Adaptive timing of motor output in the mouse: the role of movement oscillations in eyelid conditioning

Abstract

To survive, animals must learn to control their movements with millisecond-level precision, and adjust the kinematics if conditions, or task requirements, change. Here, we examine adaptive timing of motor output in mice, using a simple eyelid conditioning task. Mice were trained to blink in response to a light stimulus that was always followed by a corneal air-puff at a constant time interval. Different mice were trained with different intervals of time separating the onset of the light and the air-puff. As in previous work in other animal species, mice learned to control the speed of the blink, such that the time of maximum eyelid closure matched the interval used during training. However, we found that the time of maximum eyelid speed was always in the first 100 ms after movement onset and did not scale with the training interval, indicating that adaptive timing is not accomplished by slowing down (or speeding up) the eyelid movement uniformly throughout the duration of the blink. A new analysis, specifically designed to examine the kinematics of blinks in single trials, revealed that the underlying control signal responsible for the eyelid movement is made up of oscillatory bursts that are time-locked to the light stimulus at the beginning of the blink, becoming desynchronized later on. Furthermore, mice learn to blink at different speeds and time the movement appropriately by adjusting the amplitude, but not the frequency of the bursts in the eyelid oscillation.

Keywords: cerebellum; eyeblink; interstimulus interval; invariance; learning.

Figure 1

Kinematic properties of the conditioned eyelid movement. (A) Average eyelid position, and (B) average eyelid velocity for mice trained with four different ISIs. (C) Asterisks indicate the average maximum position and maximum velocity for each ISI. X-range of the linear regression lines is mean of position maxima ± std across all trials for a particular ISI. (D) Normalized phase plots for the data shown in (A,B). Color code for all plots indicates trained ISI and is shown in (B). FEC, fraction eyelid closure.

Figure 2

Temporal profile of the conditioned eyelid movement in individual mice. (A) Normalized average position, and (B) normalized average velocity, of each mouse’s conditioned eyelid responses. Color code indicates trained ISI and is shown in (B). (C) Probability density functions for latency to maximum eyelid closure, and (D) latency to maximum velocity. Each row of the heat map contains the probability density function for an individual mouse, with trained ISI indicated on the y-axis. For display purposes, each row is normalized to have the same maximum (white).

Figure 3

Analysis of velocity peaks. (A) Normalized eyelid velocity averaged over all trials (black), and averaged separately for individual sessions (red) for a mouse trained with a 175-ms, and (B) for a mouse trained with a 400-ms ISI (individual sessions shown in blue). (C) Eyelid velocity, and (D) eyelid position for three trials taken from the subject in (B). (E) Number of velocity peaks found in single trials between 100 and 200 ms from light onset, and (F) the interval of time between consecutive velocity peaks. Histograms were computed separately for each mouse and averaged over ISI condition. (G) Difference between the probability of finding a peak and a trough at each ms from light onset. Each row of the heat map contains the difference in peak and trough probability functions for an individual mouse, with trained ISI indicated on the y-axis. For display purposes, each row is converted to a z-score. (H) Difference, and (I) sum of probability functions for peaks and troughs, averaged over ISI condition. Color code for (E), (F), (H), and (I) indicates trained ISI and is shown in (H). FEC, fraction eyelid closure.

Figure 4

Eyelid acceleration. Top: mean eyelid acceleration (black) ±1 SD (gray shaded region), and example acceleration records from two randomly selected trials (green and purple), for one of the mice trained with a 175-ms ISI (same mouse as in Figure 3A). Bottom: mean of all single-trial spectrograms of the eyelid acceleration signal for the same mouse (see Materials and Methods for details about spectrogram generation).

Figure 5

Oscillatory properties of the conditioned eyelid response. (A) Average power spectrum of eyelid acceleration during the first 200 ms, and (B) from 200 to 500 ms after light onset. Spectra are plotted for each ISI separately. (C) Maximum power, and (D) dominant frequency of oscillation, in the eyelid acceleration signal. The value plotted at each moment in time in (C,D) is computed over a ~50 ms spectrogram window centered at that moment (see Materials and Methods). Each ISI plotted separately. For all plots, power is in units of (fraction eyelid closure/s²)² Hz⁻¹ × 10⁴, and color code indicates trained ISI as shown in (C).

Figure 6

Conditioned eyelid movements with a 100-ms ISI. (A) Normalized average eyelid position, and (B) normalized average velocity for mice trained with a 100-ms (black) or 175 ms ISI (red). (C) Difference between the probability of finding a peak and a trough at each ms from light onset. Color code for all plots indicates ISI and is shown in (B).

Figure 7

Acquisition of the conditioned eyelid response. (A) Eyelid velocity averaged across different sessions during the training phase, for mice trained with ISIs of 175 ms, (B) 250 ms, (C) 325 ms, and (D) 400 ms. The sessions that were used to compute each average are indicated by the color code shown in (D), where n represents number of the last training session for each mouse. FEC, fraction eyelid closure.

Figure 8

Comparison of video and MDMT. (A) Normalized average position, and (B) normalized average velocity. (C) Difference between the probability of finding a peak and a trough at each millisecond from light onset. (D) Normalized average power spectrum of eyelid acceleration. Color code for all plots indicates the type of recording system and is shown in (A). All data is from two 300-trial sessions with a mouse trained with a 250-ms ISI.