Computation in spiking neural networks
The brain has always been known to be a powerful computational tool as it possesses problem solving and exceptional calculating abilities. As such, neuroscientists and researchers are always fascinated by how the brain works as this knowledge will help in treating brain disorder and create better ma...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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2012
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| Online Access: | http://hdl.handle.net/10356/49436 |
| _version_ | 1826125742289715200 |
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| author | Koh, Lynn. |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Koh, Lynn. |
| author_sort | Koh, Lynn. |
| collection | NTU |
| description | The brain has always been known to be a powerful computational tool as it possesses problem solving and exceptional calculating abilities. As such, neuroscientists and researchers are always fascinated by how the brain works as this knowledge will help in treating brain disorder and create better machines that use brain-like computing principles.
With the recent research development in spiking neural network models, we have come to know that unlike the classic neural network models, these models communicate though the precise timing of neuron spikes, hence making them more biologically realistic. To support the field of artificial intelligence which implements spiking neural network models, this paper presents different phase locking behaviour of two popular formal spiking neural network models - Leaky Integrate and Fire and Resonate and Fire neurons to various periodic input stimulus. Differences in computation are also observed when both neuron models are coupled to form a Winner Take All circuit. All interpretations of neural responses presented in this paper are based on results obtained from numerical simulations performed using MATLAB. |
| first_indexed | 2024-10-01T06:41:39Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/49436 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:41:39Z |
| publishDate | 2012 |
| record_format | dspace |
| spelling | ntu-10356/494362023-07-07T17:43:40Z Computation in spiking neural networks Koh, Lynn. School of Electrical and Electronic Engineering Arindam Basu DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering The brain has always been known to be a powerful computational tool as it possesses problem solving and exceptional calculating abilities. As such, neuroscientists and researchers are always fascinated by how the brain works as this knowledge will help in treating brain disorder and create better machines that use brain-like computing principles. With the recent research development in spiking neural network models, we have come to know that unlike the classic neural network models, these models communicate though the precise timing of neuron spikes, hence making them more biologically realistic. To support the field of artificial intelligence which implements spiking neural network models, this paper presents different phase locking behaviour of two popular formal spiking neural network models - Leaky Integrate and Fire and Resonate and Fire neurons to various periodic input stimulus. Differences in computation are also observed when both neuron models are coupled to form a Winner Take All circuit. All interpretations of neural responses presented in this paper are based on results obtained from numerical simulations performed using MATLAB. Bachelor of Engineering 2012-05-18T07:19:24Z 2012-05-18T07:19:24Z 2012 2012 Final Year Project (FYP) http://hdl.handle.net/10356/49436 en Nanyang Technological University 80 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering Koh, Lynn. Computation in spiking neural networks |
| title | Computation in spiking neural networks |
| title_full | Computation in spiking neural networks |
| title_fullStr | Computation in spiking neural networks |
| title_full_unstemmed | Computation in spiking neural networks |
| title_short | Computation in spiking neural networks |
| title_sort | computation in spiking neural networks |
| topic | DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering |
| url | http://hdl.handle.net/10356/49436 |
| work_keys_str_mv | AT kohlynn computationinspikingneuralnetworks |