1998 Special Issue Coordinate-free sensorimotor processing: computing with population codes Pietro G. Morasso a , *, Vittorio Sanguineti b , Francesco Frisone a , Luca Perico a a Department of Informatics, Systems and Telecommunications, University of Genova, Genova, Italy b Department of Physiology, Northwestern University Medical School, Chicago, USA Received 13 November 1997; revised 4 May 1998; accepted 4 May 1998 Abstract The purpose of the study is to outline a computational architecture for the intelligent processing of sensorimotor patterns. The focus is on the nature of the internal representations of the outside world which are necessary for planning and other goal-oriented functions. A model of cortical map dynamics and self-organization is proposed that integrates a number of concepts and methods partly explored in the field. The novelty and the biological plausibility is related to the global architecture which allows one to deal with sensorimotor patterns in a coordinate-free way, using population codes as distributed internal representations of external variables and the coupled dynamics of cortical maps as a general tool of trajectory formation. The basic computational features of the model are demonstrated in the case of articulatory speech synthesis and some of the metric properties are evaluated by means of simple simulation studies.  1998 Elsevier Science Ltd. All rights reserved. Keywords: Population code; Cortical map; Cortical dynamics; Field computing; Self-organization; Speech; Topology representing network; Hebbian learning 1. Introduction A fundamental feature of sensorimotor processing in bio- logical or robotic organisms is its ecological nature, i.e. the fact that the relevant dynamics applies to the whole ensem- ble ‘‘organism þ environment’’ and the latter is a full part- ner, not a mere passive ‘‘slave’’ of the former. There is no doubt that the implications of this concept of circularity have not been explored to their full extent, although the main idea has been around for some time (since the pioneer- ing work of J. Piaget and J.J. Gibson), shifting from one research field to another: cognitive psychology, cognitive neuroscience, robotics, neural networks, artificial life, etc. In AL, for example, the attention is focused on very simple organisms which are able to exhibit some form of intelligent behavior, without any explicit form of internal intelligence, a situation that has been described also as pre-rational intel- ligence (Cruse, 1996). Such simple organisms have ‘‘sim- ple’’ sensory and motor organs and the related sensorimotor processing can be reduced to a rather straightforward (although tunable) analog circuitry, which directly trans- forms sensory signals into motor commands. The richness of the observed behavior is mainly a consequence of the complexity and, in a sense, creativity of the non-linear, dis- sipative, non-equilibrium dynamics of the environment; thus, a small amount of adaptability is sufficient for the organism to tailor its behavior to the essential constraints. However, adaptability does not necessarily imply intelligence. With the exception of ‘‘hard-wired’’ tropistic organisms, most existing organisms must be adaptable (in order to survive) but not all of them can be considered ‘‘intelligent’’, whatever the specific definition we use for such an elusive concept (Fig. 1). For the scope of the paper, we limit ourselves to the domain of sensorimotor processing and we argue that in such context a thing called intelligence is a necessity for organisms which have the burden to manage the complex sensory and motor organs required for complex tasks, such as manipulation and phonation. ‘‘Complex organs’’ of this kind would be useless for an insect and, in general, for an organism that could only rely on reflexive processing mod- ules, although adaptable. What is needed, in general, is the ability to build internal representations of the external world, which allow the organism to anticipate, plan, and * Requests for reprints should be sent to Dr P.G. Morasso, University of Genova, DIST, via Opera Pia 13, I-16145 Genova, Italy. Tel.: +39-10- 3532749; fax: +39-10-3532154; e-mail: morasso@dist.unige.it 0893–6080/98/$19.00  1998 Elsevier Science Ltd. All rights reserved. PII: S0893-6080(98)00065-3 Neural Networks 11 (1998) 1417–1428 PERGAMON Neural Networks