Testimonies and Truth-tellings: Women in the United Methodist Tradition.

Published version of the Keynote address at "Struggle, Faith and Vision: Celebrating Women in the United Methodist Tradition, 1788 to Today," March 9, 2007, Nashville, Tennessee.

Computer-Mediated Communication and Ecclesiological Challenges To and From the Reformed Tradition

Communities of faith have appeared online since the inception of computer -­ mediated communication (CMC)and are now ubiquitous. Yet the character and legitimacy of Internet communities as ecclesial bodies is often disputed by traditional churches; and the Internet's ability to host the church as church for online Christians remains a question. This dissertation carries out a practical theological conversation between three main sources: the phenomenon of the church online; ecclesiology (especially that characteristic of Reformed communities); and communication theory. After establishing the need for this study in Chapter 1, Chapter 2 investigates the online presence of Christians and trends in their Internet use, including its history and current expressions. Chapter 3 sets out an historical overview of the Reformed Tradition, focusing on the work of John Calvin and Karl Barth, as well as more contemporary theologians. With a theological context in which to consider online churches in place, Chapter 4 introduces four theological themes prominent in both ecclesiology and CMC studies: authority; community; mediation; and embodiment. These themes constitute the primary lens through which the dissertation conducts a critical-­confessional interface between communication theory and ecclesiology in the examination of CMC. Chapter 5 continues the contextualization of online churches with consideration of communication theories that impact CMC, focusing on three major communication theories: Narrative Theory; Interpretive Theory; and Speech Act Theory. Chapter 6 contains the critical conversation between ecclesiology and communication theory by correlating the aforementioned communication theories with Narrative Theology, Communities of Practice, and Theo-­Drama, and applying these to the four theological themes noted above. In addition, new or anticipated developments in CMC investigated in relationship to traditional ecclesiologies and the prospect of cyber-­ecclesiology. Chapter 7 offers an evaluative tool consisting of a three-­step hermeneutical process that examines: 1) the history, tradition, and ecclesiology of the particular community being evaluated; 2) communication theories and the process of religious-­social shaping of technology; and 3) CMC criteria for establishing the presence of a stable, interactive, and relational community. As this hermeneutical process unfolds, it holds the church at the center of the process, seeking a contextual yet faithful understanding of the church.

Vector Associative Maps: Unsupervised Real-time Error-based Learning and Control of Movement Trajectories

This article describes neural network models for adaptive control of arm movement trajectories during visually guided reaching and, more generally, a framework for unsupervised real-time error-based learning. The models clarify how a child, or untrained robot, can learn to reach for objects that it sees. Piaget has provided basic insights with his concept of a circular reaction: As an infant makes internally generated movements of its hand, the eyes automatically follow this motion. A transformation is learned between the visual representation of hand position and the motor representation of hand position. Learning of this transformation eventually enables the child to accurately reach for visually detected targets. Grossberg and Kuperstein have shown how the eye movement system can use visual error signals to correct movement parameters via cerebellar learning. Here it is shown how endogenously generated arm movements lead to adaptive tuning of arm control parameters. These movements also activate the target position representations that are used to learn the visuo-motor transformation that controls visually guided reaching. The AVITE model presented here is an adaptive neural circuit based on the Vector Integration to Endpoint (VITE) model for arm and speech trajectory generation of Bullock and Grossberg. In the VITE model, a Target Position Command (TPC) represents the location of the desired target. The Present Position Command (PPC) encodes the present hand-arm configuration. The Difference Vector (DV) population continuously.computes the difference between the PPC and the TPC. A speed-controlling GO signal multiplies DV output. The PPC integrates the (DV)·(GO) product and generates an outflow command to the arm. Integration at the PPC continues at a rate dependent on GO signal size until the DV reaches zero, at which time the PPC equals the TPC. The AVITE model explains how self-consistent TPC and PPC coordinates are autonomously generated and learned. Learning of AVITE parameters is regulated by activation of a self-regulating Endogenous Random Generator (ERG) of training vectors. Each vector is integrated at the PPC, giving rise to a movement command. The generation of each vector induces a complementary postural phase during which ERG output stops and learning occurs. Then a new vector is generated and the cycle is repeated. This cyclic, biphasic behavior is controlled by a specialized gated dipole circuit. ERG output autonomously stops in such a way that, across trials, a broad sample of workspace target positions is generated. When the ERG shuts off, a modulator gate opens, copying the PPC into the TPC. Learning of a transformation from TPC to PPC occurs using the DV as an error signal that is zeroed due to learning. This learning scheme is called a Vector Associative Map, or VAM. The VAM model is a general-purpose device for autonomous real-time error-based learning and performance of associative maps. The DV stage serves the dual function of reading out new TPCs during performance and reading in new adaptive weights during learning, without a disruption of real-time operation. YAMs thus provide an on-line unsupervised alternative to the off-line properties of supervised error-correction learning algorithms. YAMs and VAM cascades for learning motor-to-motor and spatial-to-motor maps are described. YAM models and Adaptive Resonance Theory (ART) models exhibit complementary matching, learning, and performance properties that together provide a foundation for designing a total sensory-cognitive and cognitive-motor autonomous system.

Adaptive Resonance Associative Map: A Hierarchical ART System for Fast Stable Associative Learning

This paper introduces a new class of predictive ART architectures, called Adaptive Resonance Associative Map (ARAM) which performs rapid, yet stable heteroassociative learning in real time environment. ARAM can be visualized as two ART modules sharing a single recognition code layer. The unit for recruiting a recognition code is a pattern pair. Code stabilization is ensured by restricting coding to states where resonances are reached in both modules. Simulation results have shown that ARAM is capable of self-stabilizing association of arbitrary pattern pairs of arbitrary complexity appearing in arbitrary sequence by fast learning in real time environment. Due to the symmetrical network structure, associative recall can be performed in both directions.