Brain Activity Is Sequenced In Time As Well As Space
Humanoid robots have been used to show that that functional hierarchy in the brain is linked to time as well as space.
Researchers from RIKEN Brain Science Institute, Japan, have created a new type of neural network model which adds to the previous literature that suggests neural activity is linked solely to spatial hierarchy within the animal brain.
Details are published November 7 in the open-access journal PLoS Computational Biology.
An animal’s motor control system contains a functional hierarchy, whereby small, reusable parts of movements are flexibly integrated to create various action sequences. For example, the action of drinking a cup of coffee can be broken down into a combination of small movements including the motions of reaching for a cup, grasping the cup, and bringing it to one’s mouth.
Earlier studies suggested that this functional hierarchy results from an explicit spatial hierarchical structure, but this has not been seen in anatomical studies of the brain. The underlying neural mechanisms for functional hierarchy, thus, had not yet been definitively determined.
In this study, Yuichi Yamashita and Jun Tani demonstrate that even without explicit spatial hierarchical structure a, functional hierarchy can self-organize through multiple timescales in neural activity. Their model was proven viable when tested with the physical body of a humanoid robot.
Results suggest that it is not only the spatial connections between neurons, but also the timescales of neural activity, that act as important mechanisms in neural system
PLoS Comput Biol, 2008; 4(11): e1000220 DOI: 10.1371/journal.pcbi.1000220
Emergence of Functional Hierarchy in a Multiple Timescale Neural Network Model: A Humanoid Robot Experiment.
Yamashita Y, Tani J.
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.