V. Khatri, R. Bermejo, J. C. Brumberg, A. Keller and H. P. Zeigler
J Neurophysiol 101:1836-1846, 2009. First published Dec 24, 2008; doi:10.1152/jn.90655.2008
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J Neurophysiol 101: 1836 –1846, 2009.
First published December 24, 2008; doi:10.1152/jn.90655.2008.
Whisking in Air: Encoding of Kinematics by Trigeminal Ganglion Neurons
in Awake Rats
V. Khatri,1 R. Bermejo,2 J. C. Brumberg,3 A. Keller,4 and H. P. Zeigler2
1
Department of Hearing and Speech Sciences, Vanderbilt University Nashville, Tennessee; 2Department of Psychology, Hunter College
New York; 3Department of Psychology, Queens College, Queens, New York; and 4Program in Neuroscience and Department
of Anatomy and Neurobiology, University of Maryland School of Medicine, Baltimore, Maryland
Submitted 10 June 2008; accepted in final form 17 December 2008
Active sensing, a perceptual strategy employed by all sensorimotor systems, requires the brain to distinguish signals
produced by external inputs (exafference) from those generated
by the animal’s own movements (reafference). In some systems (e.g., electrolocation, oculomotor saccades, somatosensation), such disambiguation could be mediated by a “corollary
discharge” from motor to sensory regions, which has the effect
of canceling the reafference generated during the movement
(Cullen 2004). Here we address the question of how the rat
distinguishes movements produced during contact with objects
in the environment from its own whisker movements in air. A
first step is to determine whether and how reafferent information about movement kinematics is encoded during whisking in
air, and how such encoding differs from that of active whisker
contacts.
The rodent vibrissa system has served as an excellent model
for studies of active sensing (Kleinfeld et al. 2006; Sachdev
et al. 2001). A mobile subset of the whiskers function as an
active sensor; their rhythmic oscillations are used for dynamic
exploration of the environment. Sensory input from the vibrissae contributes both to orienting behavior (Vincent 1912) and
tactile discriminations (Carvell and Simons 1990; Harvey et al.
2001; Mehta et al. 2007). Moreover, during tactile discriminations, rats can modulate whisker movement parameters such as
amplitude, speed, and frequency, to meet task demands
(Carvell and Simons 1990; Harvey et al. 2001).
Active whisking typically includes both rhythmic whisker
movements in air (“whisking”) and whisker contacts with
objects in the environment. Both behaviors result in deformation of vibrissa mechanoreceptors that generate neural signals
along the trigeminal sensory pathway. Such signals could
provide the brain with information about the position of the
whisker in air and/or textural properties of the contacted object.
However, for neural representations of contacts or self-generated whisker movements to be useful, kinematics must be
reliably encoded by neurons of the rat trigeminal system.
In anesthetized animals, whisker-related neurons throughout
the brain can reliably encode several parameters of the movements generated passively by whisker contact, including direction, amplitude, and speed (Jones et al. 2004a,b; Pinto et al.
2000; Shipley 1973; Simons 1978; Waite 1973; Zucker and
Welker 1969). However, we have little information about the
encoding of signals generated during active whisker movements in air.
In the absence of evidence for proprioceptors in whisker
musculature (Bowden and Mahran 1956), whisker-related neurons in the rodent trigeminal ganglion (TG) could initially
generate the signals on which subsequent processing of movement-related input is based. Many studies have demonstrated
that ganglion neurons in anesthetized animals encode responses to passive vibrissa displacement (Jones et al. 2004a,b;
Lichtenstein et al. 1990; Shoykhet et al. 2000; Stüttgen et al.
2006; Zucker and Welker 1969). These studies indicate that the
receptive field of each ganglion neuron consists of a single
vibrissa and that neurons are readily activated by vibrissal
displacement. However, the ability of a specific whisker deflection to evoke a neuronal response varies with the neuron’s
particular amplitude threshold, speed threshold, and directional
preference (Lichtenstein et al. 1990; Shoykhet et al. 2000;
Stüttgen et al. 2006; Zucker and Welker 1969).
Encoding of whisker movement in the absence of contact
was first demonstrated in anesthetized animals using electrical
stimulation of the facial motor nerve (Zucker and Welker
1969). Approximately 50% of the sampled neurons responded
during electrically evoked vibrissa movements in air, but the
number significantly increased if the movement terminated in
Address for reprint requests and other correspondence: V. Khatri, Dept. of
Hearing and Speech Sciences, 465 21st Ave. South, 7114 MRB III, Vanderbilt
University Nashville, TN (E-mail:khatri1976@gmail.com).
The costs of publication of this article were defrayed in part by the payment
of page charges. The article must therefore be hereby marked “advertisement”
in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
INTRODUCTION
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0022-3077/09 $8.00 Copyright © 2009 The American Physiological Society
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Khatri V, Bermejo R, Brumberg JC, Keller A, Zeigler HP.
Whisking in air: encoding of kinematics by trigeminal ganglion
neurons in awake rats. J Neurophysiol 101: 1836 –1846, 2009. First
published December 24, 2008; doi:10.1152/jn.90655.2008. Active
sensing requires the brain to distinguish signals produced by external
inputs from those generated by the animal’s own movements. Because
the rodent whisker musculature lacks proprioceptors, we asked
whether trigeminal ganglion neurons encode the kinematics of the
rat’s own whisker movements in air. By examining the role of
kinematics, we have extended previous findings showing that many
neurons that respond during such movements do not do so consistently. Nevertheless, the majority (⬃70%) of trigeminal ganglion
neurons display significant correlations between firing rate and a
kinematic parameter, and a subset, ⬃30%, represent kinematics with
high reliability. Preferential firing to movement direction was observed but was strongly modulated by movement amplitude and
speed. However, in contrast to the precise time-locking that occurs in
response to active whisker contacts, whisker movements in air generate temporally dispersed responses that are not time-locked to the
onset of either protractions or retractions.
