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Neuron
Article
A Cortical Substrate for Memory-Guided
Orienting in the Rat
Jeffrey C. Erlich,1 Max Bialek,1 and Carlos D. Brody1,*
1Howard Hughes Medical Institute, Princeton Neuroscience Institute and Department of Molecular Biology, Princeton University,
Princeton NJ 08544, USA
*Correspondence: [email protected]
DOI 10.1016/j.neuron.2011.07.010
SUMMARY
Anatomical, stimulation, and lesion data have suggested a homology between the rat frontal orienting
fields (FOF) (centered at +2 AP, ±1.3 ML mm from
Bregma) and primate frontal cortices such as the
frontal or supplementary eye fields. We investigated
the functional role of the FOF using rats trained to
perform a memory-guided orienting task, in which
there was a delay period between the end of a
sensory stimulus instructing orienting direction and
the time of the allowed motor response. Unilateral
inactivation of the FOF resulted in impaired contralateral responses. Extracellular recordings of single
units revealed that 37% of FOF neurons had delay
period firing rates that predicted the direction of the
rats’ later orienting motion. Our data provide the first
electrophysiological and pharmacological evidence
supporting the existence in the rat, as in the primate,
of a frontal cortical area involved in the preparation
and/or planning of orienting responses.
INTRODUCTION
Behaviors that require the planning and execution of orienting
decisions have long been investigated in rodents. A classic
example is navigation through mazes (Tolman, 1938; Hull,
1932; Olton and Samuelson, 1976). Recordings from the rodent
hippocampus and entorhinal cortex have led to important
discoveries about the neural encoding of navigation and the
representation of space (McNaughton et al., 2006; Moser
et al., 2008). Navigation is composed of a sequence of individual
orienting motions, but in contrast to rodent studies of spatial
navigation, the neural control of individual orienting motions
has been studied most thoroughly in primates, specifically with
regard to the control of gaze by the frontal and supplementary
eye fields (FEF and SEF) (Schall and Thompson, 1999; Schiller
and Tehovnik, 2005). As a result of being separated by both
different model species and by different behavioral paradigms,
literature for the navigation system and literature for the orienting
systems have remained far apart, making few references to each
other (but see Arbib, 1997; Corwin and Reep, 1998; Kargo et al.,
2007). Yet the two systems must necessarily interact (Whitlock
et al., 2008). As part of bridging the gap between these two fields
330 Neuron 72, 330–343, October 20, 2011 ª2011 Elsevier Inc.
of research, we took a classic primate behavioral paradigm,
memory-guided orienting (Gage et al., 2010; Funahashi et al.,
1991), which is known to be FEF-dependent (Bruce and Goldberg, 1985; Bruce et al., 1985), and adapted it to rats. Then, in
rats performing the task, we studied a rat cortical area that has
long been suggested as homologous to the primate FEF.
The area we studied appears in the literature under a large
variety of names. These include M2 (Paxinos and Watson,
2004), anteromedial cortex (Sinnamon and Galer, 1984), dorsomedial prefrontal cortex (Cowey and Bozek, 1974), medial precentral cortex (Leichnetz et al., 1987), Fr2 (Zilles, 1985), medial
agranular cortex (Donoghue and Wise, 1982; Neafsey et al.,
1986), primary whisker motor cortex (Brecht et al., 2004), and
rat frontal eye fields (Neafsey et al., 1986; Guandalini, 1998). A
theme common to many studies of this area, and shared with
the primate FEF, is a role in guiding orienting movements. We
targeted a particular point at the center of the areas investigated
in the studies cited above (+2 AP, ±1.3 ML mm from Bregma),
and refer to the cortex around this point as the frontal orienting
field (FOF).
