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PubMed2005

A field-theoretic approach to understanding scale-free neocortical dynamics.

A mesoscopic field-theoretic approach is compared with neural network and brain imaging approaches to understanding brain dynamics. Analysis of high spatiotemporal resolution rabbit electroencephalogram (EEG) reveals neural fields in the form of spatial patterns in amplitude (AM) and phase (PM) modulation of gamma and beta carrier waves that serve to classify EEGs from trials with differing conditioned stimuli (CS+/-). Paleocortex exemplified by olfactory EEG has one AM-PM pattern at a time that forms by an input-dependent phase transition. Neocortex shows multiple overlapping AM-PM patterns before and during presentation of CSs. Modeling suggests that neocortex is stabilized in a scale-free state of self-organized criticality, enabling cooperative domains to form virtually instantaneously by phase transitions ranging in size from a few hypercolumns to an entire hemisphere. Self-organized local domains precede formation of global domains that supervene and contribute global modulations to local domains. This mechanism is proposed to explain Gestalt formation in perception.

Animals↗

PubMed2006

Avoiding spurious submovement decompositions II: a scattershot algorithm.

Evidence for the existence of discrete submovements underlying continuous human movement has motivated many attempts to "extract" them. Although they produce visually convincing results, all of the methodologies that have been employed are prone to produce spurious decompositions. In previous work, a branch-and-bound algorithm for submovement extraction, capable of global nonlinear minimization, and hence, capable of avoiding spurious decompositions, was presented [Rohrer and Hogan (Biol Cybern 39:190-199, 2003)]. Here, we present a scattershot-type global nonlinear minimization algorithm that requires approximately four orders of magnitude less time to compute. A sensitivity analysis reveals that the scattershot algorithm can reliably detect changes in submovement parameters over time, e.g., over the course of neuromotor recovery.

Algorithms↗

PubMed2006

The optimal human ventral stream from estimates of the complexity of visual objects.

The part of the primate visual cortex responsible for the recognition of objects is parcelled into about a dozen areas organized somewhat hierarchically (the region is called the ventral stream). Why are there approximately this many hierarchical levels? Here I put forth a generic information-processing hierarchical model, and show how the total number of neurons required depends on the number of hierarchical levels and on the complexity of visual objects that must be recognized. Because the recognition of written words appears to occur in a similar part of inferotemporal cortex as other visual objects, the complexity of written words may be similar to that of other visual objects for humans; for this reason, I measure the complexity of written words, and use it as an approximate estimate of the complexity more generally of visual objects. I then show that the information-processing hierarchy that accommodates visual objects of that complexity possesses the minimum number of neurons when the number of hierarchical levels is approximately 15.

Humans↗

PubMed2006

The influence of wavelets on multiscale analysis and parametrization of midlatency auditory evoked potentials.

This work shows methodological aspects of heuristic pattern recognition in auditory evoked potentials. A linear and a nonlinear transformation based on wavelet transform are presented. They result in a statistical error model and an entropy function related to the Gibbs function and describe changes in midlatency auditory evoked potentials induced by general anaesthesia. The same transformations were calculated using 12 common wavelets. We present a method to compare the two defined parametrizations with respect to their ability to discriminate two defined states which is responsive and unresponsive depending on the wavelet used for the analysis. Auditory evoked potentials of 60 patients undergoing general anaesthesia were analysed. We propose the defined statistical error model and the entropy function as a very robust measure of changes in auditory evoked potentials. The influence of the wavelets suggest that for each parametrization the goodness of the wavelet should be validated.

Acoustic Stimulation↗

PubMed2006

Use of a neural mass model for the analysis of effective connectivity among cortical regions based on high resolution EEG recordings.

Assessment of brain connectivity among different brain areas during cognitive or motor tasks is a crucial problem in neuroscience today. Aim of this work is to use a neural mass model to assess the effect of various connectivity patterns in cortical electroencephalogram (EEG) power spectral density, and investigate the possibility to derive connectivity circuits from EEG data. To this end, a model of an individual region of interest (ROI) has been built as the parallel arrangement of three populations, each described as in Wendling et al. (Eur J Neurosci 15:1499-1508, 2002). Connectivity among ROIs includes three parameters, which specify the strength of connection in the different frequency bands. The following main steps have been followed: (1) we analyzed how the power spectral density (PSD) is significantly modified by the kind of coupling hypothesized among the ROIs; (2) with the model, and using an automatic fitting procedure, we looked for a simple connectivity circuit able to reproduce PSD of cortical EEG in three ROIs during a finger-movement task. The estimated parameters represent the strength of connections among the ROIs in the different frequency bands. Cortical EEGs were computed with an inverse propagation algorithm, starting from measurement performed with 96 electrodes on the scalp. The present study suggests that the model can be used as a simulation tool, able to mimic the effect of connectivity on EEG. Moreover, it can be used to look for simple connectivity circuits, able to explain the main features of observed cortical PSD. These results may open new prospectives in the use of neurophysiological models, instead of empirical models, to assess effective connectivity from neuroimaging information.