KINEMATICS AND TG NEURONS IN RATS
METHODS
All procedures were in accordance with National Institutes of
Health guidelines for the use and care of experimental animals and
were approved by an institutional animal use and care committee.
Animal preparation
Data were obtained from seven adult female Long Evans rats
(200 –300 g). Prior to behavioral testing, subjects were anesthetized
with isofluorane and fitted with a dental cement headmount, and a
stainless-steel ground screw was inserted into the skull. A small
window was left in the dental cement at the stereotaxic coordinates of
the ganglion to provide access for electrophysiological recordings.
Following adaptation to head fixation and consistent performance on
the operant task described in the following text, the animals were
re-anesthetized, a 1.5 ⫻ 1.5-mm opening was made in the skull, and
the opening was covered with moist cotton and a thin layer of dental
acrylic.
using an optoelectronic monitoring system described in detail elsewhere (Bermejo et al. 1998). Bermejo et al. (2005; Fig. 2) have shown
that during whisking in air, vibrissae in different rows and columns
(e.g., B1 and C3) on one side of the face move synchronously, with
amplitudes differing by ⬍1°. Note that independent movements of
adjacent whiskers have only been observed during a whisker contact
task (Sachdev et al. 2002). Therefore in the present study, kinematic
data tracked in the anterior-posterior plane for a single whisker (C2),
served as a surrogate for the kinematics of all surrounding whiskers.
Our neuronal data set includes all neurons that responded unambiguously to manual deflection. A list of the whiskers for each ganglion
neuron is given in Table 1. Because all data were gathered in awake
“whisking” animals, we could not determine whether a neuron was
rapidly adapting or slowly adapting.
An operant conditioning procedure was employed to generate
voluntary whisking behavior (Gao et al. 2003). Whisker movements
were monitored in real-time and reinforced on a fixed ratio (FR)
schedule contingent on protraction amplitudes ⱖ5 mm. Whisker
movements exceeding the criterion amplitude triggered a brief visual
signal. A series of 10 such movements (FR 10) was followed by
delivery of the reinforcer (Yoo-hoo, a commercially available chocolate drink).
Kinematic analysis
For kinematic analyses, the 500 Hz movement record was
smoothed using a 4-ms moving window. The nth bin of the record was
replaced with the average of bins N and n ⫹ 1. To enable pooling of
data across rats, a calibration procedure (Bermejo et al. 1998) for each
rat was used to convert micrometer measurements on the sensor to
degrees of displacement of the whisker, which are independent of the
distance of the CCD device from the whisker shaft. To avoid errors of
measurement (see Knutsen et al. 2005) the laser curtain of the
optoelectric CCD array was always between 13 and 20 mm away from
the face and calibrated for each animal in such a way that it was a
constant throughout the different experimental sessions for the animal.
Our measurement of a whisker’s angle is relative to the face with 90°
being orthogonal to the pad. In all figures displaying whisker trajectories, increases of whisker angle correspond to protractions, and
decreases correspond to retractions.
TABLE
1. Whisker location and number of whisker movements
Cell Number
Number of Analyzed Whisks
Whisker
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
494
151
248
10
17
247
261
307
248
162
53
17
325
97
107
27
18
23
11
68
25
C2
*
A2
C1
B3
C1
␤
D1
B1
Y
C2
Y
C3
D1
D4
B2
B1
*
*
E3
C3
Monitoring of movements and behavioral training
Whisker movements were monitored with high spatiotemporal
precision (7 ␮m, 500 Hz) in head-fixed rats with all whiskers intact,
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*Because of high levels of background activity, the associated whisker
could not be precisely determined; however, all were clearly within the
ganglion representation and responded strongly to manual whisker deflection.
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contact. An “electrical whisking “ paradigm has been used in
anesthetized animals by Ahissar and colleagues to examine the
response properties of neurons during whisker movements with
and without contact (Szwed et al. 2003;Yu et al. 2006). They
concluded that, “whisking-responsive neurons fired at specific
deflection angles, reporting the actual whiskers’ position with
high precision” (Szwed et al. 2003; p.1). A subsequent study
from the Ahissar lab reported that some neurons in the lemniscal ventral posterior medial thalamic nucleus (VPM), and all
neurons in the paralemniscal thalamic nucleus posteromedial
(POm), respond strongly to electrically elicited whisker movements in the absence of contact (Yu et al. 2006). However, it
is not clear whether responses to electrical whisking reliably
model voluntary, active whisking responses. Indeed, we have
recently reported that voluntary whisking in awake rats fails to
evoke firing in POm neurons (Masri et al. 2008).
Leiser and Moxon (2007) recorded ganglion neuron responses in freely moving rats as they explored a small chamber. They found that all TG neurons responded with relatively
low firing rates during whisker movements in air. Moreover,
many neurons which did respond during such movements did
so in an inconsistent manner: (1) only some movements were
accompanied by spikes (2) spikes were not triggered reliably
by movement onsets. All neurons increased their frequency of
firing when the whisker movements terminated in contact, and
no units responded exclusively during whisker movements in
air. The fact that some, but not all whisker movements evoked
neuronal activity could reflect differences in their movement
kinematics, such as speed or amplitude. To address this possibility it is necessary to obtain correlated data on both neuronal activity and movement kinematics, data that were not
available in previous studies. These correlated data are necessary also to determine whether the kinematics of self-generated
movements in air are reliably encoded by ganglion cells in
awake animals. To provide such data, we have simultaneously
recorded ganglion cell activity and movement kinematics in
awake, behaving rats during operantly conditioned (voluntary)
whisker movements in air.
