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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.


Some population characteristics of all recordings.(A) Latency from touch onset to onset of spiking response for 31 L4 cells in C2 (red), four L4 outside C2 (grey) and eleven L5 near C2 (yellow). Latencies could not be determined for six L4 outside cells and one L5 cell due to lack of touch response. Same colors in B, C. (B) Modulation of spike rate by whisking phase. Angle of the peak phase response is plotted vs modulation depth. (C) Histogram of the minimum interspike interval measured for each neuron.DOI:http://dx.doi.org/10.7554/eLife.06619.008
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fig2s1: Some population characteristics of all recordings.(A) Latency from touch onset to onset of spiking response for 31 L4 cells in C2 (red), four L4 outside C2 (grey) and eleven L5 near C2 (yellow). Latencies could not be determined for six L4 outside cells and one L5 cell due to lack of touch response. Same colors in B, C. (B) Modulation of spike rate by whisking phase. Angle of the peak phase response is plotted vs modulation depth. (C) Histogram of the minimum interspike interval measured for each neuron.DOI:http://dx.doi.org/10.7554/eLife.06619.008

Mentions: Neuronal variability can arise from external factors, such as trial-to-trial variations in behavior, or internal factors, such as synaptic noise and fluctuating motivation and arousal (Renart and Machens, 2014). The Fano factor (FF) is a widely used measure of variability in spike trains (Berry et al., 1997). FF is defined as the variance of the spike count divided by the mean spike count over some time window. For a Poisson process, FF = 1, independent of the window size. In our task, mice are free to explore the object differently in each trial. At a coarse scale, behavior and neural responses were irregular during object localization. FF computed by counting spikes over the entire sample epoch (stimulus presentation), was huge (FF = 7.51). Since each trial corresponds to different whisker movements and different patterns of touches this value of FF includes extrinsic variability due to behavior, in addition to intrinsic variability. Aligning spikes to the fine-scale structure of behavior revealed that spikes were mainly coupled to temporally irregular sensory input from object contact (Figure 1E). Spike rate was sharply elevated shortly after touch onset (Figure 1F). Each touch evoked on the order of one spike (1.51 spikes/first touch; 0.31/later touches) with short latency (onset, 8 ms) (Table 1; Figure 2—figure supplement 1). The large FF when computing spikes over the sample period is therefore at least in part due to trial-to-trial variability in active touch.10.7554/eLife.06619.006Table 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)

Some population characteristics of all recordings.(A) Latency from touch onset to onset of spiking response for 31 L4 cells in C2 (red), four L4 outside C2 (grey) and eleven L5 near C2 (yellow). Latencies could not be determined for six L4 outside cells and one L5 cell due to lack of touch response. Same colors in B, C. (B) Modulation of spike rate by whisking phase. Angle of the peak phase response is plotted vs modulation depth. (C) Histogram of the minimum interspike interval measured for each neuron.DOI:http://dx.doi.org/10.7554/eLife.06619.008
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4525079&req=5

fig2s1: Some population characteristics of all recordings.(A) Latency from touch onset to onset of spiking response for 31 L4 cells in C2 (red), four L4 outside C2 (grey) and eleven L5 near C2 (yellow). Latencies could not be determined for six L4 outside cells and one L5 cell due to lack of touch response. Same colors in B, C. (B) Modulation of spike rate by whisking phase. Angle of the peak phase response is plotted vs modulation depth. (C) Histogram of the minimum interspike interval measured for each neuron.DOI:http://dx.doi.org/10.7554/eLife.06619.008
Mentions: Neuronal variability can arise from external factors, such as trial-to-trial variations in behavior, or internal factors, such as synaptic noise and fluctuating motivation and arousal (Renart and Machens, 2014). The Fano factor (FF) is a widely used measure of variability in spike trains (Berry et al., 1997). FF is defined as the variance of the spike count divided by the mean spike count over some time window. For a Poisson process, FF = 1, independent of the window size. In our task, mice are free to explore the object differently in each trial. At a coarse scale, behavior and neural responses were irregular during object localization. FF computed by counting spikes over the entire sample epoch (stimulus presentation), was huge (FF = 7.51). Since each trial corresponds to different whisker movements and different patterns of touches this value of FF includes extrinsic variability due to behavior, in addition to intrinsic variability. Aligning spikes to the fine-scale structure of behavior revealed that spikes were mainly coupled to temporally irregular sensory input from object contact (Figure 1E). Spike rate was sharply elevated shortly after touch onset (Figure 1F). Each touch evoked on the order of one spike (1.51 spikes/first touch; 0.31/later touches) with short latency (onset, 8 ms) (Table 1; Figure 2—figure supplement 1). The large FF when computing spikes over the sample period is therefore at least in part due to trial-to-trial variability in active touch.10.7554/eLife.06619.006Table 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.