107 research outputs found

    Firing Pattern Formation by Transient Calcium Current in Model Motoneuron

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    Firing properties of neurons are important in signal detection and generation in nervous system. Spinal cord motoneurons have membrane properties adapted to muscle properties. In this work we explored the relation between the first interspike interval and the amplitude of injected current. It was shown that increased sensitivity of firing frequency to the injected current at higher firing frequencies is due to the transient calcium current. Particularly, the specific density of N-type channels and the speed of their deactivation define the steepness and onset of the second range in frequency versus current relation

    Auto and crosscorrelograms for the spike response of LIF neurons with slow synapses

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    An analytical description of the response properties of simple but realistic neuron models in the presence of noise is still lacking. We determine completely up to the second order the firing statistics of a single and a pair of leaky integrate-and-fire neurons (LIFs) receiving some common slowly filtered white noise. In particular, the auto- and cross-correlation functions of the output spike trains of pairs of cells are obtained from an improvement of the adiabatic approximation introduced in \cite{Mor+04}. These two functions define the firing variability and firing synchronization between neurons, and are of much importance for understanding neuron communication.Comment: 5 pages, 3 figure

    Neuron dynamics in the presence of 1/f noise

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    Interest in understanding the interplay between noise and the response of a non-linear device cuts across disciplinary boundaries. It is as relevant for unmasking the dynamics of neurons in noisy environments as it is for designing reliable nanoscale logic circuit elements and sensors. Most studies of noise in non-linear devices are limited to either time-correlated noise with a Lorentzian spectrum (of which the white noise is a limiting case) or just white noise. We use analytical theory and numerical simulations to study the impact of the more ubiquitous "natural" noise with a 1/f frequency spectrum. Specifically, we study the impact of the 1/f noise on a leaky integrate and fire model of a neuron. The impact of noise is considered on two quantities of interest to neuron function: The spike count Fano factor and the speed of neuron response to a small step-like stimulus. For the perfect (non-leaky) integrate and fire model, we show that the Fano factor can be expressed as an integral over noise spectrum weighted by a (low pass) filter function. This result elucidates the connection between low frequency noise and disorder in neuron dynamics. We compare our results to experimental data of single neurons in vivo, and show how the 1/f noise model provides much better agreement than the usual approximations based on Lorentzian noise. The low frequency noise, however, complicates the case for information coding scheme based on interspike intervals by introducing variability in the neuron response time. On a positive note, the neuron response time to a step stimulus is, remarkably, nearly optimal in the presence of 1/f noise. An explanation of this effect elucidates how the brain can take advantage of noise to prime a subset of the neurons to respond almost instantly to sudden stimuli.Comment: Phys. Rev. E in pres

    Universal properties of correlation transfer in integrate-and-fire neurons

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    One of the fundamental characteristics of a nonlinear system is how it transfers correlations in its inputs to correlations in its outputs. This is particularly important in the nervous system, where correlations between spiking neurons are prominent. Using linear response and asymptotic methods for pairs of unconnected integrate-and-fire (IF) neurons receiving white noise inputs, we show that this correlation transfer depends on the output spike firing rate in a strong, stereotyped manner, and is, surprisingly, almost independent of the interspike variance. For cells receiving heterogeneous inputs, we further show that correlation increases with the geometric mean spiking rate in the same stereotyped manner, greatly extending the generality of this relationship. We present an immediate consequence of this relationship for population coding via tuning curves

    Spiking Neurons Learning Phase Delays

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    Time differences between the two ears are an important cue for animals to azimuthally locate a sound source. The first binaural brainstem nucleus, in mammals the medial superior olive, is generally believed to perform the necessary computations. Its cells are sensitive to variations of interaural time differences of about 10 μs. The classical explanation of such a neuronal time-difference tuning is based on the physical concept of delay lines. Recent data, however, are inconsistent with a temporal delay and rather favor a phase delay. By means of a biophysical model we show how spike-timing-dependent synaptic learning explains precise interplay of excitation and inhibition and, hence, accounts for a physical realization of a phase delay

