Principles of Brain Functioning
نویسنده
چکیده
A continuous time, on-line training algorithm is presented, which learns to classify input patterns P in a Euclidean space R (or a Hilbert space) by attractors of a differential equation dx/dt = F(x) in R (or a Banach space). These nets include simple one-layer adeline-like nets as well as many recurrent nets. The attractors can be quite arbitrary—fixed points, limit cycles, fractals, etc.— but the training algorithm requires that we know approximately where they are. A time-varying d x m memory matrix M feeds an input pattern P of norm 1 into the activation level x = MP € R . We assume both x and M change simultaneously, according to differential equations of the following form:
منابع مشابه
Editorial : Sustainable Development of Cognitive Science and Technology Ecosystem an Overview to the “Human Brain Project” as a Functioning Sample
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