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Computational Neurobiology
(32 References)
Songnian, Z., X. Xiaoyun, et al. (2003). "A computational model as neurodecoder based on synchronous oscillation in the visual cortex." Neural Comput 15(10): 2399-418.
Based on synchronized responses of neuronal populations in the visual cortex to external stimuli, we proposed a computational model consisting primarily of a neuronal phase-locked loop (NPLL) and multiscaled operator. The former reveals the function of synchronous oscillations in the visual cortex. Regardless of which of these patterns of the spike trains may be an average firing-rate code, a spike-timing code, or a rate-time code, the NPLL can decode original visual information from neuronal spike trains modulated with patterns of external stimuli, because a voltage-controlled oscillator (VCO), which is included in the NPLL, can precisely track neuronal spike trains and instantaneous variations, that is, VCO can make a copy of an external stimulus pattern. The latter, however, describes multi-scaled properties of visual information processing, but not merely edge and contour detection. In this study, in which we combined NPLL with a multiscaled operator and maximum likelihood estimation, we proved that the model, as a neurodecoder, implements optimum algorithm decoding visual information from neuronal spike trains at the system level. At the same time, the model also obtains increasingly important supports, which come from a series of experimental results of neurobiology on stimulus-specific neuronal oscillations or synchronized responses of the neuronal population in the visual cortex. In addition, the problem of how to describe visual acuity and multiresolution of vision by wavelet transform is also discussed. The results indicate that the model provides a deeper understanding of the role of synchronized responses in decoding visual information.
Nisenbaum, L. K. (2002). "The ultimate chip shot: can microarray technology deliver for neuroscience?" Genes Brain Behav 1(1): 27-34.
The use of cDNA and oligonucleotide microarrays, or 'chips', is emerging as a powerful, new technology in the field of neuroscience for examining gene expression in a high-throughput fashion. The application of microarray technology to the study of brain and behavior has lagged behind other areas of biology such as cancer and yeast genetics due to the challenges presented by the heterogeneous and complex organization of the nervous system. This review provides a brief overview of available microarray technology as well as a description of experimental considerations in planning and implementing a neuroscience-based array study. Successful implementation of microarray technology within the field of neuroscience will provide a molecular approach to studying systems neurobiology, leading to insights into areas ranging from fundamental questions of developmental neurobiology to neurological and psychiatric disorders.
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.
Clifford, C. W. (2002). "Perceptual adaptation: motion parallels orientation." Trends Cogn Sci 6(3): 136-143.
Adaptation phenomena provide striking examples of perceptual plasticity and offer valuable insight into the mechanisms of visual coding. Within the context of recent progress in neurobiology and computational modelling, I review evidence from studies employing psychophysical adaptation to investigate orientation and motion processing. These studies reveal marked similarities between the orientation and motion domains, raising the possibility that common computational principles underlie the processing of orientation and motion despite apparently distinct cortical substrates.
Carson, J. P., C. Thaller, et al. (2002). "A transcriptome atlas of the mouse brain at cellular resolution." Curr Opin Neurobiol 12(5): 562-5.
A genome-wide expression atlas of the nervous system at cellular resolution would be a valuable resource for neurobiology, genetics, developmental biology and medicine. Progress in automation of in situ hybridization makes such an atlas possible. Standardized and computerized annotation of expression patterns will be critical for producing a searchable atlas database that can be accessed through the internet.
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.
Loew, L. M. and J. C. Schaff (2001). "The Virtual Cell: a software environment for computational cell biology." Trends Biotechnol 19(10): 401-6.
The newly emerging field of computational cell biology requires software tools that address the needs of a broad community of scientists. Cell biological processes are controlled by an interacting set of biochemical and electrophysiological events that are distributed within complex cellular structures. Computational modeling is familiar to researchers in fields such as molecular structure, neurobiology and metabolic pathway engineering, and is rapidly emerging in the area of gene expression. Although some of these established modeling approaches can be adapted to address problems of interest to cell biologists, relatively few software development efforts have been directed at the field as a whole. The Virtual Cell is a computational environment designed for cell biologists as well as for mathematical biologists and bioengineers. It serves to aid the construction of cell biological models and the generation of simulations from them. The system enables the formulation of both compartmental and spatial models, the latter with either idealized or experimentally derived geometries of one, two or three dimensions.
Higgins, C. M. (2001). "Sensory architectures for biologically inspired autonomous robotics." Biol Bull 200(2): 235-42.
