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Author(s): Carpenter, G.A. | Grossberg, S. |
Year: 1990
Citation: Neural Networks, 3(2), 129-152
Abstract: A model to implement parallel search of compressed or distributed pattern recognition codes in a neural network hierarchy is introduced. The search process functions well with either fast learning or slow learning, and can robustly cope with sequences of asynchronous input patterns in real-time. The search process emerges when computational properties of the chemical synapse, such as transmitter accumulation, release, inactivation, and modulation, are embedded within an Adaptive Resonance Theory architecture called ART 3. Formal analogs of ions such as Na− and Ca2− control nonlinear feedback interactions that enable presynaptic transmitter dynamics to model the postsynaptic short-term memory representation of a pattern recognition code. Reinforcement feedback can modulate the search process by altering the ART 3 vigilance parameter or directly engaging the search mechanism. The search process is a form of hypothesis testing capable of discovering appropriate representations of a nonstationary input environment.
Topics:
Machine Learning,
Models:
ART 3,
