2001 Fiscal Year Final Research Report Summary
The Supervised Learning Rules of the Pulsed Neuron Model
| Project/Area Number |
11650422
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Measurement engineering
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| Research Institution | Nagoya Institute of Technology |
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Principal Investigator |
IWATA Akira Nagoya Inst. Of Tech., Faculty of Eng., Professor, 工学部, 教授 (10093098)
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| Co-Investigator(Kenkyū-buntansha) |
KUROYANAGI Susumu Nagoya Inst. Of Tech., Faculty of Eng., Research Associate, 工学部, 助手 (10283475)
MATSUO Hiroshi Nagoya Inst. Of Tech., Faculty of Eng., Associate Professor, 工学部, 助教授 (00219396)
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| Project Period (FY) |
1999 – 2000
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| Keywords | Neural Network / Pulsed Neuron Model / Temporal Information Processing / Supervised Learning / Unsupervised Learning / Sound Localization / Sound Recognition |
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| Research Abstract |
We propose an Auditory Neural Network Model using the Pulsed Neuron Model (PNM) to deal with the information which is presented as pulse. In the previous progress of this study, the parameters used in PNM were not configured automatically. Therefore, it was difficult to implement the model into real applications. This limitation became the motivation to build a general-purpose algorithms dedicated for the Pulsed Neuron Model. In the first year of this study, we proposed 2 new algorithms which were suitable for PNM based implementation. The first one was supervised algorithm to learn the specific phase-difference between the pulses. The second one was an unsupervised algorithm for pattern classification. By implementing these algorithms, PNM had the capability of non-linear mapping to make it suitable for real-world domain application. In the second year, the performance of the supervised algorithm was evaluated on sound source localization problem and sound source recognition problem. The experiments were performed under the condition which was using real-life sound database. In these study, we also attempted to evaluate the system in an environment which comes with various sounds. To deal with this condition, a time-difference extraction module was added to the system as preprocessing part to classify the sound of the input signal. The experiment results showed that the system had the capability to recognize specific sound from specific direction. A part of this study was also dedicated on the cost evaluation for the hardware implementation of PNM. We proposed efficient method to implement PNM as logic circuit and the experiments showed promised result. The progress of this study was presented in IEICE Neurocomputing-Technical Group Workshop on December 1999, March 2001, International Conference On Neural Information Processing '99 (November 1999) and International Joint Conference on Neural Network 2000 (July 2000).
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