Engineers have a lot to gain from studying biology. The study of biological neural systems alone provides numerous examples of computational systems that are far more complex than any man-made system and perform real-time sensory and motor tasks in a manner that humbles the most advanced artificial systems. Despite the evolutionary genesis of these systems and the vast apparent differences between species, there are common design strategies employed by biological systems that span taxa, and engineers would do well to emulate these strategies. However, biologically-inspired computational architectures, which are continuous-time and parallel in nature, do not map well onto conventional processors, which are discrete-time and serial in operation. Rather, an implementation technology that is capable of directly realizing the layered parallel structure and nonlinear elements employed by neurobiology is required for power- and space-efficient implementation. Custom neuromorphic hardware meets these criteria and yields low-power dedicated sensory systems that are small, light, and ideal for autonomous robot applications. As examples of how this technology is applied, this article describes both a low-level neuromorphic hardware emulation of an elementary visual motion detector, and a large-scale, system-level spatial motion integration system.
Braver, T. S., D. M. Barch, et al. (2001). "Context processing in older adults: evidence for a theory relating cognitive control to neurobiology in healthy aging." J Exp Psychol Gen 130(4): 746-63.
A theory of cognitive aging is presented in which healthy older adults are hypothesized to suffer from disturbances in the processing of context that impair cognitive control function across multiple domains, including attention, inhibition, and working memory. These cognitive disturbances are postulated to be directly related to age-related decline in the function of the dopamine (DA) system in the prefrontal cortex (PFC). A connectionist computational model is described that implements specific mechanisms for the role of DA and PFC in context processing. The behavioral predictions of the model were tested in a large sample of older (N = 81) and young (N = 175) adults performing variants of a simple cognitive control task that placed differential demands on context processing. Older adults exhibited both performance decrements and, counterintuitively, performance improvements that are in close agreement with model predictions.
Aharonov-Barki, R., T. Beker, et al. (2001). "Emergence of memory-driven command neurons in evolved artificial agents." Neural Comput 13(3): 691-716.
Using evolutionary simulations, we develop autonomous agents controlled by artificial neural networks (ANNs). In simple lifelike tasks of foraging and navigation, high performance levels are attained by agents equipped with fully recurrent ANN controllers. In a set of experiments sharing the same behavioral task but differing in the sensory input available to the agents, we find a common structure of a command neuron switching the dynamics of the network between radically different behavioral modes. When sensory position information is available, the command neuron reflects a map of the environment, acting as a location-dependent cell sensitive to the location and orientation of the agent. When such information is unavailable, the command neuron's activity is based on a spontaneously evolving short-term memory mechanism, which underlies its apparent place-sensitive activity. A two-parameter stochastic model for this memory mechanism is proposed. We show that the parameter values emerging from the evolutionary simulations are near optimal; evolution takes advantage of seemingly harmful features of the environment to maximize the agent's foraging efficiency. The accessibility of evolved ANNs for a detailed inspection, together with the resemblance of some of the results to known findings from neurobiology, places evolved ANNs as an excellent candidate model for the study of structure and function relationship in complex nervous systems.
Yoshizawa, T., K. T. Mullen, et al. (2000). "Absence of a chromatic linear motion mechanism in human vision." Vision Res 40(15): 1993-2010.
We have investigated motion mechanisms in central and perifoveal vision using two-frame random Gabor kinematograms with isoluminant red-green or luminance stimuli. In keeping with previous results, we find that performance dominated by a linear motion mechanism is obtained using high densities of micropatterns and small temporal intervals between frames, while nonlinear performance is found with low densities and longer temporal intervals [Boulton, J. C., & Baker, C. L. (1994) Proceedings of SPIE, computational vision based on neurobiology, 2054, 124-133]. We compare direction discrimination and detection thresholds in the presence of variable luminance and chromatic noise. Our results show that the linear motion response obtained from chromatic stimuli is selectively masked by luminance noise; the effect is selective for motion since luminance noise masks direction discrimination thresholds but not stimulus detection. Furthermore, we find that chromatic noise has the reverse effect to luminance noise: detection thresholds for the linear chromatic stimulus are masked by chromatic noise but direction discrimination is relatively unaffected. We thus reveal a linear 'chromatic' mechanism that is susceptible to luminance noise but relatively unaffected by color noise. The nonlinear chromatic mechanism behaves differently since both detection and direction discrimination are unaffected by luminance noise but masked by chromatic noise. The double dissociation between the effects of chromatic and luminance noise on linear and nonlinear motion mechanisms is not based on stimulus speed or differences in the temporal presentations of the stimuli. We conclude that: (1) 'chromatic' linear motion is solely based on a luminance signal, probably arising from cone-based temporal phase shifts; (2) the nonlinear chromatic motion mechanism is purely chromatic; and (3) we find the same results for both perifoveal and foveal presentations.
