Magnetic polarization of the Schumann resonances: An asymptotic theory

Bezrodny, V. G.

doi:10.1016/j.jastp.2007.03.007

Cited by 7 publications

(8 citation statements)

References 11 publications

(19 reference statements)

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“…It can be assumed that the excitatory cells in the S1 hind limb area represent information related to a narrow stimulus range at the single cell level and cover all stimulus frequencies used in this study collectively as a population. In previous studies investigating the rat barrel cortex, cortical cells were found to encode whisker deflection velocity and vibration speed (Pinto et al., 2000; Arabzadeh et al., 2004; Gerdjikov et al., 2010). Although our results did not reveal specific stimulus parameters encoded in the cortical cells (e.g., frequency, amplitude, velocity, etc.…”

Section: Discussionmentioning

confidence: 99%

“…However, with advances in imaging techniques, the whisker selectivity of individual neurons within a barrel has been reported to be “salt and pepper”-like, that is, more heterogeneous than formerly imagined (Sato et al., 2007; Clancy et al., 2015). It has also been reported that the neurons in the barrel code the velocity of passive whisker vibrations (Pinto et al., 2000; Arabzadeh et al., 2004; Gerdjikov et al., 2010).…”

Section: Introductionmentioning

confidence: 96%

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Cell Type Specific Representation of Vibro-tactile Stimuli in the Mouse Primary Somatosensory Cortex

Hayashi

Yoshida

Ohki

2018

Front. Neural Circuits

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Although the processing of whisker deflections in the barrel area of the rodent primary somatosensory cortex (S1) has been studied extensively, how cutaneous vibro-tactile stimuli are processed in the rodent S1 outside the barrel area has not been fully examined. Particularly, the cell-type specific representation of multiple vibration frequencies in genetically identified inhibitory cells in the S1 has not been examined. Using two-photon calcium imaging, we examined the responses to vibration stimuli of excitatory and inhibitory neurons in the S1 hind limb area of male and female mice. The excitatory cells showed relatively sharp selectivity to vibration stimuli, whereas the inhibitory cells exhibited less selectivity. The excitatory and inhibitory cells with different preferred stimuli were intermingled in a “salt and pepper” manner. Furthermore, the noise correlation tended to be especially strong in excitatory-inhibitory and inhibitory-inhibitory cell pairs that have similar stimulus selectivity. These results suggest that excitatory cells tend to represent specific stimulus information and work together with similarly tuned inhibitory cells as a functionally connected network.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

Cell Type Specific Representation of Vibro-tactile Stimuli in the Mouse Primary Somatosensory Cortex

Hayashi

Yoshida

Ohki

2018

Front. Neural Circuits

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“…We found that NTM substantially increased the number of stimulus-evoked spikes detected after CW deflection. The greatest gain in detected spikes was observed in L4, where spiking probability and spike timing precision are greatest 10,25 . Most spikes missed by the standard voltage threshold were due to the shadow period enforced after preceding voltage threshold crossing events.…”

Section: Discussionmentioning

confidence: 95%

“…Strongly correlated firing of nearby neurons on a short time scale and high temporal precision of neural responses will increase the number of shadowed spikes, as will greater overall mean firing rate. These conditions often occur in topographically organized brain regions, such as primary sensory cortex, when an optimal sensory stimulus for the local population is presented [10][11][12] and are important for precise sensory feature representations [13][14][15] . As a result, more spikes are likely to be missed in response to locally preferred stimuli, which will distort measurements of neural tuning.…”

Section: Introductionmentioning

confidence: 99%

A normalized template matching method for improving spike detection in extracellular voltage recordings

Laboy-Juárez

Ahn

Feldman

2018

Preprint

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Spike sorting is the process of detecting and clustering action potential waveforms from extracellular voltage recordings to identify spikes of putative single neurons. Typically, spike detection is done using a fixed voltage threshold and shadow period, but this approach can lead to missed spikes during high firing rate epochs or noisy conditions. We developed a novel spike detection method utilizing a computationally simple form of template matching that efficiently detects spikes from candidate single units and is tolerant of high firing rates and electrical noise without a whitening filter. Template matching was based on a sliding cosine similarity between mean spike waveforms of candidate single units and the extracellular voltage signal. Performance was tested in whisker somatosensory cortex (S1) of anesthetized mice in vivo. The method consistently detected whisker-evoked spikes that were missed by a standard fixed voltage threshold. Detection was most improved for spikes evoked by strong stimuli (40-70% increase), less improved for weaker stimuli, and unchanged for spontaneous spiking. This reflected the failure of standard detection during spatiotemporally dense spiking. Template-based detection revealed higher signal-to-noise ratio for sensory responses and sharper sensory tuning. Thus, this template matching method (and other model-based spike detection methods) critically improve the quantification of single-unit spiking activity.

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“…The most popular Schumann resonance observation method is to use three extremely low frequency (ELF) sensors, including East-West and North-South direction for magnetic field and a vertical one for electric field [7] [14,15]. The schematic block diagram of a typical SR measurement equipment is shown in Fig.1.…”

Section: Measurement Equipment Of Schumann Resonancementioning

confidence: 99%