I am reading the literature on sequential learning and it is often mention that in order to handle sequential/temporal data, there two categories of work in sequential learning

  1. External Representation of Time
  2. Internal Representation of Time

I am not able to understand the difference between two, for example in the paragraphs below, it is hinted that external representation can be inserted directly by preprocessing time through a time to space transformation. Following example is given to elaborate this.

For instance, one effective way of constructing a spatial representation of temporally-
occurring information (which is not only limited to neural computing) is to create
the power spectrum of the incoming information and use it as a static input image.

Is there any simple and intuitive example to understand this? I cant get time to space transformation or power spectrum thing.



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