Abstract
Support vector machines are state-of-the-art pattern recognition algorithms that are well founded in optimization and generalization theory but not obviously applicable to the brain. This paper presents Bio-SVM, a biologically feasible support vector machine. An unstable associative memory oscillates between support vectors and interacts with a feed-forward classification pathway. Kernel neurons blend support vectors and sensory input. Downstream temporal integration generates the classification. Instant learning of surprising events and off-line tuning of support vector weights trains the system. Emotion-based learning, forgetting trivia, sleep and brain oscillations are phenomena that agree with the Bio-SVM model. A mapping to the olfactory system is suggested.
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