Supervisors info:
Φώτιος Διάκονος, Αναπληρωτής Καθηγητής, Τμήμα φυσικής, ΕΚΠΑ
Ιωάννης Λελίδης, Επίκουρος Καθηγητής, Τμήμα φυσικής, ΕΚΠΑ
Ευστράτιος Κοσμίδης, Επίκουρος Καθηγητής, Τμήμα Ιατρικής, ΑΠΘ
Summary:
In our time, development of research in the field of computational neuroscience, as well as its coupling with technological applications in various fields, from medicine to artificial intelligence, has become important. In this paper we present the Hodgkin and Huxley model, which incorporates information about the ion channels of the membrane and offers very accurate results for the Action Potential. It also offers the ability to reproduce the noise of membrane potential, Vm, in a way that Vm values show critical behavior. However, the noise generated by this model presents critical exponents which, ultimately, are inconsistent with corresponding experimental data. Thus, having demonstrated this weakness of the model, we proceed to present a neural network model that can reproduce the A.P., reproduce noise, display critical behavior and the critical exponents are in agreement with experimental data. At the same time, there is an inherent parameter in the model that we can identify with an experimental physical quantity, such as current Is, which is used as external stimulus in experiments.
Keywords:
Membrane Potential, Noise, Critical fluctuations, Hodkin-Huxley, Neural network model