Probing the dynamics of identified neurons with a data-driven modeling approach.
In controlling animal behavior the nervous system has to perform within the operational limits set by the requirements of each specific behavior. The implications for the corresponding range of suitable network, single neuron, and ion channel properties have remained elusive. In this article we approach the question of how well-constrained properties of neuronal systems may be on the neuronal level. We used large data sets of the activity of isolated invertebrate identified cells and built an accurate conductance-based model for this cell type using customized automated parameter estimation techniques. By direct inspection of the data we found that the variability of the neurons is larger when they are isolated from the circuit than when in the intact system. Furthermore, the responses of the neurons to perturbations appear to be more consistent than their autonomous behavior under stationary conditions. In the developed model, the constraints on different parameters that enforce appropriate model dynamics vary widely from some very tightly controlled parameters to others that are almost arbitrary. The model also allows predictions for the effect of blocking selected ionic currents and to prove that the origin of irregular dynamics in the neuron model is proper chaoticity and that this chaoticity is typical in an appropriate sense. Our results indicate that data driven models are useful tools for the in-depth analysis of neuronal dynamics. The better consistency of responses to perturbations, in the real neurons as well as in the model, suggests a paradigm shift away from measuring autonomous dynamics alone towards protocols of controlled perturbations. Our predictions for the impact of channel blockers on the neuronal dynamics and the proof of chaoticity underscore the wide scope of our approach.
Nowotny
Thomas
T
Centre for Computational Neuroscience and Robotics, Department of Informatics, University of Sussex, Falmer, Brighton, United Kingdom. T.Nowotny@sussex.ac.uk
Levi
Rafael
R
Selverston
Allen I
AI
D000818:Animals; D005071:Evoked Potentials / Q000502:physiology; D005724:Ganglia / Q000502:physiology; D008959:Models, Neurological; D008121:Nephropidae; D009474:Neurons / Q000502:physiology*; D013569:Synapses / Q000502:physiology