1993 Fiscal Year Final Research Report Summary
Computational and Experimental Research on Higher Order Functions of the Brain
| Project/Area Number |
04246103
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| Research Category |
Grant-in-Aid for Scientific Research on Priority Areas
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| Allocation Type | Single-year Grants |
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| Research Institution | The University of Tokyo |
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Principal Investigator |
AMARI Shun-ichi University of Tokyo, Faculty of Engineering, Professor, 工学部, 教授 (80010726)
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| Co-Investigator(Kenkyū-buntansha) |
TOYAMA Keisuke Kyoto Prefecture Medical University, Professor, 医学部, 教授 (10073074)
TSUKADA Minoru Tamagawa University, Faculty of Engineering, Professor, 工学部, 教授 (80074392)
SUZUKI Ryouji University of Tokyo, Faculty of Engineering, Professor, 工学部, 教授 (80013811)
SUGIE Noboru Nagoya University, Faculty of Engineering, Professor, 工学部, 教授 (30126867)
SAKATA Hideo Nihon University, The Medical Depertment, Professor, 医学部, 教授 (10073066)
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| Project Period (FY) |
1990 – 1993
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| Keywords | brain / higher function / neural net model |
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| Research Abstract |
We have summarized three-years research on higher order functions of the brain for further developments. The following chapters explain the present state-of-the-art, our original results and future directions of research.
1.Introduction
2.Computational neuroscience and neurocomputing
3.Role of higher visual cortices on visual cognition
4.Computational theory of vision and psychophysics
5.Brain mechanism of object recognition
6.Neural network model of visual pattern recognition
7.Spatial cognition and action-oriented perception
8.Computational theory of spatial cognition by vision and audition
9.Neural network model of associative memory
10.Role of hippocumpus on memory
11.Partition of roles on memory into hippocumpus of neocortex
12.Programming of action in frontal cortex
13.Neural network which learns sequential information
14.Motion programming in motion-related cortices
15.Role of basal ganglion on voluntary motion
16.Computational theory of voluntary motion control
17.Synapses for learning and memory
18.Plastiaty of neural network and its model
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