"Construction of predictive neuron models using large scale data assimilation"　

［ 2017年03月01日 ］

RIKEN International Symposium on Data Assimilation 2017
"Construction of predictive neuron models using large scale data assimilation"

We report on the construction of neuron models by assimilating electrophysiological data with large-scale constrained nonlinear optimization. The method implements interior point line parameter search to determine parameters from the responses to intracellular current injections of zebra finch HVC neurons. We incorporated these parameters into a nine ionic channel conductance model to obtain completed models which we then use to predict the state of the neuron under arbitrary current stimulation. Each model was validated by successfully predicting the dynamics of the membrane potential induced by 20-50 different current protocols. By extending constrained nonlinear optimization with Bayesian inference, we obtain model solutions which satisfy the conditions of uniqueness and consistency with biological parameters. The assimilation method may be extended to building models of the small neural networks which control biological rhythms. I will show some of the benefits of artificially restoring coupling between respiration and heart rate to provide novel therapy for chronic cardiorespiratory diseases.
http://www.data-assimilation.riken.jp/risda2017/

講師プロフィール
名前:Alain Nogaret	所属:University of Bath
略歴:外部ページ

eラーニングアーカイブ カテゴリー