van Ooyen, A. and D. J. Willshaw (2000). "Development of nerve connections under the control of neurotrophic factors: parallels with consumer-resource systems in population biology." J Theor Biol 206(2): 195-210.
The development of connections between neurons and their target cells involves competition between axons for target-derived neurotrophic factors. Although the notion of competition is commonly used in neurobiology, the process is not well understood, and only a few formal models exist. In population biology, in contrast, the concept of competition is well developed and has been studied by means of many formal models of consumer-resource systems. Here we show that a recently formulated model of axonal competition can be rewritten as a general consumer-resource system. This allows neurobiological phenomena to be interpreted in population biological terms and, conversely, results from population biology to be applied to neurobiology. Using findings from population biology, we have studied two extensions of our axonal competition model. In the first extension, the spatial dimension of the target is explicitly taken into account. We show that distance between axons on their target mitigates competition and permits the coexistence of axons. The model can account for the fact that in many types of neurons a positive correlation exists between the size of the dendritic tree and the number of innervating axons surviving into adulthood. In the second extension, axons are allowed to respond to more than one neurotrophic factor. We show that this permits competitive exclusion among axons of one type, while at the same time there is coexistence with axons of another type innervating the same target. The model offers an explanation for the innervation pattern found on cerebellar Purkinje cells, where climbing fibres compete with each other until only a single one remains, which coexists with parallel fibre input to the same Purkinje cell.
Toschi, N. (2000). "Influence of mRNA self-structure on hybridization: computational tools for antisense sequence selection." Methods 22(3): 261-9.
Antisense targeting, an innovative technology based on preventing biosynthesis through sequence-specific hybridization of mRNA to synthetic oligodeoxynucleotides (ODNs), is used to selectively and transiently downregulate the expression of any gene product. Its potential applications are both investigative (neurobiology and related disciplines) and therapeutic (oncology, virology, genetic diseases), and several antisense-based drugs are currently undergoing clinical trials. However, the reported efficiencies vary and there is still a lack of clarity in the underlying mechanisms of action. A critical factor of antisense efficiency is the issue of target site selection, as both mRNA and ODN molecules exhibit a significant amount of highly heterogeneous self-structure and the region selected for targeting may well be sterically or energetically inaccessible. Because of the prohibitively large chain length, mRNA structural information is not accessible by X-ray crystallography or NMR, making a modeling approach indispensable. I outline how the latest molecular modeling techniques can be employed to establish the secondary (2D) and tertiary (3D) structures into which a given mRNA folds during and after transcription. The aim should be to integrate 2D prediction results achieved by (a) free-energy minimization, (b) kinetic folding simulations, (c) iterative population breeding by genetic algorithms, and (d) phylogenetic comparison techniques. These results can form the input of a 3D structure prediction paradigm based on constraint-satisfying programming, governed by experimental molecular mechanical constraints, and refined by molecular dynamics simulations. Finally, the automated docking (by simulated annealing) of ODN molecules to the mRNA structure can provide information about the accessibility of target mRNA regions for hybridization. To date, the great majority of studies that employ antisense as a tool select their target sequences more or less randomly. Although the wealth of molecular interactions that take place within a cell makes complete predictability unrealistic, the kind of information that can be extracted from such techniques is of relevance to every application of antisense technology, both investigative and therapeutic.
Basheer, I. A. and M. Hajmeer (2000). "Artificial neural networks: fundamentals, computing, design, and application." J Microbiol Methods 43(1): 3-31.
