Starting with Adrian's recordings the idea was that the neural code was embedded in temporal patterns. Therefore, in the last sixty years everything was felt to revolve around temporal patterns.
However, the main hypothesis of computational neuroscience ( digital-like uniformity of action potentials) is not validated by recent experimental data ,.
From a false hypothesis (stereotypical spike) one can reach false conclusions following a correct analysis of data. The unrealistic hypothesis impacted model validity, created a strong debate and generated unrealistic predictions in behavioral studies, visual object recognition (e.g. grandmother cell).
The power of counterexamples
We completely understand the frustration of neuroscientists regarding temporal coding. For more than 60 years they have tried to prove that temporal coding is the right model of information processing that reflects the subtle nature of neural code. The paradox is that the same experiment used to show the organization of temporal patterns proves the fallacy of temporal coding. The procedural T-maze learning task became the best counterexample for temporal coding theory http://neuroelectrodynamics.blogspot.com/p/spike-directivity.html. Currently, we can provide other examples, however there is no need to generate new evidence. That’s the beauty of counterexamples and experimental design. You only need one single counterexample to throw down a "solid"theoretical construct. Quoting Feynman "It doesn't matter how beautiful your theory is, it doesn't matter how smart you are or how many years you can keep a theory alive ! If it doesn't agree with experiment, it's wrong."
All issues in interpretation are generated by this FALSE hypothesis of a digital (stereotype) spike, see the analogy:
a. 3=-3 ------- (3)2=(-3)2 ----- 9=9
b. (FALSE hypothesis) ------ (operation/transformation) ---- (CORRECT/ FALSE ?)
A.Stereotype spike -------- adding spikes/firing rate -------CONTROVERSIES
B. (FALSE hypothesis) ------- (transformation, STATISTICS ) -- CORRECT /FALSE ?
2. Takuya Sasaki, Norio Matsuki, Yuji Ikegaya, 2011, Action-potential modulation during axonal conduction Science 331 (6017), pp. 599-601