Literature DB >> 17925247

How tightly tuned are network parameters? Insight from computational and experimental studies in small rhythmic motor networks.

Eve Marder1, Anne-Elise Tobin, Rachel Grashow.   

Abstract

We describe theoretical and experimental studies that demonstrate that a given pattern of neuronal activity can be produced by variable sets of underlying conductances. Experimental work demonstrates that individual identified neurons in different animals may show variations as large as 2-5 fold in the conductance densities of specific ion channels. Theoretical work shows that models with this range of variation in many of their maximal conductances can produce similar activity. Together, these observations suggest that neurons and networks may be less tightly tuned than previously thought. Consequently, we argue that instead of attempting to construct single canonical models of neuronal function, it might be more useful to construct and analyze large families of models that give similar behavior.

Mesh:

Year:  2007        PMID: 17925247     DOI: 10.1016/S0079-6123(06)65012-7

Source DB:  PubMed          Journal:  Prog Brain Res        ISSN: 0079-6123            Impact factor:   2.453


  12 in total

1.  GABAA transmission is a critical step in the process of triggering homeostatic increases in quantal amplitude.

Authors:  Jennifer C Wilhelm; Peter Wenner
Journal:  Proc Natl Acad Sci U S A       Date:  2008-08-04       Impact factor: 11.205

Review 2.  Coping with variability in small neuronal networks.

Authors:  Ronald L Calabrese; Brian J Norris; Angela Wenning; Terrence M Wright
Journal:  Integr Comp Biol       Date:  2011-06-30       Impact factor: 3.326

Review 3.  Computational models and emergent properties of respiratory neural networks.

Authors:  Bruce G Lindsey; Ilya A Rybak; Jeffrey C Smith
Journal:  Compr Physiol       Date:  2012-07       Impact factor: 9.090

4.  Coregulation of ion channel conductances preserves output in a computational model of a crustacean cardiac motor neuron.

Authors:  David J Schulz; Satish S Nair; John M Ball; Clarence C Franklin; Anne-Elise Tobin
Journal:  J Neurosci       Date:  2010-06-23       Impact factor: 6.167

5.  Constancy and variability in the output of a central pattern generator.

Authors:  Brian J Norris; Angela Wenning; Terrence Michael Wright; Ronald L Calabrese
Journal:  J Neurosci       Date:  2011-03-23       Impact factor: 6.167

Review 6.  Network homeostasis: a matter of coordination.

Authors:  Arianna Maffei; Alfredo Fontanini
Journal:  Curr Opin Neurobiol       Date:  2009-06-18       Impact factor: 6.627

7.  A role for compromise: synaptic inhibition and electrical coupling interact to control phasing in the leech heartbeat CpG.

Authors:  Adam L Weaver; Rebecca C Roffman; Brian J Norris; Ronald L Calabrese
Journal:  Front Behav Neurosci       Date:  2010-07-12       Impact factor: 3.558

8.  The stomatogastric nervous system as a model for studying sensorimotor interactions in real-time closed-loop conditions.

Authors:  Nelly Daur; Florian Diehl; Wolfgang Mader; Wolfgang Stein
Journal:  Front Comput Neurosci       Date:  2012-03-14       Impact factor: 2.380

9.  Successful reconstruction of a physiological circuit with known connectivity from spiking activity alone.

Authors:  Felipe Gerhard; Tilman Kispersky; Gabrielle J Gutierrez; Eve Marder; Mark Kramer; Uri Eden
Journal:  PLoS Comput Biol       Date:  2013-07-11       Impact factor: 4.475

10.  morphforge: a toolbox for simulating small networks of biologically detailed neurons in Python.

Authors:  Michael J Hull; David J Willshaw
Journal:  Front Neuroinform       Date:  2014-01-28       Impact factor: 4.081

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