Evolutionary computing in neuronal modeling

Doctoral Thesis / Dissertation from the year 2018 in the subject Biology - Neurobiology, grade: 10.0, University of Wisconsin-Madison, language: English, abstract: The efficacy of genetic algorithms in the design of models that model specific and experimental aspects of action potentials in a wide variety of organisms is proven. A specific example of a plant action potential is used to illustrate the use of genetic algorithms in the search for parameters of models. The efficiency of the genetic algorithms as a search method is in the short generation span of the convergence of the algorithm. We use the genesis simulator to simulate a single compartmental model of a plant cell. The plant cell has a delayed rectification K channel, a Ca channel and a Ca dependent Cl channel. We have omitted the H+ ATPase pump from the model. The depolarization is by the release of Ca from stores and due to a Ca channel and due to the chloride channel that depends on Calcium. There is an IP3 mediated calcium release mechanism which we have simplified in a model of calcium concentration that decays exponentially with time. The genesis package comes inbuilt with search algorithms that include the genetic algorithm. We use this to run a simulation to search for many parameters including the gbar for the chloride and calcium channels and the time constant and middle point of the potassium channel parameters, Ninf and tauN. The simulations have a time step of 20 micro seconds and are responses of the single compartmental model to current injections of .1 nanoampere to 1.0 nano ampere.