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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

Effects of running speed on activity and wall distance tuning.(a) Four example units that showed significant tuning to running speed when the wall was out of reach. The activity of the top left unit increased linearly with speed over a large range, the activity of top right unit increased rapidly and then saturated at low speeds, the activity of the bottom right unit peaked at low speeds and then decreased at higher speeds, and the activity of bottom left unit decreased with running speed. (b) Histogram of the peak speed tuning for units significantly tuned to speed (31%; 46/148) from the population used in Figure 4. (c) Example wall distance tuning curve while the mouse is running in fast trials (top) and slow trials (bottom). Fast and slow trials were split based on the median of the trial speeds in trials when the mouse was running. This unit is the same as used in Figure 4—figure supplement 3a). (d) Example wall distance tuning curve while the mouse is running fast trials (top) and slow trials (bottom). This unit is the same as used in Figure 4—figure supplement 3b). (e) Histogram of the modulation of wall distance tuning by speed for all 148 units. This index was the log of the gain parameter that describes a multiplicative scaling of the slow tuning curve to the fast tuning curve. Units that are more active during fast running have positive modulation indices, units that are less active during fast running have negative indices.DOI:http://dx.doi.org/10.7554/eLife.12559.011
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fig4s4: Effects of running speed on activity and wall distance tuning.(a) Four example units that showed significant tuning to running speed when the wall was out of reach. The activity of the top left unit increased linearly with speed over a large range, the activity of top right unit increased rapidly and then saturated at low speeds, the activity of the bottom right unit peaked at low speeds and then decreased at higher speeds, and the activity of bottom left unit decreased with running speed. (b) Histogram of the peak speed tuning for units significantly tuned to speed (31%; 46/148) from the population used in Figure 4. (c) Example wall distance tuning curve while the mouse is running in fast trials (top) and slow trials (bottom). Fast and slow trials were split based on the median of the trial speeds in trials when the mouse was running. This unit is the same as used in Figure 4—figure supplement 3a). (d) Example wall distance tuning curve while the mouse is running fast trials (top) and slow trials (bottom). This unit is the same as used in Figure 4—figure supplement 3b). (e) Histogram of the modulation of wall distance tuning by speed for all 148 units. This index was the log of the gain parameter that describes a multiplicative scaling of the slow tuning curve to the fast tuning curve. Units that are more active during fast running have positive modulation indices, units that are less active during fast running have negative indices.DOI:http://dx.doi.org/10.7554/eLife.12559.011

Mentions: These tuning curves were generated during open-loop movements of the wall, but during natural behavior the wall will move in closed-loop with locomotion. To investigate whether tuning under these conditions is similar, we characterized wall distance tuning during closed-loop wall-movements for a subset of mice (10 mice; 114 regular spiking units). In closed-loop, wall distance sampling is non-uniform because the sensory stimulus depends on behavior. We combined epochs across several widths and bends in the corridor to approximately sample wall distance as in the open-loop condition. Units showed similar tuning curves under both open-loop and closed-loop (Figure 4—figure supplement 3a,b), with similar average rates over the same wall distances range (107/114; 94% -0.5 < open vs. closed modulation < 0.5) (Figure 4—figure supplement 3c,d). A substantial fraction of all units (31%; 46/148) were significantly modulated by running speed in the absence of the walls (Figure 4—figure supplement 4a,b) (Keller et al., 2012; Saleem et al., 2013). However, the wall distance tuning curves computed during slow running and fast running were similar on average (modulation index was −0.10 ± 0.55) (Figure 4—figure supplement 4c,d,e).


Neural coding in barrel cortex during whisker-guided locomotion.

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

Effects of running speed on activity and wall distance tuning.(a) Four example units that showed significant tuning to running speed when the wall was out of reach. The activity of the top left unit increased linearly with speed over a large range, the activity of top right unit increased rapidly and then saturated at low speeds, the activity of the bottom right unit peaked at low speeds and then decreased at higher speeds, and the activity of bottom left unit decreased with running speed. (b) Histogram of the peak speed tuning for units significantly tuned to speed (31%; 46/148) from the population used in Figure 4. (c) Example wall distance tuning curve while the mouse is running in fast trials (top) and slow trials (bottom). Fast and slow trials were split based on the median of the trial speeds in trials when the mouse was running. This unit is the same as used in Figure 4—figure supplement 3a). (d) Example wall distance tuning curve while the mouse is running fast trials (top) and slow trials (bottom). This unit is the same as used in Figure 4—figure supplement 3b). (e) Histogram of the modulation of wall distance tuning by speed for all 148 units. This index was the log of the gain parameter that describes a multiplicative scaling of the slow tuning curve to the fast tuning curve. Units that are more active during fast running have positive modulation indices, units that are less active during fast running have negative indices.DOI:http://dx.doi.org/10.7554/eLife.12559.011
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fig4s4: Effects of running speed on activity and wall distance tuning.(a) Four example units that showed significant tuning to running speed when the wall was out of reach. The activity of the top left unit increased linearly with speed over a large range, the activity of top right unit increased rapidly and then saturated at low speeds, the activity of the bottom right unit peaked at low speeds and then decreased at higher speeds, and the activity of bottom left unit decreased with running speed. (b) Histogram of the peak speed tuning for units significantly tuned to speed (31%; 46/148) from the population used in Figure 4. (c) Example wall distance tuning curve while the mouse is running in fast trials (top) and slow trials (bottom). Fast and slow trials were split based on the median of the trial speeds in trials when the mouse was running. This unit is the same as used in Figure 4—figure supplement 3a). (d) Example wall distance tuning curve while the mouse is running fast trials (top) and slow trials (bottom). This unit is the same as used in Figure 4—figure supplement 3b). (e) Histogram of the modulation of wall distance tuning by speed for all 148 units. This index was the log of the gain parameter that describes a multiplicative scaling of the slow tuning curve to the fast tuning curve. Units that are more active during fast running have positive modulation indices, units that are less active during fast running have negative indices.DOI:http://dx.doi.org/10.7554/eLife.12559.011
Mentions: These tuning curves were generated during open-loop movements of the wall, but during natural behavior the wall will move in closed-loop with locomotion. To investigate whether tuning under these conditions is similar, we characterized wall distance tuning during closed-loop wall-movements for a subset of mice (10 mice; 114 regular spiking units). In closed-loop, wall distance sampling is non-uniform because the sensory stimulus depends on behavior. We combined epochs across several widths and bends in the corridor to approximately sample wall distance as in the open-loop condition. Units showed similar tuning curves under both open-loop and closed-loop (Figure 4—figure supplement 3a,b), with similar average rates over the same wall distances range (107/114; 94% -0.5 < open vs. closed modulation < 0.5) (Figure 4—figure supplement 3c,d). A substantial fraction of all units (31%; 46/148) were significantly modulated by running speed in the absence of the walls (Figure 4—figure supplement 4a,b) (Keller et al., 2012; Saleem et al., 2013). However, the wall distance tuning curves computed during slow running and fast running were similar on average (modulation index was −0.10 ± 0.55) (Figure 4—figure supplement 4c,d,e).

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