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Individual whisker movements were identified using a computer
algorithm that defines three critical points: protraction onset, peak
amplitude, and end of retraction. Protraction onset is identified by
locating an increase between consecutive position measurements
(every 2 ms) that is ⬎0.14°. Similarly, the end of a retraction was
defined as the first time point, after the peak, where the decrease
was ⬍0.14°. To minimize the possibility of including movements
produced passively during other behaviors (e.g., jaw opening) only
whisker movements with well-defined protractions and retractions,
and with minimum amplitudes of 4°, were included in the analysis.
Due to the limited range of our CCD device, the monitored whisker
would sometimes move out of range. Individual movements were
only analyzed if the whisker was in range throughout the movement. The largest detectable movements ranged from 80 to 110°,
varying with the individual calibration of each rat. Because “inrange” movements ⬎80° were rare, they were not included in our
analyses.
Electrophysiological recording
Data analysis
Note that all analyses were performed on individual whisker movements (“whisks”) except for the decimation and coherence analyses,
which were performed on the entire movement record collected on the
C2 whisker for each neuron.
We also assessed the relation between spike
activity and kinematic data using a coherence analysis available
on-line in the Chronux toolbox for Matlab (www.chronux.org). Whisker-movement bouts ⱖ1 s in duration were selected, and the multitaper technique was used to estimate spectra of bouts and their
corresponding spiking activity (bandwidth ⫽ 2; tapers ⫽ 3). Coherence between the movement and spike data were computed for the
peak movement frequency between 3 and 20 Hz. Coherence was
considered significant if it was in excess of the value of the 95%
jackknife error bar.
COHERENCE ANALYSIS.
The latency of the first spike as a function of
speed was determined for both protractions and retractions. For this
analysis, spikes were not binned. “First-spike” latency is the time of
the spike’s occurrence relative to movement onset. Speed was computed by determining the slope from 20 to 80% of the peak amplitude.
Additionally, the temporal jitter of a first spike was computed by
calculating the variance (␴2) of its time of occurrence. Visual and
quantitative comparisons of linear and power-law regressions revealed that in the few cells that did display a relationship between
speed and first-spike latency, power law functions best captured the
relationship. Thus for all neurons, power law functions were used in
the regression between speed and first-spike latency. Five speed bins
were sufficient to capture the observed power-law relationships between speed and first-spike latency. A minimum of 5 whisks was
required in each bin. The bins spanned the range of observed speeds.
For similar analyses, see Heil and Irvine (1997) and Shoykhet et al.
(2000).
FIRST-SPIKE LATENCY.
CORRELATIONS BETWEEN FIRING RATE AND WHISKER-MOVEMENT
KINEMATICS. Neural activity was quantified for each neuron by
DISCRIMINATION OF STATIONARY FROM MOVING WHISKERS. For
each neuron, we calculated firing rates for periods of whisker movement
(protractions and retractions) and periods during which whisker position
did not change. The significance of differences in firing rates between the
two periods was assessed using Student’s t-test (P ⬍ 0.05).
determining the firing rate during the protraction and retraction
portion of each movement. This is different from the coherence and
decimination analyses, for which, firing rate was binned at 2 ms.
Firing rate was then correlated with each of the four kinematic
measures independently of set-point (protraction amplitude, protraction speed, retraction amplitude, and retraction speed), using Spearman’s rho.
DECIMATION PROCEDURE. If a neuron’s activity reliably represents
changes in whisker-movement trajectory, there should be significant
correlations between its firing rate and movement kinematics. To
determine whether and with what resolution a neuron’s firing rate
could be used to reconstruct whisker-movement trajectories, we used
a decimation analysis. The entire movement record, originally acquired at 500 Hz, was down-sampled to frequencies from 250 to 12.5
Hz. The decimated movement records were then interpolated back to
500 Hz so that the position, amplitude, and speed of the movement
traces could be correlated with instantaneous firing rates calculated in
2-ms bins.
The kinematic data were not band-pass filtered to avoid possible
artifacts produced by edge effects. Firing rate was then correlated with
RECEIVER OPERATING CHARACTERISTIC (ROC) ANALYSIS. For each
neuron, we quantified the accuracy with which a movement kinematic
could be predicted using firing rate. Because all significant firing ratecorrelations were positively monotonic, (i.e., large firing rates were
indicative of larger or faster movements), we determined whether a
higher firing rate indicated that a movement’s actual class was relatively
large or fast. For both amplitude and speed parameters, movements were
assigned to one of three classes that encompassed the entire range of
observed kinematic values (see Table 2). The kinematic values for the
three amplitude classes were: ⬍20°, 20 – 40°, and 40 – 60°; for protraction
speed: ⬍430°/s, 430 –760°/s, and 760 –1,100°/s. Since retraction speeds
tend to be faster, different kinematic values were employed: ⬍1,090°/s,
1,090 –1,920°/s, and 1,920 –2,750°/s. For each binary comparison of
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Following head-fixation, the moist cotton and dental acrylic atop
the opening in the skull were removed and neuronal activity from
single TG neurons was recorded while rats generated whisker movements in the operant task. Using a manual stereotaxic microdrive, an
electrode (FHC, stainless steel, 3–5 M⍀, 250-␮m shank) was inserted
through the dura and slowly lowered down to the TG. Electrophysiological activity was band-pass-filtered from 1 to 10 kHz and then
acquired with a sampling rate of 20 kHz. Entry into the ganglion (⬎9
mm in depth) was usually preceded by a brief period of silence. The
whisker representation within the ganglion was identified by manual
stimulation of the vibrissa array, the receptive fields of individual
whiskers were defined by audiovisual isolation, and neuronal responses to self-generated whisker movements were recorded. Off-line
spike sorting was performed using principal component analysis with
the Plexon off-line sorter (Dallas, TX). Activity generated during
body movements was eliminated from the analysis as were periods of
licking, which were monitored by connecting the metal sipper tube to
the input of a standard A/D converter, and connecting the rat to
ground (see Hayar et al. 2005). Contact between the rat’s tongue and
the spout produced a junction potential that could be identified in the
electrophysiological recording.
position, amplitude, and speed, using a nonparametric Spearman’s
rho, which has several advantages over a Pearson’s correlation coefficient. Spearman’s rho does not assume that the variables have a
Gaussian distribution, and the variables can be related in any monotonic manner, not just linearly. Use of rho is justified for whisker
kinematics since the distributions were skewed; smaller and slower
movements are more common than larger and faster movements. The
calculation of rho is identical to the more traditionally used Pearson
correlation coefficient with the exception that the former transforms x
and y values to ranks.
