Title :
A theory of architecture for spatial abstraction
Author_Institution :
Michigan State Univ., East Lansing, MI, USA
Abstract :
A great mystery is how the brain abstracts during the process of development. It is also unclear how motor actions alter cortical representation. The architecture theory introduced here indicates that for each cortical area, the bottom-up space and top-down space are two sources of its representation-bridge representation which embeds manifolds of both spaces into a single space. A bridge representation has the following properties (a) responses from developed neurons are relatively less sensitive to irrelevant sensory information (i.e., invariants) but are relatively more sensitive to relevant sensory information for classification (i.e., discriminants), (b) neurons form topographic cortical areas according to abstract classes. Both properties transform meaningless (iconic, pixel like) raw sensory inputs into an internal representation with abstract meanings. The most abstract area can be considered as frontal cortex (or motor area if each firing pattern of the motor represents a unique abstract class). Such a cortical representation system is neither a purely symbolic system nor a monolithic meaning system, but is iconic-abstract two-way: bottom-up attention, top-down attention and recognition are all tightly integrated and highly distributed throughout the developmental network.
Keywords :
brain; network theory (graphs); neural nets; neurophysiology; architecture theory; bottom-up attention; bottom-up space; brain; bridge representation; cortical representation; developed neuron responses; developmental neuron network; discriminants; frontal cortex; internal representation; invariants; motor actions; raw sensory input; sensory information; spatial abstraction; top-down attention; top-down space; topographic cortical areas; Biological system modeling; Buildings; Cognitive robotics; Computational and artificial intelligence; Computational intelligence; Computational modeling; Humans; Information science; Intelligent robots; Intelligent systems;
Conference_Titel :
Development and Learning, 2009. ICDL 2009. IEEE 8th International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-4117-4
Electronic_ISBN :
978-1-4244-4118-1
DOI :
10.1109/DEVLRN.2009.5175545