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Query: UMLS:C0019829 (Hodgkin's disease)
 30,247 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Neuronal output properties for input stimuli that evoke a deterministic response can be efficiently described by the interspike-interval function (Awiszus 1988a). It is shown in this paper that there are stimuli for which both the Hodgkin-Huxley (HH-) model of an action potential encoding membrane (Hodgkin and Huxley 1952) and a muscle spindle primary afferent generate responses which violate the conditions for a deterministic one. Instead of being stochastic these responses follow systematic rules, namely those for a semi-deterministic response, a class of neuronal responses established in this paper that includes the deterministic one. Instead of being stochastic these output properties are best described by the interspike-interval curve. A phase plane analysis of the internal properties of the HH-model underlying such responses shows that it is reasonable to assume that responses of an HH-model and consequently, all neurons for which an HH-model is a valid description of the action potential encoding process, always fall into the class of semi-deterministic responses, regardless of the input current density time course as long as it is large enough to maintain spike activity. Consequences of this assumption for the analysis of neuronal output properties are discussed with respect to output measures and efficient input stimuli.
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PMID:On the description of neuronal output properties using spike train data. 265 Jul 40

Neuronal excitability under stimuli with a complex time course is investigated on the basis of the numerical solution of the Hodgkin-Huxley equations. Each stimulus is composed of 100-1000 unitary excitatory postsynaptic potentials (uEPSP) that start randomly within a definite time window. Probability of initiating a spike [firing probability, FP(W)] as a function of the window width W is calculated by the Monte Carlo method. FP(W) has a step-like shape: it becomes equal to 1 for small W and almost vanishes as W exceeds some value Ws. The role of long-lasting somatic inhibition is analysed. Ws depends on the inhibition potential, but the step-like shape of FP is preserved. It is concluded that the capability of multisynaptic stimulation to cause a spike can be expressed in terms of temporal coherence between the synaptic inputs. Namely, the spike is initiated if the temporal coherence between active inputs is above a definite threshold. The threshold value can be effectively regulated by varying the inhibition potential.
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PMID:Neuron as time coherence discriminator. 867 60

Neuronal excitable membrane integrative function during temporal summation of EPSPs from multiple synaptic inputs has been studied by means of numerical solution of the Hodgkin and Huxley-type equations. A criterion for discrimination between successful and unsuccessful compound multisynaptic stimuli has been formulated in terms of temporal coherence between the unitary EPSPs composing the stimulus.
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PMID:[The neuron as a time-coherence detector during stimulation from multiple synaptic inputs]. 877 50

We study the stability and information encoding capacity of synchronized states in a neuronal network model that represents part of thalamic circuitry. Our model neurons have a Hodgkin-Huxley-type low-threshold calcium channel, display postinhibitory rebound, and are connected via GABAergic inhibitory synapses. We find that there is a threshold in synaptic strength, tau(c), below which there are no stable spiking network states. Above threshold the stable spiking state is a cluster state, where different groups of neurons fire consecutively, and each neuron fires with the same cluster each time. Weak noise destabilizes this state, but stronger noise drives the system into a different, self-organized, stochastically synchronized state. Neuronal firing is still organized in clusters, but individual neurons can hop from cluster to cluster. Noise can actually induce and sustain such a state below the threshold of synaptic strength. We do find a qualitative difference in the firing patterns between small (approximately 10 neurons) and large (approximately 1000 neurons) networks. We determine the information content of the spike trains in terms of two separate contributions: the spike-time jitter around cluster firing times, and the hopping from cluster to cluster. We quantify the information loss due to temporally correlated interspike intervals. Recent experiments on the locust olfactory system and striatal neurons suggest that the nervous system may actually use these two channels to encode separate and unique information.
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PMID:Synchronous clusters in a noisy inhibitory neural network. 1094 92

Neuronal action potentials are generated by clusters of ion channels between the Hillock and the first segment. If the clusters comprise a large number of sodium and potassium channels, action potentials are generated if the membrane potential exceeds a threshold of about -55 mV. Such behavior is well described by an excitable model such as, for example, the Hodgkin-Huxley equations. In this paper we show through stochastic modeling that if the size of the generating ion channel cluster is small, action potentials are generated regardless of whether the membrane potential is below or above the excitation threshold. Action potential generation is then determined by single-channel kinetics. We further show that this switch in generation mechanism manifests itself in peculiar statistical properties of the generated spike trains at small cluster sizes.
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PMID:Mechanism for neuronal spike generation by small and large ion channel clusters. 1532 84

