To explain how stimuli cause consciousness, we have to explain causality. We can't trace linear causal chains from receptors after the first cortical synapse, so we use circular causality to explain neural pattern formation by self-organizing dynamics. But an aspect of intentional action is causality, which we extrapolate to material objects in the world. Thus causality is a property of mind, not matter.

l networks, and that recording wave activity is equivalent to observing an engine with a stethoscope or a computer with a D'Arsonval galvanometer. However, one can learn a lot about a system by listening and watching, if one knows what to seek and find. Numerous recent studies of the behavioral correlates of so-called "unit activity" of single neurons in sensory and motor systems have shown that the carrier of behaviorally significant information is not the pulse train of the single neuron, but instead the organized activity of arrays of neurons (see review in Note 3.7 in Freeman 1995). How many neurons are needed to make an array? Does the number exceed the number that can be accessed by current methods of recording pulse trains (on the order of 100)? Where do they form, what fractions of neurons in local neighborhoods suffice, and how are their outputs selectively read by their targets of transmission? In my view these questions have no answers, because the objects of their inquiry

RESEARCH OBJECTIVES 1 1.1. DYNAMICAL MODELS OF SENSORY FUSION 2 1.2. EMBODIED CATEGORY FORMATION 2 1.3. ACTION-ORIENTED REPRESENTATIONS 2 1.4. ONTOGENETIC DEVELOPMENT 3 2. SCIENTIFIC RELEVANCE 3 2.1. DYNAMICAL BRAIN MODELS 3 2.2. EMBODIED COGNITION 5 2.3. NEURAL DARWINISM 5 3. TECHNICAL APPROACH 6 3.1. PRINCIPLES OF COGNITIVE DYNAMICS 6 3.2. THE LIMBIC SYSTEM MODEL OF INTENTIONAL BEHAVIOR 8 3.3. SELECTIONAL MECHANISMS 9 3.4. VALUE SYSTEMS, PLASTICITY AND HOMEOSTATIC REGULATION 10 3.5. COMPUTATIONAL APPROACH 10 4. EXPECTED RESULTS 11 4.1. CONCEPTUAL DESIGN 11 4.2. MOTOR COORDINATION TASKS 11 4.3. REAL-TIME TASK ENVIRONMENTS 12 4.4. MOBILE ROBOTIC SIMULATORS 12 4.5. AUTONOMOUS AGENTS 13 5. REFERENCES 13 6. MANAGEMENT PLAN 16 7. COST PLAN 18 7.1. YEAR 1 18 SODAS - iii - 10/20/2000 7.4. SUMMARY 21 7.5. EXPLANATION 22 8. RESUMES 24 ROBERT KOZMA 25 STANLEY P. FRANKLIN 27 WALTER J. FREEMAN 29 DEREK HARTER 31 9. DECLARATIONS AND CERTIFICATIONS 33 10. APPENDIX 34 10.1. CHAOTIC RESONANCE -- METHODS AND APPLICATIONS FOR ROBUST CLASSIFICATION OF NOISY AND VARIABLE PATTERNS 34 10.2. EMERGENCE OF UN-CORRELATED COMMON-MODE OSCILLATIONS IN THE SENSORY CORTEX 35 10.3. LOCAL-GLOBAL INTERACTIONS AND THE ROLE OF MESOSCOPIC (INTERMEDIATE-RANGE) ELEMENTS IN BRAIN DYNAMICS 36 List of Figures Figure 1 - KII scheme 7 Figure 2 - KIII model 8 Figure 3 - Limbic System 9 Figure 4 - Brain State Schematic 9 Abstract Biological organisms show an amazing ability during their ontogenetic development to adaptively develop strategies and solutions to the various problems of survival that their environments present to them. Dynamical and embodied models of cognition are beginning to offer new insights into how the numerous, heterogeneous elements of neur...