The homology between rat FOF and primate FEF was first
proposed four decades ago by C.M. Leonard (1969), based on
the anatomical finding that the FOF, like the FEF, receives
projections from the mediodorsal nucleus of the thalamus
(Reep et al., 1984), and projects to the superior colliculus (SC)
(Reep et al., 1987). Later, Stuesse and Newman (1990) found
that the rat FOF also projects to other oculomotor centers in
the rat’s brainstem, in a pattern that mimics the oculomotor
brainstem projections of the primate FEF. Also like the FEF, the
FOF receives inputs from multiple sensory cortices, including
visual, auditory, and somatosensory cortices (Condé et al.,
1995), and has strong reciprocal connections with the prefrontal
(Condé et al., 1995) and parietal cortices (Corwin and Reep,
1998). The rat FOF, like the primate FEF, is thus well-placed to
integrate information from many different sources in the service
of guiding orienting motions. Leonard’s proposal led to studies
that found that unilateral lesions of the FOF produced effects
consistent with contralateral neglect (Cowey and Bozek, 1974;
Crowne and Pathria, 1982; Crowne et al., 1986), which is a
classic symptom of FEF damage in humans and monkeys
(Ferrier, 1875; Hebb and Penfield, 1940). Further support for
Leonard’s proposal came from studies that revealed orienting
motions in response to intracortical microstimulation of the
FOF (Sinnamon and Galer, 1984). This parallels the orienting
motions produced by stimulation of the primate FEF in headfixed (Bruce et al., 1985) as well as head-free animals (Monteon
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Cortical Basis of Memory-Guided Orienting in Rats
et al., 2010). Neafsey et al. (1986) reported that stimulation of the
FOF in anesthetized, head-fixed rats produced both eye and
whisker motions and suggested it was an eye-head orientation
cortex, homologous to the FEF. More recently, based on the
whisker motions evoked by electrical stimulation of the FOF,
the area has been studied as a whisker motor cortex (Brecht
et al., 2004), with particular attention paid to its role in vibrissal
active sensing (reviewed in Kleinfeld et al., 2006).
To our knowledge, there are only a few electrophysiological
studies recording single neurons of awake animals in this area
(we are aware of only three, Carvell et al., 1996; Kleinfeld et al.,
2002; Mizumori et al., 2005), and they have not focused on the
FOF’s role in orienting motions. Kleinfeld et al. (2002) used
head-fixed rats, precluding the study of head- or body-orienting
movements. Carvell et al. (1996) recorded from awake rats that
were whisking freely while being held in the experimenter’s
hands, but orienting movements were not recorded, and the
rats were not required to perform any task. Mizumori et al.
(2005) reported head direction tuning (Taube, 2007) in the FOF.
Mizumori et al. (2005) also mentioned observing neurons that
encoded egocentric motions, including orienting movements,
but they did not elaborate on this observation.
To further investigate the role of the FOF in the control of
orienting, we carried out unilateral pharmacological inactivations
of the FOF and recorded extracellular neural spiking signals from
the FOF, while rats were performing a memory-guided orienting
task (Gage et al., 2010; Funahashi et al., 1991). Our findings
provide the first pharmacological and electrophysiological
evidence that the FOF plays an important role in the preparation
(Riehle and Requin, 1993) of orienting movements.
RESULTS
The Memory-Guided Orienting Task
We developed a computerized protocol to train rats to perform
a two-alternative forced-choice memory-guided orienting task
(Figure 1A). Training took place in a behavior box with three
nose ports arranged side-by-side along one wall, and with two
speakers, placed above the right and left nose ports. Each trial
began with a visible light-emitting diode (LED) turning on in the
center port. In response to this, rats were trained to place their
noses in the center port, and remain there until the LED was
turned off. We refer to this period as the ‘‘nose in center’’ or
‘‘fixation’’ period, and varied its duration randomly from trial to
trial (range: 0.9–1.5 s). During the fixation period, an auditory
stimulus, consisting of a periodic train of clicks, was played for
300 ms. Click rates greater than 50 clicks/s indicated that a water
reward would be available on the left port; click rates less than 50
clicks/s indicated that a water reward would be available on the
right port. On ‘‘memory trials,’’ the click train was played shortly
after the rat placed its nose in the center port, and was followed
by a silent delay period before the fixation period ended and the
animal was allowed to make its response. On ‘‘nonmemory
trials,’’ the click train ended at the same time as the fixation
period, and the animal could respond immediately after the
end of the stimulus. The two types of trials were randomly interleaved with each other in each session. For animals in behavioral
and pharmacological experiments, we also interleaved, across
trials within each session, six different click rate values, ranging
from easy trials, with click rates far from 50 clicks/s, to difficult
trials, with click rates close to 50 clicks/s. To maximize the
number of identically prepared trials, animals in electrophysiological experiments were presented with only two click rates,
100 and 25 clicks/s, again randomly interleaved across trials
(Figure 1C, filled circles).
Here we present data from 25 male Long-Evans rats, five of
which were implanted with bilateral FOF cannula for infusions,
four of which were implanted with bilateral M1 cannula, and
another five of which were implanted with microdrives for tetrode
recording. Four of the five tetrode-implanted rats performed
memory-guided click rate discrimination, as described in Figure 1. As a preliminary test of the effects of a different class
of instruction stimulus, the fifth tetrode-implanted rat was
trained on a memory-guided spatial location task, in which the
click train rate was always 100 clicks/s, and the rewarded
side was indicated by playing the click train from either the
left or the right speaker. The behavioral performance and physiological results were similar for the two stimulus classes (i.e.,
click rate discrimination and location discrimination; see Figure S4 available online), and are reported together in the main
text.
