Abstract
It is generally thought that skilled behavior in human beings results from a functional hierarchy of the motor control system, within which reusable motor primitives are flexibly integrated into various sensori-motor sequence patterns. The underlying neural mechanisms governing the way in which continuous sensori-motor flows are segmented into primitives and the way in which series of primitives are integrated into various behavior sequences have, however, not yet been clarified. In earlier studies, this functional hierarchy has been realized through the use of explicit hierarchical structure, with local modules representing motor primitives in the lower level and a higher module representing sequences of primitives switched via additional mechanisms such as gate-selecting. When sequences contain similarities and overlap, however, a conflict arises in such earlier models between generalization and segmentation, induced by this separated modular structure. To address this issue, we propose a different type of neural network model. The current model neither makes use of separate local modules to represent primitives nor introduces explicit hierarchical structure. Rather than forcing architectural hierarchy onto the system, functional hierarchy emerges through a form of self-organization that is based on two distinct types of neurons, each with different time properties (“multiple timescales”). Through the introduction of multiple timescales, continuous sequences of behavior are segmented into reusable primitives, and the primitives, in turn, are flexibly integrated into novel sequences. In experiments, the proposed network model, coordinating the physical body of a humanoid robot through high-dimensional sensori-motor control, also successfully situated itself within a physical environment. Our results suggest that it is not only the spatial connections between neurons but also the timescales of neural activity that act as important mechanisms leading to functional hierarchy in neural systems.
Highlights
Functional hierarchy, defined broadly as the principle that complex entities may be segmented into simpler elements and that simple elements may be integrated into a complex entity, is a ubiquitous feature of information processing in biological neural systems [1,2,3,4]
Functional hierarchy in neural systems, defined as the principle that complex entities may be segmented into simpler elements and that simple elements may be integrated into a complex entity, is a challenging area of study in neuroscience
Hierarchy in time is exemplified by auditory information processing, where syllable-level information within a short time window is integrated into word-level information over a longer time window
Summary
Functional hierarchy, defined broadly as the principle that complex entities may be segmented into simpler elements and that simple elements may be integrated into a complex entity, is a ubiquitous feature of information processing in biological neural systems [1,2,3,4]. In primary sensory areas such as VI and SI, the receptive field of neurons is relatively small, and these neurons respond to features of the stimulus that are simpler than those responded to by higher associative areas. Determining how these functional hierarchies are implemented in neural systems is a fundamental challenge in neuroscience. In adapting to various situations, series of motor primitives are in turn integrated into diverse sequential behavior The idea underlying this basic process was proposed by Arbib in terms of ‘‘schema theory’’ [5], and has since been used as the basis for many studies The idea underlying this basic process was proposed by Arbib in terms of ‘‘schema theory’’ [5], and has since been used as the basis for many studies (e.g. [6,7])
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