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Biomedical subjects

O Sporns

Publications and source records attributed to O Sporns.

At least 19 recordsLinked to original sources
PubMed2000

Theoretical neuroanatomy: relating anatomical and functional connectivity in graphs and cortical connection matrices.

Neuroanatomy places critical constraints on the functional connectivity of the cerebral cortex. To analyze these constraints we have examined the relationship between structural features of networks (expressed as graphs) and the patterns of functional connectivity to which they give rise when implemented as dynamical systems. We selected among structurally varying graphs using as selective criteria a number of global information-theoretical measures that characterize functional connectivity. We selected graphs separately for increases in measures of entropy (capturing statistical independence of graph elements), integration (capturing their statistical dependence) and complexity (capturing the interplay between their functional segregation and integration). We found that dynamics with high complexity were supported by graphs whose units were organized into densely linked groups that were sparsely and reciprocally interconnected. Connection matrices based on actual neuroanatomical data describing areas and pathways of the macaque visual cortex and the cat cortex showed structural characteristics that coincided best with those of such complex graphs, revealing the presence of distinct but interconnected anatomical groupings of areas. Moreover, when implemented as dynamical systems, these cortical connection matrices generated functional connectivity with high complexity, characterized by the presence of highly coherent functional clusters. We also found that selection of graphs as they responded to input or produced output led to increases in the complexity of their dynamics. We hypothesize that adaptation to rich sensory environments and motor demands requires complex dynamics and that these dynamics are supported by neuroanatomical motifs that are characteristic of the cerebral cortex.

Animals↗
PubMed1999

Measures of degeneracy and redundancy in biological networks.

Degeneracy, the ability of elements that are structurally different to perform the same function, is a prominent property of many biological systems ranging from genes to neural networks to evolution itself. Because structurally different elements may produce different outputs in different contexts, degeneracy should be distinguished from redundancy, which occurs when the same function is performed by identical elements. However, because of ambiguities in the distinction between structure and function and because of the lack of a theoretical treatment, these two notions often are conflated. By using information theoretical concepts, we develop here functional measures of the degeneracy and redundancy of a system with respect to a set of outputs. These measures help to distinguish the concept of degeneracy from that of redundancy and make it operationally useful. Through computer simulations of neural systems differing in connectivity, we show that degeneracy is low both for systems in which each element affects the output independently and for redundant systems in which many elements can affect the output in a similar way but do not have independent effects. By contrast, degeneracy is high for systems in which many different elements can affect the output in a similar way and at the same time can have independent effects. We demonstrate that networks that have been selected for degeneracy have high values of complexity, a measure of the average mutual information between the subsets of a system. These measures promise to be useful in characterizing and understanding the functional robustness and adaptability of biological networks.

Animals↗
PubMed1999

BDNF-dependent enhancement of exocytosis in cultured cortical neurons requires translation but not transcription.

Neurotrophic factors, such as brain-derived neurotrophic factor (BDNF), are involved in acute modulation of synaptic plasticity. Different modes of action of BDNF have been described with time courses ranging from seconds to hours, but the sequence of cellular processes responsible for BDNF-dependent modulation of synaptic plasticity is unknown. We have used optical imaging of the styryl dye, FM1-43, which selectively labels synaptic vesicles, to investigate potential presynaptic effects of BDNF. Addition of BDNF to cultured cortical neurons for 3 h produced a significant enhancement of exocytosis upon modest depolarization. BDNF had no effect on exocytosis either immediately or after incubation for 30 min. BDNF-dependent enhancement of exocytosis was blocked by the tyrosine kinase inhibitor, K252a, but not by K252b, consistent with signalling via the TrkB receptor. Having demonstrated that the BDNF-dependent enhancement of synaptic vesicle release was present only after 1 h, we investigated whether de novo gene transcription and/or protein synthesis were involved. Addition of the inhibitors of RNA synthesis, actinomycin D, or 5,6-dichloro-1-beta-D-ribofuranosyl benzimidazole (DRB), did not affect the enhancement of exocytosis produced by BDNF. However, the effect of BDNF was blocked by the inhibitors of translation, cycloheximide or anisomycin. Our results indicate a rapid BDNF-dependent enhancement of neurotransmitter release that requires translation but not transcription.

Animals↗
PubMed1998

Behavioral constraints in the development of neuronal properties: a cortical model embedded in a real-world device.

