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Low-noise encoding of active touch by layer 4 in the somatosensory cortex.

Hires SA, Gutnisky DA, Yu J, O'Connor DH, Svoboda K - Elife (2015)

Bottom Line: The amount of irreducible internal noise in spike trains, an important constraint on models of cortical networks, has been difficult to estimate, since behavior and brain state must be precisely controlled or tracked.The variance of touch responses was smaller than expected from Poisson processes, often reaching the theoretical minimum.Layer 4 spike trains thus reflect the millisecond-timescale structure of tactile input with little noise.

View Article: PubMed Central - PubMed

Affiliation: Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States.

ABSTRACT
Cortical spike trains often appear noisy, with the timing and number of spikes varying across repetitions of stimuli. Spiking variability can arise from internal (behavioral state, unreliable neurons, or chaotic dynamics in neural circuits) and external (uncontrolled behavior or sensory stimuli) sources. The amount of irreducible internal noise in spike trains, an important constraint on models of cortical networks, has been difficult to estimate, since behavior and brain state must be precisely controlled or tracked. We recorded from excitatory barrel cortex neurons in layer 4 during active behavior, where mice control tactile input through learned whisker movements. Touch was the dominant sensorimotor feature, with >70% spikes occurring in millisecond timescale epochs after touch onset. The variance of touch responses was smaller than expected from Poisson processes, often reaching the theoretical minimum. Layer 4 spike trains thus reflect the millisecond-timescale structure of tactile input with little noise.

No MeSH data available.


L4 spike count varies with touch properties.(A) Left, touch aligned spike rasters for a single cell, sorted by one of three touch properties: Order of touch in trial (top), whisker velocity at touch onset (middle), maximum whisker curvature during touch (bottom). Value of touch property corresponding to the touch (red line). Spike integration window for binned touch response (pink), first touches in trials highlighted (grey). Right, average spikes per touch for a binned range of touch property (10 bins with equal number of touches) (black line), 95% confidence interval (grey line). Same example cell as in Figures 1, 3. (B) Heatmap of the response of each L4 excitatory cell inside C2 (n = 31) to the three touch characteristics across 10 equal element bins. Responses normalized to peak for each cell. Cells are ordered by the mean tuning to maximum touch curvature. Example cell highlighted by black arrow. (C) Heatmap of the modulation index of the same cells and touch characteristics (max bin − min bin)/(max bin + min bin).DOI:http://dx.doi.org/10.7554/eLife.06619.014
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fig5: L4 spike count varies with touch properties.(A) Left, touch aligned spike rasters for a single cell, sorted by one of three touch properties: Order of touch in trial (top), whisker velocity at touch onset (middle), maximum whisker curvature during touch (bottom). Value of touch property corresponding to the touch (red line). Spike integration window for binned touch response (pink), first touches in trials highlighted (grey). Right, average spikes per touch for a binned range of touch property (10 bins with equal number of touches) (black line), 95% confidence interval (grey line). Same example cell as in Figures 1, 3. (B) Heatmap of the response of each L4 excitatory cell inside C2 (n = 31) to the three touch characteristics across 10 equal element bins. Responses normalized to peak for each cell. Cells are ordered by the mean tuning to maximum touch curvature. Example cell highlighted by black arrow. (C) Heatmap of the modulation index of the same cells and touch characteristics (max bin − min bin)/(max bin + min bin).DOI:http://dx.doi.org/10.7554/eLife.06619.014

Mentions: In our active object localization task, mice produce tactile input through whisker movements, which varies greatly across trials and individual touches within a trial (O'Connor et al., 2010). To explore how sensory responses depended on different tactile stimulus features we sorted touches and the L4 neuron responses by one of three behavioral variables: the order of touch within each trial, to account for adaptation; whisker velocity just before touch, to account for rate of change of impact forces; maximum whisker curvature during touch, which is proportional to peak touch force (Figure 5A,B). Touch response magnitude was highly modulated by each of the three variables (mean modulation index: touch order, 0.71 ± 0.25; velocity, 0.71 ± 0.27; max curvature, 0.72 ± 0.23; mean ± std; Figure 5A–C). Responsiveness to pretouch velocity and max curvature covaried strongly (pairwise correlation coefficient 0.80, p = 4.3e-8), whereas touch order response was somewhat less correlated with velocity and curvature (correlation coefficients 0.70, 0.65, p = 1.0e-5, p = 8.2e-5). The deep modulation index of sensory responses to tactile stimulus features indicates that stimulus variability likely accounts for a significant component of variability in the touch response.10.7554/eLife.06619.014Figure 5.L4 spike count varies with touch properties.


