Artificial neural nets and biological neural nets share many common characteristics, but one big difference is that artificial neurons typically operate in a static framework by outputting a single scalar value in response to their inputs, while biological neurons have a rich life in the time dimension and output sequences of pulses. Nobody is exactly sure what that means yet, but it’s pretty clear that our artificial neural nets do not yet model the time dimension of biological nets very well.
Here’s an article that explains how that the thousands of synaptic inputs to a neuron help it recognize sequences of patterns, not just static patterns. The authors say that they have discovered that the physical arrangement of input synapses can cause the “emergence of a computationally sophisticated sequence memory.” Also see this commentary about the article.
I’m very interested in hearing about your experiments with neural nets recognizing time-dependent sequences of patterns.