Whisker primary afferents encode temporal frequency of moving gratings

Jones, Lauren M.; Kwegyir-Afful, Ernest E.; Keller, Asaf

doi:10.1080/08990220600702707

Cited by 8 publications

(6 citation statements)

References 31 publications

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“…21,30 According to the results of numerous studies of the last decade, neurons of the barrel field are sensitive to the frequency of the whisker stimulation. 2,16,37,48 We believe that frequency selective neurons in the barrel are characterized by a V-shaped histogram of the stimulation frequency/stimulation intensity similar to how the neurons of the auditory cortex have a Vshaped histogram of sound frequency/sound intensity. 45 We hypothesize that the frequency selectivity in the somatosensory cortex is organized similarly to the auditory cortex.…”

Section: Discussionmentioning

confidence: 72%

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Study of the cortical representation of whisker frequency selectivity using voltage-sensitive dye optical imaging

et al. 2016

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The facial whiskers of rodents act as a high-resolution tactile apparatus that allow the animal to detect the finest details of its environment. Previously it was shown that whisker-sensitive neurons in the somatosensory cortex show frequency selectivity to small amplitude stimuli, An intravital voltage-sensitive dye optical imaging (VSDi) method in combination with the different frequency whisker stimulation was used in order to visualize neural activity in the mice somatosensory cortex in response to the stimulation of a single whisker by different frequencies. Using the intravital voltage-sensitive dye optical imaging (VSDi) method in combination with the different frequency whisker stimulation we visualized neural activity in the mice somatosensory cortex in response to the stimulation of a single whisker by different frequencies. We found that whisker stimuli with different frequencies led to different optical signals in the barrel field. Our results provide evidence that different neurons of the barrel cortex have different frequency preferences. This supports prior research that whisker deflections cause responses in cortical neurons within the barrel field according to the frequency of the stimulation. Many studies of the whisker frequency selectivity were performed using unit recording but to map spatial organization, imaging methods are essential. In the work described in the present paper, we take a serious step toward detailed functional mapping of the somatosensory cortex using VSDi. To our knowledge, this is the first demonstration of whisker frequency sensitivity and selectivity of barrel cortex neurons with optical imaging methods.

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Section: Discussionmentioning

confidence: 72%

“…Thus, it is a logical inference that the whisker's frequency selectivity is an essential element of barrel cortex information processing. Frequency selectivity of the neurons have been described at the cortical level of the whisker's system 14,16 but its mechanism is not clarified yet.…”

Section: Discussionmentioning

confidence: 99%

Study of the cortical representation of whisker frequency selectivity using voltage-sensitive dye optical imaging

et al. 2016

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“…Moreover, they follow faithfully, but at lower efficacy, whisker stimulation up to 2800 Hz. In rats, trigeminal ganglion cells respond with high temporal fidelity to sinusoidal stimulation up to 1000 Hz (Gibson and Welker, 1983;Deschênes et al, 2003;Jones et al, 2006). [Sinusoidal stimuli may specifically affect neural responses and inform the response analyses (Khatri et al, 2004;Mehta and Kleinfeld, 2004).…”

Section: Introductionmentioning

confidence: 99%

Stimulus Frequency Processing in Awake Rat Barrel Cortex

Melzer¹,

Sachdev²,

Jenkinson³

et al. 2006

J. Neurosci.

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In awake rats, we examined the relationship between neural spiking activity in primary somatic sensory cortex and the frequency of whisker stimulation. Neural responses were recorded extracellularly in barrel cortex while single whiskers were deflected with 0.5-18 air puffs per second (apps), a range that includes the whisk rates observed when rats explore their environment and discriminate surfaces with their whiskers. Twenty-nine neurons in layers III and IV were isolated in three rats (23 in barrel columns and 6 in septum columns). At Յ9 apps, cortical neurons responded with one to two spikes per stimulus, whereas at Ͼ9 apps, the response efficacy was reduced to only 0.2-0.4 spikes per stimulus. Several mechanisms are discussed that could account for the decrement in responsiveness. Despite this adaptation, neural spike rates increased in direct proportion with stimulus frequency when cast on logarithmic scales. At Ͼ9 apps, however, this relationship deteriorated in barrel columns in which the response approximately halved. In contrast, septum column cells continued to increase their spike rates linearly up to 18 apps, although they responded at lower magnitude than the barrel column cells. Our findings suggest that septum column neurons are potential candidates to encode stimulus frequency using spike rate across the entire frequency range relevant to rats' whisking behavior.

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“…Considering the robotic aspects, a limitation of the physical simulator (Gazebo) regards the properties of the materials that can be used. Rodents rely on whiskers bending to recognise the shape of objects and on their resonance frequencies to detect textures (58,59), but the current state of the simulators used by the NRP supports only rigid bodies. Using only rigid whiskers makes object recognition tasks more difficult, unless maybe using large arrays of finely spaced whiskers.…”

Section: Discussionmentioning

confidence: 99%

Brain-inspired spiking neural network controller for a neurorobotic whisker system

Antonietti

Geminiani

Negri

et al. 2021

Preprint

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It is common for animals to use self-generated movements to actively sense the surrounding environment. For instance, rodents rhythmically move their whiskers to explore the space close to their body. The mouse whisker system has become a standard model to study active sensing and sensorimotor integration through feedback loops. In this work, we developed a bioinspired spiking neural network model of the sensorimotor peripheral whisker system, modelling trigeminal ganglion, trigeminal nuclei, facial nuclei, and central pattern generator neuronal populations. This network was embedded in a virtual mouse robot, exploiting the Neurorobotics Platform, a simulation platform offering a virtual environment to develop and test robots driven by brain-inspired controllers. Eventually, the peripheral whisker system was properly connected to an adaptive cerebellar network controller. The whole system was able to drive active whisking with learning capability, matching neural correlates of behaviour experimentally recorded in mice.

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Whisker primary afferents encode temporal frequency of moving gratings

Cited by 8 publications

References 31 publications

Study of the cortical representation of whisker frequency selectivity using voltage-sensitive dye optical imaging

Study of the cortical representation of whisker frequency selectivity using voltage-sensitive dye optical imaging

Stimulus Frequency Processing in Awake Rat Barrel Cortex

Brain-inspired spiking neural network controller for a neurorobotic whisker system

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