Synchrony among multicortical EEGs 2 Freeman, Gaál & Jörnsten Information transfer and integration among functionally distinct areas of cerebral cortex of oscillatory activity requires some degree of phase synchrony of the trains of action potentials that carry the information prior to the integration. However, propagation delays are obligatory. Delays vary with the lengths and conduction velocities of the axons carrying the information, causing phase dispersion. In order to determine how synchrony is achieved despite dispersion, we recorded EEG signals from multiple electrode arrays on five cortical areas in cats and rabbits, that had been trained to discriminate visual or auditory conditioned stimuli. Analysis by time-lagged correlation, multiple correlation and PCA, showed that maximal correlation was at zero lag and averaged.7, indicating that 50 % of the power in the gamma range among the five areas was at zero lag irrespective of phase or frequency. There were no stimulus-related episodes of transiently increased phase locking among the areas, nor EEG "bursts " of transiently increased amplitude above the sustained level of synchrony. Three operations were identified to account for the sustained correlation. Cortices broadcast their outputs over divergent-convergent axonal

How can we build artificial agents that can autonomously explore and understand their environments? An immediate requirement for such an agent is to learn how its own sensory state corresponds to the external world properties: It needs to learn the semantics of its internal state (i.e., grounding). In principle, we as programmers can provide the agents with the required semantics, but this will compromise the autonomy of the agent. To overcome this problem, we may fall back on natural agents and see how they acquire meaning of their own sensory states, their neural firing patterns. We can learn a lot about what certain neural spikes mean by carefully controlling the input stimulus while observing how the neurons fire. However, neurons embedded in the brain do not have direct access to the outside stimuli, so such a stimulus-to-spike association may not be learnable at all. How then can the brain solve this problem? (We know it does.) We propose that motor interaction with the environment is necessary to overcome this conundrum. Further, we provide a simple yet powerful criterion, sensory invariance, for learning the meaning of sensory states. The basic idea is that a particular form of action sequence that maintains invariance of a sensory state will express the key property of the environmental stimulus that gave rise to the sensory state. Our experiments with a sensorimotor agent trained on natural images show that sensory invariance can indeed serve as a powerful objective for semantic grounding.

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Mesoscopic level neurodynamics study the collective dynamical behavior of neural populations. Such models are becoming increasingly important in understanding large-scale brain processes. Brains exhibit aperiodic oscillations with a much more rich dynamical behavior than fixed-point and limitcycle approximation allow. Here we present a discretized model inspired by Freeman’s K-set mesoscopic level population model. We show that this version is capable of replicating the important principles of aperiodic/chaotic neurodynamics while being fast enough for use in real-time autonomous agent applications. This simplification of the K model provides many advantages not only in terms of efficiency but in simplicity and its ability to be analyzed in terms of its dynamical properties. We study the discrete version using a multi-layer, highly recurrent model of the neural architecture of perceptual brain areas. We use this architecture to develop example action selection mechanisms in an autonomous agent.

Workforce population trends have increased the numbers and kinds of culturally diverse people who work together. Researchers in organizational behavior have often examined culture through values; however, cultural values can be based on collections of people other than traditional nation states. A cultural mosaic is presented as a framework to identify demographic, geographic, and associative features underlying culture. An individual’s unique collage of multiple cultural identities yields a complex picture of the cultural influences on that person. Developments in chaos and complexity theories are proposed as a theoretical base for study on the complexity of culture at the individual level. Additional developments in network theory serve as a theoretical base for cultural research at the group level. The cultural mosaic is described as a complex system with localized structures, linking cultural tiles in ordered and chaotic ways. Research propositions examining multiple cultural identities at individual and group levels are discussed.

Aperiodic dynamics are known to be essential in the formation of perceptual mechanisms and representations in biological organisms. Advances in neuroscience and computational neurodynamics are helping us understand the properties of nonlinear systems that are fundamental in the self-organization of stable, complex patterns in many types of systems, from biological ecosystems to human economies and in biological brains. In this paper we introduce a neurological population model that is capable of replicating the important aperiodic dynamics observed in biological brains. We use the mechanism to self-organize cognitive maps in an autonomous agent.