The ability of organisms to categorize diverse and often novel stimuli depends on ongoing interactions with their environment. In a modality such as vision, categorization requires the generation of both selective and invariant responses of cortical neurons to complex visual stimuli. How does behavior contribute to shaping the responses of these neurons? Analysis of this question is made difficult by the complex multilevel interactions between many neural and behavioral variables. To mitigate this difficulty, we studied the development and ongoing plasticity of pattern-selective neuronal responses by means of synthetic neural modeling. For this purpose, we constructed Darwin V, which consists of a simulated neuronal model embedded in a real-world device that is capable of motion and autonomous behavior. The neuronal model consists of four major components: a visual system (containing cortical and subcortical networks); a taste system based on conductance; sets of motor neurons capable of triggering behavior; and a diffuse ascending (value) system. The modeled visual cortex consists of two areas: a topographic map responsive to elementary features connected to a higher-order map composed of initially non-selective neuronal units. During behavior over time in its environment, Darwin V encounters numerous objects consisting of black metal cubes displaying different patterns of white blobs and stripes. Initially, the lack of specific higher-order visual responses does not allow visual pattern discrimination, and appetitive and aversive behaviors are triggered by the 'taste' (surface conductivity of objects) alone. In the course of sensory experience, however, changes occur in visual and sensorimotor connection strengths, with two major consequences. First, units within the higher visual area acquire responses that are both pattern selective and translation invariant. Second, as a result of the operation of the value system, these responses become linked to appropriate behaviors. Analysis of Darwin V after such changes indicates that the continuity of self-generated movements is essential for the development of pattern-selective and translation-invariant responses. The concomitant development of a preference for foveal over parafoveal objects was found to be due to increased behavioral interactions with object cubes gripped by the centrally mounted effector (snout) of Darwin V. Finally, even after development of higher-order visual responses, visual responses to more frequently encountered objects continued to be enhanced, while other responses were diminished. Overall, the detailed study of Darwin V over multiple levels of organization provides a heuristically revealing example of the crucial role played by behavioral and environmental interactions in the development of complex responses by specialized neurons.

Behavior↗
PubMed1997

Potassium ion- and nitric oxide-induced exocytosis from populations of hippocampal synapses during synaptic maturation in vitro.

The development of mechanisms of neurotransmitter release is an important component in the formation of functional synaptic connections. Synaptic neurotransmitter release can be modulated by nitric oxide, a compound shown to have a variety of physiologic functions in the nervous system. The goal of this study was to determine whether, during synaptic maturation, nitric oxide is capable of affecting exocytosis of synaptic vesicles, and to compare its effects with those elicited by strongly depolarizing stimuli. To address these questions we examined vesicle release from large numbers of individual synapses of hippocampal neurons between five and 13 days in culture. Synaptic vesicles were labelled by uptake of the styrylpyridinium dye N-(3-triethylammoniumpropyl)-4-(4-(dibutylamino)styryl)pyridinium dibromide (FM1-43) and their release was monitored by fluorescence imaging. Across populations of developing synapses, there was a good correspondence between FM1-43 staining and synapsin immunocytochemistry. A marked heterogeneity was observed in the ability to release vesicles both after potassium and nitric oxide stimulation. In less mature populations of synapses, the rate of potassium- and nitric oxide-induced exocytosis gradually increased, while at later stages nitric oxide-induced responses levelled off and potassium-induced responses continued to rise. Application of nitric oxide donors did not trigger any detectable changes in intracellular calcium. Combined immunocytochemical analysis of cultured hippocampal neurons for neuronal nitric oxide synthase and synapsin revealed that nitric oxide synthase was present within neurites of cultured hippocampal neurons, largely distributed in a bead-like pattern which partially overlapped presynaptic sites. Stimulation of the N-methyl-D-aspartate receptor while blocking propagation of action potentials with tetrodotoxin resulted in exocytosis from numerous individually resolved sites. Preincubation of neurons with an nitric oxide synthase inhibitor or addition of an nitric oxide scavenger eliminated these responses indicating a role for nitric oxide in N-methyl-D-aspartate-stimulated exocytosis. Using fluorescence imaging of individually resolved synaptic sites, we provide direct evidence for an effect of nitric oxide on vesicular neurotransmitter release in intact neurons. Nitric oxide is capable to produce this effect at all stages of synaptic development and acts independently of calcium influx. We show that nitric oxide synthase is present at synaptic sites and endogenously produced nitric oxide is sufficient to cause exocytosis. Taken together, these experiments suggest a possible role for nitric oxide in calcium-independent transmitter release in populations of synapses at all stages of maturation.

Animals↗
PubMed1996

A complexity measure for selective matching of signals by the brain.

