A Neiman

AA Lazar

AB Neiman

AG Dimitrov

CK Machens

GC Horwitz

Jan-Hendrik Schleimer

JH Schleimer

MH Rowe

Susanne Schreiber

BMC Neuroscience

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POSTER PRESENTATION Open AccessInfluence of biophysical properties ontemporal filters in a sensory neuronJan-Hendrik Schleimer1*, Susanne Schreiber2From Twenty Second Annual Computational Neuroscience Meeting: CNS*2013Paris, France. 13-18 July 2013Sensory pathways implement filters that extract relevantinformation from the environment [1]. These filteringproperties depend in intricate ways on the biophysicalparameters of the underlying neuronal architecture.Understanding the link between computational aspects,such as response properties or spike statistics, and theunderlying biophysics is a question that can beaddressed with theoretical methods.Many primary sensory neurons operate in a mean-dri-ven regime, where mean intensity is represented by theaverage firing rate, while the temporal structure of thestimulus around its mean is encoded into the particularstructure of the spike train in analogy to an irregularsampler [2]. Examples of neurons applying this strategyinclude the paddlefish’s electrosensory system [3], grass-hopper auditory receptors [4], or the vestibular systemof the turtle [5].Spikes are generated from the dynamics of voltage-dependent ion channels. Different sensory neurons,however, may use different compositions of spike-gener-ating channel types. These do not only render the cellexcitable in the first place, they also have a large influ-ence on a neuron’s transfer function. In addition, someion channels shape the subthreshold dynamics of neu-rons, but it is less clear, how they affect the temporalstructure of spike trains when the neurons are driventonically.Several channel types, including HCN channels, areknown to cause subthreshold resonance. This propertyresults in largest subthreshold response amplitudes of aneuron at a particular frequency, hence termed the reso-nance frequency. The resulting band-pass like filterproperties have a strong effect on fluctuation-drivenneurons. Their influence on tonic spiking, however, hasbeen less explored. Accordingly, the influence of chan-nels that cause subthreshold resonance, such as HCN,on sensory systems like the hair cells of the mouseinner ear is still under debate [6]. Here, we use phase-response-curve-based methods within the framework ofnumerical continuation to resolve whether and how theeffects of these channels carry over into the suprathres-hold, mean-driven dynamics.To this end, we use computational models of hair-cellmechanoreceptors [7]. Our approach is to, first, derivethe phase oscillator that is input-output equivalent tothe full biophysical model. Then, applying linearresponse theory, the transfer function can be estimatedin the regime of weak-amplitude stimulation [8]. Thistransfer function maps time-dependent stimuli to theinstantaneous firing rate. We show that the poles andzeros of the transfer spectrum are related to the Fouriercomponents of the systems phase response curve (PRC)as well as the average intrinsic noise level, which is dueto a finite ensemble of stochastic ion channels.We find that both properties (1) the PRC - a conse-quence of the deterministic part of the dynamical system -and (2) the intrinsic stochasticity, are affected by the pre-sence of HCN channels. Further, we use an overall mea-sure of information transmission, the stimulus-responseinformation, to approximate the contribution of HCNchannels to information transfer in given frequency bands.Author details1Bernstein Center for Computational Neuroscience, Berlin, Germany.2Institute for Theoretical Biology, Humboldt University, Berlin, Germany.Published: 8 July 2013References1. Dimitrov AG, Lazar AA, Victor JD: Information theory in neuroscience.J Comput Neurosci 2011, 30:1-5.2. Lazar AA: Information Representation with an Ensemble of Hodgkin-Huxley Neurons. Neurocomputing 2007, 70:1764-1771.* Correspondence: schleimj@hu-berlin.de1Bernstein Center for Computational Neuroscience, Berlin, GermanyFull list of author information is available at the end of the articleSchleimer and Schreiber BMC Neuroscience 2013, 14(Suppl 1):P347http://www.biomedcentral.com/1471-2202/14/S1/P347© 2013 Schleimer and Schreiber; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of theCreative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, andreproduction in any medium, provided the original work is properly cited.3. Neiman A, Russell D: Sensory coding in oscillatory electroreceptors ofpaddlefish. Chaos 2011, 21(4):047505.4. Machens CK, Stemmler MB, Prinz P, Krahe R, Ronacher B, Herz AVM:Representation of acoustic communication signals in insect auditoryreceptors. J Neurosci 2001, 21:3215.5. Rowe MH, Neiman AB: Information analysis of posterior canal afferents inthe turtle Trachemys scripta elegans. Brain Research 2012, 1434:226-242.6. Horwitz GC, Lelli A, Géléoc GSG, Holt JR: HCN Channels Are Not Requiredfor Mechanotransduction in Sensory Hair Cells of the Mouse Inner Ear.PLoS ONE 2010, 5:e8627.7. Neiman AB, Dierkes K, Lindner B, Han L, Shilnikov AL: Spontaneous voltageoscillations and response dynamics of a Hodgkin-Huxley type model ofsensory hair cells. J Math Neurosci 2011, 1:11.8. Schleimer JH, Stemmler M: Coding of information in limit cycle oscillators.Phys Rev Lett 2009, 103(24):248105.doi:10.1186/1471-2202-14-S1-P347Cite this article as: Schleimer and Schreiber: Influence of biophysicalproperties on temporal filters in a sensory neuron. BMC Neuroscience2013 14(Suppl 1):P347.Submit your next manuscript to BioMed Centraland take full advantage of: • Convenient online submission• Thorough peer review• No space constraints or color figure charges• Immediate publication on acceptance• Inclusion in PubMed, CAS, Scopus and Google Scholar• Research which is freely available for redistributionSubmit your manuscript at www.biomedcentral.com/submitSchleimer and Schreiber BMC Neuroscience 2013, 14(Suppl 1):P347http://www.biomedcentral.com/1471-2202/14/S1/P347Page 2 of 2

Influence of biophysical properties on temporal filters in a sensory neuron

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Influence of biophysical properties on temporal filters in a sensory neuron

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