We have chosen the decimation analysis over spike-triggered covariance because the former can readily handle the fact that rats do not
produce a normal distribution of whisker-movement amplitudes and
speeds. The method of spike-triggered covariance method may not be
valid for non-Gaussian variables (Schwartz et al. 2006).
KINEMATICS AND TG NEURONS IN RATS
2. Average whisking kinematics (Mean ⫹ SD): Freely
moving versus head-fixed rats
TABLE
Kinematic
Freely Moving
Head-Fixed
Protraction amplitude (°)
31.6 ⫾ 14.7*
30 to 40†
56 ⫾ 14‡
696 ⫾ 330*
717 ⫾ 186‡
32.1 ⫾ 15.3*
30 to 40†
56 ⫾ 14‡
1106 ⫾ 565*
1484 ⫾ 752‡
25.6 ⫾ 8.7
Protraction speed (°/sec)
Retraction amplitude (°)
Retraction speed (°/sec)
354.4 ⫾ 76.4
26.1 ⫾ 9.3
618.4 ⫾ 209.7
*Carvell and Simons (1990); †Towal and Hartmann (2008); ‡Jin et al.
(2004).
RESULTS
In rats operantly conditioned to generate whisking in air, we
recorded the responses of 21 isolated TG neurons while simultaneously monitoring large numbers of associated whisker
movements (median ⫽ 98, see Table 1) with high spatiotemporal resolution. All 21 TG neurons responded to manual
deflections of the whiskers, and their receptive fields were
distributed throughout the whisker array (see Table 1). There
was no apparent relationship between whisker length or location and any aspect of neural responsiveness. A statistical
comparison of whisker-movement kinematics in our head-fixed
animals and those reported for exploratory and discriminative
behaviors in freely moving rats (Carvell and Simons 1990; Jin
et al. 2004; Towal and Hartmann 2008) indicates that, while
there are some differences for both protraction and retraction
movements, the 95% confidence intervals for the two populations clearly overlap (see Table 2).
Figure 1 shows whisker movements and associated unit
activity for three neurons and illustrates the range of responsiveness exhibited during self-generated whisker movements in
air. The movements shown are variable in amplitude and speed
as is typical of exploratory whisking (Bermejo et al. 2002;
Towal and Hartmann 2008). One group of neurons (Fig. 1, top)
rarely fired during the rat’s own whisker movements, but, like
all neurons in our sample, could be reliably driven by manual
deflections of the whisker. Other neurons emitted spikes during
some, but not all, self-generated whisker movements in air,
even when the movements were of similar amplitude or speed
(Fig. 1, middle). At the other end of the continuum were
neurons that fire during almost all whisker movements, producing at least one spike for most movements (Fig. 1, bottom).
Temporal relationship between movement features
and spike activity
To determine whether, and with what temporal resolution, a
TG neuron’s firing rate tracks movement trajectories, we correlated instantaneous firing rate (2-ms bins) with the entire
movement record (recorded at 500 Hz). Based on findings of
an average response latency of 2–3 ms in the anesthetized rat
(Minnery and Simons 2003) the spike record was shifted 2 ms
backward. There were no strong correlations between instan-
FIG. 1. Trigeminal ganglion activity during whisker movements in air. Each panel
shows the spikes (red ticks) generated by a
trigeminal ganglion neuron during a 2-s episode of whisking. Right: spike waveforms. The
arrangement of the panels from top to bottom
reflects increasing correlations between movement kinematics and spiking probability. Top:
a ganglion neuron that fired consistently in
response to passive deflections but rarely fired
during self-generated whisker movements.
Even movements as large as 40° failed to
evoke a spike in this neuron. Middle: a neuron
that responded during some but not all whisker
movements. Bottom: a neuron that responded
consistently throughout the episode.
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classes (e.g., ⬍20 vs. 20 – 40°), an ROC curve was constructed. The ROC
curve displays the rate of true positives versus the rate of false positives
as the criterion firing rate for assignment to the larger or faster movement
class is decreased. The area under the curve was used to determine the
percentage of accurate classifications. The statistical significance of the
calculated area was determined by comparing it with the results of a
bootstrap analysis (1,000 repetitions) with randomly shuffled firing rates
and kinematic measurements. Calculated ROC areas for individual neurons were considered significant if not contained within the 95% confidence interval of the bootstrap-obtained ROC areas. The 95% confidence
interval of the upper and lower bounds of the bootstrapped ROC values
were always ⬃0.5.
Information theory and ROC curves from ideal observer analysis
are common and complementary methods by which one can evaluate
the ability of a neural code to “predict” stimuli. We have chosen to use
the ROC curve from the ideal observer analysis because it directly
translates to performance (e.g., 70% correct), whereas interpreting the
bits from information theory is less straightforward.
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FIG. 2. Spikes of trigeminal ganglion (TG) neurons are not reliably triggered by the onset of protraction or retraction. For each of the 4 cells, 100
whisker movements are aligned to protraction onset, retraction onset, and end.
Spike rasters are shown above the movements.
of five movements in each class was required (see METHODS for
details). For each class, first-spike latency means and variances
(jitter) were computed. Seventeen TG neurons qualified for
this analysis. Power functions were fit to plots of speed versus
mean and variance of the first-spike latency. In only 1 of the 17
cells, and only for retraction, was there a negative correlation
between speed and first-spike latency (P ⬍ 0.05). Decreases in
first-spike jitter were displayed by just two neurons for increases in retraction speed.
