Probing the Dynamics of Identified Neurons with a Data-Driven Modeling Approach

Abstract

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.

How Multiple Conductances Determine Electrophysiological Properties in a Multicompartment Model

“…Previous models of LP have captured some of these features but not all of them (Buchholtz et al, 1992; Nowotny et al, 2008). One difference between the behavior of the model and that of the biological LP neuron (Golowasch and Marder, 1992a) is that the model fires more rapidly than the biological neuron, but without this we were not able to obtain the appropriate firing rates in response to inhibitory inputs.…”

“…Previous multi-compartment models of STG neurons have had only two compartments, one to model the generation of spikes, and another that contains the currents typically measured in voltage-clamp experiments (Soto-Trevino et al, 2005; Nowotny et al, 2008). Additional compartments were needed to accurately model both the input impedance of LP (not shown) and the attenuation of spikes when measured in the soma.…”

Robust Microcircuit Synchronization by Inhibitory Connections

“…Furthermore, it induces variability across the neurons of both CPGs. Equations for the IA current are the same as in (Nowotny et al, 2008). …”

Identification and modelling of fast and slow Ih current components in vestibular ganglion neurons

“…Several studies have revisited the full-trace method: using data from patch-clamp experiments to identify the parameters of Markov models on rat cortex pyramidal neurons (Gurkiewicz & Korngreen, 2007); to highlight the neural dynamics in lobster lateral pyloric neurons (Nowotny et al, 2008); and to estimate a conductance gradient along a dendrite (Keren et al, 2009). Only one study of this type concerns the identification of two ionic populations, one inactivated and one non-inactivated potassium conductance (Vavoulis et al, 2010).…”