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Neural coding in barrel cortex during whisker-guided locomotion.

Sofroniew NJ, Vlasov YA, Andrew Hires S, Freeman J, Svoboda K - Elife (2015)

Bottom Line: We measured neural activity using two-photon calcium imaging and extracellular recordings.Neurons were tuned to the distance between the animal snout and the contralateral wall, with monotonic, unimodal, and multimodal tuning curves.This rich representation of object location in the barrel cortex could not be predicted based on simple stimulus-response relationships involving individual whiskers and likely emerges within cortical circuits.

View Article: PubMed Central - PubMed

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

ABSTRACT
Animals seek out relevant information by moving through a dynamic world, but sensory systems are usually studied under highly constrained and passive conditions that may not probe important dimensions of the neural code. Here, we explored neural coding in the barrel cortex of head-fixed mice that tracked walls with their whiskers in tactile virtual reality. Optogenetic manipulations revealed that barrel cortex plays a role in wall-tracking. Closed-loop optogenetic control of layer 4 neurons can substitute for whisker-object contact to guide behavior resembling wall tracking. We measured neural activity using two-photon calcium imaging and extracellular recordings. Neurons were tuned to the distance between the animal snout and the contralateral wall, with monotonic, unimodal, and multimodal tuning curves. This rich representation of object location in the barrel cortex could not be predicted based on simple stimulus-response relationships involving individual whiskers and likely emerges within cortical circuits.

No MeSH data available.


Related in: MedlinePlus

Tuning to direction of wall movement.(a) Example spike rasters of regular spiking units that showed strong modulation by wall direction during open-loop trials during locomotion (running speed over 3 cm/s). (b) Spike rate as a function of time during epochs when wall is moving towards/away from the mice for the same regular spiking units as in a. (c) Heatmap of time profiles curves normalized by maximum for symmetric and asymmetric units, sorted by time to peak. (d) Scatter of the range of spike rates as the wall moved towards and away from the mouse. The range of spike rates is the difference between the maximum and minimum rate during the 1 s when the wall was moving towards or away from the mouse. (e) Histogram of direction modulation index, which is the range difference in spike rates during wall movement towards and away from the mouse divided by the sum of the towards range and away range. Units that respond only when the wall approaches have modulation 1, units that respond only when the wall moves away have modulation −1, and units that respond to both the wall approaching and moving away have a modulation near 0.DOI:http://dx.doi.org/10.7554/eLife.12559.013
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fig5: Tuning to direction of wall movement.(a) Example spike rasters of regular spiking units that showed strong modulation by wall direction during open-loop trials during locomotion (running speed over 3 cm/s). (b) Spike rate as a function of time during epochs when wall is moving towards/away from the mice for the same regular spiking units as in a. (c) Heatmap of time profiles curves normalized by maximum for symmetric and asymmetric units, sorted by time to peak. (d) Scatter of the range of spike rates as the wall moved towards and away from the mouse. The range of spike rates is the difference between the maximum and minimum rate during the 1 s when the wall was moving towards or away from the mouse. (e) Histogram of direction modulation index, which is the range difference in spike rates during wall movement towards and away from the mouse divided by the sum of the towards range and away range. Units that respond only when the wall approaches have modulation 1, units that respond only when the wall moves away have modulation −1, and units that respond to both the wall approaching and moving away have a modulation near 0.DOI:http://dx.doi.org/10.7554/eLife.12559.013

Mentions: (a) Image acquired with green filter of a whole brain removed from the skull of a Scnn1a-Tg3-Cre x RCL-ChR2-EYFP mouse. Overlay of 13 recordings sites identified by DiI tracing of silicon probe tracks. An image acquired with orange filter and superimposed on top for one example. The recording site is shown by arrow. (b) Illustration of three complementary methods used for identification of the depth of recorded units. Left: Coronal section of barrel cortex acquired with green and orange filters showing with dotted square the location of recording site. Center: Blown-up portion of the same area of the coronal section (turned rotated by 39 degrees) taken with green filter showing of L4 barrels. The location of two electrolytic lesions on top and bottom electrodes and the center of layer 4 are marked with dashed white lines. Right: Current source density trace used to identify the middle of Layer 4 as a short-time (<3 ms) minimum. The schematics of electrodes positions on the silicon probe that is aligned with the centers of the top and bottom lesions. (c) Inter-spike-interval distributions for units presented in Figure 4 and Figure 5. (d) Histogram of spike widths. Fast spikers (< 350 μs): pink, intermediate: gray, and regular spikers (> 450 μs): purple). (e) Z-scored waveforms from all units. (fast spikers: pink, intermediate: gray, regular spikers: purple). (f) Scatter plot of waveform SNR against ISI false alarm rate. The area shown by colored square corresponds to accepted units with SNR > 6 and false alarm rate < 1.5%


Neural coding in barrel cortex during whisker-guided locomotion.

