5,557 research outputs found

    Channel noise induced stochastic facilitation in an auditory brainstem neuron model

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    Neuronal membrane potentials fluctuate stochastically due to conductance changes caused by random transitions between the open and close states of ion channels. Although it has previously been shown that channel noise can nontrivially affect neuronal dynamics, it is unknown whether ion-channel noise is strong enough to act as a noise source for hypothesised noise-enhanced information processing in real neuronal systems, i.e. 'stochastic facilitation.' Here, we demonstrate that biophysical models of channel noise can give rise to two kinds of recently discovered stochastic facilitation effects in a Hodgkin-Huxley-like model of auditory brainstem neurons. The first, known as slope-based stochastic resonance (SBSR), enables phasic neurons to emit action potentials that can encode the slope of inputs that vary slowly relative to key time-constants in the model. The second, known as inverse stochastic resonance (ISR), occurs in tonically firing neurons when small levels of noise inhibit tonic firing and replace it with burst-like dynamics. Consistent with previous work, we conclude that channel noise can provide significant variability in firing dynamics, even for large numbers of channels. Moreover, our results show that possible associated computational benefits may occur due to channel noise in neurons of the auditory brainstem. This holds whether the firing dynamics in the model are phasic (SBSR can occur due to channel noise) or tonic (ISR can occur due to channel noise).Comment: Published by Physical Review E, November 2013 (this version 17 pages total - 10 text, 1 refs, 6 figures/tables); Associated matlab code is available online in the ModelDB repository at http://senselab.med.yale.edu/ModelDB/ShowModel.asp?model=15148

    Stable Propagation of a Burst Through a One-Dimensional Homogeneous Excitatory Chain Model of Songbird Nucleus HVC

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    We demonstrate numerically that a brief burst consisting of two to six spikes can propagate in a stable manner through a one-dimensional homogeneous feedforward chain of non-bursting neurons with excitatory synaptic connections. Our results are obtained for two kinds of neuronal models, leaky integrate-and-fire (LIF) neurons and Hodgkin-Huxley (HH) neurons with five conductances. Over a range of parameters such as the maximum synaptic conductance, both kinds of chains are found to have multiple attractors of propagating bursts, with each attractor being distinguished by the number of spikes and total duration of the propagating burst. These results make plausible the hypothesis that sparse precisely-timed sequential bursts observed in projection neurons of nucleus HVC of a singing zebra finch are intrinsic and causally related.Comment: 13 pages, 6 figure

    Probing the dynamics of identified neurons with a data-driven modeling approach

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    In controlling animal behavior the nervous system has to perform within the operational limits set by the requirements of each specific behavior. The implications for the corresponding range of suitable network, single neuron, and ion channel properties have remained elusive. In this article we approach the question of how well-constrained properties of neuronal systems may be on the neuronal level. We used large data sets of the activity of isolated invertebrate identified cells and built an accurate conductance-based model for this cell type using customized automated parameter estimation techniques. By direct inspection of the data we found that the variability of the neurons is larger when they are isolated from the circuit than when in the intact system. Furthermore, the responses of the neurons to perturbations appear to be more consistent than their autonomous behavior under stationary conditions. In the developed model, the constraints on different parameters that enforce appropriate model dynamics vary widely from some very tightly controlled parameters to others that are almost arbitrary. The model also allows predictions for the effect of blocking selected ionic currents and to prove that the origin of irregular dynamics in the neuron model is proper chaoticity and that this chaoticity is typical in an appropriate sense. Our results indicate that data driven models are useful tools for the in-depth analysis of neuronal dynamics. The better consistency of responses to perturbations, in the real neurons as well as in the model, suggests a paradigm shift away from measuring autonomous dynamics alone towards protocols of controlled perturbations. Our predictions for the impact of channel blockers on the neuronal dynamics and the proof of chaoticity underscore the wide scope of our approach

    Somatic and dendritic GABAB receptors regulate neuronal excitability via different mechanisms

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    GABAB receptors play a key role in regulating neuronal excitability in the brain. Whereas the impact of somatic GABAB receptors on neuronal excitability has been studied in some detail, much less is known about the role of dendritic GABAB receptors. Here, we investigate the impact of GABAB receptor activation on the somato-dendritic excitability of layer 5 pyramidal neurons in the rat barrel cortex. Activation of GABAB receptors led to hyperpolarization and a decrease in membrane resistance that was greatest at somatic and proximal dendritic locations. These effects were occluded by low concentrations of barium (100 μM), suggesting that they are mediated by potassium channels. In contrast, activation of dendritic GABAB receptors decreased the width of backpropagating action potential (APs) and abolished dendritic calcium electrogenesis, indicating that dendritic GABAB receptors regulate excitability, primarily via inhibition of voltage-dependent calcium channels. These distinct actions of somatic and dendritic GABAB receptors regulated neuronal output in different ways. Activation of somatic GABAB receptors led to a reduction in neuronal output, primarily by increasing the AP rheobase, whereas activation of dendritic GABAB receptors blocked burst firing, decreasing AP output in the absence of a significant change in somatic membrane properties. Taken together, our results show that GABAB receptors regulate somatic and dendritic excitability of cortical pyramidal neurons via different cellular mechanisms. Somatic GABAB receptors activate potassium channels, leading primarily to a subtractive or shunting form of inhibition, whereas dendritic GABAB receptors inhibit dendritic calcium electrogenesis, leading to a reduction in bursting firing.NHMR

