| dc.contributor |
H. Sebastian Seung. |
|
| dc.contributor |
Massachusetts Institute of Technology. Dept. of Brain and Cognitive Sciences. |
|
| dc.contributor |
Massachusetts Institute of Technology. Dept. of Brain and Cognitive Sciences. |
|
| dc.creator |
Xie, Xiaohui, 1972- |
|
| dc.date |
2005-08-23T19:49:39Z |
|
| dc.date |
2005-08-23T19:49:39Z |
|
| dc.date |
2002 |
|
| dc.date |
2002 |
|
| dc.identifier |
http://hdl.handle.net/1721.1/8393 |
|
| dc.identifier |
50593549 |
|
| dc.description |
Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2002. |
|
| dc.description |
Includes bibliographical references (p. 141-151). |
|
| dc.description |
This thesis is a study of dynamics and learning in recurrent neural networks. Many computations of neural systems are carried out through a network of a large number of neurons. With massive feedback connections among these neurons, a study of its dynamics is necessary in order to understand the network's function. In this thesis, I aim at studying several recurrent network models and relating the dynamics with the networks' computation. For this purpose, three systems are studied and analyzed in detail: The first one is a network model for direction selectivity; the second one is a generalized network of Winner-Take-All; the third one is a model for integration in head-direction systems. One distinctive feature of neural systems is the ability of learning. The other part of my thesis is on learning in biologically motivated neural networks. Specifically, I study how the spike-time-dependent synaptic plasticity helps to stabilize persistent neural activities in the ocular motor integrator. I study the connections between back-propagation and contrastive-Hebbian learning, and show how backpropagation could be equivalently implemented by contrastive-Hebbian learning in a layered network. I also propose a learning rule governing synaptic plasticity in a network of spiking neurons and compare it with recent experimental results on spike-time-dependent plasticity. |
|
| dc.description |
by Xiaohui Xie. |
|
| dc.description |
Ph.D. |
|
| dc.format |
151 p. |
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| dc.format |
10109796 bytes |
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| dc.format |
10109554 bytes |
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| dc.format |
application/pdf |
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| dc.format |
application/pdf |
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| dc.format |
application/pdf |
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| dc.language |
eng |
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| dc.publisher |
Massachusetts Institute of Technology |
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| dc.rights |
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. |
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| dc.rights |
http://dspace.mit.edu/handle/1721.1/7582 |
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| dc.subject |
Brain and Cognitive Sciences. |
|
| dc.title |
Dynamics and learning in recurrent neural networks |
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| dc.type |
Thesis |
|