Neuronal Firing Sensitivity to Morphologic and Active Membrane Parameters

Abstract

Both the excitability of a neuron's membrane, driven by active ion channels, and dendritic morphology contribute to neuronal firing dynamics, but the relative importance and interactions between these features remain poorly understood. Recent modeling studies have shown that different combinations of active conductances can evoke similar firing patterns, but have neglected how morphology might contribute to homeostasis. Parameterizing the morphology of a cylindrical dendrite, we introduce a novel application of mathematical sensitivity analysis that quantifies how dendritic length, diameter, and surface area influence neuronal firing, and compares these effects directly against those of active parameters. The method was applied to a model of neurons from goldfish Area II. These neurons exhibit, and likely contribute to, persistent activity in eye velocity storage, a simple model of working memory. We introduce sensitivity landscapes, defined by local sensitivity analyses of firing rate and gain to each parameter, performed globally across the parameter space. Principal directions over which sensitivity to all parameters varied most revealed intrinsic currents that most controlled model output. We found domains where different groups of parameters had the highest sensitivities, suggesting that interactions within each group shaped firing behaviors within each specific domain. Application of our method, and its characterization of which models were sensitive to general morphologic features, will lead to advances in understanding how realistic morphology participates in functional homeostasis. Significantly, we can predict which active conductances, and how many of them, will compensate for a given age-or development-related structural change, or will offset a morphologic perturbation resulting from trauma or neurodegenerative disorder, to restore normal function. Our method can be adapted to analyze any computational model. Thus, sensitivity landscapes, and the quantitative predictions they provide, can give new insight into mechanisms of homeostasis in any biological system.

Parallel deconvolution of large 3D images obtained by confocal laser scanning microscopy

“…Over a century ago, Santiago Ramon y Cajal suggested that the great variety of neuronal morphologies reflected different physiological properties of these cells and, consequently, their information-processing functions. This hypothesis has been confirmed by contemporary research (Mainen and Sejnowski, 1996;Nasuto et al, 2001;Sakatani and Hirose, 2003;Weaver and Wearne, 2008).…”

“…Computer-assisted microscopy opens the possibility of 3D reconstruction of cell morphologies; but, in most studies, axon or dendrites still are traced manually or semi-manually with a mouse cursor moving over the computer screen (Brown et al, 2005). In the recent years, significant progress has been made in de-velopment of fully automated methods Bjornsson et al, 2008;Kozubek et al, 2004;Pool et al, 2008;Rodriguez et al, 2003Rodriguez et al, , 2006Rodriguez et al, , 2008Roysam et al, 2006;Srinivasan et al, 2007;Vasilkoski and Stepanyants, 2009;Weaver et al, 2004), but several problems still need to be resolved before these methods can widely replace manual methods.…”

Degeneracy in hippocampal physiology and plasticity

“…The use of multiple measurements to establish the validity of models is essential because of aforementioned (Section 2.1-2.3) dissociation between different forms of homeostasis and the differential dependence of different measurements on distinct constitutive components (Figures 2 and 3). It is well recognized in the design principle of these techniques that establishing physiological equivalence of only a partial set of measurements does not necessarily ensure that the other measurements which have not been constrained by the validation process are within the physiological ranges (Achard & De Schutter, 2006;Beining, Mongiat, Schwarzacher, Cuntz, & Jedlicka, 2017;Foster et al, 1993;Goldman et al, 2001;Hobbs & Hooper, 2008;Marder, 2011;Marder & Taylor, 2011;Prinz et al, 2003;Prinz et al, 2004;Rathour & Narayanan, 2012a;Rathour & Narayanan, 2014;Srikanth & Narayanan, 2015;Taylor et al, 2009;Tobin, Van Hooser, & Calabrese, 2006;Weaver & Wearne, 2008). If such a stochastic search algorithm fails to yield any valid model that satisfies all the physiological objectives, the interpretation should not be that the specified model configuration is incapable of achieving all objectives.…”

Robust emergence of sharply tuned place cell responses in hippocampal neurons with structural and biophysical heterogeneities

“…From the standpoint of cellular neurophysiology, two major factors that impact neuronal function are its morphology and the ion channels and receptors that are expressed throughout neuronal membranes. Neuronal morphology has been shown to have strong links with its physiology, spanning a wide variety of functional characteristics, including neuronal excitability, firing patterns, intraneuronal coupling, intrinsic functional maps, somatodendritic subthreshold resonance, back-and forward propagation of electrical potentials and frequency selectivity in firing (Vetter et al 2001;Mainen and Sejnowski 1996;Stiefel and Sejnowski 2007;Ostojic et al 2015;Cannon et al 2010;van Elburg and van Ooyen 2010;van Ooyen et al 2002;Narayanan and Chattarji 2010;Narayanan et al 2005;Dhupia et al 2014;Krichmar et al 2002;Ferrante et al 2013;Beining et al 2017;Weaver and Wearne 2008;Schaefer et al 2003). Neuronal morphology has also been viewed to be a critical cog in solving the wiring optimization problem, which minimizes the cost of space, time, and matter (Cajal 1992;Cuntz et al 2010;Cherniak 1992;Kim et al 2012) towards transmitting input signals most efficiently (Chklovskii 2004).…”

“…A second class of studies have assessed pathology-induced or behaviorally relevant dendritic remodeling (atrophy or hypertrophy) in neuronal structures (van Elburg and van Ooyen 2010; van Ooyen et al 2002; Narayanan and Chattarji 2010; Narayanan et al 2005; Dhupia et al 2014). A third class of studies explored the implications for intrinsic morphological heterogeneities within a neuronal subtype(Krichmar et al 2002;Ferrante et al 2013;Beining et al 2017;Weaver and Wearne 2008;Otopalik et al 2017b;Otopalik et al 2019;Otopalik et al 2017a;Schaefer et al 2003).…”