Theoretical modes for learning and memory in the microbrain system
| Project/Area Number |
11168224
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research on Priority Areas (A)
|
| Allocation Type | Single-year Grants |
|---|
| Review Section |
Biological Sciences
|
|---|
| Research Institution | Kyushu Institute of Technology |
|---|
Principal Investigator |
MATSUOKA Kiyotoshi Graduate School of Life Science and Systems Engineering, Professor, 大学院・生命体工学研究科, 教授 (90110840)
|
|---|
| Co-Investigator(Kenkyū-buntansha) |
徳成 剛 九州工業大学, 工学部, 助手 (00237075)
黒木 秀一 九州工業大学, 工学部, 助教授 (40178124)
|
|---|
| Project Period (FY) |
1999 – 2001
|
| Project Status |
Completed (Fiscal Year 2002)
|
| Budget Amount *help |
¥4,200,000 (Direct Cost: ¥4,200,000)
Fiscal Year 2001: ¥1,500,000 (Direct Cost: ¥1,500,000)
Fiscal Year 2000: ¥1,500,000 (Direct Cost: ¥1,500,000)
Fiscal Year 1999: ¥1,200,000 (Direct Cost: ¥1,200,000)
|
|---|
| Keywords | learning / memory / Hebbian learning / anti-Hebbian learning / ニューラルネット / リズム / 相互抑制 |
|---|
| Research Abstract |
The purpose of this study was to investigate learning / memory mechanisms in lower animals by means of mathematical models. In particular we have focused on a Hebb-type learning.
A typical model for Hebbian / anti-Hebbian learning is as follows : when the pre- and post-synaptic activations occur at the same time, the relevant synaptic weight increases / decreases. According to recent neuro-physiological findings, however, the change in the synaptic weight depends on the timing between pre- and post-synaptic activities. Namely, if the pre-synaptic activation precedes the post-synaptic activation, then LTP is induced. On the other hand, if the order is reversed, then LTP appears. This kind of asymmetric feature in synaptic plasticity must play a very important role in the learning of temporal patterns in animals.
In this study we first built a basic mathematical model for temporally asymmetric Hebbian learning. Using the basic model, we devised more elaborated models that would explain some neuronal phenomena, for example, (1) oscillatory behavior of a neural circuit, (2) neural integrators, (3) neural memory for certain periodic stimuli as seen in the visual system of the crayfish. Through the modeling, we were able to clarify some functional mechanisms of the temporally Hebbian learning rule.
|
Report
(4 results)
Research Products
(9 results)
