Natural patterns of neural activity: how physiological mechanisms are orchestrated to cope with real life

Physiological mechanisms of neuronal information processing have been shaped during evolution by a continual interplay between organisms and their sensory surroundings. Thus, when asking for the functional significance of such mechanisms, the natural conditions under which they operate must be considered. This has been done successfully in several studies that employ sensory stimulation under in vivo conditions. These studies address the question of how physiological mechanisms within neurons are properly adjusted to the characteristics of natural stimuli and to the demands imposed on the system being studied. Results from diverse animal models show how neurons exploit natural stimulus statistics efficiently by utilizing specific filtering capacities. Mechanisms that allow neurons to adapt to the currently relevant range from an often immense stimulus spectrum are outlined, and examples are provided that suggest that information transfer between neurons is shaped by the system-specific computational tasks in the behavioral context.     

Natural patterns of activity and long-term synaptic plasticity

Long-term potentiation (LTP) of synaptic transmission is traditionally elicited by massively synchronous, high-frequency inputs, which rarely occur naturally. Recent in vitro experiments have revealed that both LTP and long-term depression (LTD) can arise by appropriately pairing weak synaptic inputs with action potentials in the postsynaptic cell. This discovery has generated new insights into the conditions under which synaptic modification may occur in pyramidal neurons in vivo. First, it has been shown that the temporal order of the synaptic input and the postsynaptic spike within a narrow temporal window determines whether LTP or LTD is elicited, according to a temporally asymmetric Hebbian learning rule. Second, backpropagating action potentials are able to serve as a global signal for synaptic plasticity in a neuron compared with local associative interactions between synaptic inputs on dendrites. Third, a specific temporal pattern of activity--postsynaptic bursting--accompanies synaptic potentiation in adults.     

Techniques for extracting single-trial activity patterns from large-scale neural recordings

Large, chronically implanted arrays of microelectrodes are an increasingly common tool for recording from primate cortex and can provide extracellular recordings from many (order of 100) neurons. While the desire for cortically based motor prostheses has helped drive their development, such arrays also offer great potential to advance basic neuroscience research. Here we discuss the utility of array recording for the study of neural dynamics. Neural activity often has dynamics beyond that driven directly by the stimulus. While governed by those dynamics, neural responses may nevertheless unfold differently for nominally identical trials, rendering many traditional analysis methods ineffective. We review recent studies - some employing simultaneous recording, some not - indicating that such variability is indeed present both during movement generation and during the preceding premotor computations. In such cases, large-scale simultaneous recordings have the potential to provide an unprecedented view of neural dynamics at the level of single trials. However, this enterprise will depend not only on techniques for simultaneous recording but also on the use and further development of analysis techniques that can appropriately reduce the dimensionality of the data, and allow visualization of single-trial neural behavior.     

After-effects and the integration of patterns of neural activity within a channel

Prolonged inspection of an adapting simulus changes the appearance of a subsequent test stimulus. There are five distinct viewing conditions under which such 'after-effects' may be generated. There are MON-MON (inspect with one eye, same eye), BIN-BIN (inspect with both eyes, test both eyes), BIN-MON (inspect with both eyes, test only one eye), MON-BIN (inspect with one eye, test with both) and TRANSFER (inpect with one eye, test with the other eye). A model based upon the assumption of the linearly additive effects of adaptation generated in 'dominance classes' or cortical units that are driven either by one eye, or the other eye, or by either or both eyes together, is described. This model generates predictions concerning the expected ralative magnitudes of after-effects generated under the five viewing modes described above, and experiments are described that confirm these predictions. The model can be extended to gaenerate predictions about other experimental conditions. A more complex version of the model is consistent with electrophysiologically derived estimates of the proportion of cortical units in each dominance class.     

On how network architecture determines the dominant patterns of spontaneous neural activity

In the absence of sensory stimulation, neocortical circuits display complex patterns of neural activity. These patterns are thought to reflect relevant properties of the network, including anatomical features like its modularity. It is also assumed that the synaptic connections of the network constrain the repertoire of emergent, spontaneous patterns. Although the link between network architecture and network activity has been extensively investigated in the last few years from different perspectives, our understanding of the relationship between the network connectivity and the structure of its spontaneous activity is still incomplete. Using a general mathematical model of neural dynamics we have studied the link between spontaneous activity and the underlying network architecture. In particular, here we show mathematically how the synaptic connections between neurons determine the repertoire of spatial patterns displayed in the spontaneous activity. To test our theoretical result, we have also used the model to simulate spontaneous activity of a neural network, whose architecture is inspired by the patchy organization of horizontal connections between cortical columns in the neocortex of primates and other mammals. The dominant spatial patterns of the spontaneous activity, calculated as its principal components, coincide remarkably well with those patterns predicted from the network connectivity using our theory. The equivalence between the concept of dominant pattern and the concept of attractor of the network dynamics is also demonstrated. This in turn suggests new ways of investigating encoding and storage capabilities of neural networks.     

