title: Intermediate intrinsic diversity enhances neural population coding.
abstract: Cell-to-cell variability in molecular, genetic, and physiological features is increasingly recognized as a critical feature of complex biological systems, including the brain. Although such variability has potential advantages in robustness and reliability, how and why biological circuits assemble heterogeneous cells into functional groups is poorly understood. Here, we develop analytic approaches toward answering how neuron-level variation in intrinsic biophysical properties of olfactory bulb mitral cells influences population coding of fluctuating stimuli. We capture the intrinsic diversity of recorded populations of neurons through a statistical approach based on generalized linear models. These models are flexible enough to predict the diverse responses of individual neurons yet provide a common reference frame for comparing one neuron to the next. We then use Bayesian stimulus decoding to ask how effectively different populations of mitral cells, varying in their diversity, encode a common stimulus. We show that a key advantage provided by physiological levels of intrinsic diversity is more efficient and more robust encoding of stimuli by the population as a whole. However, we find that the populations that best encode stimulus features are not simply the most heterogeneous, but those that balance diversity with the benefits of neural similarity.
authors: Program in Neural Computation, Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
authors: Shreejoy J
authors: SJ
authors: Tripathy
authors: Krishnan
authors: K
authors: Padmanabhan
authors: Richard C
authors: RC
authors: Gerkin
authors: Nathaniel N
authors: NN
authors: Urban
mesh_terms: D000200:Action Potentials / Q000502:physiology; D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D004594:Electrophysiology; D007473:Ion Channels / Q000378:metabolism; D016014:Linear Models; D051379:Mice; D008959:Models, Neurological; D009474:Neurons / Q000378:metabolism / Q000502:physiology*; D009830:Olfactory Bulb / Q000473:pathology; D015203:Reproducibility of Results
chemical_list: D007473:Ion Channels
keywords: generalized linear models; intrinsic biophysics; ion channels; neural variability; stimulus coding
references: Proc Natl Acad Sci U S A. 2001 Nov 20;98(24):13763-8
references: 11698650
references: Nat Neurosci. 2010 Oct;13(10):1276-82
references: 20802489
references: Nat Neurosci. 2011 Feb;14(2):133-8
references: 21270780
references: J Physiol. 2011 May 1;589(Pt 9):2261-73
references: 21486768
references: J Neurophysiol. 2010 Jun;103(6):3248-65
references: 20357062
references: Phys Rev Lett. 2000 Mar 13;84(11):2310-3
references: 11018872
references: J Neurosci. 2003 Dec 17;23(37):11539-53
references: 14684857
references: J Neurophysiol. 1997 Nov;78(5):2336-50
references: 9356386
references: J Neurophysiol. 1993 Jun;69(6):1948-65
references: 7688798
references: Nature. 2008 Aug 21;454(7207):995-9
references: 18650810
references: Nat Commun. 2012 Feb 28;3:703
references: 22426224
references: J Neurophysiol. 2009 Feb;101(2):1073-88
references: 19091924
references: Sci Rep. 2011;1:50
references: 22355569
references: Neural Comput. 2011 Jan;23(1):1-45
references: 20964538
references: Proc Natl Acad Sci U S A. 2011 Apr 5;108(14):5843-8
references: 21436050
references: Nature. 2012 Aug 16;488(7411):375-8
references: 22820253
references: Neuron. 2012 Nov 21;76(4):776-89
references: 23177962
references: J Neurosci. 2006 Jan 25;26(4):1247-59
references: 16436612
references: Proc Natl Acad Sci U S A. 2010 Aug 10;107(32):14419-24
references: 20660781
references: Biol Cybern. 2008 Nov;99(4-5):417-26
references: 19011928
references: J Comput Neurosci. 2001 Mar-Apr;10(2):213-21
references: 11361260
references: Cell. 2010 May 14;141(4):559-63
references: 20478246
references: J Neurosci. 2005 Oct 26;25(43):9978-88
references: 16251446
references: Science. 2009 Oct 16;326(5951):379-80
references: 19833951
references: Nat Neurosci. 2011 Feb;14(2):208-16
references: 21217763
references: Nature. 2007 Sep 6;449(7158):92-5
references: 17805296
references: Proc Natl Acad Sci U S A. 2005 Sep 27;102(39):14063-8
references: 16157876
references: Nat Neurosci. 2011 Jul 17;14(8):1039-44
references: 21765422
references: Nat Neurosci. 2012 Feb 12;15(3):441-8, S1-2
references: 22327473
references: Nat Neurosci. 2007 May;10(5):631-9
references: 17450136
references: PLoS Comput Biol. 2012;8(3):e1002398
references: 22442645
references: Phys Rev Lett. 2012 Jun 1;108(22):228102
references: 23003656
references: Nature. 2008 Aug 14;454(7206):881-5
references: 18633351
references: Cell. 2010 Jul 23;142(2):189-93
references: 20655460
references: Nat Neurosci. 2010 Nov;13(11):1404-12
references: 20953197
references: J Neurosci. 1999 Mar 15;19(6):2209-23
references: 10066274
references: Neural Comput. 2001 Aug;13(8):1713-20
references: 11506667
references: J Neurosci. 2005 Apr 27;25(17):4207-16
references: 15858046
references: Neuron. 2001 Aug 30;31(4):639-51
references: 11545722
references: J Neurophysiol. 2010 Nov;104(5):2543-55
references: 20631220
references: Neuron. 2005 May 5;46(3):493-504
references: 15882648
references: Nat Neurosci. 2008 Jan;11(1):80-7
references: 18084286