Taken together, the results of our first-spike latency, decimation, and coherence analyses indicate that spikes were not
time-locked to movement onsets and could not track the
trajectory of movements. Thus all subsequent analyses examine firing rate during time windows that encompass the duration (⬃50 –100 ms) of protractions and retractions, i.e., forward and backward movements.
Can TG neurons distinguish between stationary
and moving whiskers?
Firing rates were computed separately for individual protractions and retractions, and rates during movement were
compared with those observed when the whisker was stationary. As Fig. 3 shows, no TG neurons generated lower firing
rates during periods of movement, while 11 of the 21 (⬃52%)
produced higher firing rates during movements (t-test, P ⬍
0.05). Thus 50% of the TG neurons recorded can signal the
occurrence of whisker movements.
How reliably do TG neurons encode the kinematics of self
generated whisker movements?
The question was motivated by the fact that individual TG
neurons encode the kinematics of passive whisker deflections
in anesthetized rats (Shoykhet et al. 2000; Stüttgen et al. 2006).
To answer this question, we carried out two complementary
analyses. For both analyses, individual whisker movements
were separated into protraction and retraction components and
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taneous firing rate and position, amplitude, or speed, for any of
the neurons (rho’s ⬍ 0.08, P ⬍ 0.0001 to ⬍ 0.05). It is
possible, however, that temporal integration of trigeminal activity is necessary to encode movement trajectories; that is,
movement trajectories might be encoded at a lower temporal
resolution. To address this possibility, we correlated the firing
rates with movement records sampled with progressively
coarser temporal resolution (downsampled from 500 to 12.5
Hz, see METHODS). Regardless of the movement sampling frequency, firing rate did not reliably correlate with position,
speed, or acceleration (mean rho across neurons did not exceed
0.08). An additional analysis carried out using an “unrealistic”
0-ms latency (no shift of spike times) produced identical
results. This is likely due to the relative smooth trajectory of
movements; adjacent segments of the whisker-movement trace
will have highly similar positions, speeds, and accelerations.
The decimation analysis examined the ability of TG neurons to
track the ongoing trajectory of whisker movements. To determine
whether TG neurons could be entrained by the dominant frequency within a bout of whisker movements, we also performed
a coherence analysis (see METHODS for details). Only bouts ⱖ1 s in
duration were used for this analysis: 18 neurons qualified for this
analysis (median ⫽ 27 bout/neuron). The percentage of bouts per
neuron with significant coherence ranged from 0 to only 47.5%
(mean ⫽ 18%). Thus neither the decimation nor the coherence
analyses support the hypothesis that TG spiking activity provides
a reliable representation of the kinematics of self-generated whisker-movement trajectories in air.
To evaluate the ability of a TG neuron to phase-lock to
particular movement features, individual whisker movements
were aligned at three critical points: protraction onset, retraction onset, and retraction end. For this purpose, the movements
were rescaled in amplitude and matched in phase at the critical
points. Individual whisker movements (whisks) are consistently characterized by a protraction followed by a retraction.
The three components that define these whisks (protraction
onset, peak protraction, and end of retraction) were obtained
using computer algorithms (see METHODS). In this way, a
collection of individual whisks was available for further analysis of where, within each whisk, the neuron produces spikes.
The assignment of the retraction to begin at 2/3 of the whisk
has been done only for graphical purposes and to facilitate
visual identification of phase-locking. As Fig. 2 indicates,
some neurons did appear to fire preferentially during certain
phases of the movements. For example, cell 2 fired preferentially during retractions, although it responded only during a
small proportion of the movements. Cell 4 fired preferentially
near the peak amplitude or start of retraction. In other cells,
spikes were dispersed throughout the movement. However, in
none of the cells were spikes time-locked to the onset of either
protraction or retraction movements.
In a previous study using controlled whisker deflections in
anesthetized rats, Shoykhet et al. (2000) found that the variances (and means) of TG neuron first-spike latencies decreased
as deflection speed was increased. We reasoned therefore that
time-locking might occur preferentially in response to fast
deflections. To assess the relationship between first-spike latency and the speed of self-generated whisker movements, we
assigned movements to one of five classes that spanned the
range of observed speeds. Speed was defined as the slope from
20 to 80% of the peak amplitude. For each neuron, a minimum
KINEMATICS AND TG NEURONS IN RATS
1841
FIG. 3. Comparison of spiking activity for stationary and
moving whiskers. Relative to nonwhisking, 10 of 21 (⬃48%)
TG neurons produce higher firing rates (P ⬍ 0.05) during
protractions (A) and 7 of 21 (⬃33%) do so during retractions
(B). F, significant differences.
CORRELATING FIRING RATE AND KINEMATICS. We sampled 21
TG neurons, all of which could be readily driven by manual
whisker deflections. Seven did not show any correlations
between firing rate and any of the four kinematic variables
(protraction amplitude, protraction speed, retraction amplitude,
and retraction speed), even though movements covered a range
of ⬃70° in amplitude and ⬃3,000°/s in speed. The majority of
neurons (14/21) did display significant correlations (P ⬍
0.0001 to ⬍ 0.05) between firing rate and the magnitude of one
or more kinematic variables (see Fig. 4 and Table 3), and are
hereafter referred to as “kinematic-responsive neurons.” Only
3 of the 14 neurons displayed a P value for just one of the four
kinematics that was ⬎0.025 (the Bonferoni corrected value for
a criterion P value of 0.05; each firing rate was used twice,
once for amplitude and again for speed). Unless otherwise
specified, all subsequent analyses were performed on this
subset of our sample. Note that the data plotted in Fig. 2 are
from neurons with significant kinematic correlations. Some
neurons could not differentiate whisker movements from stationary periods when all whisker movements were considered,
but they did display significant correlations because larger or
faster movements evoked more spikes than slower or smaller
movements.