Brain Mapping↗

PubMed2001

Evaluating causal relations in neural systems: granger causality, directed transfer function and statistical assessment of significance.

We consider the question of evaluating causal relations among neurobiological signals. In particular, we study the relation between the directed transfer function (DTF) and the well-accepted Granger causality, and show that DTF can be interpreted within the framework of Granger causality. In addition, we propose a method to assess the significance of causality measures. Finally, we demonstrate the applications of these measures to simulated data and actual neurobiological recordings.

Action Potentials↗

PubMed1996

A psychophysical and computational analysis of intensity-based stereo.

We describe two psychophysical experiments testing predictions of the square difference mechanism we have previously proposed for intensity-based stereo. Experiment 1 assesses the relative contributions of disparity and contrast to intensity-based stereo by measuring detection thresholds. The product of disparity and contrast at threshold is shown to be constant. In experiment 2, we measure quantitatively the global depth position perceived in stereograms of curved, smoothly shaded surfaces. The results show that disparity averaging over the surface involves a contrast-dependent weighting function. The results from both experiments are consistent with predictions derived from the square difference mechanism. The relation of this mechanism to feature correspondence stereopsis and shape-from-shading is discussed and a general framework for assessing the modularity of stereopsis is presented.

Adult↗

PubMed1998

Statistical model of the hippocampal CA3 region II. The population framework: model of rhythmic activity in the CA3 slice.

A statistical model is given to describe the electrical activity patterns of large neural populations of the hippocampal CA3 region. A continuous model has been formalized to describe the statistical processes governing the interactions within and between neural fields. The system of partial differential equations contains diffusion terms which determine the evolution of second moments of the probability distribution functions. The model is supplemented with a differential description of post-synaptic potentials. The discretization procedure has been designed so as to make the discrete equations scaling invariant. Population activities as well as underlying single-cell voltages are simulated during normal and epileptiform activities in the hippocampal CA3 slice. It is demonstrated that our model can reproduce electrophysiological phenomena characteristic to both single-cell and population activities. Specifically, fully synchronized population bursts, synchronized synaptic potentials, and low amplitude population oscillation were obtained.

Action Potentials↗

PubMed1999

Coefficient of variation of interspike intervals greater than 0.5. How and when?

Using Stein's model with and without reversal potentials, we investigated the mechanism of production of spike trains with a CV (ISI)(standard deviation/mean interspike interval) greater than 0.5, as observed in the visual cortex. When the attractor of the deterministic part of the dynamics is below the firing threshold, spike generation results primarily from random fluctuations. Using computer simulation for a range of membrane decay times and with other model parameters set to values appropriate for the visual cortex, we demonstrate that CV (ISI) is then usually greater than 0.5; if the attractor is above the threshold, spike generation is mainly due to deterministic forces, and CV (ISI) is then usually lower than 0.5. The critical value of the inhibitory postsynaptic potential (IPSP) rate at which CV (ISI) becomes greater than 0.5 is determined, resulting in specifications of how neurones might adjust their synaptic inputs to elicit irregular spike trains.

Analysis of Variance↗

PubMed1999

A joint interspike interval difference stochastic spike train analysis: detecting local trends in the temporal firing patterns of single neurons.

We introduce a stochastic spike train analysis method called joint interspike interval difference (JISID) analysis. By design, this method detects changes in firing interspike intervals (ISIs), called local trends, within a 4-spike pattern in a spike train. This analysis classifies 4-spike patterns that have similar incremental changes. It characterizes the higher-order serial dependence in spike firing relative to changes in the firing history. Mathematically, this spike train analysis describes the statistical joint distribution of consecutive changes in ISIs, from which the serial dependence of the changes in higher-order intervals can be determined. It is similar to the joint interspike interval (JISI) analysis, except that the joint distribution of consecutive ISI differences (ISIDs) is quantified. The graphical location of points in the JISID scatter plot reveals the local trends in firing (i.e., monotonically increasing, monotonically decreasing, or transitional firing). The trajectory of these points in the serial-JISID plot traces the time evolution of these trends represented by a 5-spike pattern, while points in the JISID scatter plot represent trends of a 4-spike pattern. We provide complete theoretical interpretations of the JISID analysis. We also demonstrate that this method indeed identifies firing trends in both simulated spike trains and spike trains recorded from cultured neurons.

