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.National Science Foundation (IRI-87-16960, IRI-87-6960); Air Force Office of Scientific Research (90-0175); Defense Advanced Research Projects Agency (90-0083

Electrophysiological and Morphological Analyses of Mouse Spinal Cord Mini-Cultures Grown on Multimicroelectrode Plates

The electrophysiological and morphological properties of small networks of mammalian neurons were investigated with mouse spinal cord monolayer cultures of 2 mm diameter grown on multimicroelectrode plates (MMEPs). Such cultures were viewed microscopically and their activity simultaneously recorded from 2 of any 36 fixed recording sites. The specific aims achieved were: development of techniques for production of functional MMEPs and maintenance of mini-cultures, characterization of the spontaneous activity of mini-cultures, application of inhibitory and disinhibitory agents, development of staining methods for cultured neurons and initial light microscopic analysis with correlation of electrophysiological and morphological characteristics

An experimental design framework for evolutionary robotics

Based on the failures of work in the area of machine intelligence in the past, a new paradigm has been proposed: for a machine to develop intelligence it should be able to interact with and survive within a hostile dynamic environment. It should therefore be able to display adaptive behaviour and respond correctly to changes in its situation. This means that before higher cognitive properties can be modeled, the modeling of the lower levels of intelligence would be achieved first. Only by building on this platform of physical and mental abilities may it be possible to develop true intelligence. One train of thought for implementing this is to control and design a robot by modeling the neuroethology of simpler animals such as insects. This thesis outlines one approach to the design and development of such a robot, controlled by a neural network, by combining the work of a number of researchers in the areas of machine intelligence and artificial life. It involves Rodney Brooks’ subsumption architecture, Randall D. Beer’s work in the area of computational neuroethology, Richard Dawkins’ work in the area of biomorphs and computational embryology and finally the work of John Holland and David Goldberg in genetic algorithms. This thesis will demonstrate the method and reasoning behind the combination of the work of the above named researchers. It will also detail and analyse the results obtained by their application