Figure 4, A and B, presents two examples that illustrate the
range of correlations exhibited by kinematic-responsive neurons. Not only was there considerable variability in the magnitude of calculated rho’s (see Fig. 4, C and D), but for both
amplitude and speed parameters, cells with significant kinematic correlations could be highly variable in their responses
during movements of similar amplitudes or speeds (see Fig.
4A). That is, even kinematic-responsive neurons responded to
some movements within a bout but not to other movements of
similar magnitude (note failures in Fig. 4, A and B). Nevertheless, for each movement parameter, some neurons displayed
strong correlations (Fig. 4B).
The presence
of a significant correlation between firing rate and a kinematic
parameter, while indicative of an association between the two
variables, is not informative as to the reliability with which the
parameter is encoded. We used a receiver operating analysis
RECEIVER OPERATING CHARACTERISTIC ANALYSIS.
FIG. 4. Correlations between kinematic parameters and firing rates of TG neurons. A: a typical significant (P ⬍ 0.05)
relationship between firing rate and a kinematic parameter for
cell 2 from D. B: an example of a less common, strong
relationship (cell 18 from D). Significant correlations for amplitude (C) and speed (D) in our sample of TG cells. Absence
of a bar indicates that none of the correlations were significant.
For a given parameter, correlations could be significant in 1
direction but not in the other. 14 of 21 neurons displayed
significant correlations with ⱖ1 movement parameters.
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four kinematic parameters were calculated (protraction amplitude, protraction speed, retraction amplitude, and retraction
speed). In the first analysis, we computed Spearman’s rho to
determine whether there was a significant association between
each neuron’s firing rate and one or more of the four kinematic
parameters.
1842
V. KHATRI, R. BERMEJO, J. C. BRUMBERG, A. KELLER, AND H. P. ZEIGLER
3. Significant correlations between kinematics
and firing rate
TABLE
Kinematic
Number of Cells (%)
Mean rho
Median rho
Protraction amplitude
Protraction speed
Retraction amplitude
Retraction speed
7 (33%)
9 (43%)
11 (52%)
11 (52%)
0.34 ⫾ 0.07
0.44 ⫾ 0.07
0.44 ⫾ 0.05
0.45 ⫾ 0.05
0.27
0.41
0.45
0.46
Do trigeminal neurons encode whisker position
or movement direction?
Here, we ask whether cells preferentially respond to protractions or retractions, whether these directional preferences are
influenced by either the amplitudes or speeds of the movements, and whether there is a neural correlate of whisker
location relative to the face, i.e., a “position” code.
We first asked whether ganglion cells have a preference for
either forward or backward movements. An example of such
direction-specific firing is shown in Fig. 6A for a neuron which
fires preferentially during backward movements. For each of
the 14 kinematic-responsive cells we computed a measure of
the cell’s directional tuning by determining a 95% CI for the
difference in firing rate between the protraction and retraction
components of individual whisker movements. Preferences for
protraction or retraction are indicated, respectively, by values
FIG. 5. Receiver operating characteristic (ROC) analysis for
each movement kinematic. For amplitude, movements were
sorted into 3 classes: small (S), intermediate (I), and large (L)
movements. For speed, the classes were slow (S), intermediate
(I), and fast (F). We plot those neurons for which ROC curves
had areas ⱖ0.7 and thus could reliably encode movement
kinematics.
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(ROC) to address this issue. An ROC analysis allows one to
determine the accuracy with which a variable can be classified
while systematically varying the decision criteria but making
no assumptions about underlying distributions.
In the preceding section, we reported that for all firing
rate-kinematic correlations, the firing rate increased with
movement amplitude or speed. In the ROC analysis, we asked
whether a higher firing rate indicated that a movement’s actual
class was relatively large or fast. For both amplitude (small,
intermediate, large) and speed (slow, intermediate, fast) movements were assigned to one of three classes. Fourteen of our 21
neurons qualified for this analysis by having at least nine
whisks per class. All possible binary movement class comparisons were made (see Fig. 5 for details). The integral of the
ROC curve for each comparison was used to determine each
neuron’s percentage of correct classifications. By shuffling the
data and by randomly matching firing rates and kinematic
measures, we determined with a 95% confidence interval (CI)
whether the calculated areas differed significantly from chance
(0.50). Note that in Fig. 5 all reported ROC values differed
from chance, and some neurons are represented in more than
one movement type. Across all four movement components
(protraction amplitude, protraction speed, retraction amplitude,
retraction speed), five different TG neurons (5/14, 36%) were
able to distinguish the smallest and largest or slowest and
fastest movements with ⱖ70% accuracy. Some neurons could
also make the finer discriminations between the intermediate
and other classes. Thus a subpopulation of TG neurons can
reliably encode whisker-movement kinematics.
KINEMATICS AND TG NEURONS IN RATS
1843
FIG. 6. Evaluating the ability of kinematic-responsive neurons to differentiate protractions from retractions. A: directional
response difference (Hz) as a function of relative speed (protraction minus retraction) for cell 2 in B–D. Directional response difference is computed for each whisk with protraction
firing rate minus retraction firing rate. Similarly, relative speed
for each whisk, is protraction speed minus retraction speed.
B: mean difference in firing rate for protractions vs. retractions
is plotted for all movements. Bars are only shown for those
cells (10/14, ⬃70%) for which the difference is significant (P ⬍
0.05). C: R2 for the extent to which relative amplitudes and
speeds modulate direction preferences. Speed has a stronger
effect than amplitude in modulating the amount of directional
tuning. D: the presence of a significant y intercept indicates a
directional preference persisting after contributions from amplitude or speed are removed. Eight of the 10 cells displayed
preferences after eliminating the influence of amplitude differences, and 5 of them responded more during protractions. Six of
the 10 originally direction-selective cells continued to differentiate directions after accounting for speed, and they all
preferred protractions.
