Principles of Neural Design by Peter Sterling and Simon Laughlin describes a usage of information theory in calculating the rate of information transfer in the brain.
...when successive signal states are uncorrelated... the information rate is I = R \log2 (1 + S/N) bits per second. This equation assumes that redundancy is zero, that is, there is no correlation between signal states. To achieve this , signal states must change randomly. To be truly random, the signal must be able to jump from any state to any other. but this ability is constrained by the time needed to make the jump... Shannon solved this problem by using the Fourier transform to convert the continuous analogue signal and noise into their frequency components... It follow sthat the total information carried by the signal is the sum of the information carried by each of its component frequencies. I = int(0-co)log2[1 + S(f) / N(f)] * df
All states can be reached in a certain amount of time. Why applying Fourier Transform solves this issue?
