Figure 1From: Efficient supervised learning in networks with binary synapsesLearning capacity and learning time. (left) achieved capacity vs. the number of synapses N, with different numbers of hidden states, in the sparse coding case: the algorithm can achieve up to 70% of the maximal theoretical capacity at N ~10000 with 10 hidden states; (right) average learning time (number of presentations per pattern) versus number of patterns to be learned, for N = 64000: less than 100 presentations are required up to the critical point where learning fails.Back to article page