Rats performed about 300 trials per 1.5 hr session each day,
7 days a week, for 6 months to 1.5 years. After each animal
was fully trained, an average of !66,000 trials per rat were
collected. Maintaining fixation is likely to require inhibitory
control (Narayanan and Laubach, 2006; Munoz and Wurtz,
1992), and individual rats varied in the percentage of trials in
which they broke fixation (range: 10%–50%). There were
consistently more broken fixation trials for memory trials
(mean ± standard error [SE], 37% ± 2%) than for nonmemory
trials (mean ± SE, 29% ± 2%, paired t test, p < 10"5). Unless
otherwise specified, all trials where rats prematurely broke fixation were excluded from analyses.
For each rat, we combined the data across sessions and fitted
four-parameter logistic functions to generate one psychometric
curve for memory trials, and another curve for nonmemory trials
(Figure 1C, thin lines). Percent correct on the easiest memory
trials was similar to the easiest nonmemory trials (94% versus
95%, paired t test, p > 0.49). Click frequency discrimination
ability, as assayed by the slopes of the psychometric fits at their
inflection point, was also similar for memory and nonmemory
trials ("2.3% versus "2.1% went-right per click/sec, paired
t test, p > 0.35). This suggests that the two types of trials are
of similar difficulty.
We tested whether whisking played a role in performance of
the memory-guided orienting task in three ways. First, we cut
off the whiskers of three rats bilaterally. This manipulation had
no statistically significant effect on psychometric function slopes
or endpoints, although it did produce a small effect on overall
percent correct performance (83% ± 1% without whiskers
versus 87% ± 1% with whiskers, t test, p < 0.05). There was no
differential effect on memory versus nonmemory trials (t test,
p > 0.5; Figures 1D and 1F). Second, we probed whether asymmetric whisking played a role in task performance by using
unilateral subcutaneous lidocaine injections to temporarily
paralyze the whiskers on one side of the face of four rats.
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Cortical Basis of Memory-Guided Orienting in Rats
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B
C
D
E
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Figure 1. Memory-Guided Frequency Discrimination Task and Behavioral Performance
(A) Task schematic, showing a cartoon of a rat in the behavior box and the timing of the events in the task. Onset of the Center LED indicated to the rat it should put
its nose in the center port, and remain there until the LED was turned off. During this variable-duration ‘‘nose fixation’’ or ‘‘nose-in-center’’ period, a 300 ms-long
periodic train of auditory clicks was played. Click rates higher than 50 clicks/s indicated that a water reward would be available from the left port; click rates lower
than 50 clicks/s indicated reward would be available from the right port. On memory trials (orange), the click train was played near the beginning of the fixation
period, and there was a several hundred ms delay between the end of the click train and the end of the nose fixation signal. On nonmemory trials (green) the click
train ended at the same time as the nose fixation signal.
(B) An example of performance data for a single rat. Each circle indicates the percentage of trials in which the subject chose the right port for a given stimulus in
a single session. There were six stimuli presented in each session. The thick line shows the psychometric curve, drawn as a 4-parameter sigmoidal fit to the
circles. The left panel shows data from nonmemory trials, and the right panel shows data from memory trials.
(C) Psychometric curves showing performance of 20 rats. Thin lines are the fits to individual rats, as in (B). Thick lines are the fits to the data combined across rats.
The performance of electrode implanted rats (n = 5) is shown by the small filled circles at the two stimuli used with these animals (25 clicks/s and 100 clicks/s).
(D) Bilateral whisker trimming (3 rats) has a minimal effect on performance. The gray line is the average of memory and nonmemory trials for control sessions
before whisker trimming. Diamonds are data after trimming, solid lines are sigmoid fits. Memory trials are in orange, nonmemory trials in green.
(E) Unilateral whisker pad anesthesia and paralysis (four rats) also has a minimal effect on performance. Open circles are data from lidocaine sessions. Color
conventions as in (D).
(F) Summary of effects of whisker trimming and lidocaine. See also Figure S1, Movie S1, Movie S2, and Movie S3.
This manipulation did not generate any lateralized effects on
performance, but led instead to a small bilateral effect, indistinguishable from that of bilateral whisker trimming (Figures 1E
and 1F). Third, we performed video analysis of regular sessions
(no drug, no whisker trimming), searching for differences in delay
period whisking preceding leftward versus rightward movements. No significant differences were found (Figure S1).