We have previously derived a theoretical measure of neural complexity (CN) in an attempt to characterize functional connectivity in the brain. CN measures the amount and heterogeneity of statistical correlations within a neural system in terms of the mutual information between subsets of its units. CN was initially used to characterize the functional connectivity of a neural system isolated from the environment. In the present paper, we introduce a related statistical measure, matching complexity (CM), which reflects the change in CN that occurs after a neural system receives signals from the environment. CM measures how well the ensemble of intrinsic correlations within a neural system fits the statistical structure of the sensory input. We show that CM is low when the intrinsic connectivity of a simulated cortical area is randomly organized. Conversely, CM is high when the intrinsic connectivity is modified so as to differentially amplify those intrinsic correlations that happen to be enhanced by sensory input. When the input is represented by an individual stimulus, a positive value of CM indicates that the limited mutual information between sensory sheets sampling the stimulus and the rest of the brain triggers a large increase in the mutual information between many functionally specialized subsets within the brain. In this way, a complex brain can deal with context and go "beyond the information given."

Animals↗
PubMed1995

Neural cell adhesion molecule (N-CAM) inhibits astrocyte proliferation after injury to different regions of the adult rat brain.

After a penetrating lesion in the central nervous system, astrocytes enlarge, divide, and participate in creating an environment that adversely affects neuronal regeneration. We have recently shown that the neural cell adhesion molecule (N-CAM) partially inhibits the division of early postnatal rat astrocytes in vitro. In the present study, we demonstrate that addition of N-CAM, the third immunoglobulin-like domain of N-CAM, or a synthetic decapeptide corresponding to a putative homophilic binding site in N-CAM partially inhibits astrocyte proliferation after a stab lesion in the adult rat brain. Animals were lesioned in the cerebral cortex, hippocampus, or striatum with a Hamilton syringe and needle at defined stereotaxic positions. On one side, the lesions were concomitantly infused with N-CAM or with one of the N-CAM-related molecules. As a control, a peptide of the same composition as the N-CAM decapeptide but of random sequence was infused on the contralateral side of the brain. We consistently found that the population of dividing astrocytes was significantly smaller on the side in which N-CAM or one of the N-CAM-related molecules was infused than on the opposite side. The inhibition was greatest in the cortical lesion sites (approximately 50%) and was less pronounced in the hippocampus (approximately 25%) and striatum (approximately 20%). Two weeks after the lesion, the cerebral cortical sites infused with N-CAM continued to exhibit a significantly smaller population of dividing astrocytes than the sites on the opposite side. When N-CAM and basic fibroblast growth factor, which is known to stimulate astrocyte division in vitro, were coinfused into cortical lesion sites, astrocyte proliferation was still inhibited. These results suggest the hypothesis that, by reducing glial proliferation, N-CAM or its peptides may help create an environment that is more suitable for neuronal regeneration.

Amino Acid Sequence↗
PubMed1995

The neural cell adhesion molecule (N-CAM) inhibits proliferation in primary cultures of rat astrocytes.

Cell proliferation is a key primary process during neural development and also plays an important role in the regenerative response of neural tissue to injury. It has been reported that glial cell proliferation is, at least in part, controlled by a neuronal signal, possibly involving cell surface molecules. We report here that the addition of purified rat neural cell adhesion molecule (N-CAM) to primary cultures of rat forebrain astrocytes inhibits their proliferation. This inhibitory effect can be elicited in cultures grown in chemically defined serum-free medium or in medium that had been supplemented with growth factors. Polyclonal antibodies to N-CAM or their Fab' fragments elicited a similar inhibitory effect. The magnitude of the inhibitory effect of N-CAM was dependent on cell density: it was maximal at low cell densities and weakened progressively as cells approached confluency. Synthetic peptides with sequences identical to a putative homophilic binding region of N-CAM mimicked the effect of purified N-CAM, while peptides of the same length and amino acid composition but with a randomized sequence did not. The addition of N-CAM antisense oligonucleotides to primary astrocyte cultures for 48 h resulted in reduced levels of N-CAM expression. After N-CAM levels on astrocytes were diminished by this treatment, the antiproliferative effect of N-CAM added to the medium was significantly reduced. The combined results suggest that N-CAM homophilic binding may be involved in the control of glial cell proliferation.

Animals↗
PubMed1994

A measure for brain complexity: relating functional segregation and integration in the nervous system.

