Intrinsic biophysical diversity decorrelates neuronal firing while increasing information content

Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.
Nature Neuroscience (Impact Factor: 14.98). 10/2010; 13(10):1276-82. DOI: 10.1038/nn.2630
Source: PubMed

ABSTRACT Although examples of variation and diversity exist throughout the nervous system, their importance remains a source of debate. Even neurons of the same molecular type have notable intrinsic differences. Largely unknown, however, is the degree to which these differences impair or assist neural coding. We examined the outputs from a single type of neuron, the mitral cells of the mouse olfactory bulb, to identical stimuli and found that each cell's spiking response was dictated by its unique biophysical fingerprint. Using this intrinsic heterogeneity, diverse populations were able to code for twofold more information than their homogeneous counterparts. In addition, biophysical variability alone reduced pair-wise output spike correlations to low levels. Our results indicate that intrinsic neuronal diversity is important for neural coding and is not simply the result of biological imprecision.

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Available from: Nathaniel Urban, Aug 25, 2015
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    • ", 2007 ; Calabrese , Schumacher , Schneider , Paninski , & Woolley , 2011 ) . Such reliable spiking has been shown in a number of contexts and systems , including as a general feature of neuronal membranes stimulated using somatic current injection in vitro ( Bryant & Segundo , 1976 ; Mainen & Sejnowski , 1995 ; Padmanabhan & Urban , 2010 ) , as well as resulting from multiple stages of neuronal circuit processing in vivo ( Butts et al . , 2007 ; Kelly , Smith , Kass , & Lee , 2010 ) . "
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    Neural Computation 06/2015; 27(8):1-15. DOI:10.1162/NECO_a_00754 · 1.69 Impact Factor
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    • "Indeed, there are many potential biophysical sources of heterogeneity in neural systems, both at the network level (i.e., heterogeneity in the network connectivity , as in Olmi et al., 2010) and at the neuron level. In this second group, possible heterogeneity sources can be defined in terms of anatomical and morphological properties, or also at a functional level, including neuronal excitability (Tessone et al., 2006; Perez et al., 2010), different degrees of spike frequency adaptation (Hemond et al., 2008; Nicola and Campbell, 2013), or other biophysical properties (Padmanabhan and Urban, 2010; Tripathy et al., 2013), to name a few. Understanding their individual or joint role in neural dynamics will require future modeling work at different scales and levels of detail, for which mean-field approaches could be of great help. "
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    Frontiers in Computational Neuroscience 09/2014; 8:107. DOI:10.3389/fncom.2014.00107 · 2.23 Impact Factor
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    • "Early work by Eliasmith and Anderson (2003) demonstrated that heterogeneity increases both the ability of a population to represent an input signal and the range of functions computable by the population. More recent work showed that heterogeneity allows neurons to use combinatorial coding schemes (Osborne et al., 2008) and in general increase the information encoded by a population (Shamir and Sompolinsky, 2006; Chelaru and Dragoi, 2008; Padmanabhan and Urban, 2010; Ecker et al., 2011). In the field of stochastic resonance, the effects of noise in a heterogeneous population have been examined, but only in specific cases such as for input signals much larger than the range of heterogeneity (Stocks, 2000) or for very large levels of noise (McDonnell et al., 2006). "
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