Peng, Y and Munro, PW
(2005)
Learning arbitrary functions with spike-timing dependent plasticity learning rule.
In: UNSPECIFIED.
Abstract
A neural network model based on spike-timing-dependent plasticity (STOP) learning rule, where afferent neurons will excite both the target neuron and interneurons that in turn project to the target neuron, is applied to the tasks of learning AND and XOR functions. Without inhibitory plasticity, the network can learn both AND and XOR functions. Introducing inhibitory plasticity can improve the performance of learning XOR function. Maintaining a training pattern set is a method to get feedback of network performance, and will always improve network performance. © 2005 IEEE.
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Details
| Item Type: |
Conference or Workshop Item
(UNSPECIFIED)
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| Status: |
Published |
| Creators/Authors: |
| Creators | Email | Pitt Username | ORCID  |
|---|
| Peng, Y | | | | | Munro, PW | pwm@pitt.edu | PWM | |
|
| Date: |
1 December 2005 |
| Date Type: |
Publication |
| Access Restriction: |
No restriction; Release the ETD for access worldwide immediately. |
| Journal or Publication Title: |
Proceedings of 2005 International Conference on Neural Networks and Brain Proceedings, ICNNB'05 |
| Volume: |
3 |
| Page Range: |
1344 - 1349 |
| Event Type: |
Conference |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
School of Information Sciences > Information Science |
| Refereed: |
Yes |
| ISBN: |
0780394224, 9780780394223 |
| Date Deposited: |
29 Jun 2012 20:20 |
| Last Modified: |
02 Feb 2019 16:55 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/12632 |
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