Literature DB >> 27760819

Optimizing computer models of corticospinal neurons to replicate in vitro dynamics.

Samuel A Neymotin1, Benjamin A Suter2, Salvador Dura-Bernal3, Gordon M G Shepherd2, Michele Migliore4, William W Lytton3,5,6,7.   

Abstract

Corticospinal neurons (SPI), thick-tufted pyramidal neurons in motor cortex layer 5B that project caudally via the medullary pyramids, display distinct class-specific electrophysiological properties in vitro: strong sag with hyperpolarization, lack of adaptation, and a nearly linear frequency-current (F-I) relationship. We used our electrophysiological data to produce a pair of large archives of SPI neuron computer models in two model classes: 1) detailed models with full reconstruction; and 2) simplified models with six compartments. We used a PRAXIS and an evolutionary multiobjective optimization (EMO) in sequence to determine ion channel conductances. EMO selected good models from each of the two model classes to form the two model archives. Archived models showed tradeoffs across fitness functions. For example, parameters that produced excellent F-I fit produced a less-optimal fit for interspike voltage trajectory. Because of these tradeoffs, there was no single best model but rather models that would be best for particular usages for either single neuron or network explorations. Further exploration of exemplar models with strong F-I fit demonstrated that both the detailed and simple models produced excellent matches to the experimental data. Although dendritic ion identities and densities cannot yet be fully determined experimentally, we explored the consequences of a demonstrated proximal to distal density gradient of Ih, demonstrating that this would lead to a gradient of resonance properties with increased resonant frequencies more distally. We suggest that this dynamical feature could serve to make the cell particularly responsive to major frequency bands that differ by cortical layer. NEW & NOTEWORTHY: We developed models of motor cortex corticospinal neurons that replicate in vitro dynamics, including hyperpolarization-induced sag and realistic firing patterns. Models demonstrated resonance in response to synaptic stimulation, with resonance frequency increasing in apical dendrites with increasing distance from soma, matching the increasing oscillation frequencies spanning deep to superficial cortical layers. This gradient may enable specific corticospinal neuron dendrites to entrain to relevant oscillations in different cortical layers, contributing to appropriate motor output commands.
Copyright © 2017 the American Physiological Society.

Entities:  

Keywords:  HCN channel; computer simulation; corticospinal neuron; hyperpolarization-activated cyclic nucleotide-gated channel; layer 5; motor cortex; neocortex

Mesh:

Substances:

Year:  2016        PMID: 27760819      PMCID: PMC5209548          DOI: 10.1152/jn.00570.2016

Source DB:  PubMed          Journal:  J Neurophysiol        ISSN: 0022-3077            Impact factor:   2.714


  57 in total

Review 1.  Emerging rules for the distributions of active dendritic conductances.

Authors:  Michele Migliore; Gordon M Shepherd
Journal:  Nat Rev Neurosci       Date:  2002-05       Impact factor: 34.870

2.  ModelDB: A Database to Support Computational Neuroscience.

Authors:  Michael L Hines; Thomas Morse; Michele Migliore; Nicholas T Carnevale; Gordon M Shepherd
Journal:  J Comput Neurosci       Date:  2004 Jul-Aug       Impact factor: 1.621

3.  ModelDB: making models publicly accessible to support computational neuroscience.

Authors:  Michele Migliore; Thomas M Morse; Andrew P Davison; Luis Marenco; Gordon M Shepherd; Michael L Hines
Journal:  Neuroinformatics       Date:  2003

4.  A database of computational models of a half-center oscillator for analyzing how neuronal parameters influence network activity.

Authors:  Anca Doloc-Mihu; Ronald L Calabrese
Journal:  J Biol Phys       Date:  2011-02-12       Impact factor: 1.365

5.  Laminarly orthogonal excitation of fast-spiking and low-threshold-spiking interneurons in mouse motor cortex.

Authors:  Alfonso J Apicella; Ian R Wickersham; H Sebastian Seung; Gordon M G Shepherd
Journal:  J Neurosci       Date:  2012-05-16       Impact factor: 6.167

6.  Corticospinal-specific HCN expression in mouse motor cortex: I(h)-dependent synaptic integration as a candidate microcircuit mechanism involved in motor control.

Authors:  Patrick L Sheets; Benjamin A Suter; Taro Kiritani; C Savio Chan; D James Surmeier; Gordon M G Shepherd
Journal:  J Neurophysiol       Date:  2011-07-27       Impact factor: 2.714

7.  Distribution and function of HCN channels in the apical dendritic tuft of neocortical pyramidal neurons.

Authors:  Mark T Harnett; Jeffrey C Magee; Stephen R Williams
Journal:  J Neurosci       Date:  2015-01-21       Impact factor: 6.167

8.  Dual γ rhythm generators control interlaminar synchrony in auditory cortex.

Authors:  Matthew Ainsworth; Shane Lee; Mark O Cunningham; Anita K Roopun; Roger D Traub; Nancy J Kopell; Miles A Whittington
Journal:  J Neurosci       Date:  2011-11-23       Impact factor: 6.167