J Neurophysiol • VOL
continues to respond preferentially to retractions even when the
effect of amplitude is removed but loses a directional preference when speed differences are eliminated.
Only two cells lose a direction preference after the role of
amplitude is eliminated. However, of the six cells that had
retraction preferences across all whisking movements, five do
not show a preference when the speed difference is set to zero
in the regression, and one cell’s preference actually changes to
protractions. Therefore only preferences for protraction remain
after removing the effect of speed.
In our experimental paradigm, the rat’s “whisking space” is
determined by the length of our CCD array, and it covers the
area from 30 to 140° (90° being perpendicular to the face).
Position coding within the space was examined by dividing it
into 10° equal bins. For each of the 21 units, we determined
whether the spikes are uniformly distributed throughout that
space. Nine of the 21 neurons displayed a significantly nonuniform distribution (Kruskall-Wallis test, P ⬍ 0.05). To
determine the preferred location with respect to the sampled
range of locations for each neuron, we divided the bin with
maximal response by the range of possible locations to derive
a location index. A location index ⬎0.5 indicates that the cell
preferred relatively protracted positions. The mean location
index for the nine cells with significant preferred locations is
0.78 ⫾ 0.10, indicating that most of these cells responded
preferentially at protracted positions.
DISCUSSION
We evaluated the ability of TG neurons in awake rats to
reliably encode the kinematics of self-generated whisker movements in air. While the majority (⬃70%) of TG neurons
displayed significant correlations between firing rate and some
kinematic parameter, only a subset, ⬃30%, represented kinematics with high reliability. Moreover, neurons that did respond during movements did not do so consistently. Preferential firing to movement direction was observed, but it was
strongly modulated by movement amplitude and speed. Finally, in contrast to the precise time-locking reported in previous studies of active whisker contacts, movements in air
generated temporally dispersed responses that were not time-
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greater or less than zero. Figure 6B plots the mean difference
in firing rate between protractions and retractions for cells in
which the 95% CI of which differed from zero. Four cells
display preferences for protractions and six prefer retractions.
Thus 10/21 cells have significant directional preferences.
Because protractions and retractions can vary in amplitude
and/or speed, we next determined whether the observed direction preference was influenced by these kinematic parameters.
To do so, we linearly regressed the amplitude or speed difference between the protraction and retraction of an individual
whisker movement against the corresponding firing rate difference. The coefficient of determination (R2) quantifies the
ability of speed or amplitude to modulate a directional preference. With the y intercept from the regression analysis, we
determine whether the observed direction preference is maintained even when protractions and retractions are of equal
amplitude or speed. Data for a sample cell are shown in Fig.
6A. The directional response difference (protraction minus
retraction) for each individual whisker movement is plotted as
function of relative speed. Across all movements, there is a
stronger response for retractions (the plot in Fig. 6A is for cell
2 in B–D). However, as protractions become more similar to
retractions in speed, the preference for retractions decreases.
Relative speed has a stronger effect than amplitude on modulating direction preferences as indicated by the higher R2
values (Fig. 6C). Eight of 14 cells (⬃60%) displayed a significant R2 for speed, whereas only 3 of 14 (⬃20%) did so for
amplitude. Additionally, the R2 values for speed were larger
than those for amplitude (Mann-Whitney test, P ⫽ 0.01). For
seven of the eight cells with significant speed correlations, the
regression slopes are positive, indicating that the direction
preference is biased toward the faster movement. Therefore
direction preferences are modulated by both movement speed
and amplitude.
Some TG neurons continue to display a direction preference
even when the effects of amplitude and speed are removed.
Figure 6D plots the mean firing rate difference when the
amplitude or speed difference is fixed at zero, by using the y
intercept (P ⬍ 0.0001 to ⬍ 0.05). The y intercept corresponds
to the direction preference of a cell when the influences of
amplitude and speed are removed. Cell 2 (shown in Fig. 6A)
1844
V. KHATRI, R. BERMEJO, J. C. BRUMBERG, A. KELLER, AND H. P. ZEIGLER
locked to the onset of either protractions or retractions. These
findings are consistent with observations in awake, behaving
animals (Leiser and Moxon 2007). Our results differ from
those in a report based on “electrical whisking” in anesthetized
animals, which concluded that trigeminal neurons report “the
actual whiskers’ position with high precision” (Szwed et al.
2003). Because of this inconsistency and because our findings
address the question of whether and how behaving rats know
the position of their whiskers, we consider first the limitations
of our study and the possibility of other interpretations of the
data.
Methodological considerations and
alternative interpretations
J Neurophysiol • VOL
The failure of TG neurons to respond consistently and
reliably during whisking in air is surprising because these
neurons are driven reliably by passive deflections (contacts) of
similar amplitude and speeds (Jones et al. 2004a,b; Shoykhet
et al. 2000; Stüttgen et al. 2006). Such differences suggest that
follicle-whisker biomechanical interactions contribute critically to the probability of evoking spikes in TG neurons. One
possibility is that both manual deflections and self-generated
contacts produce relative motion between the whisker and its
follicle, and this relative motion is critical for driving TG
neurons. Self-generated whisker movements in air may generate less relative motion and, consequently, be less likely to
drive some TG neurons. Our observed responses to whisking
could be due to whisker-related muscles actually squeezing the
follicle during whisker movements. Alternatively, contact may
produce larger forces or torques at the whisker base (Birdwell
et al. 2007), whereas whisking in air may produce smaller
forces or torques. Future studies will be required to distinguish
between these possibilities and to clarify the manner in which
the kinematics of a whisker movement and its biomechanical
consequence interact with the response thresholds of individual
ganglion cells.