In brains of higher vertebrates, the functional segregation of local areas that differ in their anatomy and physiology contrasts sharply with their global integration during perception and behavior. In this paper, we introduce a measure, called neural complexity (CN), that captures the interplay between these two fundamental aspects of brain organization. We express functional segregation within a neural system in terms of the relative statistical independence of small subsets of the system and functional integration in terms of significant deviations from independence of large subsets. CN is then obtained from estimates of the average deviation from statistical independence for subsets of increasing size. CN is shown to be high when functional segregation coexists with integration and to be low when the components of a system are either completely independent (segregated) or completely dependent (integrated). We apply this complexity measure in computer simulations of cortical areas to examine how some basic principles of neuroanatomical organization constrain brain dynamics. We show that the connectivity patterns of the cerebral cortex, such as a high density of connections, strong local connectivity organizing cells into neuronal groups, patchiness in the connectivity among neuronal groups, and prevalent reciprocal connections, are associated with high values of CN. The approach outlined here may prove useful in analyzing complexity in other biological domains such as gene regulation and embryogenesis.

Animals↗
PubMed1994

Cholinesterases and peanut agglutinin binding related to cell proliferation and axonal growth in embryonic chick limbs.

Embryonic cholinesterases are assigned important functions during morphogenesis. Here we describe the expression of butyrylcholinesterase and acetylcholinesterase, and the binding of peanut agglutinin, and relate the results to mitotic activity in chick wing and leg buds from embryonic day 4 to embryonic day 9. During early stages, butyrylcholinesterase is elevated in cells under the apical ectodermal ridge and around invading motoraxons, while acetylcholinesterase is found in the chondrogenic core, on motoraxons and along the ectoderm. Peanut agglutinin binds to the apical ectodermal ridge and most prominently to the chondrogenic core. Measurements of thymidine incorporation and enzyme activities were consistent with our histological findings. Butyrylcholinesterase is concentrated near proliferative zones and periods, while acetylcholinesterase is associated with low proliferative activity. At late stages of limb development, acetylcholinesterase is concentrated in muscles and nonexistent within bones, while butyrylcholinesterase shows an inverse pattern. Thus, as in other systems, in limb formation butyrylcholinesterase is a transmitotic marker preceding differentiation, acetylcholinesterase is found on navigating axons, while peanut agglutinin appears in non-invaded regions. These data suggest roles for cholinesterases as positive regulators and peanut-agglutinin-binding proteins as negative regulators of neural differentiation.

Acetylcholinesterase↗
PubMed1994

Value-dependent selection in the brain: simulation in a synthetic neural model.

Many forms of learning depend on the ability of an organism to sense and react to the adaptive value of its behavior. Such value, if reflected in the activity of specific neural structures (neural value systems), can selectively increase the probability of adaptive behaviors by modulating synaptic changes in the circuits relevant to those behaviors. Neuromodulatory systems in the brain are well suited to carry out this process since they respond to evolutionarily important cues (innate value), broadcast their responses to widely distributed areas of the brain through diffuse projections, and release substances that can modulate changes in synaptic strength. The main aim of this paper is to show that, if value-dependent modulation is extended to the inputs of neural value systems themselves, initially neutral cues can acquire value. This process has important implications for the acquisition of behavioral sequences. We have used a synthetic neural model to illustrate value-dependent acquisition of a simple foveation response to a visual stimulus. We then examine the improvement that ensues when the connections to the value system are themselves plastic and thus become able to mediate acquired value. Using a second-order conditioning paradigm, we demonstrate that auditory discrimination can occur in the model in the absence of direct positive reinforcement and even in the presence of slight negative reinforcement. The discriminative responses are accompanied by value-dependent plasticity of receptive fields, as reflected in the selective augmentation of unit responses to valuable sensory cues. We then consider the time-course during learning of the responses of the value system and the transfer of these responses from one sensory modality to another. Finally, we discuss the relation of value-dependent learning to models of reinforcement learning. The results obtained from these simulations can be directly related to various reported experimental findings and provide additional support for the application of selectional principles to the analysis of brain and behavior.

Animals↗
PubMed1993

Behaviorally based modeling and computational approaches to neuroscience.