Artificial neural networks (ANNs) are relatively new computational tools that have found extensive utilization in solving many complex real-world problems. The attractiveness of ANNs comes from their remarkable information processing characteristics pertinent mainly to nonlinearity, high parallelism, fault and noise tolerance, and learning and generalization capabilities. This paper aims to familiarize the reader with ANN-based computing (neurocomputing) and to serve as a useful companion practical guide and toolkit for the ANNs modeler along the course of ANN project development. The history of the evolution of neurocomputing and its relation to the field of neurobiology is briefly discussed. ANNs are compared to both expert systems and statistical regression and their advantages and limitations are outlined. A bird's eye review of the various types of ANNs and the related learning rules is presented, with special emphasis on backpropagation (BP) ANNs theory and design. A generalized methodology for developing successful ANNs projects from conceptualization, to design, to implementation, is described. The most common problems that BPANNs developers face during training are summarized in conjunction with possible causes and remedies. Finally, as a practical application, BPANNs were used to model the microbial growth curves of S. flexneri. The developed model was reasonably accurate in simulating both training and test time-dependent growth curves as affected by temperature and pH.
Wallace, R. and H. Price (1999). "Neuromolecular computing: a new approach to human brain evolution." Biol Cybern 81(3): 189-97.
Evolutionary approaches in human cognitive neurobiology traditionally emphasize macroscopic structures. It may soon be possible to supplement these studies with models of human information-processing of the molecular level. Thin-film, simulation, fluorescence microscopy, and high-resolution X-ray crystallographic studies provide evidence for transiently organized neural membrane molecular systems with possible computational properties. This review article examines evidence for hydrophobic-mismatch molecular interactions within phospholipid microdomains of a neural membrane bilayer. It is proposed that these interactions are a massively parallel algorithm which can rapidly compute near-optimal solutions to complex cognitive and physiological problems. Coupling of microdomain activity to permenant ion movements at ligand-gated and voltage-gated channels permits the conversion of molecular computations into neuron frequency codes. Evidence for microdomain transport of proteins to specific locations within the bilayer suggests that neuromolecular computation may be under some genetic control and thus modifiable by natural selection. A possible experimental approach for examining evolutionary changes in neuromolecular computation is briefly discussed.
McIntosh, A. R. (1999). "Mapping cognition to the brain through neural interactions." Memory 7(5-6): 523-48.
Brain imaging methods, such as positron emission tomography (PET) and functional magnetic resonance imaging (fMRI), provide a unique opportunity to study the neurobiology of human memory. As these methods can measure most of the brain, it is possible to examine the operations of large-scale neural systems and their relation to cognition. Two neuroimaging studies, one concerning working memory and the other episodic memory retrieval, serve as examples of application of two analytic methods that are optimised for the quantification of neural systems, structural equation modelling, and partial least squares. Structural equation modelling was used to explore shifting prefrontal and limbic interactions from the right to the left hemisphere in a delayed match-to-sample task for faces. A feature of the functional network for short delays was strong right hemisphere interactions between hippocampus, inferior prefrontal, and anterior cingulate cortices. At longer delays, these same three areas were strongly linked, but in the left hemisphere, which was interpreted as reflecting change in task strategy from perceptual to elaborate encoding with increasing delay. The primary manipulation in the memory retrieval study was different levels of retrieval success. The partial least squares method was used to determine whether the image-wide pattern of covariances of Brodmann areas 10 and 45/47 in right prefrontal cortex (RPFC) and the left hippocampus (LGH) could be mapped on to retrieval levels. Area 10 and LGH showed an opposite pattern of functional connectivity with a large expanse of bilateral limbic cortices that was equivalent for all levels of retrieval as well as the baseline task. However, only during high retrieval was area 45/47 included in this pattern. The results suggest that activity in portions of the RPFC can reflect either memory retrieval mode or retrieval success depending on other brain regions to which it is functionally linked, and imply that regional activity must be evaluated within the neural context in which it occurs. The general hypothesis that learning and memory are emergent properties of large-scale neural network interactions is discussed, emphasising that a region can play a different role across many functions and that role is governed by its interactions with anatomically related regions.
Aakerlund, L. and R. Hemmingsen (1998). "Neural networks as models of psychopathology." Biol Psychiatry 43(7): 471-82.
Neural network modeling is situated between neurobiology, cognitive science, and neuropsychology. The structural and functional resemblance with biological computation has made artificial neural networks (ANN) useful for exploring the relationship between neurobiology and computational performance, i.e., cognition and behavior. This review provides an introduction to the theory of ANN and how they have linked theories from neurobiology and psychopathology in schizophrenia, affective disorders, and dementia.
Ritz, R. and T. J. Sejnowski (1997). "Synchronous oscillatory activity in sensory systems: new vistas on mechanisms." Curr Opin Neurobiol 7(4): 536-46.
