Neural computation : theory and practice behind brain
Liquid State Machine is a relatively new system which is capable of recognising real-world temporal patterns on noisy continuous input streams. We will also investigate on its applicability for practical usage. By first looking at how the human brain model and biological neural network has brought a...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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2010
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| Online Access: | http://hdl.handle.net/10356/40145 |
| _version_ | 1811690861331742720 |
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| author | Wang, Justin Chang Li. |
| author2 | Quah Tong Seng |
| author_facet | Quah Tong Seng Wang, Justin Chang Li. |
| author_sort | Wang, Justin Chang Li. |
| collection | NTU |
| description | Liquid State Machine is a relatively new system which is capable of recognising real-world temporal patterns on noisy continuous input streams. We will also investigate on its applicability for practical usage. By first looking at how the human brain model and biological neural network has brought about the development of artificial neural networks. We will also get to see how the spiking neuron model would have an advantage over conventional artificial neural network in classifying the real-world temporal patterns. In this project, we selected a practical input type for implementation which would be visual input, inspired by how human see. The challenges of such an implementation would be in creating a good encoding scheme to change the 2-dimensional input into spike train(s). A possible encoding scheme has been created in this project which has been successful in classifying the input. The implementation was created in the Matlab environment. |
| first_indexed | 2024-10-01T06:10:43Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/40145 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:10:43Z |
| publishDate | 2010 |
| record_format | dspace |
| spelling | ntu-10356/401452023-07-07T15:49:27Z Neural computation : theory and practice behind brain Wang, Justin Chang Li. Quah Tong Seng School of Electrical and Electronic Engineering A*STAR Institute for Infocomm Research Tang Huajin DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems Liquid State Machine is a relatively new system which is capable of recognising real-world temporal patterns on noisy continuous input streams. We will also investigate on its applicability for practical usage. By first looking at how the human brain model and biological neural network has brought about the development of artificial neural networks. We will also get to see how the spiking neuron model would have an advantage over conventional artificial neural network in classifying the real-world temporal patterns. In this project, we selected a practical input type for implementation which would be visual input, inspired by how human see. The challenges of such an implementation would be in creating a good encoding scheme to change the 2-dimensional input into spike train(s). A possible encoding scheme has been created in this project which has been successful in classifying the input. The implementation was created in the Matlab environment. Bachelor of Engineering 2010-06-11T01:32:29Z 2010-06-11T01:32:29Z 2010 2010 Final Year Project (FYP) http://hdl.handle.net/10356/40145 en Nanyang Technological University 46 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems Wang, Justin Chang Li. Neural computation : theory and practice behind brain |
| title | Neural computation : theory and practice behind brain |
| title_full | Neural computation : theory and practice behind brain |
| title_fullStr | Neural computation : theory and practice behind brain |
| title_full_unstemmed | Neural computation : theory and practice behind brain |
| title_short | Neural computation : theory and practice behind brain |
| title_sort | neural computation theory and practice behind brain |
| topic | DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems |
| url | http://hdl.handle.net/10356/40145 |
| work_keys_str_mv | AT wangjustinchangli neuralcomputationtheoryandpracticebehindbrain |