Furthermore, in the video analyzed, the whiskers were held still
during the memory delay period (Movie S2, compare to exploratory whisking in Movie S1 and out-of-task whisking Movie
S3). In sum, whisking appears to play a negligible role in the
memory-guided orienting task.
332 Neuron 72, 330–343, October 20, 2011 ª2011 Elsevier Inc.
Muscimol Inactivation of the FOF Generates
a Contralateral Impairment
In contrast to the negligible effects found from manipulating the
whiskers themselves, we found that manipulating neural activity
in the FOF produced strong effects on memory-guided orienting.
Unilateral inactivation of the FOF generated a clear impairment
on trials where the animal was instructed to orient contralateral
to the infusion site. (Figure 2, Contra trials). Performance on ipsilaterally-orienting trials was unaffected (Figure 2, Ipsi trials).
Contralateral impairment was observed for both memory and
nonmemory trials, which were randomly interleaved with each
other. However, the effect was markedly stronger on memory
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Cortical Basis of Memory-Guided Orienting in Rats
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Figure 2. Unilateral Inactivation of FOF Generates a Contralateral Impairment that Is Larger for Memory Trials Compared to Nonmemory
Trials
(A) Behavioral performance on control and muscimol-infusion days. Top row: nonmemory trials. Bottom row: memory trials. Left column: muscimol infusions into
left FOF. Right column: musicmol infusions into right FOF. Open circles, data from muscimol infusions. Closed circles: control data from days immediately
preceding infusion days. Dashed lines: sigmoidal fits to muscimol data. Solid lines: sigmoidal fits to control data. Error bars are standard error of the mean. Error
bars for control data were smaller than the marker in most cases. Underbraces at bottom indicate the sets of trials in which animals were instructed to orient
ipsilaterally or contralaterally to the site of infusion. The percentages aligned to the dashed curves indicate the endpoint performance for the trials contralateral to
the infusion.
(B) Combined data from left and right infusion sessions and collapsed across all stimulus difficulty levels. The ‘‘No Drug’’ data come from the 20 sessions one day
before infusion sessions. The Ipsi and Contra Muscimol data are the performance on ipsilateral trials and contralateral trials on infusion sessions (n = 20). See also
Figure S2.
trials (Figure 2A; compare top row to bottom row). Left infusions
impaired rightward-instructed trials to the same degree that
right infusions impaired leftward-instructed trials (four t tests:
contra/mem p > 0.5, contra/nonmem p > 0.26, ipsi/mem p > 0.1,
ipsi/nonmem p > 0.4). We therefore combined data from left
and right infusion days for an overall population analysis,
and confirmed that performance was worse for contralateral
memory trials than nonmemory trials (Figure 2B, permutation
test p < 0.001). Since memory and nonmemory trials are of
similar difficulty (see above), the greater impairment on memory
trials suggests that, in addition to a potential role in direct motor
control of orienting movements, there is a memory-specific
component to the role of the FOF.
To test whether unilateral inactivation of primary motor
cortex could produce a similar effect to inactivation of the
FOF, we repeated the experiment, in the neck region of M1
(+3.5 AP, +3.5 ML). This is the same region in which Gage
et al. (2010) recorded single-units during a memory-guided orienting task. Unilateral muscimol in M1 produced a pattern of
impairment that was different, and much weaker, than that
produced in the FOF. In particular, we found no difference in
the impairment of contra-memory versus ipsi-memory trials
(t test, p > 0.35) (Figures S2A–S2D).
Neurons in the FOF Prospectively Encode Future
Orienting Movements
We obtained spike times of 242 well-isolated neurons from five
rats performing the memory-guided orienting task. No significant
differences were found across recordings from the left and
right sides of the brain. Accordingly, we grouped left and right
FOF recording data together. Below we distinguish between
trials in which animals were instructed to orient in a direction
opposite to the recorded side (‘‘contralateral trials’’) and trials
in which they were instructed to orient to the same side (‘‘ipsilateral trials’’).
We first analyzed spike trains from correct trials, with a particular interest in cells that had differential contra versus ipsi firing
rates during the delay period, i.e., after the end of the click train
stimulus but before the Go signal (see Figure 1A). We identified
such cells by obtaining the firing rate from each correct trial,
averaged over the entire delay period, and using ROC analysis
(Green and Swets, 1974) to query whether the contra and ipsi
firing rate distributions were significantly different. By this
measure, we found that 89/242 (37%) of cells had significantly
different contra versus ipsi delay period firing rates (permutation
test, p < 0.05). We refer to these cells as ‘‘delay period neurons.’’