Encoding of the spatiotemporal properties of “whisking”
movements in air
In our awake animals, responses during whisking in air were
not time-locked to the three analyzed temporal features of the
whisking cycle (protraction onset, retraction onset, or end).
This finding differs with the observation by Szwed et al. (2003)
that ganglion responses during “electrical whisking” in anesthetized rats are time-locked to movement onset during the
initial portion of the protraction. The observed time-locking
may reflect the presence of a timing signal in the repetitive
facial nerve stimulation used to evoke the movements. The
absence of precise time locking seen in our awake animals is
also in contrast to findings in studies of the effects of passive
vibrissae deflections (contacts) throughout the trigeminal
neuraxis. For example, Jones et al. (2004a) demonstrated in
anesthetized rats that TG neurons respond reliably and in a
time-locked fashion to whisker deflections at frequencies as
high as 312 Hz. Also in anesthetized rats, the initial firing rate
of a TG neuron in the first 2 ms after deflection onset increases
with stimulus speed but not amplitude (Shoykhet et al. 2000).
In the ventroposterior thalamus, speed is encoded by the
average firing rate across the thalamic population within 2–7
ms after the onset of population activity, and this component of
the thalamic population response strongly predicts the response
magnitude of individual layer 4 excitatory neurons in barrel
cortex (Pinto et al. 2000). Precisely time-locked onset responses to self-generated contacts have also been reported for
barrel cortex neurons in awake rats (Hentschke et al. 2006).
Therefore the temporal dispersion of responses we observed
raises doubts as to the utility of ganglion activity during
whisking in air for the downstream encoding of whisking
kinematics. To derive useful kinematic information from such
dispersed ganglion responses, downstream neurons in whiskerrelated brain stem structures would need to integrate those
inputs over a relatively long time window that matches the
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Could the variability in neuronal responses to whisking
movements in air be due to differences between the kinematics
of our representative whisker (C2) and those of the other
whiskers? Bermejo et al. (2005: see Fig. 2) have shown that
whiskers in different rows and columns (e.g., B1/C3) exhibit
highly synchronous movements, differing in amplitude by less
than a degree. Thus it seems unlikely that the relation between
neural activity and whisker motion might have been obscured
by slight differences in kinematics of different whiskers. Moreover it seems equally unlikely that our optoelectronic monitoring system lacked sufficient spatiotemporal resolution. As for
spatial resolution, it is true that ganglion neurons in anesthetized animals are sensitive to inputs ⬍1 ␮m and frequencies as
high as 1 kHz (Gibson and Welker 1983), but it is hard to
imagine how movements ⬍7 ␮m—that is, below the resolution
of our detector— could be significant to a ganglion neuron
when much larger movements (ranging from 4 to ⬃80°) did
not always evoke spikes, even in neurons that did display
significant correlations between firing rates and kinematics.
Additionally, movement of the whisker pad itself and the
activity of its three related extrinsic muscles (Hill et al. 2008),
could be contributing to the responses of our TG neurons for
position. Because the pad moves with the whisker, we do not
believe this is likely to be affecting the neural activity for
changes in whisker position as measured by amplitude and
speed. Regarding temporal resolution, while we can make no
statements about the encoding by TG neurons of frequencies
exceeding our maximum resolution of 250 Hz, the available
data (see Fig. 4 of Wolfe et al. 2008), suggest that frequencies
⬎200 Hz are not normally produced during whisking in air.
Although we could not classify our neurons as either RA or
SA, several pieces of evidence suggest that our conclusions
apply to both classes. First, Leiser and Moxon (2007) found
identical correlations between the frequency within a bout of
whisking and its corresponding firing rate for both RA and SA
neurons (r ⫽ 0.52). Second, for “pink noise” stimuli covering
a range of several hundred hertz, the spike trains of RA and SA
neurons were identical in their ability to reconstruct stimuli
(Jones et al. 2004a). Third, whether or not a neuron is classified
as RA or SA varies with the direction of whisker stimulation
(Jones et al. 2004b). Nonetheless, both SA and RA neurons,
when driven by ramp-and-hold whisker deflections, respond
primarily to movement onsets and offsets (Lichtenstein et al.
1990). However because we could not operationally distinguish between the two classes, the extent to which our findings
may be generalized to both remains unclear.
Biomechanical considerations
KINEMATICS AND TG NEURONS IN RATS
1845
duration of whisker movements (⬃50 –100 ms). While individual TG neurons do not emit reproducible spike trains during
whisking in air, our ROC analysis indicates that ⬃1/3 of the
neurons are able to accurately discriminate kinematics with
ⱖ70% accuracy. As each whisker is innervated by 100 –150
ganglion neurons (Lee and Woolsey 1975), it is possible that
the population activity of a subset of TG neurons might
transmit reliable kinematic information to downstream structures. Because downstream neurons receive converging sensory inputs, they could represent kinematics more reliably than
their presynaptic inputs, due to an increase in the signal-tonoise ratio (e.g., the receptive fields of superior colliculus
neurons include ⬃14.5 vibrissae) (Hemelt and Keller 2007).
For example, in the both the auditory and the electrosensory
systems, the ability to track ongoing input changes is greater in
neurons of downstream structures than in the responses of
single primary afferents (Carlson and Kawasaki 2008; Hoffman et al. 2008; Joris et al. 1994; Louage et al. 2005).
⬃30% of ganglion neurons exhibit reliable kinematic encoding
and that preferential responsiveness to movement direction is
strongly modulated by movement amplitude and speed. Thus
the utility of these signals for processing by downstream
structures remains to be determined by recording experiments
throughout the trigeminal neuraxis in awake, actively whisking
animals.
Encoding of whisker position or direction of movement
in awake animals
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Conclusions
Our findings demonstrate that during whisking in air, some
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J Neurophysiol • VOL
The authors thank V. Lawson for preparing the animals and assisting in
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GRANTS
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