Animals↗

PubMed1999

Analytical description of the evolution of neural networks: learning rules and complexity.

We present the foundation of a physical formalism that allows us to characterize the dynamics of the evolution of neural networks both in regard to the network configuration and to network performance. Model runs were performed on a simple network consisting of six neurons, allowing complete analytical description of the network's behaviour. Order parameters are characterized that allow an analytical description of critical periods in network evolution. Thus, correlations of the local dynamics and the system's global behaviour could be computed. It is shown that local learning rules are sufficient to model complex dynamical aspects of the evolution of networks. It is demonstrated in how far novel statistical formalisms, e.g. neural complexity, can be employed to evaluate the system's dynamics. The introduction of order parameters allows an analytical characterization of transient phases in the network's behaviour, correlating network connectivity with neuronal firing patterns. The relevance of this approach for the interpretation of physiological data is discussed.

Animals↗

PubMed1999

Subordinate-level object classification reexamined.

The classification of a table as round rather than square, a car as a Mazda rather than a Ford, a drill bit as 3/8-inch rather than 1/4-inch, and a face as Tom have all been regarded as a single process termed "subordinate classification." Despite the common label, the considerable heterogeneity of the perceptual processing required to achieve such classifications requires, minimally, a more detailed taxonomy. Perceptual information relevant to subordinate-level shape classifications can be presumed to vary on continua of (a) the type of distinctive information that is present, nonaccidental or metric, (b) the size of the relevant contours or surfaces, and (c) the similarity of the to-be-discriminated features, such as whether a straight contour has to be distinguished from a contour of low curvature versus high curvature. We consider three, relatively pure cases. Case 1 subordinates may be distinguished by a representation, a geon structural description (GSD), specifying a nonaccidental characterization of an object's large parts and the relations among these parts, such as a round table versus a square table. Case 2 subordinates are also distinguished by GSDs, except that the distinctive GSDs are present at a small scale in a complex object so the location and mapping of the GSDs are contingent on an initial basic-level classification, such as when we use a logo to distinguish various makes of cars. Expertise for Cases 1 and 2 can be easily achieved through specification, often verbal, of the GSDs. Case 3 subordinates, which have furnished much of the grist for theorizing with "view-based" template models, require fine metric discriminations. Cases 1 and 2 account for the overwhelming majority of shape-based basic- and subordinate-level object classifications that people can and do make in their everyday lives. These classifications are typically made quickly, accurately, and with only modest costs of viewpoint changes. Whereas the activation of an array of multiscale, multiorientation filters, presumed to be at the initial stage of all shape processing, may suffice for determining the similarity of the representations mediating recognition among Case 3 subordinate stimuli (and faces), Cases 1 and 2 require that the output of these filters be mapped to classifiers that make explicit the nonaccidental properties, parts, and relations specified by the GSDs.

Classification↗

PubMed2001

Probabilistic approaches for atlasing normal and disease-specific brain variability.

The extreme variability in the structural conformation of the human brain poses significant challenges for the creation of population-based atlases. The ability to statistically and visually compare and contrast brain image data from multiple individuals is essential to understanding normal variability within a particular population as well as differentiating normal from diseased populations. This paper introduces the application of probabilistic atlases that describe specific subpopulations, measures their variability and characterizes the structural differences between them. Utilizing data from structural MRI, we have built atlases with defined coordinate systems creating a framework for mapping data from functional, histological and other studies of the same population. This paper describes the basic approach and a brief description of the underlying mathematical constructs that enable the calculation of probabilistic atlases and examples of their results from several different normal and diseased populations.

Brain↗

PubMed1999

A concept of consciousness.