The almost incredible advances that have recently occurred in the power of computers available to scientists in all disciplines have encouraged an explosion of neural network and behavioral models. Some of these have been constrained more by the imagination of the programmer than by rude biological facts. Their relevance for the experimental neuroscientist thus varies from case to case. Some models (e.g. Grillner's model of lamprey swimming movements) are so closely based on known neuroanatomy and neurophysiology that it becomes possible to generate and test precise experimental predictions. Other models (such as MURPHY and NOMAD) use neurobiological principles in their architectures, but do not portray any particular organism. Although it is harder to relate the study of these models of the study of real animals, they fulfill an important explanatory role. They make possible insights into how behavior is controlled by neuronal activity that would be unobtainable in real animals using present methods. Thus, even the excesses of neural modeling have provided a useful impetus to what is undoubtedly a most promising approach to integrating data from the various disciplines concerned with behavior and the mind. The problems have been pointed out by many authors (see citations in our introduction), and a phase of more critical evaluation appears to have begun. We hope that our brief survey of models based on widely different theoretical approaches, but all aimed at explaining behavior, will encourage critical comparisons to be made. As in more mature fields, such as thermodynamics, we can expect that more complete models will force an evaluation of theoretical hypotheses against the entire body of available evidence, rather than just a few pertinent test cases. Such evaluation will make possible a much more rigorous exclusion of invalid or inconsistent theoretical ideas. From such studies, a much smaller, but more robust, set of basic principles can be expected to emerge. From the perspective afforded by our own modeling studies, it appears essential that modeling be informed by a general theory of brain function. In this work, the theory of neuronal group selection provides a useful basis for further work by virtue of its consistency with basic evolutionary and physiological principles and the power of the selection paradigm to shape neural networks in behaviorally adaptive directions.

Animals↗
PubMed1993

Solving Bernstein's problem: a proposal for the development of coordinated movement by selection.

In recent years, many established concepts in the theory of human motor development have undergone profound change, and our knowledge has increased greatly. Nevertheless, some outstanding problems remain unsolved. A central problem concerns the redundancy of effective movements, first pointed out by N. A. Bernstein. The human motor system is mechanically complex and can make use of a large number of degrees of freedom. The controlled operation of such a system requires a reduction of mechanical redundancy, effectively by reducing the number of degrees of freedom. More recent work has shown that this problem is hard to solve explicitly by computing solutions to the equations of motion of the system. Equally challenging to traditional computational approaches is the fact the motor systems show remarkable adaptability and flexibility in the presence of changing biomechanical properties of motor organs during development and when faced with different environmental conditions or tasks. Solutions to these problems would have a large impact on a variety of issues in child development. In this article, we stress the importance of the somatic selection of neuronal groups in maps for the progressive transformation of a primary movement repertoire into a set of motor synergies and adaptive action patterns. We present results from computer simulations of a simple motor system that works according to such selectional principles. This approach suggests a provisional solution to Bernstein's problem and provides new parameters to guide experimental approaches to the development of sensorimotor coordination.

Biomechanical Phenomena↗
PubMed1992

Synthetic neural modeling applied to a real-world artifact.

We describe the general design, operating principles, and performance of a neurally organized, multiply adaptive device (NOMAD) under control of a nervous system simulated in a computer. The complete system, Darwin IV, is the latest in a series of models based on the theory of neuronal group selection, which postulates that adaptive behavior is the result of selection in somatic time among synaptic populations. The simulated brain of Darwin IV includes visual and motor areas that are connected with NOMAD by telemetry. Under suitable conditions, Darwin IV can be trained to track a light moving in a random path. After such training, it can approach colored blocks and collect them to a home position. Following a series of contacts with such blocks, value signals received through a "snout" that senses conductivity allow it to sort these blocks on the basis of differences in color associated with differences in their conductivity. Darwin IV represents a new approach to synthetic neural modeling (SNM), a technique in which large-scale computer simulations are employed to analyze the interactions among the nervous system, the phenotype, and the environment of a designed organism as behavior develops. Darwin IV retains the advantages of SNM while avoiding the difficulties and pitfalls of attempting to simulate a rich environment in addition to a brain.

Behavior↗
PubMed1991

Modeling perceptual grouping and figure-ground segregation by means of active reentrant connections.

The segmentation of visual scenes is a fundamental process of early vision, but the underlying neural mechanisms are still largely unknown. Theoretical considerations as well as neurophysiological findings point to the importance in such processes of temporal correlations in neuronal activity. In a previous model, we showed that reentrant signaling among rhythmically active neuronal groups can correlate responses along spatially extended contours. We now have modified and extended this model to address the problems of perceptual grouping and figure-ground segregation in vision. A novel feature is that the efficacy of the connections is allowed to change on a fast time scale. This results in active reentrant connections that amplify the correlations among neuronal groups. The responses of the model are able to link the elements corresponding to a coherent figure and to segregate them from the background or from another figure in a way that is consistent with the so-called Gestalt laws.

Animals↗