Complex Spatiotemporal Information Research Articles

Spatiotemporal memory is an intrinsic property of networks of dissociated cortical neurons.

The ability to process complex spatiotemporal information is a fundamental process underlying the behavior of all higher organisms. However, how the brain processes information in the temporal domain remains incompletely understood. We have explored the spatiotemporal information-processing capability of networks formed from dissociated rat E18 cortical neurons growing in culture. By combining optogenetics with microelectrode array recording, we show that these randomly organized cortical microcircuits are able to process complex spatiotemporal information, allowing the identification of a large number of temporal sequences and classification of musical styles. These experiments uncovered spatiotemporal memory processes lasting several seconds. Neural network simulations indicated that both short-term synaptic plasticity and recurrent connections are required for the emergence of this capability. Interestingly, NMDA receptor function is not a requisite for these short-term spatiotemporal memory processes. Indeed, blocking the NMDA receptor with the antagonist APV significantly improved the temporal processing ability of the networks, by reducing spontaneously occurring network bursts. These highly synchronized events have disastrous effects on spatiotemporal information processing, by transiently erasing short-term memory. These results show that the ability to process and integrate complex spatiotemporal information is an intrinsic property of generic cortical networks that does not require specifically designed circuits.

Quantifying human sensitivity to spatio-temporal information in dynamic faces.

A great deal of perceptual and social information is conveyed by facial motion. Here, we investigated observers' sensitivity to the complex spatio-temporal information in facial expressions and what cues they use to judge the similarity of these movements. We motion-captured four facial expressions and decomposed them into time courses of semantically meaningful local facial actions (e.g., eyebrow raise). We then generated approximations of the time courses which differed in the amount of information about the natural facial motion they contained, and used these and the original time courses to animate an avatar head. Observers chose which of two animations based on approximations was more similar to the animation based on the original time course. We found that observers preferred animations containing more information about the natural facial motion dynamics. To explain observers' similarity judgments, we developed and used several measures of objective stimulus similarity. The time course of facial actions (e.g., onset and peak of eyebrow raise) explained observers' behavioral choices better than image-based measures (e.g., optic flow). Our results thus revealed observers' sensitivity to changes of natural facial dynamics. Importantly, our method allows a quantitative explanation of the perceived similarity of dynamic facial expressions, which suggests that sparse but meaningful spatio-temporal cues are used to process facial motion.

The multiscale character of evoked cortical activity

Both the architecture and the dynamics of the brain have characteristic features at different spatial scales. However, the existence, nature and function of dynamical interdependencies between such scales have not been investigated. We studied the multiscale properties of functional magnetic resonance imaging (fMRI) data acquired while human subjects viewed a visual image. Traditional “region of interest” analysis of this data set revealed evoked activity in primary and extrastriate visual cortex. Wavelet transform in the spatial domain provides a multiscale representation of this evoked brain activity. Studying the correlation structure of this representation revealed strong and novel interdependencies in these data within and between different spatial scales. We found that such correlations are stronger than those evident in the original data and comparable in magnitude to those obtained after Gaussian smoothing. However, analysis of the data in the wavelet domain revealed additional structure such as positive correlations, strong anti-correlations and phase-lagged interdependencies. Statistical significance of these effects was inferred through nonparametric bootstrap techniques. We conclude that the spatial analysis of functional neuroimaging data in the wavelet domain provides novel information which may reflect complex spatiotemporal neuronal activity and information encoding. It also affords a quantitative means of testing hierarchical and multiscale models of cortical activity.

Design and Usability of an Enhanced Geographic Information System for Exploration of Multivariate Health Statistics*

The multidimensional nature of many types of data in modern geography calls for creative and innovative approaches to their analysis. Statisticians have recently developed methods for exploring and visualizing large, multivariate datasets, but cartographers and geographers in general have only recently begun to integrate these methods for use with spatial and spatiotemporal datasets that are multivariate in character. This article will present an example of such an integration—an environment for visualization of health statistics—as a case study to demonstrate the philosophical and practical advantages of geovisualization systems for the exploration of complex spatiotemporal information. Emphasis is placed on the encouragement of creative thinking about geographic phenomena through the use of such data-rich graphical tools. *I am pleased to acknowledge support for portions of this work provided by a contract from the NIH-National Cancer Institute and a grant from the U.S. National Science Foundation (#EIA9983451). Special thanks to both Dr. Alan M. MacEachren and Dr. Linda J. Pickle for their generous assistance through all phases of this research. The ArcView 3.2 application (including all scripts and interface tools) described herein is available for no charge. Please contact the author at robedsall@asu.edu, or by standard mail.

The parallel coordinate plot in action: design and use for geographic visualization

Implementations of interactive parallel coordinate plots in geographic visualization systems are presented. The plots represent spatial and spatio-temporal data, and are linked to maps and scatterplots. The interactive features of the parallel coordinate representations are discussed, with particular emphasis on their ability to facilitate geographic data exploration and understanding. The paper will present the systems as “case studies” to demonstrate the philosophical and practical advantages of highly interactive, multiple-perspective environments, including parallel coordinate plots, for the exploration of complex spatial and spatiotemporal information. Emphasis is placed on the encouragement and facilitation of creative thinking about geographic phenomena through the use of such data-rich graphical tools.

Stimulus-induced bifurcations in discrete-time neural oscillators

Based on theoretical issues and neurobiological evidence, considerable interest has recently focused on dynamic computational elements in neural systems. Such elements respond to stimuli by altering their dynamical behavior rather than by changing a scalar output. In particular, neural oscillators capable of chaotic dynamics represent a potentially very rich substrate for complex spatiotemporal information processing. However, the response properties of such systems must be studied in detail before they can be used as computational elements in neural models. In this paper, we focus on the response of a very simple discrete-time neural oscillator model to a fixed input. We show that the oscillator responds to the stimulus through a fairly complex set of bifurcations, and shows critical switching between attractors. This information can be used to construct very sophisticated dynamic computational elements with well-understood response properties. Examples of such elements are presented in the paper. We end with a brief discussion of simple architectures for networks of dynamical elements, and the relevance of our results to neurobiological models.

Comparison of Z and multivariate statistical brain electromagnetic maps for the localization of brain lesions

Conventional Z maps provide probability statements about the deviation of observed values from the norm. Galán et al. (1994) introduced Simultaneous Significance Probability Scales to detect abnormalities over the whole map, making use of the information provided by the topographic structure of dependencies. They also described multivariate brain electromagnetic (MBE) maps for compact presentation of complex spatio-temporal information. In this paper, using the distance-based localization receiver operating characteristic curves (DL-ROC curves), we compare the localization provided by computed tomography with that provided by Z and MBE maps in 61 patients with brain lesions. Maps were calculated for absolute power and relative power in delta, theta, alpha and beta bands for voltage and current source densities (CSD). In each patient, all maps were compared and the map with the highest value of the area of the DL-ROC curve was considered to be the “best map.” Z maps of CSD were the “best maps” in 24 patients. In the voltage montage, we observed that multivariate maps add some information not contained in the Z maps. However, for CSD, Z maps were more accurate than multivariate maps. A very consistent finding was the observation that lesions were better detected by maps analyzing the delta band, while edema was better represented by maps in the theta range.