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However, despite the accumulating evidence for widespread sparse coding and theoretical arguments for its importance, a demonstration that sparse coding improves the stimulus-specificity of associative memory has been difficult to obtain.
=== '''Implications''' ===
What are the tasks of neurons when processing signals from the external and internal environment? First, neurons must create information-efficient code. Second, neurons must create energy-efficient code. These requirements lead to code sparseness in the sense of a small number of elements in a fast time window and a small set of basic code units that can encode complex information in their combinations. It follows that each component should be information-rich. In other words, the neural code must combine sparseness and richness. These are not mutually exclusive but complementary requirements.
The question arises: can a neuron spike be information-rich if it is a discrete event that does not have internal characteristics? In this formulation, the question becomes rhetorical, and the answer is negative. Unfortunately, all of the above models are based on the assumption that the action potentials of the neurons are the same. But is it really so? Moreover, the question arises: are the action potentials spikes? It may sound strange as most studies use the words as synonyms. The reason is that there is an old tradition to portrait actions potentials as identical sharp points distributed along the time axis with varying density.
Maybe the action potentials are actually sharp points? Not really. They are simplified this way to make them convenient for the model. "The spike is added manually for aesthetic purposes and to fool the reader into believing that this is a spiking neuron …All spikes are implicitly assumed to be identical in size and duration … Despite all these drawbacks, the integrate-and-fire model is an acceptable sacrifice for a mathematician who wants to prove theorems and derive analytical expressions. However, using the model might be a waste of time."<ref>{{Cite book|last=Izhikevich|first=Eugene M.|url=https://www.worldcat.org/oclc/457159828|title=Dynamical systems in neuroscience : the geometry of excitability and bursting|date=2010|publisher=MIT Press|isbn=978-0-262-51420-0|edition=|___location=Cambridge, Mass.|oclc=457159828}}</ref>
To understand whether any spiking neuron model reflects reality or not, we must turn to the temporal level of the neuron itself. If we increase the resolution along the time axis, the picture changes dramatically and shows that neurons do not fire with sharp spikes but vibrate with soft waves. This fact is known to everyone involved in brain research. However, the paradigm, which has existed for almost a hundred years, still prevails despite all the internal contradictions and inconsistencies with reality.
After decades of looking at discrete units where there are actually are waves, we come back to the question: "What is the structure of a neural code that allows such high rates of information transmission? ... Nature has built computing machinery of surprising precision and adaptability ... Our story began, more or less, with Adrian’s discovery that spikes are the units out of which our perceptions must be built. We end with the idea that each of these units makes a definite and measurable contribution to those perceptions. The individual spike, so often averaged in with its neighbors, deserves more respect."<ref>{{Cite book|url=https://www.worldcat.org/oclc/42274482|title=Spikes : exploring the neural code|date=1999|publisher=MIT|others=Fred Rieke|isbn=0-262-68108-0|___location=Cambridge, Mass.|oclc=42274482}}</ref>
== See also ==
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