The Experts below are selected from a list of 10596 Experts worldwide ranked by ideXlab platform
Jurgen Jost - One of the best experts on this subject based on the ideXlab platform.
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Weak Noise in Neurons May Powerfully Inhibit the Generation of Repetitive Spiking but Not Its Propagation
2013Co-Authors: Henry C Tuckwell, Jurgen JostAbstract:Many Neurons have epochs in which they fire action potentials in an approximately periodic fashion. To see what effects noise of relatively small amplitude has on such repetitive activity we recently examined the response of the Hodgkin-Huxley (HH) space-clamped system to such noise as the mean and variance of the applied current vary, near the bifurcation to periodic firing. This article is concerned with a more Realistic Neuron model which includes spatial extent. Employing the Hodgkin-Huxley partial differential equation system, the deterministic component of the input current is restricted to a small segment whereas the stochastic component extends over a region which may or may not overlap the deterministic component. For mean values below, near and above the critical values for repetitive spiking, the effects of weak noise of increasing strength is ascertained by simulation. As in the point model, small amplitude noise near the critical value dampens the spiking activity and leads to a minimum as noise level increases. This was the case for both additive noise and conductance-based noise. Uniform noise along the whole Neuron is only marginally more effective in silencing the cell than noise which occurs near the region of excitation. In fact it is found that if signal and noise overlap in spatial extent, then weak noise may inhibit spiking. If, however, signal and noise are applied on disjoint intervals, then the noise has no effect on the spiking activity, no matter how large its region of application, though the trajectories are naturally altered slightly by noise. Such effects could not be discerned in a point model and are important for Real Neuron behavior. Interference wit
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weak noise in Neurons may powerfully inhibit the generation of repetitive spiking but not its propagation
PLOS Computational Biology, 2010Co-Authors: Henry C Tuckwell, Jurgen JostAbstract:Many Neurons have epochs in which they fire action potentials in an approximately periodic fashion. To see what effects noise of relatively small amplitude has on such repetitive activity we recently examined the response of the Hodgkin-Huxley (HH) space-clamped system to such noise as the mean and variance of the applied current vary, near the bifurcation to periodic firing. This article is concerned with a more Realistic Neuron model which includes spatial extent. Employing the Hodgkin-Huxley partial differential equation system, the deterministic component of the input current is restricted to a small segment whereas the stochastic component extends over a region which may or may not overlap the deterministic component. For mean values below, near and above the critical values for repetitive spiking, the effects of weak noise of increasing strength is ascertained by simulation. As in the point model, small amplitude noise near the critical value dampens the spiking activity and leads to a minimum as noise level increases. This was the case for both additive noise and conductance-based noise. Uniform noise along the whole Neuron is only marginally more effective in silencing the cell than noise which occurs near the region of excitation. In fact it is found that if signal and noise overlap in spatial extent, then weak noise may inhibit spiking. If, however, signal and noise are applied on disjoint intervals, then the noise has no effect on the spiking activity, no matter how large its region of application, though the trajectories are naturally altered slightly by noise. Such effects could not be discerned in a point model and are important for Real Neuron behavior. Interference with the spike train does nevertheless occur when the noise amplitude is larger, even when noise and signal do not overlap, being due to the instigation of secondary noise-induced wave phenomena rather than switching the system from one attractor (firing regularly) to another (a stable point).
Henry C Tuckwell - One of the best experts on this subject based on the ideXlab platform.