Low-noise encoding of active touch by layer 4 in the somatosensory cortex.

Hires SA, Gutnisky DA, Yu J, O'Connor DH, Svoboda K - Elife (2015)

L4 spike count varies with touch properties.(A) Left, touch aligned spike rasters for a single cell, sorted by one of three touch properties: Order of touch in trial (top), whisker velocity at touch onset (middle), maximum whisker curvature during touch (bottom). Value of touch property corresponding to the touch (red line). Spike integration window for binned touch response (pink), first touches in trials highlighted (grey). Right, average spikes per touch for a binned range of touch property (10 bins with equal number of touches) (black line), 95% confidence interval (grey line). Same example cell as in Figures 1, 3. (B) Heatmap of the response of each L4 excitatory cell inside C2 (n = 31) to the three touch characteristics across 10 equal element bins. Responses normalized to peak for each cell. Cells are ordered by the mean tuning to maximum touch curvature. Example cell highlighted by black arrow. (C) Heatmap of the modulation index of the same cells and touch characteristics (max bin − min bin)/(max bin + min bin).DOI:http://dx.doi.org/10.7554/eLife.06619.014
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fig5: L4 spike count varies with touch properties.(A) Left, touch aligned spike rasters for a single cell, sorted by one of three touch properties: Order of touch in trial (top), whisker velocity at touch onset (middle), maximum whisker curvature during touch (bottom). Value of touch property corresponding to the touch (red line). Spike integration window for binned touch response (pink), first touches in trials highlighted (grey). Right, average spikes per touch for a binned range of touch property (10 bins with equal number of touches) (black line), 95% confidence interval (grey line). Same example cell as in Figures 1, 3. (B) Heatmap of the response of each L4 excitatory cell inside C2 (n = 31) to the three touch characteristics across 10 equal element bins. Responses normalized to peak for each cell. Cells are ordered by the mean tuning to maximum touch curvature. Example cell highlighted by black arrow. (C) Heatmap of the modulation index of the same cells and touch characteristics (max bin − min bin)/(max bin + min bin).DOI:http://dx.doi.org/10.7554/eLife.06619.014
Mentions: In our active object localization task, mice produce tactile input through whisker movements, which varies greatly across trials and individual touches within a trial (O'Connor et al., 2010). To explore how sensory responses depended on different tactile stimulus features we sorted touches and the L4 neuron responses by one of three behavioral variables: the order of touch within each trial, to account for adaptation; whisker velocity just before touch, to account for rate of change of impact forces; maximum whisker curvature during touch, which is proportional to peak touch force (Figure 5A,B). Touch response magnitude was highly modulated by each of the three variables (mean modulation index: touch order, 0.71 ± 0.25; velocity, 0.71 ± 0.27; max curvature, 0.72 ± 0.23; mean ± std; Figure 5A–C). Responsiveness to pretouch velocity and max curvature covaried strongly (pairwise correlation coefficient 0.80, p = 4.3e-8), whereas touch order response was somewhat less correlated with velocity and curvature (correlation coefficients 0.70, 0.65, p = 1.0e-5, p = 8.2e-5). The deep modulation index of sensory responses to tactile stimulus features indicates that stimulus variability likely accounts for a significant component of variability in the touch response.10.7554/eLife.06619.014Figure 5.L4 spike count varies with touch properties.

Bottom Line: The amount of irreducible internal noise in spike trains, an important constraint on models of cortical networks, has been difficult to estimate, since behavior and brain state must be precisely controlled or tracked.The variance of touch responses was smaller than expected from Poisson processes, often reaching the theoretical minimum.Layer 4 spike trains thus reflect the millisecond-timescale structure of tactile input with little noise.

View Article: PubMed Central - PubMed

Affiliation: Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States.

ABSTRACT
Cortical spike trains often appear noisy, with the timing and number of spikes varying across repetitions of stimuli. Spiking variability can arise from internal (behavioral state, unreliable neurons, or chaotic dynamics in neural circuits) and external (uncontrolled behavior or sensory stimuli) sources. The amount of irreducible internal noise in spike trains, an important constraint on models of cortical networks, has been difficult to estimate, since behavior and brain state must be precisely controlled or tracked. We recorded from excitatory barrel cortex neurons in layer 4 during active behavior, where mice control tactile input through learned whisker movements. Touch was the dominant sensorimotor feature, with >70% spikes occurring in millisecond timescale epochs after touch onset. The variance of touch responses was smaller than expected from Poisson processes, often reaching the theoretical minimum. Layer 4 spike trains thus reflect the millisecond-timescale structure of tactile input with little noise.

No MeSH data available.