    Fine Gating Properties of Channels Responsible for Persistent Sodium Current Generation in Entorhinal Cortex Neurons

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    The gating properties of channels responsible for the generation of persistent Na+ current (INaP) in entorhinal cortex layer II principal neurons were investigated by performing cell-attached, patch-clamp experiments in acutely isolated cells. Voltage-gated Na+-channel activity was routinely elicited by applying 500-ms depolarizing test pulses positive to −60 mV from a holding potential of −100 mV. The channel activity underlying INaP consisted of prolonged and frequently delayed bursts during which repetitive openings were separated by short closings. The mean duration of openings within bursts was strongly voltage dependent, and increased by e times per every ∼12 mV of depolarization. On the other hand, intraburst closed times showed no major voltage dependence. The mean duration of burst events was also relatively voltage insensitive. The analysis of burst-duration frequency distribution returned two major, relatively voltage-independent time constants of ∼28 and ∼190 ms. The probability of burst openings to occur also appeared largely voltage independent. Because of the above “persistent” Na+-channel properties, the voltage dependence of the conductance underlying whole-cell INaP turned out to be largely the consequence of the pronounced voltage dependence of intraburst open times. On the other hand, some kinetic properties of the macroscopic INaP, and in particular the fast and intermediate INaP-decay components observed during step depolarizations, were found to largely reflect mean burst duration of the underlying channel openings. A further INaP decay process, namely slow inactivation, was paralleled instead by a progressive increase of interburst closed times during the application of long-lasting (i.e., 20 s) depolarizing pulses. In addition, long-lasting depolarizations also promoted a channel gating modality characterized by shorter burst durations than normally seen using 500-ms test pulses, with a predominant burst-duration time constant of ∼5–6 ms. The above data, therefore, provide a detailed picture of the single-channel bases of INaP voltage-dependent and kinetic properties in entorhinal cortex layer II neurons

    Noise induced processes in neural systems

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    Real neurons, and their networks, are far too complex to be described exactly by simple deterministic equations. Any description of their dynamics must therefore incorporate noise to some degree. It is my thesis that the nervous system is organized in such a way that its performance is optimal, subject to this constraint. I further contend that neuronal dynamics may even be enhanced by noise, when compared with their deterministic counter-parts. To support my thesis I will present and analyze three case studies. I will show how noise might (i) extend the dynamic range of mammalian cold-receptors and other cells that exhibit a temperature-dependent discharge; (ii) feature in the perception of ambiguous figures such as the Necker cube; (iii) alter the discharge pattern of single cells

    The what and where of adding channel noise to the Hodgkin-Huxley equations

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    One of the most celebrated successes in computational biology is the Hodgkin-Huxley framework for modeling electrically active cells. This framework, expressed through a set of differential equations, synthesizes the impact of ionic currents on a cell's voltage -- and the highly nonlinear impact of that voltage back on the currents themselves -- into the rapid push and pull of the action potential. Latter studies confirmed that these cellular dynamics are orchestrated by individual ion channels, whose conformational changes regulate the conductance of each ionic current. Thus, kinetic equations familiar from physical chemistry are the natural setting for describing conductances; for small-to-moderate numbers of channels, these will predict fluctuations in conductances and stochasticity in the resulting action potentials. At first glance, the kinetic equations provide a far more complex (and higher-dimensional) description than the original Hodgkin-Huxley equations. This has prompted more than a decade of efforts to capture channel fluctuations with noise terms added to the Hodgkin-Huxley equations. Many of these approaches, while intuitively appealing, produce quantitative errors when compared to kinetic equations; others, as only very recently demonstrated, are both accurate and relatively simple. We review what works, what doesn't, and why, seeking to build a bridge to well-established results for the deterministic Hodgkin-Huxley equations. As such, we hope that this review will speed emerging studies of how channel noise modulates electrophysiological dynamics and function. We supply user-friendly Matlab simulation code of these stochastic versions of the Hodgkin-Huxley equations on the ModelDB website (accession number 138950) and http://www.amath.washington.edu/~etsb/tutorials.html.Comment: 14 pages, 3 figures, review articl
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