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Weak Noise in Neurons May Powerfully Inhibit the Generation of Repetitive Spiking but Not Its Propagation
2013Co-Authors: Henry C Tuckwell, Jurgen JostAbstract:Many Neurons have epochs in which they fire action potentials in an approximately periodic fashion. To see what effects noise of relatively small amplitude has on such repetitive activity we recently examined the response of the Hodgkin-Huxley (HH) space-clamped system to such noise as the mean and variance of the applied current vary, near the bifurcation to periodic firing. This article is concerned with a more Realistic Neuron model which includes spatial extent. Employing the Hodgkin-Huxley partial differential equation system, the deterministic component of the input current is restricted to a small segment whereas the stochastic component extends over a region which may or may not overlap the deterministic component. For mean values below, near and above the critical values for repetitive spiking, the effects of weak noise of increasing strength is ascertained by simulation. As in the point model, small amplitude noise near the critical value dampens the spiking activity and leads to a minimum as noise level increases. This was the case for both additive noise and conductance-based noise. Uniform noise along the whole Neuron is only marginally more effective in silencing the cell than noise which occurs near the region of excitation. In fact it is found that if signal and noise overlap in spatial extent, then weak noise may inhibit spiking. If, however, signal and noise are applied on disjoint intervals, then the noise has no effect on the spiking activity, no matter how large its region of application, though the trajectories are naturally altered slightly by noise. Such effects could not be discerned in a point model and are important for Real Neuron behavior. Interference wit
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weak noise in Neurons may powerfully inhibit the generation of repetitive spiking but not its propagation
PLOS Computational Biology, 2010Co-Authors: Henry C Tuckwell, Jurgen JostAbstract:Many Neurons have epochs in which they fire action potentials in an approximately periodic fashion. To see what effects noise of relatively small amplitude has on such repetitive activity we recently examined the response of the Hodgkin-Huxley (HH) space-clamped system to such noise as the mean and variance of the applied current vary, near the bifurcation to periodic firing. This article is concerned with a more Realistic Neuron model which includes spatial extent. Employing the Hodgkin-Huxley partial differential equation system, the deterministic component of the input current is restricted to a small segment whereas the stochastic component extends over a region which may or may not overlap the deterministic component. For mean values below, near and above the critical values for repetitive spiking, the effects of weak noise of increasing strength is ascertained by simulation. As in the point model, small amplitude noise near the critical value dampens the spiking activity and leads to a minimum as noise level increases. This was the case for both additive noise and conductance-based noise. Uniform noise along the whole Neuron is only marginally more effective in silencing the cell than noise which occurs near the region of excitation. In fact it is found that if signal and noise overlap in spatial extent, then weak noise may inhibit spiking. If, however, signal and noise are applied on disjoint intervals, then the noise has no effect on the spiking activity, no matter how large its region of application, though the trajectories are naturally altered slightly by noise. Such effects could not be discerned in a point model and are important for Real Neuron behavior. Interference with the spike train does nevertheless occur when the noise amplitude is larger, even when noise and signal do not overlap, being due to the instigation of secondary noise-induced wave phenomena rather than switching the system from one attractor (firing regularly) to another (a stable point).
Dieter Jaeger - One of the best experts on this subject based on the ideXlab platform.
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characterizing the heterogeneity of globus pallidus Neuron behavior by comparing a Real Neuron database with model databases of varying conductance parameters
BMC Neuroscience, 2007Co-Authors: Cengiz Gunay, Jeremy R Edgerton, Dieter JaegerAbstract:Background The function of brain networks is highly dependent on the dynamical properties of single Neurons, whose activity ranges from complex spontaneous activity patterns such as oscillations and bursting, to a variety of synaptic response patterns serving functions such as coincidence detection or rebound firing. These dynamical properties vary in time through modulation and plasticity, and are also heterogeneous across individual Neurons of the same type. Commonly, Neurons show two to five-fold variability in the density of voltage-gated conductances, which accounts for large variations in dynamical behavior.
Cengiz Gunay - One of the best experts on this subject based on the ideXlab platform.
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characterizing the heterogeneity of globus pallidus Neuron behavior by comparing a Real Neuron database with model databases of varying conductance parameters
BMC Neuroscience, 2007Co-Authors: Cengiz Gunay, Jeremy R Edgerton, Dieter JaegerAbstract:Background The function of brain networks is highly dependent on the dynamical properties of single Neurons, whose activity ranges from complex spontaneous activity patterns such as oscillations and bursting, to a variety of synaptic response patterns serving functions such as coincidence detection or rebound firing. These dynamical properties vary in time through modulation and plasticity, and are also heterogeneous across individual Neurons of the same type. Commonly, Neurons show two to five-fold variability in the density of voltage-gated conductances, which accounts for large variations in dynamical behavior.
Wilson Rosa De Oliveira - One of the best experts on this subject based on the ideXlab platform.
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quantum Neuron with Real weights
Neural Networks, 2021Co-Authors: Claudio A Monteiro, Gustavo I S Filho, Matheus Hopper J Costa, Fernando M De Paula Neto, Wilson Rosa De OliveiraAbstract:Abstract This paper proposes a new model of a Real weights quantum Neuron exploiting the so-called quantum parallelism which allows for an exponential speedup of computations. The quantum Neurons were trained in a classical–quantum approach, considering the delta rule to update the values of the weights in an image database of three distinct patterns. We performed classical simulations and also executed experiments in an actual small-scale quantum processor. The results of the experiments show that the proposed quantum Real Neuron model has a good generalisation capacity, demonstrating better accuracy than the traditional binary quantum perceptron model.
