We describe a theoretical network analysis that can distinguish statistically causal interactions in population neural activity leading to a specific output. We introduce the concept of a causal core to refer to the set of neuronal interactions that are causally significant for the output, as assessed by Granger causality. Because our approach requires extensive knowledge of neuronal connectivity and dynamics, an illustrative example is provided by analysis of Darwin X, a brain-based device that allows precise recording of the activity of neuronal units during behavior. In Darwin X, a simulated neuronal model of the hippocampus and surrounding cortical areas supports learning of a spatial navigation task in a real environment. Analysis of Darwin X reveals that large repertoires of neuronal interactions contain comparatively small causal cores and that these causal cores become smaller during learning, a finding that may reflect the selection of specific causal pathways from diverse neuronal repertoires.

A new RoboCup soccer league is being developed, focusing on human-robot interaction. In this league each team consists of both a human player, mounted on a Segway HT scooter, and a robotic version of the Segway; both human and robot players must cooperate to score goals. This paper details the design of our robotic Segway soccer brain-based device (SS-BBD). The SS-BBD control system is based on a large scale neural simulation, whose design is dictated by details from the published literature on vertebrate neuroanatomy, neurophysiology, and psychophysics. The physical device is completely autonomous, and possesses special manipulators for kicking and capturing a full-sized soccer ball. The SS-BBD uses visual and laser rangefinder information to recognize a variety of game related objects, which enables it to perform actions such as capturing the ball, kicking the ball to another player, shooting a goal, and maneuvering safely across the field. The SS-BBD can act autonomously or obey voice commands from the human player. This is an unprecedented level of human-robot teamwork on a soccer field, in that our players are not merely acting autonomously, but also communicate with each other and support each other on the field

The simultaneous study of brain function at all levels of organization is difficult to undertake with current experimental tools. Present day electrophysiology only allows the recording of at most hundreds of neurons while an animal is performing a behavioral task. Because of this limitation and the sheer complexity of the nervous system, computational modeling has become essential in developing theories of brain function. Accordingly, our group has constructed a series of brain-based devices (BBDs), that is, physical devices with simulated nervous systems that guide behavior, to serve as a heuristic for testing theories of brain function. Unlike animal models, BBDs permit analysis of activity at all levels of the nervous system as the device behaves in its environment. Although the principal focus of developing BBDs has been to test theories of brain function, this type of modeling may also provide a basis for robotic design and practical applications.

Whiskers are widely used by many animal species for navigation and texture discrimination. This paper describes Darwin IX, a mobile physical device equipped with artificial whiskers, the behavior of which is controlled by a neural simulation based on the rat somatosensory system. During its autonomous behavior, Darwin IX is able to discriminate among textures in its environment and learns to avoid textures that are paired with aversive events.

Our group has constructed a series of brain-based devices (BBDs); i.e. physical devices with simulated nervous systems that guide behavior, to serve as a heuristic for understanding brain function. Unlike conventional robots designed by engineering principles, BBDs are based on biological principles and alter their behavior to the environment through self-learning. The resulting systems autonomously generalize signals from the environment into perceptual categories and through adaptive behavior become increasingly successful in coping with the environment. Although the principal focus of developing BBDs has been to test theories of the nervous system, this approach may also provide a basis for robotic design and practical applications.

Autonomous robotic systems need to adjust their sensorimotor coordinations so as to maintain good performance in the presence of changes in their sensory and motor characteristics. Biological systems are able to adapt to large variations in their physical and functional properties. In the last decade, the adjustment of orienting behavior has been carefully investigated in the barn owl, a nocturnal predator with highly developed auditory capabilities. We have previously proposed that the development and maintenance of the barn owl's accurate orienting behavior can be explained through a process of learning based on the saliency of sensorimotor events. In this paper we consider the application of a detailed computer model of the principal neural structures involved in the process of spatial localization in the barn owl to the control of the orienting behavior of a robotic system, in the presence of auditory and visual stimulation. The system is composed of a robotic head equipped with two lateral microphones and a camera. We show that the model produces accurate orienting behavior toward both auditory and visual targets and is able to quickly recover good performance after alterations of the sensory inputs and motor outputs. The results illustrate that an architecture specifically designed to account for biological phenomena can produce flexible and robust motor control of a robotic system operating in the real world.

Autonomous robotic systems need to adjust their sensorimotor coordinations so as to maintain good performance in the presence of changes in their sensory and motor characteristics. Biological systems are able to adapt to large variations in their physical and functional properties. The adjustment of orienting behavior has been carefully investigated in the barn owl, a nocturnal predator with highly developed auditory capabilities. In the optic tectum of the barn owl, an area well-known to be involved in the production of orienting behavior, neural maps of space in the visual, auditory, and motor modalities are found in close alignment with each other. As a neurophysiological correlate of the adjustment of motor responses, neural maps in the tectum tend to realign if the sensory inputs are manipulated. We have recently proposed that the development and maintenance of such map alignment can be explained through a process of learning, in which plasticity is mediated by the activation of diffuse-projecting neuromodulatory systems which respond to innate or acquired salient cues. This proposal was tested using a detailed computer model of the principal neural structures involved in the process of spatial localisation in the barn owl. Here we consider the application of this model to the control of the orienting behavior of a robotic system in the presence of auditory and visual stimulation. The system we consider is composed of a robotic head equipped with two lateral microphones and a camera. We show that the model produces accurate orienting behavior toward both auditory and visual stimuli during normal visual experience, after alteration of the visual inputs, and after the reestablishment of normal visual conditions. The results illustrate that an architecture specifically designed to account for biological phenomena can produce flexible and robust control of a robotic system.

The authors describe how the theory of neuronal group selection (TNGS) can form the basis for an approach to computer modeling of the nervous system. Three examples of synthetic neural modeling are discussed. Darwin I was designed to examine the process of pattern recognition and some general factors relating to degeneracy and amplification in selective systems. Darwin II introduced recognition units with some of the properties of neuronal groups, connected in reentrant networks to permit exploration of aspects of the recognition process leading to categorization, generalization, and associative memory. Darwin III is a behaving automation whose behavior is not programmed but results from its encounter with events in its world under constraints of neuronal and synaptic selection. The oculomotor, reaching, touch, and categorization subsystems of the Darwin-III system are discussed. Darwin III is compared to models based on neurobiology, models based on artificial intelligence, and connectionist models.>