Abstract
A number of artificial neural models have been presented in the literature in an effort to suggest a more accurate representation of a single biological neuron. There are numerous publications on synthetic neurons that attemptedtoreplicateasinglebiologicalneuron,however,suchmodelswereunabletogeneratethespikingpatterns of a real biological neuron. Therefore, there is still scope to design and research improved spiking neural models that more accurately reflect the functions of a biological neuron. This motivation drives extensive modification of an artificial neuron model to produce the spike patterns of a real biological neuron. The modified single artificial neuronmodel thathasbeen proposedexhibitsthefunctions ofabiological neuron.It’sstill crucialtomodel spiking bio-neuronbehavior.Modelingaspikingbio-neuronisstillanimportantexerciseinviewofpossibleapplicationsof the underlying features in the areas of neuromorphic engineering, cognitive radio, and spiking neural networks.
Highlights
In 2017 a very powerful AI tool has been established for predicting lysine phosphoglycerylation sites in proteins, one of the most important post modifications in proteins [1]
Either type or copy/paste your query protein sequences into the input box at the center of Figure 1
The input sequences should be in the FASTA format
Summary
In 2017 a very powerful AI (artificial intelligence) tool has been established for predicting lysine phosphoglycerylation sites in proteins, one of the most important post modifications in proteins [1]. To see how the web-server is working, please do the following. Click on the Read Me button to see a brief introduction about this predictor.
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