Spatially distributed dendritic resonance selectively filters synaptic input

© 2014 Laudanski et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.An important task performed by a neuron is the selection of relevant inputs from among thousands of synapses impinging on the dendritic tree. Synaptic plasticity enables this by strenghtening a subset of synapses that are, presumably, functionally relevant to the neuron. A different selection mechanism exploits the resonance of the dendritic membranes to preferentially filter synaptic inputs based on their temporal rates. A widely held view is that a neuron has one resonant frequency and thus can pass through one rate. Here we demonstrate through mathematical analyses and numerical simulations that dendritic resonance is inevitably a spatially distributed property; and therefore the resonance frequency varies along the dendrites, and thus endows neurons with a powerful spatiotemporal selection mechanism that is sensitive both to the dendritic location and the temporal structure of the incoming synaptic inputs.Peer reviewe

Calcium window currents, periodic forcing and chaos: understanding single neuron response with a discontinuous one-dimensional map

Thalamocortical (TC) neurones are known to express the low-voltage activated, inactivating Ca2+ current IT. The triggering of this current underlies the generation of low threshold Ca2+ potentials that may evoke single or bursts of action potentials. Moreover, this current can contribute to an intrinsic slow (<1 Hz) oscillation whose rhythm is partly determined by the steady state component of IT and its interaction with a leak current. This steady state, or window current as it is so often called, has received relatively little theoretical attention despite its importance in determining the electroresponsiveness and input-output relationship of TC neurones. In this paper, we introduce an integrate-and-fire spiking neuron model that includes a biophysically realistic model of IT. We briefly review the subthreshold bifurcation diagram of this model with constant current injection before moving on to consider its response to periodic forcing. Direct numerical simulations show that as well as the expected mode-locked responses there are regions of parameter space that support chaotic behavior. To reveal the mechanism by which the window current generates a chaotic response to periodic forcing we consider a piecewise linear caricature of the dynamics for the gating variables in the model of IT. This model can be analyzed in closed form and is shown to support an unstable set of periodic orbits. Trajectories are repelled from these organizing centers until they reach the threshold for firing. By determining the condition for a grazing bifurcation (at the border between a spiking and nonspiking event) we show how knowledge of the unstable periodic orbits (existence and stability) can be combined with the grazing condition to determine an effective one-dimensional map that captures the essentials of the chaotic behavior. This map is discontinuous and has strong similarities with the universal limit mapping in grazing bifurcations derived in the context of impacting mechanical systems [A. B. Nordmark, Phys. Rev. E 55, 266 (1997)]

Integrate-and-fire models in the auditory system : Dynamics of single neurons and neural populations

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A 3D Model-based Simulation of the Electric Field During Cochlear Stimulation

International audienceThe goal of research is to acquire an accurate description of the electric field inside an implanted cochlea through validated 3D numerical simulations. The simulation process should accommodate different cochlear geometries and stimulation patterns. Results from this simulation tool will be used to design better personalized cochlear implants with less energy consumptions in the future

Comparison between the STRF parameters derived from the STRF_voc and the STRF_dmr.

<p>A. Scattergram showing the values of the BF derived from the STRF_voc (abscissa) against the values of the BF derived from the STRF_dmr (ordinates). For half of the cases, the values are similar (dots around the diagonal line) whereas for the other half, the values derived from the STRF_dmr were higher than those derived from the STRF_voc. B. Scattergram showing the bandwidth values derived from the STRF_voc (abscissa) against the bandwidth value derived from the STRF_dmr (ordinates). In many cases, the values were lower with STRF_voc indicating a larger bandwidth of excitatory responses when tested with vocalizations. C. Scattergram showing the latency values derived from the STRF_voc (abscissa) against the bandwidth value derived from the STRF_dmr (ordinates). The latencies of the excitatory responses were often similar but, in some cases, they were lower with DMRs than with vocalizations. STRF units and scale are the same as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0050539#pone-0050539-g002" target="_blank">Figure 2</a>.</p