آزمایش های اولیه تکامل شبکه های عصبی اسپایک با قابلیت یادگیری تحت نظارت

We report on initial experiments on a relatively simple test problem, the tonic burster, using several types of learning including supervised. We see that EC can locate seemingly good solutions that actually do not solve the desired task; they “cheat” by simply exploiting the supervisory signals. A simple modification of the train-test protocol can solve this. We introduce an approach we call “artificial neurology” for systematically examining the behaviour of a SNN in order to understand how it achieves its performance. Experiments indicate that a combination of Hebbian and supervised learning works best for this task.