Simulation of alcohol action upon a detailed Purkinje neuron model and a simpler surrogate model that runs >400 times faster

Abstract

BackgroundAn approach to investigate brain function/dysfunction is to simulate neuron circuits on a computer. A problem, however, is that detailed neuron descriptions are computationally expensive and this handicaps the pursuit of realistic network investigations, where many neurons need to be simulated.ResultsWe confront this issue; we employ a novel reduction algorithm to produce a 2 compartment model of the cerebellar Purkinje neuron from a previously published, 1089 compartment model. It runs more than 400 times faster and retains the electrical behavior of the full model. So, it is more suitable for inclusion in large network models, where computational power is a limiting issue. We show the utility of this reduced model by demonstrating that it can replicate the full model’s response to alcohol, which can in turn reproduce experimental recordings from Purkinje neurons following alcohol application.ConclusionsWe show that alcohol may modulate Purkinje neuron firing by an inhibition of their sodium-potassium pumps. We suggest that this action, upon cerebellar Purkinje neurons, is how alcohol ingestion can corrupt motor co-ordination. In this way, we relate events on the molecular scale to the level of behavior.

The 40-year history of modeling active dendrites in cerebellar Purkinje cells: emergence of the first single cell “community model”

“…Importantly, the model, we now refer to as the “R-DB model”, has formed the basis for considerable subsequent work from my own students both within my laboratory (Jaeger et al, 1996 ; Baldi et al, 1998 ; Sultan and Bower, 1998 ; Jaeger and Bower, 1999 ; Mocanu et al, 2000 ; Santamaria et al, 2002 , 2007 ; Santamaria and Bower, 2004 ; Lu et al, 2005 , 2009 ; Cornelis et al, 2010 ) and within their own independent laboratories and research (Staub et al, 1994 ; De Schutter, 1998 ; Vos et al, 1999 ; Howell et al, 2000 ; Steuber and De Schutter, 2001 , 2002 ; Gauck and Jaeger, 2003 ; Solinas et al, 2003 , 2006 ; Kreiner and Jaeger, 2004 ; Koekkoek et al, 2005 ; Santamaria et al, 2006 , 2011 ; Shin and De Schutter, 2006 ; Shin et al, 2007 ; Steuber et al, 2007 ; Achard and De Schutter, 2008 ; De Schutter and Steuber, 2009 ; Anwar et al, 2012 , 2013 , 2014 ; Coop et al, 2010 ; Tahon et al, 2011 ; Cao et al, 2012 ; Couto et al, 2015 ). Perhaps more importantly the R-DB model has become a true “community model” as it is now being used by a growing number of authors as a base for further modeling work outside its laboratories of origin (Coop and Reeke, 2001 ; Mandelblat et al, 2001 ; Miyasho et al, 2001 ; Roth and Häusser, 2001 ; Chono et al, 2003 ; Khaliq et al, 2003 ; Steuber and Willshaw, 2004 ; Ogasawara et al, 2007 ; Yamazaki and Tanaka, 2007 ; Kulagina et al, 2008 ; Traub et al, 2008 ; Brown et al, 2011 ; Brown and Loew, 2012 ; Forrest et al, 2012 ; Forrest, 2015 ; Masoli et al, 2015 ). Several of these modeling efforts have now started their own lin...…”

Cancer cells have distinct electrical properties that predict a susceptibility to lipophilic anions; a new cancer drug paradigm

“…theoretical physics) and its importance is increasingly being realised in biology, in neuroscience especially (e.g. [137][138]), but relatively slowly in cancer biology due to institutional resistance (journals, funding, reviewers etc.). In this paper, and others (pre-published [23,102] and forthcoming), I have interpreted experiments to produce a new conceptually distinct, explanatory, quantitative, experimentally tractable theory of cancer, which delivers new therapeutic approaches i.e.…”

An electrodiffusive, ion conserving Pinsky-Rinzel model with homeostatic mechanisms

“…Intra-or extracellular electrodiffusion is not an issue in single-compartment models, 69 of which there are quite a few that incorporate ion concentration dynamics in a more or 70 less consistent way [28,29,[33][34][35][36][37][38][39][40][41][42][43][44][45][46][47]. There are also several morphologically explicit models 71 that have included homeostatic machinery and explicitly simulated ion concentration 72 dynamics (see e.g., [27,[48][49][50][51][52][53][54][55][56][57]). However, neither of these have accounted for the 73 electrodiffusive coupling between the movement of ions and electrical potentials (see 74 Results section titled Loss in accuracy when neglecting electrodiffusive effects on 75 concentration dynamics).…”

“…We, therefore, do not analyze it further here. e.g., [27,[48][49][50][51][52][53][54][55][56][57]). To specify, the change in the ion concentration in a given 363 compartment will, in reality, depend on (i) the transmembrane influx of ions into this 364 compartment, (ii) the diffusion of ions between this compartments and its neighboring 365 compartment(s), and (iii) the electrical drift of ions between this compartment and its 366 neighboring compartment(s).…”

“…To specify, the change in the ion concentration in a given 363 compartment will, in reality, depend on (i) the transmembrane influx of ions into this 364 compartment, (ii) the diffusion of ions between this compartments and its neighboring 365 compartment(s), and (iii) the electrical drift of ions between this compartment and its 366 neighboring compartment(s). Some of the cited models account for only (i) [27,49,51], 367 others account for (i) and (ii) [48,50,[52][53][54][55][56][57], but neither account for (iii). When (iii) is 368 not accounted for, electrical and diffusive processes are implicitly treated as independent 369 processes, a simplifying assumption which is also incorporated in the reaction-diffusion 370 module [72] in the NEURON simulation environment [73].…”