The origin and nature, as well as the functional role, of synchronous oscillatory activity in the cortex are among the major unresolved issues in systems neurobiology. Recent advances in understanding the mechanisms underlying oscillations include the description of intrinsically bursting pyramidal cells in striate cortex in vivo and the discovery of inhibitory interneurons that fire spike doublets to induce synchrony. The behavioral consequences of coordinated activity in cortical neurons remain poorly understood.
Gerstner, W., A. K. Kreiter, et al. (1997). "Neural codes: firing rates and beyond." Proc Natl Acad Sci U S A 94(24): 12740-1.
Computational neuroscience has contributed significantly to our understanding of higher brain function by combining experimental neurobiology, psychophysics, modeling, and mathematical analysis. This article reviews recent advances in a key area: neural coding and information processing. It is shown that synapses are capable of supporting computations based on highly structured temporal codes. Such codes could provide a substrate for unambiguous representations of complex stimuli and be used to solve difficult cognitive tasks, such as the binding problem. Unsupervised learning rules could generate the circuitry required for precise temporal codes. Together, these results indicate that neural systems perform a rich repertoire of computations based on action potential timing.
Friston, K. J. (1996). "Theoretical neurobiology and schizophrenia." Br Med Bull 52(3): 644-55.
This chapter addresses the idea that schizophrenia is a 'disconnection syndrome' from a theoretical and computational perspective. The distinction between anatomical and functional connectivity is reviewed and used as a framework to introduce empirical and computational evidence that schizophrenia involves, at some level, a disintegration of neuronal interactions. The chapter concludes with an example of computational neuroscience that relates observations on the dimensional complexity of neuronal dynamics in schizophrenia to the disconnection hypothesis.
Britton, N. F. and S. M. Skevington (1996). "On the mathematical modelling of pain." Neurochem Res 21(9): 1133-40.
In this review a case is presented for the use of mathematical modelling in the study of pain. The philosophy of mathematical modelling is outlined and a recommendation is made for the use of modern nonlinear techniques and computational neuroscience in the modelling of pain. Classic and more recent examples of modelling in neurobiology in general and pain in particular, at three different levels-molecular, cellular and neural networks-are described and evaluated. Directions for further progress are indicated, particularly in plasticity and in modelling brain mechanisms. Major advantages of mathematical modelling are that it can handle extremely complex theories and it is non-invasive, and so is particularly valuable in the investigation of chronic pain.
Jobe, T. H., C. G. Fichtner, et al. (1995). "Neuropoiesis: proposal for a connectionistic neurobiology." Med Hypotheses 45(2): 147-63.
Given current assumptions about the biology of neural organization, some connectionists believe that it may not be possible to accurately model the brain's neural architecture. We have identified five restrictive neurobiological dogmas that we believe have limited the exploration of more fundamental correlations between computational and biological neural networks. We postulate that: 1) the dendritic tree serves as a synapse storage device rather than a simple summation device; 2) connection strength between neurons depends on the number and location of synapses of similar weight, not on synapses of variable weights; 3) axonal sprouting occurs regularly in adult organisms; 4) the postsynaptic genome directly controls the presynaptic cell via mRNA, rather than indirectly by the expression of NCAMs, reverse neurotransmitters, etc.; 5) dendritic spines serve a trophic function by controlling development of new sprouts via a process we term retroduction. We entertain an alternative formulation of a computational neural element that is fully consistent with modern neuroscience research. We then show how our model neuron can learn under Hebbian conditions, and extend the model to explain non-Hebbian, one-trial learning. This work is significant because by stretching the theoretical boundaries of modern neuroscience, we show how connectionists can potentially create new, more biologically-based neural elements which, when, interconnected into networks, exhibit not only properties of existing backpropagation networks, but other physiological properties as well.
Turner, J. N., D. H. Szarowski, et al. (1994). "Three-dimensional imaging and image analysis of hippocampal neurons: confocal and digitally enhanced wide field microscopy." Microsc Res Tech 29(4): 269-78.