Biological organisms display an amazing ability during their ontogenetic development to adaptively develop solutions to the various problems of survival that their environments present to them. Dynamical and embodied models of cognition (Clark, 1997; Edelman & Tononi, 2000; Franklin, 1995; Freeman, 1999a, 1999b; Freeman & Kozma, 2000; Freeman, Kozma, & Werbos, 2000; Hendriks-Jansen, 1996; Kelso, 1995; Kozma & Freeman, 2001; Port & van Gelder, 1995; Skarda & Freeman, 1987; Thelen & Smith, 1994) are beginning to offer new insights into how the numerous, heterogeneous elements of neural structures may self-organize during the development of the organism in order to effectively form adaptive categories and increasingly sophisticated skills, strategies and goals. In this paper we present models of ontogenetic development built on neurologically inspired, bottom-up, dynamic approaches to embodied category formation such as those done by Freeman (1975, 1999b), Freeman and Kozma (2000), Kozma and Freeman (2001), Verschure (1998) and Edelman (1987, 1989). We believe that building on such mechanisms from an embodied dynamical perspective will produce autonomous agents that display greatly increased flexibility in their behavior. Such models will represent a better understanding of how the brains of biological organisms not only form perceptual categories of their environments during development, but also develop effective patterns of behavior through the dynamic self-organization of neurological patterns of activity.

Abstract—Vision is basically a sensory modality, so it is no surprise that the investigation into the brain’s visual functions has been focused on its sensory aspect. Thus, questions like (1) how can external geometric properties represented in internal states of the visual system be grounded, (2) how do the visual cortical receptive fields (RFs) form, and (3) how can visual shapes be recognized have all been addressed within the framework of sensory information processing. However, this view is being challenged on multiple fronts, with an increasing emphasis on the motor aspect of visual function. In this paper, we will review works that implicate the important role of motor function in vision, and discuss our latest results touching upon the issues of grounding, RF development, and shape recognition. Our main findings are that (1) motor primitives play a fundamental role in grounding, (2) RF learning can be biased and enhanced by the motor system, and (3) shape recognition is easier with motorbased representations than with sensor-based representations. The insights we gained here will help us better understand visual cortical function. Also, we expect the motor-oriented view of visual cortical function to be generalizable to other sensory cortices such as somatosensory and auditory cortices. I.

ABSTRACT. Recent findings in neuroscience strongly suggest that an object’s features (e.g., its color, texture, shape, etc.) are represented in separate areas of the visual cortex. Although represented in separate neuronal areas, somehow the feature representations are brought together as a single, unified object of visual consciousness. This raises a question of binding: how do neural activities in separate areas of the visual cortex function to produce a feature-unified object of visual consciousness? Several prominent neuroscientists have adopted neural synchrony and attention-based approaches to explain object feature binding. I argue that although neural synchrony and/or attentional mechanisms might function to disambiguate an object’s features, it is difficult to see how either of these mechanisms could fully explain the unity of an object’s features at the level of visual consciousness. After presenting a detailed critique of neural synchrony and attention-based approaches to object feature binding, I propose interactive hierarchical structuralism (IHS). This view suggests that a unified percept (i.e., a feature-unified object

1997) are stimulating a re-examination of traditional principles of designing teaching and learning experiences. Insights from this research are not only helping to deepen our understanding of traditional core learning principles, but they are also providing practical guidance on how to design learning experiences for our new high technology environments. The following ten learning principles illustrate how recent research integrated with traditional principles of pedagogy and instructional design can enrich our understanding of thinking and learning processes. The principles outlined here can serve as a guide to the design of learning experiences in both online environments and traditional campus classrooms. Core Learning Principle #1: Every Structured Learning Experience Has Four Elements with the