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| Predicate | Object |
|---|---|
| rdf:type | |
| lifeskim:mentions | |
| pubmed:issue |
6
|
| pubmed:dateCreated |
1989-2-13
|
| pubmed:abstractText |
The diffusion models of neuronal activity are general yet conceptually simple and flexible enough to be useful in a variety of modeling problems. Unfortunately, even simple diffusion models lead to tedious numerical calculations. Consequently, the existing neural net models use characteristics of a single neuron taken from the "pre-diffusion" era of neural modeling. Simplistic elements of neural nets forbid to incorporate a single learning neuron structure into the net model. The above drawback cannot be overcome without the use of the adequate structure of the single neuron as an element of a net. A linear (not necessarily homogeneous) diffusion model of a single neuron is a good candidate for such a structure, it must, however, be simplified. In the paper the structure of the diffusion model of neuron is discussed and a linear homogeneous model with reflection is analyzed. For this model an approximation is presented, which is based on the approximation of the first passage time distribution of the Ornstein-Uhlenbeck process by the delayed (shifted) exponential distribution. The resulting model has a simple structure and has a prospective application in neural modeling and in analysis of neural nets.
|
| pubmed:language |
eng
|
| pubmed:journal | |
| pubmed:citationSubset |
IM
|
| pubmed:status |
MEDLINE
|
| pubmed:issn |
0340-1200
|
| pubmed:author | |
| pubmed:issnType |
Print
|
| pubmed:volume |
59
|
| pubmed:owner |
NLM
|
| pubmed:authorsComplete |
Y
|
| pubmed:pagination |
395-404
|
| pubmed:dateRevised |
2006-11-15
|
| pubmed:meshHeading | |
| pubmed:year |
1988
|
| pubmed:articleTitle |
Delayed-exponential approximation of a linear homogeneous diffusion model of neuron.
|
| pubmed:affiliation |
Department of Electrical and Computer Engineering, Oregon State University, Corvallis 97331.
|
| pubmed:publicationType |
Journal Article,
Research Support, Non-U.S. Gov't
|