Name
Abstract | ||
|---|---|---|
Changes in neural connectivity are thought to underlie the most permanent forms of memory in the brain. We consider two models,
derived from the clusteron (Mel, Adv Neural Inf Process Syst 4:35–42, 1992), to study this method of learning. The models show a direct relationship between the speed of memory acquisition and the
probability of forming appropriate synaptic connections. Moreover, the strength of learned associations grows with the number
of fibers that have taken part in the learning process. We provide simple and intuitive explanations of these two results
by analyzing the distribution of synaptic activations. The obtained insights are then used to extend the model to perform
novel tasks: feature detection, and learning spatio-temporal patterns. We also provide an analytically tractable approximation
to the model to put these observations on a firm basis. The behavior of both the numerical and analytical models correlate
well with experimental results of learning tasks which are thought to require a reorganization of neuronal networks. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/s10827-008-0078-6 | Journal of Computational Neuroscience |
Keywords | Field | DocType |
Structural plasticity,Synaptic plasticity,LTP,LTD,Dendritic integration,Spatial summation | Memory acquisition,Feature detection,Computer science,Nerve net,Synaptic plasticity,Artificial intelligence,Neuroplasticity,Machine learning,Anti-Hebbian learning | Journal |
Volume | Issue | ISSN |
25 | 2 | 1573-6873 |
Citations | PageRank | References |
0 | 0.34 | 1 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Keith J. Kelleher | 1 | 0 | 0.34 |
| V. Hajdik | 2 | 0 | 0.34 |
| C M Colbert | 3 | 36 | 3.04 |
| Kresimir Josic | 4 | 123 | 16.63 |