The microscopy of biological specimens has traditionally been a two-dimensional imaging method for analyzing what are in reality three-dimensional (3-D) objects. This has been a major limitation of the application of one of science's most widely used tools. Nowhere has this limitation been more acute than in neurobiology, which is dominated by the necessity of understanding both large- and small-scale 3-D anatomy. Fortunately, recent advances in optical instrumentation and computational methods have provided the means for retrieving the third dimension, making full 3-D microscopic imaging possible. Optical designs have concentrated on the confocal imaging mode while computational methods have made 3-D imaging possible with wide field microscopes using deconvolution methods. This work presents a brief review of these methods, especially as applied to neurobiology, and data using both approaches. Specimens several hundred micrometers thick can be sampled allowing essentially intact neurons to be imaged. These neurons or selected components can be contrasted with either fluorescent, absorption, or reflection stains. Image analysis in 3-D is as important as visualization in 3-D. Automated methods of cell counting and analysis by nuclear detection as well as tracing of individual neurons are presented.
Gallistel, C. R. (1994). "Foraging for brain stimulation: toward a neurobiology of computation." Cognition 50(1-3): 151-70.
The self-stimulating rat performs foraging tasks mediated by simple computations that use interreward intervals and subjective reward magnitudes to determine stay durations. This is a simplified preparation in which to study the neurobiology of the elementary computational operations that make cognition possible, because the neural signal specifying the value of a computationally relevant variable is produced by direct electrical stimulation of a neural pathway. Newly developed measurement methods yield functions relating the subjective reward magnitude to the parameters of the neural signal. These measurements also show that the decision process that governs foraging behavior divides the subjective reward magnitude by the most recent interreward interval to determine the preferability of an option (a foraging patch). The decision process sets the parameters that determine stay durations (durations of visits to foraging patches) so that the ratios of the stay durations match the ratios of the preferabilities.
Borst, A. and M. Egelhaaf (1994). "Dendritic processing of synaptic information by sensory interneurons." Trends Neurosci 17(6): 257-63.
One of the most distinguishing features of nerve cells is the vast morphological diversity of their input regions, that is, their dendrites. These range from bulbous structures, with only small protrusions, to large tree-like arborizations. The diversity of nerve cells is further augmented by a continuously increasing number of types of voltage-dependent conductances in dendrites that might alter the postsynaptic signals in a pronounced way. Moreover, intracellular factors such as Ca2+ link electrical activity with biochemical processes, and can induce short and long-term changes in responsiveness. This complexity of neurons in general, and the uniqueness of each cell type, sharply contrasts with the comparatively simple and uniform design principle of the integrate-and-fire units of so-called neuronal net models. This raises the question of which particular structural and physiological details of nerve cells really matter for the performance of neuronal circuits. An answer to this basic problem of computational neurobiology might be given only if the task of the neurons and circuits is known. This review illustrates how the problem can be approached particularly well in sensory interneurons. The functional significance of sensory interneurons can often be assessed more easily than that of central nerve cells because of their vicinity to the sensory surface.
Bhalla, U. S., D. H. Bilitch, et al. (1992). "Rallpacks: a set of benchmarks for neuronal simulators." Trends Neurosci 15(11): 453-8.
The field of computational neurobiology has advanced to the point where there are several general-purpose simulators to choose from. These cater to various niches in the world of realistic neuronal models, which range from the molecular level to descriptions of entire sensory modalities. In addition, there are numerous custom-designed simulations, adaptations of electrical circuit simulators, and other specific implementations of neurobiological models. As a first step towards evaluating this disparate set of simulators and simulations, and towards establishing standards for comparisons of speed and accuracy, we describe a set of benchmarks. These have been given the name 'Rallpacks' in honor of Wilfrid Rall, who pioneered the study of neuronal systems through analytical and numerical techniques.
Ohayon, M. (1990). "[Cognitive processes and neuronal networks]." Ann Med Psychol (Paris) 148(8): 669-95.
It is clear that computers are but a poor brain models: the nervous system has many "processors" (neurons) in parallel, whereas von Neuman's machines work sequentially on a single processor. In complex systems, emergent properties cannot be inferred from the behaviour of single elements. Anthills display collective "meaningful" moves, while each ant seems to obey local interactions only. Likewise, large parallel networks of processing elements elicit emergent properties. Like brains, some of them are self-organizing systems. In large parallel processing networks, each unit performs an elementary computation: adding inputs from other units. Large nets display surprising spontaneous computational abilities: associative memories, classes, generalizations may be seen as emergent properties of the network. Symbols are dynamical entities, whose handing is driven by local interactions of activation/inhibition of related representations. In such models, representations (memories) are distributed in the whole network, as stable configurations. Indeed, the basic properties of representation in connectionist models seem closer to human mental objects than the classic Artificial Intelligence concepts. Connectionist models have been used in many fields, namely simulations of real neural networks, pattern recognition and artificial vision, speech recognition, language understanding and knowledge representation, problem solving... Connectionist models have been thus used in neurobiology as well as cognition. One basic structure seems indeed able to account for a range of cognitive functions, from perception to problem solving and high level cognitive tasks. Nevertheless studies about "pathological" networks are yet rare, still an open field... We explore some of these fields.