Natural killer activity against cultured human neural tumor and fetal brain cells

Ten human neural tumor lines and three established from normal human brain were analyzed for sensitivities to natural killer (NK) cytolysis. Compared to MOLT-4, fetal brain cells were sensitive, but those from adult brain and eight of ten neural tumor cell lines demonstrated marked NK resistance. The frequencies of target-binding cells (TBC) and single-cell lysis of glioma cells bound within tumor cell conjugates demonstrated that the resistance of two lines was explained either by a decrease in the frequencies of TBC or reduced ability of bound NK cells to lyse the tumor cell conjugates. A third resistant line demonstrated decreases in both TBC and tumor cell conjugate lysis. Two glioma lines with less NK resistance had greater frequencies of TBC or conjugate lysis than the resistant lines. Thus, NK resistance can result from decreased recognition of targets, diminished NK lysis of bound targets, or a combination of both.     

Oscillatory models of the hippocampus: A study of spatio-temporal patterns of neural activity

Spatial patterns of theta-rhythm activity in oscillatory models of the hippocampus are studied here using canonical models for both Hodgkin's class-1 and class-2 excitable neuronal systems. Dynamics of these models are studied in both the frequency domain, to determine phase-locking patterns, and in the time domain, to determine the amplitude responses resulting from phase-locking patterns. Computer simulations presented here demonstrate that phase deviations (timings) between inputs from the medial septum and the entorhinal cortex can create spatial patterns of theta-rhythm phase-locking. In this way, we show that the timing of inputs (not only their frequencies alone) can encode specific patterns of theta-rhythm activity. This study suggests new experiments to determine temporal and spatial synchronization. Received: 31 July 1998 /Accepted in revised form: 20 April 1999     

Dynamical correlation patterns and corresponding community structure in neural spontaneous activity at criticality

It has been considered that the state in the vicinity of a critical point, which is the point between ordered and disordered states, can underlie and facilitate information processing of the brain in various aspects. In this research, we numerically study the influence of criticality on one aspect of brain information processing, i.e., the community structure, which is an important characteristic of complex networks. We examine community structure of the functional connectivity in simulated brain spontaneous activity, which is based on dynamical correlations between neural activity patterns at different positions. The brain spontaneous activity is simulated by a neural field model whose parameter covers subcritical, critical, and supercritical regions. Then, the corresponding dynamical correlation patterns and community structure are compared. In the critical region, we found some distinctive properties, namely high correlation and correlation switching, high modularity and a low number of modules, high stability of the dynamical functional connectivity, and moderate flexibility of the community structure across temporal scales. We also discuss how these characteristics might improve information processing of the brain.     

Chronotype predicts activity patterns in the neural underpinnings of the motor system during the day

Neuroimaging is increasingly used to study the motor system in vivo. Despite many reports of time-of-day influences on motor function at the behavioral level, little is known about these influences on neural motor networks and their activations recorded in neuroimaging. Using functional magnetic resonance imaging (fMRI), the authors studied 15 healthy subjects (9 females; mean ± SD age: 23 ± 3 yrs) performing a self-paced finger-tapping task at different times of day (morning, midday, afternoon, and evening). Blood-oxygenation-level-dependent signal showed systematic differences across the day in task-related motor areas of the brain, specifically in the supplementary motor area, parietal cortex, and rolandic operculum (p(corr)< .0125). The authors found that these time-of-day-dependent hemodynamic modulations are associated with chronotype and not with homeostatic sleep pressure. These results show that consideration of time-of-day for the analysis of fMRI studies is imperative.     

Effect of stimulus-driven pruning on the detection of spatiotemporal patterns of activity in large neural networks

Adult patterns of neuronal connectivity develop from a transient embryonic template characterized by exuberant projections to both appropriate and inappropriate target regions in a process known as synaptic pruning. Trigger signals able to induce synaptic pruning could be related to dynamic functions that depend on the timing of action potentials. We stimulated locally connected random networks of spiking neurons and observed the effect of a spike-timing-dependent synaptic plasticity (STDP)-driven pruning process on the emergence of cell assemblies. The spike trains of the simulated excitatory neurons were recorded. We searched for spatiotemporal firing patterns as potential markers of the build-up of functionally organized recurrent activity associated with spatially organized connectivity.     

Fixed action patterns and neural Darwinism

Summary The stereotopy of the Fixed Action Pattern of classical ethology is customarily attributed to hard wiring. We submit that this explanation is akin to the 17th century use of the homunculus to explain development. We propose extendingEdelman's notions of neural Darwinism to explain the emergence of species-characteristic (innate) motor patterns.

