Article

Intrinsic biophysical diversity decorrelates neuronal firing while increasing information content

Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.
Nature Neuroscience (Impact Factor: 16.1). 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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    • "Indeed, by comparing trial ensembles of dozens of simultaneously recorded neurons previous studies already suggested that noise correlations were essential for the attentional performance enhancements[39]and that feature attention is coordinated across hemispheres whereas spatial attention correlates only local groups of neu- rons[40]. We, on the other hand, had only single cell recordings available, but they revealed a high degree of heterogeneity in tuning which may be functional, not merely reflecting noise, but carrying relevant information26272829414243. In particular, such single-cell " weird " modulations may build up in a coordinated manner to give rise to population-level representations of the attended stimulus with a higher quality of encoding or with better and faster decodability properties[44,45]. "
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    ABSTRACT: Tuning curves are the functions that relate the responses of sensory neurons to various values within one continuous stimulus dimension (such as the orientation of a bar in the visual domain or the frequency of a tone in the auditory domain). They are commonly determined by fitting a model e.g. a Gaussian or other bell-shaped curves to the measured responses to a small subset of discrete stimuli in the relevant dimension. However, as neuronal responses are irregular and experimental measurements noisy, it is often difficult to determine reliably the appropriate model from the data. We illustrate this general problem by fitting diverse models to representative recordings from area MT in rhesus monkey visual cortex during multiple attentional tasks involving complex composite stimuli. We find that all models can be well-fitted, that the best model generally varies between neurons and that statistical comparisons between neuronal responses across different experimental conditions are affected quantitatively and qualitatively by specific model choices. As a robust alternative to an often arbitrary model selection, we introduce a model-free approach, in which features of interest are extracted directly from the measured response data without the need of fitting any model. In our attentional datasets, we demonstrate that data-driven methods provide descriptions of tuning curve features such as preferred stimulus direction or attentional gain modulations which are in agreement with fit-based approaches when a good fit exists. Furthermore, these methods naturally extend to the frequent cases of uncertain model selection. We show that model-free approaches can identify attentional modulation patterns, such as general alterations of the irregular shape of tuning curves, which cannot be captured by fitting stereotyped conventional models. Finally, by comparing datasets across different conditions, we demonstrate effects of attention that are cell- and even stimulus-specific. Based on these proofs-of-concept, we conclude that our data-driven methods can reliably extract relevant tuning information from neuronal recordings, including cells whose seemingly haphazard response curves defy conventional fitting approaches.
    Full-text · Article · Jan 2016 · PLoS ONE
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    • "demands required by different cortical microcircuits ( Ascoli et al., 2008 ; Battaglia et al., 2013) . In sensory systems , firing diversity allows for an enhanced representation of complex stimuli by expanding the range of responses to varied inputs ( Padmanabhan and Urban , 2010 ) . For example , firing heterogeneity in the cochlear nuclei allows individual neurons to extract information from complex auditory stimuli in both the frequency and time domains ( Young and Oertel , 2004 ; Yang and Feng , 2007 ) . "
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    ABSTRACT: Central neurons express a variety of neuronal types and ion channels that promote firing heterogeneity among their distinct neuronal populations. Action potential (AP) phasic firing, produced by low-threshold voltage-activated potassium currents (VAKCs), is commonly observed in mammalian brainstem neurons involved in the processing of temporal properties of the acoustic information. The avian caudomedial nidopallium (NCM) is an auditory area analogous to portions of the mammalian auditory cortex that is involved in the perceptual discrimination and memorization of birdsong and shows complex responses to auditory stimuli We performed in vitro whole-cell patch-clamp recordings in brain slices from adult zebra finches (Taeniopygia guttata) and observed that half of NCM neurons fire APs phasically in response to membrane depolarizations, while the rest fire transiently or tonically. Phasic neurons fired APs faster and with more temporal precision than tonic and transient neurons. These neurons had similar membrane resting potentials, but phasic neurons had lower membrane input resistance and time constant. Surprisingly phasic neurons did not express low-threshold VAKCs, which curtailed firing in phasic mammalian brainstem neurons, having similar VAKCs to other NCM neurons. The phasic firing was determined not by VAKCs, but by the potassium background leak conductances, which was more prominently expressed in phasic neurons, a result corroborated by pharmacological, dynamic-clamp, and modeling experiments. These results reveal a new role for leak currents in generating firing diversity in central neurons.
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    • "Somewhat interfering, but also popular, is the concept that intrinsic plasticity could itself be a mechanism for learning and memory (Alkon, 1984; Marder et al., 1996; Daoudal and Debanne, 2003; Disterhoft and Oh, 2006; Mozzachiodi and Byrne, 2010; Turrigiano, 2011). Additionally, intrinsic plasticity has been discussed in the context of cell type identity and variability (Golowasch et al., 1999; Padmanabhan and Urban, 2010; Marder and Taylor, 2011), development (Turrigiano and Nelson, 2004; Marder and Goaillard, 2006), and brain diseases, in particular epilepsy (Beck and Yaari, 2008; Wolfart and Laker, 2015). Much of neuronal development depends on intrinsic plasticity. "

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