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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.


GLM modeling of L4 touch responses. (a-b) Two example neural responses (blue) aligned to touch onset and their corresponding GLM prediction (red). (c-d) Same as (a-b) but aligned to touch offset. (e-f) Touch adaptation in function of touch number. (g-h) Proportion of spikes explained as a function of the exploration time (similar to Figure 3 in the paper). The blue curve (data) was obtained as described in the paper. For the GLM we started counting spikes ranked by the maximum GLM prediction (i.e. the moments with highest probability of a spike occurring according to the GLM model).DOI:http://dx.doi.org/10.7554/eLife.06619.019
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fig7: GLM modeling of L4 touch responses. (a-b) Two example neural responses (blue) aligned to touch onset and their corresponding GLM prediction (red). (c-d) Same as (a-b) but aligned to touch offset. (e-f) Touch adaptation in function of touch number. (g-h) Proportion of spikes explained as a function of the exploration time (similar to Figure 3 in the paper). The blue curve (data) was obtained as described in the paper. For the GLM we started counting spikes ranked by the maximum GLM prediction (i.e. the moments with highest probability of a spike occurring according to the GLM model).DOI:http://dx.doi.org/10.7554/eLife.06619.019

Mentions: This is just to say that we simply don’t know how to apply ‘GLM or with similar approaches’ (even with Dr. Freeman). To show that we have seriously considered this approach we show GLMs to fit the time-course of touch-responses (see Author response image 1).10.7554/eLife.06619.019Author response image 1.


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)

GLM modeling of L4 touch responses. (a-b) Two example neural responses (blue) aligned to touch onset and their corresponding GLM prediction (red). (c-d) Same as (a-b) but aligned to touch offset. (e-f) Touch adaptation in function of touch number. (g-h) Proportion of spikes explained as a function of the exploration time (similar to Figure 3 in the paper). The blue curve (data) was obtained as described in the paper. For the GLM we started counting spikes ranked by the maximum GLM prediction (i.e. the moments with highest probability of a spike occurring according to the GLM model).DOI:http://dx.doi.org/10.7554/eLife.06619.019
© Copyright Policy
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4525079&req=5

fig7: GLM modeling of L4 touch responses. (a-b) Two example neural responses (blue) aligned to touch onset and their corresponding GLM prediction (red). (c-d) Same as (a-b) but aligned to touch offset. (e-f) Touch adaptation in function of touch number. (g-h) Proportion of spikes explained as a function of the exploration time (similar to Figure 3 in the paper). The blue curve (data) was obtained as described in the paper. For the GLM we started counting spikes ranked by the maximum GLM prediction (i.e. the moments with highest probability of a spike occurring according to the GLM model).DOI:http://dx.doi.org/10.7554/eLife.06619.019
Mentions: This is just to say that we simply don’t know how to apply ‘GLM or with similar approaches’ (even with Dr. Freeman). To show that we have seriously considered this approach we show GLMs to fit the time-course of touch-responses (see Author response image 1).10.7554/eLife.06619.019Author response image 1.

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.