Using interspike intervals to quantify noise effects on spike trains in temperature encoding neurons

Du, Yukou; Lu, Qishao; Wang, Rubin

doi:10.1007/s11571-010-9112-2

Cited by 21 publications

(18 citation statements)

References 22 publications

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“…Although the Hodgkin-Huxley-type Huber-Brown cold receptor model can simulate the temperature dependences of cold receptors [28,29], a computer simulation model that explains the temperature dependence of the firing patterns in Aplysia neurons has not yet been found. The temperature-dependent reproducible properties of the 14 AP parameters described in this study can be considered as the basis for setting up a set of nonlinear differential equations that approximate the temperature-dependent firing patterns of neurons in Aplysia .…”

Section: Discussionmentioning

confidence: 99%

Temperature Dependence of Action Potential Parameters inAplysiaNeurons

Hyun

Lee

et al. 2012

Neurosignals

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Although the effects of temperature changes on the activity of neurons have been studied in Aplysia, the reproducibility of the temperature dependence of the action potential (AP) parameters has not been verified. To this end, we performed experiments using Aplysia neurons. Fourteen AP parameters were analyzed using the long-term data series recorded during the experiments. Our analysis showed that nine of the AP parameters decreased as the temperature increased: the AP amplitude (A_AP), membrane potential at the positive peak (V_pp), interspike interval, first half (Δt_r1) and last half (Δt_r2) of the temperature rising phase, first half (Δt_f1) and last half (Δt_f2) of the temperature falling phase, AP (Δt_{AP, 1/2}), and differentiated signal (Δt_{DS, 1/2}) half-width durations. Five of the AP parameters increased with temperature: the differentiated signal amplitude (A_DS), absolute value of the membrane potential at negative peak (∣ V_np ∣), absolute value of the maximum slope of the AP during the temperature rising (∣ –MSR ∣) and falling (∣–MSF∣) phases, and spiking frequency (Frequency). This work could provide the basis for a better understanding of the elementary processes underlying the temperature-dependent neuronal activity in Aplysia.

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Section: Discussionmentioning

confidence: 99%

Temperature Dependence of Action Potential Parameters inAplysiaNeurons

Hyun

Lee

et al. 2012

Neurosignals

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“…3c) that the performance was almost identical to the mask condition. Thus, in addition to noise (Du et al 2010) burst duration is dependent on the stimulus duration.…”

Section: Model Responses After Backward Maskingmentioning

confidence: 99%

Stimulus detection after interruption of the feedforward response in a backward masking paradigm

et al. 2012

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In backward masking, a target stimulus is rendered invisible by the presentation of a second stimulus, the mask. When the mask is effective, neural responses to the target are suppressed. Nevertheless, weak target responses sometimes may produce a behavioural response. It remains unclear whether the reduced target response is a purely feedforward response or that it includes recurrent activity. Using a feedforward neural network of biological plausible spiking neurons, we tested whether a transient spike burst is sufficient for face categorization. After training the network, the system achieved face/non-face categorization for sets of grayscale images. In a backward masking paradigm, the transient burst response was cut off thereby reducing the feedforward target response. Despite the suppressed feedforward responses stimulus classification remained robust. Thus according to our model data stimulus detection is possible with purely, suppressed feedforward responses.

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“…For example, noise can induce and enhance the synchronization of neurons in network and coupled oscillators (Neiman and Russell 2002;Zhou and Kurths 2002;He et al 2003;Xu et al 2007;Kiss et al 2003;Qu et al 2012;Shi et al 2008). Particularly, various of ordered spatiotemporal patterns and coherence resonance could be induced in the network by an optimized noise (GarciaOjalvo et al 1993;Buceta et al 2003;Perc 2005;Tang et al 2012;Liu et al 2010;Li et al 2009;Du et al 2010). More interesting, distinct transition of patterns could be induced by the fluctuation of bifurcation parameters (Vanag and Epstein 2001;Brusch et al 2004;Xie et al 2006;Ouyang and Felesselles 1996;Zhou and Ouyang 2000).…”

Section: Introductionmentioning

confidence: 99%

Noise-induced spatiotemporal patterns in Hodgkin–Huxley neuronal network

Liu

et al. 2013

Cogn Neurodyn

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The effect of noise on the pattern selection in a regular network of Hodgkin-Huxley neurons is investigated, and the transition of pattern in the network is measured from subexcitable to excitable media. Extensive numerical results confirm that kinds of travelling wave such as spiral wave, circle wave and target wave could be developed and kept alive in the subexcitable network due to the noise. In the case of excitable media under noise, the developed spiral wave and target wave could coexist and new target-like wave is induced near to the border of media. The averaged membrane potentials over all neurons in the network are calculated to detect the periodicity of the time series and the generated traveling wave. Furthermore, the firing probabilities of neurons in networks are also calculated to analyze the collective behavior of networks.

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Using interspike intervals to quantify noise effects on spike trains in temperature encoding neurons

Cited by 21 publications

References 22 publications

Temperature Dependence of Action Potential Parameters inAplysiaNeurons

Temperature Dependence of Action Potential Parameters inAplysiaNeurons

Stimulus detection after interruption of the feedforward response in a backward masking paradigm

Noise-induced spatiotemporal patterns in Hodgkin–Huxley neuronal network

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