Adaptive Behavior, 3 (4)
Adaptive Behavior
Volume 3, Number 4
Spring 1995
Table of Contents
Dave Cliff
Guest Editorial
Örjan Ekeberg, Anders Lansner, and Sten Grillner
The Neural Control of Fish Swimming Studied Through Numerical Simulations
Adaptive Behavior, 3 (4), 363-384.
H. Cruse, D. E. Brunn, Ch. Bartling, J. Dean, M. Dreifert, T. Kindermann, and J. Schmitz
Walking: A Complex Behavior Controlled by Simple Networks
Adaptive Behavior, 3 (4), 385-418.
Fernando J. Corbacho and Michael A. Arbib
Learning to Detour
Adaptive Behavior, 3 (4), 419-468.
Randall D. Beer
On the Dynamics of Small Continuous-Time Recurrent Neural Networks
Adaptive Behavior, 3 (4), 469-509.
Pages359-362
Guest Editorial
By Dave Cliff
The Neural Control of Fish Swimming Studied Through Numerical Simulations
By Örjan Ekeberg, Anders Lansner, Sten Grillner
Abstract
The neuronal generation of vertebrate locomotion has been extensively studied in the lamprey. Computer simulations of this system have been carried out with different aims and with different techniques. In this article, we review some of these simulations, particularly those leading toward models that describe the interaction that occurs between the neuronal system and its mechanical environment during swimming.Here we extend these models, enabling two new experiments to be conducted. The first one addresses the role of sensory feedback by exposing ther neuromechanical system to unexpected pertubations. The second one tests the validity of an earlier proposed hypothesis for the neural generation of three-dimensional (3D) steering by coupling this central pattern generator to a mechanical 3D simulation.
Key Words
motor control; central pattern generator; lamprey; swimming; computer simulation
Walking: A Complex Behavior Controlled by Simple Networks
By H. Cruse, D. E. Brunn, Ch. Bartling, J. Dean, M. Dreifert, T. Kindermann, J. Schmitz
Abstract
Understanding how behavior is controlled requires that modeling be combined with behavioral, electrophysiological, and neuroanatomical investigations. One problem in studying motor systems is that they have considerable autonomy; they are not driven solely by inputs. Choosing walking as the object of study is promising because it is a comparably simple and easy-to-elicit behavior, but it exhibits the special feature of most motor behavior - the interaction between central, autonomous components and peripheral, sensory influences. This article reviews the control of walking in stick insects, beginning with behavioral studies of single-leg control and the interleg coordinating mechanisms. These behavioral results are tested and supported by modeling the control system in an artificial neural network computer simulation and a six-legged robot.Supporting neurophysiological results also are considered. Together, the results indicate that high flexibility and adaptability is based on a simple distributed control structure.
Key Words
motor control; walking; stick insect; neural net
Learning to Detour
By Fernando J. Corbacho and Michael A. Arbib
Abstract
Anurans (frogs and toads) show quite flexible behavior when confronted with stationary objects on their way to prey or when escaping from a threat. Rana computatrix (Arbib, 1987), an evolving computer model of anuran visuomotor coordination, models complex behaviors such as detouring around a stationary barrier to get to prey on the basis of an understanding of anuran prey and barrier recognition, depth perception, and appropriate motor pattern generation mechanisms based on sensory perception. Our present analysis of detour behavior goes beyond other models by incorporating new data from our laboratory demonstrating a learning component in anuran detour behavior.Building on earlier work showing how interacting schemas may be used to analyze a complex environment to generate an appropriate course of behavior, we turn to the questions: How are the relevant schemas adapted? How are schemas combined to form new schema assemblages acquired for the system to become more efficient? We describe the construction mechanisms and interactions with the environment that are necessary to achieve higher levels of detour performance. We have based this article mostly on data about learning to detour when approaching prey, but the model offers a strategy for learning to detour in general. Moreover, we have attempted to solve the problem in a general way so that the model of learning to detour points the way to a general theory of schema-based learning.
Key Words
anuran; behavioral sequences; computational neuroethology; detour behavior; schema-based learning; neural networks
On the Dynamics of Small Continuous-Time Recurrent Neural Networks
By Randall D. Beer
Abstract
Dynamical neural networks are being increasingly employed in a variety of contexts, including as simple model nervous systems for autonomous agents. For this reason, there is a growing need for a comprehensive understanding of their dynamical properties. Using a combination of elementary analysis and numerical studies, this article begins a systematic examination of the dynamics of continuous-time recurrent neural networks. Specifically, a fairly complete description of the possible dynamical behavior and bifurcations of one-and two-neuron circuits is given, along with a few specific results for larger networks. This analysis provides both qualitative insight and, in many cases, quantitative formulas for predicting the dynamical behavior of particular circuits and how that behavior changes as network parameters are varied. These results demonstrate that even small circuits are capable of a rich variety of dynamical behavior (including chaotic dynamics). An approach to understanding the dynamics of circuits with time-varying inputs is also presented. Finally, based on this analysis, several strategies for focusing evolutionary searches into fruitful regions of network parameter space are suggested.Key Words
dynamical neural networks; computational neuroethology; evolutionary search; nonlinear dynamics
Pages 511-513
Author Index to Volume 3
Pages 514-516
Key Word Index to Volume 3
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