Temporal Patterns

I have finished implementing a neural network framework for detecting temporal patterns. I made a small demo of it using a single bit input. In theory the framework should scale well to much larger problems with many inputs. It is capable of detecting both spatial and temporal patterns and making predictions of what the next input will be. The framework can be used for a huge variety of applications and problems. I tried testing it for part-of-speech tagging but the results were disappointing. Due to the huge number of neurons/inputs I had to use, the network was taking several seconds to process each input. Since the network needs to be trained on huge corpora consisting of thousands of words, it would take a really long time before it can make accurate predictions. A good property of neural networks is that they are highly parrellelizable so that all of the neurons can be processing information at the same time. On a single CPU my computer processes information for one neuron at a time. Given the right hardware, neural networks can have a constant upper bound on computational time. This means that an arbitrary number of neurons/inputs could be added to the network without affecting the processing time.