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Angeborene, Stereotype Verhaltensweisen und Neuraler Darwinismus

Zusammenfassung Die in der klassischen Ethologie beschriebenen angeborenen Verhaltensweisen (sensuLorenz &Tinbergen) wurden meist durch die Annahme erklärt, daß bestimmte genetisch bedingte Nervennetze die stereotypen Bewegungen in reflektorischer Weise bestimmen. Im Computerjargon heißt das hard wiring. Wir meinen, daß diese Formulierung eher einer modernen Form des Homunculus des 17. Jh. als einer echten Erklärung entspricht, weil sie nicht erklären kann, wie das exakt reproduzierbare Nervennetz entsteht. Außerdem ist bekannt, daß dasselbe Verhalten auch bei enormen Unterschieden in der Neuralanatomie auftreten kann; stereotype Bewegungen brauchen keine spezifischen Nervennetze.G. Edelmann hat bereits vorgeschlagen, daß Nervennetze für angeborene Auslösemechanismen durch eine darwinistische Auslese von Neuronen entstehen können. Wir schlagen vor, dieses Prinzip auch zur Erklärung der Entwicklung stereotyper, artspezifischer Verhaltensweisen heranzuziehen und dadurch die sogenannte hard wiring-Erklärung abzulösen.

     

Recognition of general patterns using neural networks

The Hopfield model of neural network stores memory in its symmetric synaptic connections and can only learn to recognize sets of nearly orthogonal patterns. A new algorithm is put forth to permit the recognition of general (non-orthogonal) patterns. The algorithm specifies the construction of the new network's memory matrix T _ij, which is, in general, asymmetrical and contains the Hopfield neural network (Hopfield 1982) as a special case. We find further that in addition to this new algorithm for general pattern recognition, there exists in fact a large class of T _ij memory matrices which permit the recognition of non-orthogonal patterns. The general form of this class of T _ij memory matrix is presented, and the projection matrix neural network (Personnaz et al. 1985) is found as a special case of this general form. This general form of memory matrix extends the library of memory matrices which allow a neural network to recognize non-orthogonal patterns. A neural network which followed this general form of memory matrix was modeled on a computer and successfully recognized a set of non-orthogonal patterns. The new network also showed a tolerance for altered and incomplete data. Through this new method, general patterns may be taught to the neural network.     

Linear-nonlinear-time-warp-poisson models of neural activity

Prominent models of spike trains assume only one source of variability – stochastic (Poisson) spiking – when stimuli and behavior are fixed. However, spike trains may also reflect variability due to internal processes such as planning. For example, we can plan a movement at one point in time and execute it at some arbitrary later time. Neurons involved in planning may thus share an underlying time course that is not precisely locked to the actual movement. Here we combine the standard Linear-Nonlinear-Poisson (LNP) model with Dynamic Time Warping (DTW) to account for shared temporal variability. When applied to recordings from macaque premotor cortex, we find that time warping considerably improves predictions of neural activity. We suggest that such temporal variability is a widespread phenomenon in the brain which should be modeled.     

Gap junctional communication among motor and other neurons shapes patterns of neural activity and synaptic connectivity during development

We are studying the functional roles of neuronal gap junctional coupling during development, using motor neurons and their synapses with muscle fibers as a model system. At neuromuscular synapses, several studies have shown that the relative pattern of activity among motor inputs competing for innervation of the same target muscle fiber determines how patterns of innervation are sculpted during the first weeks after birth. We asked whether gap junctional coupling among motor neurons modulates the relative timing of motor neuron activity in awake, behaving neonatal mice. We found that the activity of motor neurons innervating the same muscle is temporally correlated perinatally, during the same period that gap junction-mediated electrical and dye coupling are present. In vivo blockade of gap junctions abolished temporal correlations in motor neuron activity, without changing overall motor behavior, motor neuron activity patterns or firing frequency. Together with preliminary studies in mice lacking gap junction protein Cx40, our data suggest that developmentally regulated gap junctional coupling among motor and other neurons affects the activity in nascent neural circuits and thus in turn affects synaptic connectivity. Dynamic monitoring of dye coupling can be used to explore this possibility in normal mice and in mice lacking gap junction proteins during embryonic and neonatal development.     

Genetically encoded neural activity indicators

Recording activity from identified populations of neurons is a central goal of neuroscience. Changes in membrane depolarization, particularly action potentials, are the most important features of neural physiology to extract, although ions, neurotransmitters, neuromodulators, second messengers, and the activation state of specific proteins are also crucial. Modern fluorescence microscopy provides the basis for such activity mapping, through multi-photon imaging and other optical schemes. Probes remain the rate-limiting step for progress in this field: they should be bright and photostable, and ideally come in multiple colors. Only protein-based reagents permit chronic imaging from genetically specified cells. Here we review recent progress in the design, optimization and deployment of genetically encoded indicators for calcium ions (a proxy for action potentials), membrane potential, and neurotransmitters. We highlight seminal experiments, and present an outlook for future progress.     

