28-03-2019 дата публикации
Номер: US20190095211A1
Принадлежит:
A computational method is disclosed for the simulation of a hierarchical artificial neural network (ANN), wherein a single correlator pools, during a single time-step, two or more consecutive feed-forward inputs from previously predicted and now active neurons of one or more lower levels. 1. A computational method for the simulation of a hierarchical artificial neural network (ANN) , wherein a single correlator pools , during a single time-step , two or more consecutive feed-forward inputs from previously predicted and now active neurons of one or more lower levels.2. The method of claim 1 , wherein the single correlation is a static correlator.3. The method of claim 1 , wherein pooling of feed-forward inputs is done by logical OR of consecutive feed-forward inputs.4. The method of claim 1 , wherein pooling of feed-forward inputs is done by concatenating consecutive inputs.5. The method of claim 1 , wherein a transformation operation is applied to each feed-forward input prior to pooling.6. The method of claim 5 , wherein the transformation operation is any of the following: a permutation operation claim 5 , a logical XOR operation claim 5 , or a logical AND operation.7. A computational method for the simulation of a hierarchical artificial neural network (ANN) claim 5 , the method comprising:(a) correlating two pooled feed-forward inputs, S(t), from time step, t, and S(t−1), from time-step, t−1 for all times t;(b) indirectly learning correlation between input S(t) and S(t−t′), where t′ is a positive integer that is ≥2; and(c) outputting correlations learned in (a) and (b).8. The method of claim 7 , wherein the output is a sparse distributed representation (SDR) matrix.9. The method of claim 7 , wherein the correlating step in (a) is done by a static correlator.10. The method of claim 7 , wherein pooling of feed-forward inputs is done by logical OR of consecutive feed-forward inputs.11. The method of claim 7 , wherein pooling of feed-forward inputs is done by ...
Подробнее