The term "consciousness", so widely used in clinical settings, is considered extremely complex and practically undefinable. Historically, consciousness was ignored by the great classical philosophers, and was regarded as a basic condition of "being". This changed with Descartes and during the ensuing centuries. A simplified, reductionistic and easily definable concept of consciousness is proposed; consciousness is proposed to consist of three main components: vigilance, mental contents, and selective attention. These three components can be investigated with modern neuroscientific methods--vigilance being the most readily explorable function. The striking differences between sleep and coma are pointed out (along with some observations on sleep initiation). Special attention is paid to epileptic impairment of consciousness and, in particular, to the spike-wave absence which is thought to be due to a temporary suspension of the "working memory circuits" within the frontal lobe.

Arousal↗

PubMed2005

A new method for detecting causality in fMRI data of cognitive processing.

One of the most important achievements in understanding the brain is that the emergence of complex behavior is guided by the activity of brain networks. To fully apply this theoretical approach fully, a method is needed to extract both the location and time course of the activities from the currently employed techniques. The spatial resolution of fMRI received great attention, and various non-conventional methods of analysis have previously been proposed for the above-named purpose. Here, we briefly outline a new approach to data analysis, in order to extract both spatial and temporal activities from fMRI recordings, as well as the pattern of causality between areas. This paper presents a completely data-driven analysis method that applies both independent components analysis (ICA) and the Granger causality test (GCT), performed in two separate steps. First, ICA is used to extract the independent functional activities. Subsequently the GCT is applied to the independent component (IC) most correlated with the stimuli, to indicate its causal relation with other ICs. We therefore propose this method as a promising data-driven tool for the detection of cognitive causal relationships in neuroimaging data.

Algorithms↗

PubMed2006

Conditional visuo-motor learning and dimension reduction.

Conditional visuo-motor learning consists in learning by trial and error to associate visual cues with correct motor responses, that have no direct link. Converging evidence supports the role of a large brain network in this type of learning, including the prefrontal and the premotor cortex, the basal ganglia BG and the hippocampus. In this paper we focus on the role of a major structure of the BG, the striatum. We first present behavioral results and electrophysiological data recorded from this structure in monkeys engaged in learning new visuo-motor associations. Visual stimuli were presented on a video screen and the animals had to learn, by trial and error, to select the correct movement of a joystick, in order to receive a liquid reward. Behavioral results revealed that the monkeys used a sequential strategy, whereby they learned the associations one by one although they were presented randomly. Human subjects, tested on the same task, also used a sequential strategy. Neuronal recordings in monkeys revealed learning-related modulations of neural activity in the striatum. We then present a mathematical model inspired by viability theory developed to implement the use of strategies during learning. This model complements existing models of the BG based on reinforcement learning RL, which do not take into account the use of strategies to reduce the dimension of the learning space.

Basal Ganglia↗

PubMed2005

[Successful coping and experience-dependent brain plasticity].

Recent progress in brain research has shown that the human brain, i.e., the neuronal and synaptic connectivity especially in higher cortical association centres, is much more plastic and use-dependent than previously thought. This contribution summarizes the present knowledge about this phenomenon of experience-dependent plasticity. Irritation, anxiety and the activation of the neuroendocrine stress-response-system is the most important trigger for the adaptive modification and reorganization of neuronal networks and synaptic connectivity. The experience and successful mastery of a large spectrum of different challenges already during early childhood and during later life diminish the risk of the acquisition and facilitation of maladaptive coping-strategies. Once established, the strongly facilitated neuronal networks involved in the generation of maladaptive behavioural patterns are difficult to reorganize. The experience of loss of control and the long-lasting activation of an uncontrollable stress response may contribute to the destabilization of the neuronal connectivity established in the course of acquisition of such maladaptive coping strategies.

Adaptation, Physiological↗

PubMed2006

Long-range correlations in rabbit brain neural activity.

We have analyzed the presence of persistence properties in rabbit brain electrical signals by means of non-equilibrium statistical physics tools. To measure long-memory properties of these experimental signals, we have first determined whether the data are fractional Gaussian noise (fGn) or fractional Brownian motion (fBm) by calculating the slope of the power spectral density plot of the series. The results show that the series correspond to fBm. Then, the data were studied by means of the bridge detrended scaled windowed variance analysis, detecting long-term correlation. Three different types of experimental signals have been studied: neural basal activity without stimulation, the response induced by a single flash light stimulus and the average of the activity evoked by 200 flash light stimulations. Analysis of the series revealed the existence of persistent behavior in all cases. Moreover, the results also exhibited an increasing correlation in the level of long-term memory from recordings without stimulation, to one sweep recording or 200 sweeps averaged recordings. Thus, brain neural electrical activity is affected not only by its most recent states, but also by previous states much more distant in the past.

Action Potentials↗