Brown, T. H., E. W. Kairiss, et al. (1990). "Hebbian synapses: biophysical mechanisms and algorithms." Annu Rev Neurosci 13: 475-511.
We have examined the evolution of the concept of a Hebbian synaptic modification and have suggested a contemporary definition. The biophysical mechanism demonstrated in vitro to control the induction of one type of hippocampal LTP has been shown to satisfy our definition of a Hebbian synaptic modification. Whether this biophysical mechanism is involved in the organization of behavior in the manner that Hebb originally envisioned remains to be seen. We have also summarized several modification algorithms that have been explored in theoretical studies of learning in adaptive networks. These algorithms also satisfied our definition of a Hebbian modification, but their relationships to known neurobiology require further exploration. By reviewing the biophysical mechanisms and formal algorithms together, we have exposed obvious similarities and differences. Such comparisons may help bridge the gap between computational theory and knowledge of the neurobiology of use-dependent synaptic change. Current models of LTP reveal that the activity-modification relationships are extremely sensitive to the biophysical/molecular details. The activity-modification relationships obviously can have a major influence on adaptive neurodynamics at the network level. As more accurate representations of the biological complexity and diversity are introduced into adaptive network simulations, we expect to gain new insights into the classes of computation that particular networks are capable of performing.
Sejnowski, T. J., C. Koch, et al. (1988). "Computational neuroscience." Science 241(4871): 1299-306.
The ultimate aim of computational neuroscience is to explain how electrical and chemical signals are used in the brain to represent and process information. This goal is not new, but much has changed in the last decade. More is known now about the brain because of advances in neuroscience, more computing power is available for performing realistic simulations of neural systems, and new insights are available from the study of simplifying models of large networks of neurons. Brain models are being used to connect the microscopic level accessible by molecular and cellular techniques with the systems level accessible by the study of behavior.
Churchland, P. S. and T. J. Sejnowski (1988). "Perspectives on cognitive neuroscience." Science 242(4879): 741-5.
How is it that we can perceive, learn and be aware of the world? The development of new techniques for studying large-scale brain activity, together with insights from computational modeling and a better understanding of cognitive processes, have opened the door for collaborative research that could lead to major advances in our understanding of ourselves.
Stowell, H. (1987). "Computation, rhythm, and cerebral slow waves." Int J Neurosci 34(1-2): 117-22.
The trajectories of data and modelling from formerly divergent research efforts now seem to be converging to an unexpected region of the phase space of neuroscience. Computational network theory and simulation assume that temporal rhythm may be a significant parameter for the successful organization of nonlinear analog computation effected by hierarchical sets of biological neurons and of nonbiological circuitry alike. For neurobiology, the apparently chaotic rhythms of cerebral compound field potentials--the electroencephalogram (EEG) and slow waves of event related brain potentials (ERBP)--have long been a phenomenological embarrassment, of only marginal clinical utility. But recent data from molecular biophysics, nonlinear dynamics, artificial intelligence, and scalp-conducted human electrocorticography suggest a possible functional role in the serial gating of neural network computations for the familiar theta-alpha-beta rhythms of the EEG clinic.
Hopfield, J. J. (1982). "Neural networks and physical systems with emergent collective computational abilities." Proc Natl Acad Sci U S A 79(8): 2554-8.
Computational properties of use of biological organisms or to the construction of computers can emerge as collective properties of systems having a large number of simple equivalent components (or neurons). The physical meaning of content-addressable memory is described by an appropriate phase space flow of the state of a system. A model of such a system is given, based on aspects of neurobiology but readily adapted to integrated circuits. The collective properties of this model produce a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size. The algorithm for the time evolution of the state of the system is based on asynchronous parallel processing. Additional emergent collective properties include some capacity for generalization, familiarity recognition, categorization, error correction, and time sequence retention. The collective properties are only weakly sensitive to details of the modeling or the failure of individual devices.