level of such data, a schema-based model may be most appropriate. To further address neurophysiological data, the Synthetic PET imaging method uses computational models of biological neural circuitry based on animal data to predict and analyze the results of human PET studies. This technique makes use of the hypothesis that rCBF (regional cerebral blood flow) is correlated with the integrated synaptic activity in a localized brain region. We also describe the possible extension of the Synthetic PET method to fMRI. The second half of the paper then exemplifies this general research program with two case studies, one on visuo-motor processing for control of grasping (Section 3 in which the focus is on Synthetic PET) and the imitation of motor skills (Sections 4 and 5, with a focus on Synthetic fMRI). Our discussion of imitation pays particular attention to data on the mirror system in monkey (neural circuitry which allows the brain to recognize actions as well as execute them). Finally, Section 6 outlines the immense challenges in integrating models of different portions of the nervous system which address detailed neurophysiological data from studies of primates and other species; summarizes key issues for developing the methodology of Synthetic Brain Imaging; and shows how comparative neuroscience and evolutionary arguments will allow us to extend Synthetic Brain Imaging even to language and other cognitive functions for which few or no animal data are available.
Moschovakis, A. K. (1999). "The intracellular HRP technique." Brain Res Bull 50(5-6): 385-6.
Flynn, J. T. (1999). "Werner Ernst Reichardt Ph.D: founder of modern computational visual neurophysiology and anti-Nazi resistance fighter." Doc Ophthalmol 99(3): 225-36.
Werner Ernst Reichardt was born on January 30, 1924 in Berlin and at age 19 was drafted into the Luftwaffe and assigned to an electronic signals section laboratory. He became an active member of a resistance group and supplied radios for the movement in Germany. He emerged from the ashes of the Second World War and dedicated his scientific life to the development of the newborn specialty of biological physics. Following graduation from the Technische Hochschule Charlottenburg, he did a fellowship at CalTech under Max Delbruck. On returning to Germany he joined the Max Planck Institut and later became Director of the Max Planck Institut fur Biologische Kybernetik in Tubingen, West Germany. Reichardt was one of the founders of the quantitative study of visually controlled orientation in animals. His work is very nearly unique in its close dialectic between elegant non-linear mathematical theory and quantitative experimental test of their predictions. During the 1950s Reichardt and his collaborators jointly developed an autocorrelation model (i.e. the firing rate of the involved visual neurones is closely correlated with the features of the pattern stimulating them) of how moving patterns are perceived by motion detectors in the visual system of the fly. This was the first mathematical description of a biological abstraction process. His findings apply to vertebrate vision, including motion detection and figure-ground description in human vision. His Max Planck Institute became a world renowned center for the computational approach to information processing by the nervous system. At his retirement party from the Institute he founded, Reichardt died on the evening of September 11th, 1992.
Dror, I. E. and D. P. Gallogly (1999). "Computational analyses in cognitive neuroscience: in defense of biological implausibility." Psychon Bull Rev 6(2): 173-82.
Because cognitive neuroscience researchers attempt to understand the human mind by bridging behavior and brain, they expect computational analyses to be biologically plausible. In this paper, biologically implausible computational analyses are shown to have critical and essential roles in the various stages and domains of cognitive neuroscience research. Specifically, biologically implausible computational analyses can contribute to (1) understanding and characterizing the problem that is being studied, (2) examining the availability of information and its representation, and (3) evaluating and understanding the neuronal solution. In the context of the distinct types of contributions made by certain computational analyses, the biological plausibility of those analyses is altogether irrelevant. These biologically implausible models are nevertheless relevant and important for biologically driven research.
Chamak, B. (1999). "The emergence of cognitive science in France." Soc Stud Sci 29(5): 643-84.
A comparison between the development of cognitive science in France and the USA enables us to analyze some national differences linked to specific connections between the scientific, military, economic and political worlds. The influence of new practices and tools developed during World War II and the Cold War appears to be of crucial importance in understanding the development of this new field, as well as that of cybernetics, computer science, artificial intelligence and molecular biology. This paper can be considered as a study in how the differing contexts in France and the USA shaped the history of the construction of cognitive science in each of these two countries. In spite of various differences, some common aspects may be pointed out: in both cases, computer experts and psychologists using a computational modelling approach were those first engaged in the construction of cognitive science. If in France neuroscience-oriented cognitive science research was stronger than in the USA, it seems that the artificial intelligence orientation is also of growing importance in France.
Beltrame, F. and S. H. Koslow (1999). "Neuroinformatics as a megascience issue." IEEE Trans Inf Technol Biomed 3(3): 239-40.
Bell, A. J. (1999). "Levels and loops: the future of artificial intelligence and neuroscience." Philos Trans R Soc Lond B Biol Sci 354(1352): 2013-20.
In discussing artificial intelligence and neuroscience, I will focus on two themes. The first is the universality of cycles (or loops): sets of variables that affect each other in such a way that any feed-forward account of causality and control, while informative, is misleading. The second theme is based around the observation that a computer is an intrinsically dualistic entity, with its physical set-up designed so as not to interfere with its logical set-up, which executes the computation. The brain is different. When analysed empirically at several different levels (cellular, molecular), it appears that there is no satisfactory way to separate a physical brain model (or algorithm, or representation), from a physical implementational substrate. When program and implementation are inseparable and thus interfere with each other, a dualistic point-of-view is impossible. Forced by empiricism into a monistic perspective, the brain-mind appears as neither embodied by or embedded in physical reality, but rather as identical to physical reality. This perspective has implications for the future of science and society. I will approach these from a negative point-of-view, by critiquing some of our millennial culture's popular projected futures.
Ascoli, G. A. (1999). "Progress and perspectives in computational neuroanatomy." Anat Rec 257(6): 195-207.
The tremendous increase in processing power of personal computers has recently allowed the construction of highly sophisticated models of neuronal function and behavior. Anatomy plays a fundamental role in supporting and shaping nervous system activity, yet to date most details of such a role have escaped the efforts of experimental and theoretical neuroscientists, mainly because of the problem's complexity. When accurate cellular morphologies are included in electrophysiological computer simulations, quantitative and qualitative effects of dendritic structure on firing properties can be extensively characterized. Complete models of dendritic morphology can be implemented to allow the computer generation of virtual neurons that model the anatomical characteristics of their real counterparts to a great degree of approximation. From a restricted and already available experimental database, stochastic and statistical algorithms can create an unlimited number of non-identical virtual neurons within several mammalian morphological classes, storing them in a compact and parsimonious format. When modeled neurons are distributed in three-dimensional and biologically plausible rules governing axonal navigation and connectivity are added to the simulations, entire portions of the nervous system can be "grown" as anatomically realistic neural networks. These computational constructs are useful to determine the influence of local geometry on system neuroanatomy, and to investigate systematically the mutual interactions between anatomical parameters and electrophysiological activity at the network level. A detailed computer model of a "virtual brain" that was truly equivalent to the biological structure could in principle allow scientists to carry out experiments that could not be performed on real nervous systems because of physical constraints. The computational approach to neuroanatomy is just at its beginning, but has a great potential to enhance the intuition of investigators and to aid neuroscience education. Anat Rec (New Anat): 257:195-207, 1999.