9.  Ephus: multipurpose data acquisition software for neuroscience experiments.

Authors:  Benjamin A Suter; Timothy O'Connor; Vijay Iyer; Leopoldo T Petreanu; Bryan M Hooks; Taro Kiritani; Karel Svoboda; Gordon M G Shepherd
Journal:  Front Neural Circuits       Date:  2010-08-26       Impact factor: 3.492

10.  Spatially distributed dendritic resonance selectively filters synaptic input.

Authors:  Jonathan Laudanski; Benjamin Torben-Nielsen; Idan Segev; Shihab Shamma
Journal:  PLoS Comput Biol       Date:  2014-08-21       Impact factor: 4.475

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  16 in total

1.  An American Physiological Society cross-journal Call for Papers on "Deconstructing Organs: Single-Cell Analyses, Decellularized Organs, Organoids, and Organ-on-a-Chip Models".

Authors:  Josephine C Adams; P Darwin Bell; Sue C Bodine; Heddwen L Brooks; Nigel Bunnett; Bina Joe; Kara Hansell Keehan; Thomas R Kleyman; André Marette; Rory E Morty; Jan-Marino Ramírez; Morten B Thomsen; Bill J Yates; Irving H Zucker
Journal:  Am J Physiol Lung Cell Mol Physiol       Date:  2020-07-01       Impact factor: 5.464

2.  Simulating Large-scale Models of Brain Neuronal Circuits using Google Cloud Platform.

Authors:  Subhashini Sivagnanam; Wyatt Gorman; Donald Doherty; Samuel A Neymotin; Stephan Fang; Hermine Hovhannisyan; William W Lytton; Salvador Dura-Bernal
Journal:  PEARC20 (2020)       Date:  2020-07

Review 3.  Pareto optimality, economy-effectiveness trade-offs and ion channel degeneracy: improving population modelling for single neurons.

Authors:  Peter Jedlicka; Alexander D Bird; Hermann Cuntz
Journal:  Open Biol       Date:  2022-07-13       Impact factor: 7.124

4.  Multiscale modeling meets machine learning: What can we learn?

Authors:  Grace C Y Peng; Mark Alber; Adrian Buganza Tepole; William R Cannon; Suvranu De; Salvador Dura-Bernal; Krishna Garikipati; George Karniadakis; William W Lytton; Paris Perdikaris; Linda Petzold; Ellen Kuhl
Journal:  Arch Comput Methods Eng       Date:  2020-02-17       Impact factor: 7.302

5.  Experimentally-constrained biophysical models of tonic and burst firing modes in thalamocortical neurons.

Authors:  Elisabetta Iavarone; Jane Yi; Ying Shi; Bas-Jan Zandt; Christian O'Reilly; Werner Van Geit; Christian Rössert; Henry Markram; Sean L Hill
Journal:  PLoS Comput Biol       Date:  2019-05-16       Impact factor: 4.475

6.  Effects of Ih and TASK-like shunting current on dendritic impedance in layer 5 pyramidal-tract neurons.

Authors:  Craig Kelley; Salvador Dura-Bernal; Samuel A Neymotin; Srdjan D Antic; Nicholas T Carnevale; Michele Migliore; William W Lytton
Journal:  J Neurophysiol       Date:  2021-03-10       Impact factor: 2.714

7.  Local glutamate-mediated dendritic plateau potentials change the state of the cortical pyramidal neuron.

Authors:  Peng P Gao; Joseph W Graham; Wen-Liang Zhou; Jinyoung Jang; Sergio Angulo; Salvador Dura-Bernal; Michael Hines; William W Lytton; Srdjan D Antic
Journal:  J Neurophysiol       Date:  2020-10-21       Impact factor: 2.714

8.  Systematic generation of biophysically detailed models for diverse cortical neuron types.

Authors:  Nathan W Gouwens; Jim Berg; David Feng; Staci A Sorensen; Hongkui Zeng; Michael J Hawrylycz; Christof Koch; Anton Arkhipov
Journal:  Nat Commun       Date:  2018-02-19       Impact factor: 14.919

9.  Parameter Optimization Using Covariance Matrix Adaptation-Evolutionary Strategy (CMA-ES), an Approach to Investigate Differences in Channel Properties Between Neuron Subtypes.

Authors:  Zbigniew Jȩdrzejewski-Szmek; Karina P Abrahao; Joanna Jȩdrzejewska-Szmek; David M Lovinger; Kim T Blackwell
Journal:  Front Neuroinform       Date:  2018-07-31       Impact factor: 4.081

10.  The physiological variability of channel density in hippocampal CA1 pyramidal cells and interneurons explored using a unified data-driven modeling workflow.

Authors:  Rosanna Migliore; Carmen A Lupascu; Luca L Bologna; Armando Romani; Jean-Denis Courcol; Stefano Antonel; Werner A H Van Geit; Alex M Thomson; Audrey Mercer; Sigrun Lange; Joanne Falck; Christian A Rössert; Ying Shi; Olivier Hagens; Maurizio Pezzoli; Tamas F Freund; Szabolcs Kali; Eilif B Muller; Felix Schürmann; Henry Markram; Michele Migliore
Journal:  PLoS Comput Biol       Date:  2018-09-17       Impact factor: 4.475

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