Robots show that brain activity is linked to time as well as space

November 7, 2008,

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 systems.

Citation: Yamashita Y, Tani J (2008) Emergence of Functional Hierarchy in a Multiple Timescale Neural Network Model: A Humanoid Robot Experiment. PLoS Comput Biol 4(11): e1000220. doi:10.1371/journal.pcbi.1000220 www.ploscompbiol.org/doi/pcbi.1000220

Source: Public Library of Science

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Quantum_Conundrum
1 / 5 (3) Nov 07, 2008
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.


Because it was designed to do so...
ofidiofile
5 / 5 (1) Nov 08, 2008
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.


Because it was designed to do so...


um, no. did you bother to RTF paper? it's free.

the researchers started with artificial neurons that operate on different timescales, slow and fast. otherwise, there was no physical/functional hierarchy imposed upon the system.

as the robot 'learned' to operate its nervous system in response to inputs from the environment, it developed its own particular patterns of activation along particular pathways of either slow or fast timescale neurons as these related to the particular timescales of environmental stimuli (e.g., spatial and balance-related orientation occured over longer timescales and so the system adapted the slow neurons to these purposes, while sensorimotor learning required faster timescales and so relied upon the fast neural connections).

note that outside of the construction of the basic system, there were no 'instructions' as to which neurons were to be used for what functions - there were no 'hints' left for the robot by the engineers. basically, it learned to use the connections more advantageous to the types of environmental cues received to create motor programs that would react appropriately. hence, "self-organized". (if it was otherwise, what would be the point of the paper? its conclusions would have obviously been moot, and it would likely not even have been published. sheesh.)

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