Sofroniew NJ, Vlasov YA, Andrew Hires S, Freeman J, Svoboda K - Elife (2015)

Tuning to direction of wall movement.(a) Example spike rasters of regular spiking units that showed strong modulation by wall direction during open-loop trials during locomotion (running speed over 3 cm/s). (b) Spike rate as a function of time during epochs when wall is moving towards/away from the mice for the same regular spiking units as in a. (c) Heatmap of time profiles curves normalized by maximum for symmetric and asymmetric units, sorted by time to peak. (d) Scatter of the range of spike rates as the wall moved towards and away from the mouse. The range of spike rates is the difference between the maximum and minimum rate during the 1 s when the wall was moving towards or away from the mouse. (e) Histogram of direction modulation index, which is the range difference in spike rates during wall movement towards and away from the mouse divided by the sum of the towards range and away range. Units that respond only when the wall approaches have modulation 1, units that respond only when the wall moves away have modulation −1, and units that respond to both the wall approaching and moving away have a modulation near 0.DOI:http://dx.doi.org/10.7554/eLife.12559.013
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Related In: Results  -  Collection

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fig5: Tuning to direction of wall movement.(a) Example spike rasters of regular spiking units that showed strong modulation by wall direction during open-loop trials during locomotion (running speed over 3 cm/s). (b) Spike rate as a function of time during epochs when wall is moving towards/away from the mice for the same regular spiking units as in a. (c) Heatmap of time profiles curves normalized by maximum for symmetric and asymmetric units, sorted by time to peak. (d) Scatter of the range of spike rates as the wall moved towards and away from the mouse. The range of spike rates is the difference between the maximum and minimum rate during the 1 s when the wall was moving towards or away from the mouse. (e) Histogram of direction modulation index, which is the range difference in spike rates during wall movement towards and away from the mouse divided by the sum of the towards range and away range. Units that respond only when the wall approaches have modulation 1, units that respond only when the wall moves away have modulation −1, and units that respond to both the wall approaching and moving away have a modulation near 0.DOI:http://dx.doi.org/10.7554/eLife.12559.013
Mentions: (a) Image acquired with green filter of a whole brain removed from the skull of a Scnn1a-Tg3-Cre x RCL-ChR2-EYFP mouse. Overlay of 13 recordings sites identified by DiI tracing of silicon probe tracks. An image acquired with orange filter and superimposed on top for one example. The recording site is shown by arrow. (b) Illustration of three complementary methods used for identification of the depth of recorded units. Left: Coronal section of barrel cortex acquired with green and orange filters showing with dotted square the location of recording site. Center: Blown-up portion of the same area of the coronal section (turned rotated by 39 degrees) taken with green filter showing of L4 barrels. The location of two electrolytic lesions on top and bottom electrodes and the center of layer 4 are marked with dashed white lines. Right: Current source density trace used to identify the middle of Layer 4 as a short-time (<3 ms) minimum. The schematics of electrodes positions on the silicon probe that is aligned with the centers of the top and bottom lesions. (c) Inter-spike-interval distributions for units presented in Figure 4 and Figure 5. (d) Histogram of spike widths. Fast spikers (< 350 μs): pink, intermediate: gray, and regular spikers (> 450 μs): purple). (e) Z-scored waveforms from all units. (fast spikers: pink, intermediate: gray, regular spikers: purple). (f) Scatter plot of waveform SNR against ISI false alarm rate. The area shown by colored square corresponds to accepted units with SNR > 6 and false alarm rate < 1.5%

Bottom Line: We measured neural activity using two-photon calcium imaging and extracellular recordings.Neurons were tuned to the distance between the animal snout and the contralateral wall, with monotonic, unimodal, and multimodal tuning curves.This rich representation of object location in the barrel cortex could not be predicted based on simple stimulus-response relationships involving individual whiskers and likely emerges within cortical circuits.

View Article: PubMed Central - PubMed

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

ABSTRACT
Animals seek out relevant information by moving through a dynamic world, but sensory systems are usually studied under highly constrained and passive conditions that may not probe important dimensions of the neural code. Here, we explored neural coding in the barrel cortex of head-fixed mice that tracked walls with their whiskers in tactile virtual reality. Optogenetic manipulations revealed that barrel cortex plays a role in wall-tracking. Closed-loop optogenetic control of layer 4 neurons can substitute for whisker-object contact to guide behavior resembling wall tracking. We measured neural activity using two-photon calcium imaging and extracellular recordings. Neurons were tuned to the distance between the animal snout and the contralateral wall, with monotonic, unimodal, and multimodal tuning curves. This rich representation of object location in the barrel cortex could not be predicted based on simple stimulus-response relationships involving individual whiskers and likely emerges within cortical circuits.

No MeSH data available.


Related in: MedlinePlus