Name
Abstract | ||
|---|---|---|
A number of neurons which respond selectively to specific stimuli are observed in many parts of the vertebrate nervous system. These neurons are developed by self-organization through sensory experiences. This is regarded as a mechanism for representing the external world structure. However, it is insufficient to think only of this learning mechanism being dependent on the input environment; other information representing mechanisms should exist in the brain. The present article proposes a neural learning model which reflects not only the input signal set but also the teacher signal. This model is constructed by adding a teacher signal layer to the conventional models for topographic organization. After learning, the network realizes the desired input-output relation. Moreover, a structure is organized in the network, not only so as to represent the input environment structure but also to fit well in the “system purpose.” The model behavior was investigated through computer simulations, and the formation of spatial maps in the brain is discussed from a viewpoint of purpose dependent representation. |
Year | DOI | Venue |
|---|---|---|
1990 | 10.1016/0893-6080(90)90024-F | Neural Networks |
Keywords | Field | DocType |
topographic organization,spatial map,inner representation,nerve field,columnar microstructure,motor map,sensory integration,teacher signal | Neural learning,Topographic map,Nervous system,Artificial intelligence,Stimulus (physiology),Sensory system,Mathematics | Journal |
Volume | Issue | ISSN |
3 | 4 | Neural Networks |
Citations | PageRank | References |
4 | 0.53 | 0 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yutaka Sakaguchi | 1 | 26 | 7.81 |