A neural support vector machine.
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.
Jändel
Magnus
M
Agora for Biosystems, Box 57 SE-193 22, Sigtuna, Sweden; Swedish Defence Research Agency, SE-164 90, Stockholm, Sweden. magnus@jaendel.se
D000465:Algorithms; D000818:Animals; D007858:Learning / Q000502:physiology; D008568:Memory / Q000502:physiology; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology; D017711:Nonlinear Dynamics; D009833:Olfactory Pathways / Q000502:physiology; D055696:Olfactory Perception / Q000502:physiology; D010363:Pattern Recognition, Automated; D010507:Periodicity; D012903:Smell / Q000502:physiology; D013997:Time Factors