T.W. Scutt

ARBIB: an Autonomous Robot Based on Inspirations from Biology

Abstract Simple artificial creatures (‘animats’), which operate as autonomous, adaptive robots in the real world, can serve both as models of biology and as a radical alternative to conventional methods of designing intelligent systems. We describe the evolution and implementation of the autonomous robot ARBIB, which learns from and adapts to its environment. A primary goal was to test the notion that effective robot learning can be based on neural habituation and sensitization, so validating the suggestion of Hawkins and Kandel that (associative) classical and ‘higher-order’ conditioning might be based on an elaboration of these (non-associative) forms of learning. Accordingly, ARBIB’s ‘nervous system’ has a non-homogeneous population of spiking neurons, and learning is by modification of basic, pre-existing (‘hard-wired’) reflexes. By monitoring firing rates of specific neurons and synaptic weights between neural connections as ARBIB learns from its environment, we confirm that both classical and higher-order conditioning occur, leading to the emergence of interesting and ecologically valid behaviors.

The Hi-NOON neural simulator and its applications

Abstract This paper describes the Hi- noon (hierarchical network of object-oriented neurons) neural simulator, originally conceived as a general-purpose, computationally efficient, object-oriented software system for the simulation of small systems of biological neurons, as an aid to the study of links between neurophysiology and behaviour in lower animals. As such, the artificial neurons employed were spiking in nature; to effect an appropriate compromise between computational complexity and biological realism, modelling was at the transmembrane potential level of abstraction. Further, since real neural systems incorporate different types of neurons specialised to somewhat different functions, the software was written to accommodate a non-homogeneous population of neurons. The computational efficiency of Hi- noon makes it eminently suitable for situated system studies (biological robotics, animats) where real-time operation is a pre-requisite. The flexibility which was a central design goal of Hi- noon means that the system is also capable of modelling interconnections of non-spiking artificial neurons with continuous or piecewise linear activation functions. The efficacy of the simulator is illustrated with respect to some recent applications to situated systems studies. We also consider prospects for integrating Hi- noon with a conventional circuit simulator in the future.