Modeling developmental patterns of spontaneous activity

Spontaneous activity is found in many regions of the developing nervous system; such activity is thought to be instructive for guiding developmental processes. In particular, the developing retina generates correlated patterns of activity known as retinal waves. We review the main theoretical models that have been developed to study the mechanisms for generation and propagation of retinal waves. Much of the progress in this field has been due to the close interaction between experimentalists and theorists in analyzing and modeling spontaneous activity. We conclude by describing spontaneous activity models in other systems and suggestions for future modeling work.     

Natural secretory products of human neural and microvessel endothelial cells

Neurons, glia, and endothelial cells of the cerebral microvasculature co-exist in intimate proximity in nervous tissues, and their homeostatic interactions in health, as well as coordinated response to injury, have led to the concept that they form the basic elements of a functional neurovascular unit. During the course of normal cellular metabolism, growth, and development, each of these brain cell types secrete various species of potentially neurotoxic peptides and factors, events that increase in magnitude as brain cells age. This article reviews contemporary research on the secretory products of the three primary cell types that constitute the neurovascular unit in deep brain regions. We provide some novel in vitro data that illustrate potentially pathogenic paracrine effects within primary cells of the neurovascular unit. For example, the pro-inflammatory cytokine interleukin (IL)-1β was found to stimulate amyloid-β (Aβ) peptide release from human neural cells, and human brain microvessel endothelial cells exposed to transient hypoxia were found to secrete IL-1β at concentrations known to induce Aβ42 peptide release from human neural cells. Hypoxia and excessive IL-1β and Aβ42 abundance are typical pathogenic stress factors implicated in the initiation and development of common, chronic neurological disorders such as Alzheimer's disease. These data support the hypothesis that paracrine effects of stressed constituent cells of the neurovascular unit may contribute to “spreading effects” characteristic of progressive neurodegenerative disorders.     

Prediction of N-linked glycan branching patterns using artificial neural networks

A model was developed for novel prediction of N-linked glycan branching pattern classification for CHO-derived N-linked glycoproteins. The model consists of 30 independent recurrent neural networks and uses predicted quantities of secondary structure elements and residue solvent accessibility as an input vector. The model was designed to predict the major component of a heterogeneous mixture of CHO-derived glycoforms of a recombinant protein under normal growth conditions. Resulting glycosylation prediction is classified as either complex-type or high mannose. The incorporation of predicted quantities in the input vector allowed for theoretical mutant N-linked glycan branching predictions without initial experimental analysis of protein structures. Primary amino acid sequence data were effectively eliminated from the input vector space based on neural network prediction analyses. This provided further evidence that localized protein secondary structure elements and conformational structure may play more important roles in determining glycan branching patterns than does the primary sequence of a polypeptide. A confidence interval parameter was incorporated into the model to enable identification of false predictions. The model was further tested using published experimental results for mutants of the tissue-type plasminogen activator protein [J. Wilhelm, S.G. Lee, N.K. Kalyan, S.M. Cheng, F. Wiener, W. Pierzchala, P.P. Hung, Alterations in the domain structure of tissue-type plasminogen activator change the nature of asparagine glycosylation. Biotechnology (N.Y.) 8 (1990) 321-325].     

Recognition of partly occluded patterns: a neural network model

 Human beings are often able to read a letter or word partly occluded by contaminating ink stains. However, if the stains are completely erased and the occluded areas of the letter are changed to white, we usually have difficulty in reading the letter. In this article I propose a hypothesis explaining why a pattern is easier to recognize when it is occluded by visible objects than by invisible opaque objects. A neural network model is constructed based on this hypothesis. The visual system extracts various visual features from the input pattern and then attempts to recognize it. If the occluding objects are not visible, the visual system will have difficulty in distinguishing which features are relevant to the original pattern and which are newly generated by the occlusion. If the occluding objects are visible, however, the visual system can easily discriminate between relevant and irrelevant features and recognize the occluded pattern correctly. The proposed model is an extended version of the neocognitron model. The activity of the feature-extracting cells whose receptive fields cover the occluding objects is suppressed in an early stage of the hierarchical network. Since the irrelevant features generated by the occlusion are thus eliminated, the model can recognize occluded patterns correctly, provided the occlusion is not so large as to prevent recognition even by human beings. Received: 21 February 2000 / Accepted in revised form: 11 September 2000