Propofol, like most general anesthetic drugs, can induce both behavioral and electroencephalographic (EEG) manifestations of excitation, rather than sedation, at low doses. Neuronal excitation is unexpected in the presence of this GABA(A)-potentiating drug. We construct a series of network models to understand this paradox. Individual neurons have ion channel conductances with Hodgkin-Huxley-type formulations. Propofol increases the maximal conductance and time constant of decay of the synaptic GABA(A) current. Networks range in size from 2 to 230 neurons. Population output is measured as a function of pyramidal cell activity, with the electroencephalogram approximated by the sum of population AMPA activity between pyramidal cells. These model networks suggest propofol-induced paradoxical excitation may result from a membrane level interaction between the GABA(A) current and an intrinsic membrane slow potassium current (M-current). This membrane level interaction has consequences at the level of multicellular networks enabling a switch from baseline interneuron synchrony to propofol-induced interneuron antisynchrony. Large network models reproduce the clinical EEG changes characteristic of propofol-induced paradoxical excitation. The EEG changes coincide with the emergence of antisynchronous interneuron clusters in the model networks. Our findings suggest interneuron antisynchrony as a potential network mechanism underlying the generation of propofol-induced paradoxical excitation. As correlates of behavioral phenomenology, these networks may refine our understanding of the specific behavioral states associated with general anesthesia.
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PMID:Potential network mechanisms mediating electroencephalographic beta rhythm changes during propofol-induced paradoxical excitation. 1907 22

Neuronal variability has been thought to play an important role in the brain. As the variability mainly comes from the uncertainty in biophysical mechanisms, stochastic neuron models have been proposed for studying how neurons compute with noise. However, most papers are limited to simulating stochastic neurons in a digital computer. The speed and the efficiency are thus limited especially when a large neuronal network is of concern. This brief explores the feasibility of simulating the stochastic behavior of biological neurons in a very large scale integrated (VLSI) system, which implements a programmable and configurable Hodgkin-Huxley model. By simply injecting noise to the VLSI neuron, various stochastic behaviors observed in biological neurons are reproduced realistically in VLSI. The noise-induced variability is further shown to enhance the signal modulation of a neuron. These results point toward the development of analog VLSI systems for exploring the stochastic behaviors of biological neuronal networks in large scale.
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PMID:Real-time simulation of biologically realistic stochastic neurons in VLSI. 2057 Jul 68

Neuronal membrane potentials fluctuate stochastically due to conductance changes caused by random transitions between the open and closed 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 hypothesized 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 burstlike 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).
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PMID:Channel-noise-induced stochastic facilitation in an auditory brainstem neuron model. 2432 11

Large-scale numerical simulations of detailed brain circuit models are important for identifying hypotheses on brain functions and testing their consistency and plausibility. An ongoing challenge for simulating realistic models is, however, computational speed. In this paper, we present the GeNN (GPU-enhanced Neuronal Networks) framework, which aims to facilitate the use of graphics accelerators for computational models of large-scale neuronal networks to address this challenge. GeNN is an open source library that generates code to accelerate the execution of network simulations on NVIDIA GPUs, through a flexible and extensible interface, which does not require in-depth technical knowledge from the users. We present performance benchmarks showing that 200-fold speedup compared to a single core of a CPU can be achieved for a network of one million conductance based Hodgkin-Huxley neurons but that for other models the speedup can differ. GeNN is available for Linux, Mac OS X and Windows platforms. The source code, user manual, tutorials, Wiki, in-depth example projects and all other related information can be found on the project website http://genn-team.github.io/genn/.
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PMID:GeNN: a code generation framework for accelerated brain simulations. 2674 Mar 69

Transcranial magneto-acoustical stimulation (TMAS) uses ultrasonic waves and a static magnetic field to generate electric current in nerve tissues for the purpose of modulating neuronal activities. It has the advantage of high spatial resolution and penetration depth. Neuronal firing rhythms carry and transmit nerve information in neural systems. In this study, we investigated the phase-locking characteristics of neuronal firing rhythms with TMAS based on the Hodgkin-Huxley neuron model. The simulation results indicate that the modulation frequency of ultrasound can affect the phase-locking behaviors. The results of this study may help us to explain the potential firing mechanism of TMAS.
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PMID:A Phase-Locking Analysis of Neuronal Firing Rhythms with Transcranial Magneto-Acoustical Stimulation Based on the Hodgkin-Huxley Neuron Model. 2816 79


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