Coincidence detection in the Hodgkin–Huxley equations

Maršálek, Petr

doi:10.1016/s0303-2647(00)00110-6

Cited by 20 publications

(11 citation statements)

References 10 publications

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“…Input synchrony enhanced the resulting AP dispersion by more than tenfold, as measured by increasing the number of independently acting presynaptic neurons from 110 to 11000. Such results are naturally expected, since synchronous or coincident synaptic events commonly result in more precise firing of APs [78, 79]. However, some studies indicate that the noise effect improves signal transmission fidelity in some population coding schemes by decreasing the synchronicity of firing neurons [80].…”

Section: Discussionmentioning

confidence: 98%

The Effect of Neural Noise on Spike Time Precision in a Detailed CA3 Neuron Model

Kuriščák

Maršálek

Stroffek

et al. 2012

Computational and Mathematical Methods in Medicine

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Experimental and computational studies emphasize the role of the millisecond precision of neuronal spike times as an important coding mechanism for transmitting and representing information in the central nervous system. We investigate the spike time precision of a multicompartmental pyramidal neuron model of the CA3 region of the hippocampus under the influence of various sources of neuronal noise. We describe differences in the contribution to noise originating from voltage-gated ion channels, synaptic vesicle release, and vesicle quantal size. We analyze the effect of interspike intervals and the voltage course preceding the firing of spikes on the spike-timing jitter. The main finding of this study is the ranking of different noise sources according to their contribution to spike time precision. The most influential is synaptic vesicle release noise, causing the spike jitter to vary from 1 ms to 7 ms of a mean value 2.5 ms. Of second importance was the noise incurred by vesicle quantal size variation causing the spike time jitter to vary from 0.03 ms to 0.6 ms. Least influential was the voltage-gated channel noise generating spike jitter from 0.02 ms to 0.15 ms.

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Section: Discussionmentioning

confidence: 98%

The Effect of Neural Noise on Spike Time Precision in a Detailed CA3 Neuron Model

Kuriščák

Maršálek

Stroffek

et al. 2012

Computational and Mathematical Methods in Medicine

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“…Computational studies of searching over information transmission have discovered some parameter sets that facilitate the information processing, highlighting the existence of optimal firing threshold for CD neuron [20], [41]–[43]. Pantic et al investigated, analytically and numerically, different types of coincidence detection tasks containing both the coincidence detection of presynaptic cells, synchrony bursts, as well as the synchronous increase of firing rate.…”

Section: Discussionmentioning

confidence: 99%

The Role of Coincidence-Detector Neurons in the Reliability and Precision of Subthreshold Signal Detection in Noise

et al. 2013

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Subthreshold signal detection is an important task for animal survival in complex environments, where noise increases both the external signal response and the spontaneous spiking of neurons. The mechanism by which neurons process the coding of signals is not well understood. Here, we propose that coincidence detection, one of the ways to describe the functionality of a single neural cell, can improve the reliability and the precision of signal detection through detection of presynaptic input synchrony. Using a simplified neuronal network model composed of dozens of integrate-and-fire neurons and a single coincidence-detector neuron, we show how the network reads out the subthreshold noisy signals reliably and precisely. We find suitable pairing parameters, the threshold and the detection time window of the coincidence-detector neuron, that optimize the precision and reliability of the neuron. Furthermore, it is observed that the refractory period induces an oscillation in the spontaneous firing, but the neuron can inhibit this activity and improve the reliability and precision further. In the case of intermediate intrinsic states of the input neuron, the network responds to the input more efficiently. These results present the critical link between spiking synchrony and noisy signal transfer, which is utilized in coincidence detection, resulting in enhancement of temporally sensitive coding scheme.

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“…Our previous description of coincidence detectors in single units in Marsalek (2000) was based on the data from detailed ion channel biophysics in the sound localization neurons from chicken brainstem slices, (Reyes et al 1994(Reyes et al , 1996, in models of Agmon-Snir et al (1998) and in combined experimental and theoretical approaches, as in (Svirskis et al 2003) and Szalisznyó and Zalányi (2004). Papers Brand et al (2002) and Svirskis et al (2003) compare experimental data with the deterministic Hodgkin-Huxley type models.…”

Section: Theoretical Issuesmentioning

confidence: 99%

Proposed mechanisms for coincidence detection in the auditory brainstem

Maršálek

Lánský

2005

Biol Cybern

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Sound localization in mammals uses two distinct neural circuits, one for low- and one for high-frequency bands. Recent experiments call for revision of the theory explaining how the direction of incoming sound is calculated. We propose such a revised theory. Our theory is based on probabilistic spiking and probabilistic delay of spikes from both sides. We have applied the mechanism originally proposed as an operation on spike trains resulting in multiplication of firing rates. We have adapted this mechanism for the case of synchronous spike trains. The mechanism has to detect spikes from both sides within a short time window. Therefore, in both circuits neurons act as coincidence detectors. In the excitatory low-frequency circuit we call the mechanism the excitatory coincidence detection, to distinguish it from the mechanism of the inhibitory coincidence detection in the high-frequency circuit. The times to first spike and gains of the two mechanisms are calculated. We show how the output gains of the mechanisms predict the dip within the human frequency sensitivity range. This dip has been described in human psychophysical experiments.

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Coincidence detection in the Hodgkin–Huxley equations

Cited by 20 publications

References 10 publications

The Effect of Neural Noise on Spike Time Precision in a Detailed CA3 Neuron Model

The Effect of Neural Noise on Spike Time Precision in a Detailed CA3 Neuron Model

The Role of Coincidence-Detector Neurons in the Reliability and Precision of Subthreshold Signal Detection in Noise

Proposed mechanisms for coincidence detection in the auditory brainstem

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