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Author(s): Hu, D.W. | Jia. P, | Yin, J.S. | Zhou, Z.T. |
Year: 2007
Citation: JOURNAL OF COMPUTATIONAL NEUROSCIENCE Volume: 23 Issue: 1 Pages: 129-141
Abstract: Adaptive resonance theory (ART) demonstrates how the brain learns to recognize and categorize vast amounts of information by using top-down expectations and attentional focusing. ART 3, one member of the ART family, embeds the computational properties of the chemical synapse in its search process, but it converges slowly and is lack of stability when being applied in pattern recognition and analysis. To overcome these problems, Nitric Oxide (NO), which serves as a newly discovered retrograde messenger in Long-Term Potentiation (LTP), is introduced in retrograde adaptive resonance theory (ReART) model presented in this paper. In the presented model a novel search hypothesis is proposed to incorporate angle and amplitude information of an external input vector to decide whether the input matches the long-term memory (LTM) weights of an active node or not, and the embedded NO retrograde mechanism makes the search procedure a closed loop, which improves the stability and convergence speed of the transmitter releasing mechanism in a synapse. To make the model more adaptive and practical, a forgetting mechanism is built to improve the weights updating process. Experimental results indicate that the proposed ReART model achieves low error rate, fast convergence and self-organizing weights regulation.
Topics:
Machine Learning,
Models:
ART 3,