    Detection of subthreshold pulses in neurons with channel noise

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    Neurons are subject to various kinds of noise. In addition to synaptic noise, the stochastic opening and closing of ion channels represents an intrinsic source of noise that affects the signal processing properties of the neuron. In this paper, we studied the response of a stochastic Hodgkin-Huxley neuron to transient input subthreshold pulses. It was found that the average response time decreases but variance increases as the amplitude of channel noise increases. In the case of single pulse detection, we show that channel noise enables one neuron to detect the subthreshold signals and an optimal membrane area (or channel noise intensity) exists for a single neuron to achieve optimal performance. However, the detection ability of a single neuron is limited by large errors. Here, we test a simple neuronal network that can enhance the pulse detecting abilities of neurons and find dozens of neurons can perfectly detect subthreshold pulses. The phenomenon of intrinsic stochastic resonance is also found both at the level of single neurons and at the level of networks. At the network level, the detection ability of networks can be optimized for the number of neurons comprising the network.Comment: 14 pages, 9 figure

    Effects of Soil Types and Fertilizers on Growth, Yield, and Quality of Edible Amaranthus tricolor lines in Okinawa, Japan

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    Soil types and fertilizer regimes were evaluated on growth, yield, and quality of Amaranthus tricolor lines, IB (India Bengal), TW (Taiwan), BB (Bangladesh B), and BC (Bangladesh C) in developing management practices in Okinawa. Growth and yield of all amaranth lines were higher in gray soil (pH 8.4) than in dark red soil (pH 6.6) and red soil (pH 5.4). The combined NPK fertilizer resulted in highest growth parameters and yield of amaranths in all soils. Nitrogen fertilizer alone did not affect growth parameters and yield of amaranths in dark red and red soils. Growth parameters and yield increased similarly with the 30, 40, and 50 g m−2 of NPK fertilizer in BB line, and with the 20, 30, 40, and 50 g m−2 in BC line. Agronomic efficiency of NPK fertilizer at 50 g m−2 was not prominent on the amaranths, compared to the fertilizer at 40 g m−2. Amaranth lines had higher Na in dark red and red soils, while K and Mg in gray soil, Ca in gray and red soils, and Fe in dark red soil. The NPK fertilizer resulted in higher Na, Ca, Mg, and P in BB line in glasshouse. These minerals in BB line were not clearly affected, but in BC line were lower with NPK fertilizer at 20–50 g m−2 in field. These studies indicate that gray soil is best for amaranth cultivation and combined NPK fertilizer at 20–40 g m−2 is effective in gray soil in Okinawa for higher yield and minerals of amaranth

    Gradients and Modulation of K+ Channels Optimize Temporal Accuracy in Networks of Auditory Neurons

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    Accurate timing of action potentials is required for neurons in auditory brainstem nuclei to encode the frequency and phase of incoming sound stimuli. Many such neurons express “high threshold” Kv3-family channels that are required for firing at high rates (>∼200 Hz). Kv3 channels are expressed in gradients along the medial-lateral tonotopic axis of the nuclei. Numerical simulations of auditory brainstem neurons were used to calculate the input-output relations of ensembles of 1–50 neurons, stimulated at rates between 100–1500 Hz. Individual neurons with different levels of potassium currents differ in their ability to follow specific rates of stimulation but all perform poorly when the stimulus rate is greater than the maximal firing rate of the neurons. The temporal accuracy of the combined synaptic output of an ensemble is, however, enhanced by the presence of gradients in Kv3 channel levels over that measured when neurons express uniform levels of channels. Surprisingly, at high rates of stimulation, temporal accuracy is also enhanced by the occurrence of random spontaneous activity, such as is normally observed in the absence of sound stimulation. For any pattern of stimulation, however, greatest accuracy is observed when, in the presence of spontaneous activity, the levels of potassium conductance in all of the neurons is adjusted to that found in the subset of neurons that respond better than their neighbors. This optimization of response by adjusting the K+ conductance occurs for stimulus patterns containing either single and or multiple frequencies in the phase-locking range. The findings suggest that gradients of channel expression are required for normal auditory processing and that changes in levels of potassium currents across the nuclei, by mechanisms such as protein phosphorylation and rapid changes in channel synthesis, adapt the nuclei to the ongoing auditory environment
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