Examples of single-trial rasters for six delay period neurons are
shown in Figure 3.
For each cell, we then took the spike train from each trial and
smoothed it with a half-Gaussian kernel to produce an estimated
firing rate as a function of time (standard deviation [SD] of whole
Gaussian = 200 ms; smoothing process is causal, i.e., looks only
backward in time). At each time point, this gave us, across
trials, a distribution of firing rates on contralateral trials and
a distribution of firing rates on ipsilateral trials. We used ROC
analysis to query whether the distributions were significantly
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Figure 3. Upcoming Choice-Dependent Delay
Period Activity in the FOF
(A and B) (A) Three contralateral preferring cells and (B)
three ipsilateral preferring cells that show delay period
activity that is dependent on the upcoming side choice.
The top half of each panel shows spike rasters sorted by
the side of the rat’s response and aligned to the time of the
Go cue. The pink shading indicates the time, for each trial,
when the stimulus was on. The brown ‘‘+’’ indicates the
time at which the rat placed its nose in the center port. The
bottom half of each panel are PETHs of the rasters
for ipsilateral (red) and contralateral (blue) trials. The two
lines are indicate the mean ± SE. PETHs were generated
using a causal half-Gaussian kernel with an SD of 200 ms.
The thick black bar just below the rasters indicates the
times when the cells response was significantly different
on ipsi- versus contralateral trials (p < 0.01, ROC analysis).
(C) Development of choice-dependent activity over the
course of the trial. The lines indicate the % of cells (out of
242 neurons) that have significantly choice-dependent
firing rate (p < 0.01) at each time point on memory trials
(orange) and nonmemory trials (green). See Figures S3A
and S3B for interspike interval histograms and waveforms
for the example neurons.
C
different at each time point. By this assay, we found that (113/
242) (47%) of cells in the FOF had significantly different contra
versus ipsi firing rates at some point in time during memory trials
(overall probability that a cell was labeled as significant by
chance p < 0.05; time window examined ran from "1.5 s before
to 0.5 s after the Go signal).
The temporal dynamics of delay period neurons were quite
heterogenous. Different cells had significantly different contra
versus ipsi firing rates at different time points during the trial (indicated for each cell in Figure 3 by black horizontal bars). At each
time point, we counted the percentage of neurons, out of the 242
recorded cells, that had significantly different contra versus ipsi
firing rates, and plotted this count as a function of time for
memory trials and for nonmemory trials (Figure 3C). For memory
trials the population first became significantly active at 850 ms
before the Go signal (Figure 3C, horizontal orange bar). For nonmemory trials the population became active 120 ms before the
Go signal (Figure 3C, horizontal green bar). At the time of the
Go signal on memory trials, 28% of cells had firing rates that predicted the choice of the rat.
We labeled cells as ‘‘contra preferring’’ if they had higher firing
rates on contra trials, and as ‘‘ipsi preferring’’ if they had higher
firing rates on ipsi trials. When firing rates were examined across
time (from "1.5 s before to 0.5 s after the Go signal), most cells
334 Neuron 72, 330–343, October 20, 2011 ª2011 Elsevier Inc.
had a label that was consistent across the
duration of the trial: 82/89 (92%) of significant
delay period neurons were labeled exclusively
as either contra-preferring or ipsi-preferring.
Seven of the 89 (8%) delay period neurons
switched preference at some point during the
trial, usually between the delay period and late
in the movement period (data not shown). For
our analyses below, we used labels based on
the average delay period firing rate.
Given the strong difference in contralateral versus ipsilateral
impairment during unilateral inactivation (Figure 2), we were
surprised to find no significant asymmetry in the number of
contra-preferring versus ipsi-preferring delay period neurons:
50/89 cells (56%) fired more on contralateral trials (three examples are shown in Figure 3A), while 39/89 (44%) fired more on
ipsilateral trials (three examples in Figure 3B). Although there
were more contra preferring cells, the difference in number of
contra versus ipsi-preferring cells was not statistically significant
(c2 test on difference, p > 0.2).
To perform population analyses of firing rates, we first Z-score
normalized each cell’s perievent time histograms (PETHs) by
subtracting their mean and dividing by their standard deviation,
and then averaged across cells to obtain population normalized
PETHs, shown in Figures 4A–4D. The early onset ramp we found
in the count of cells with significantly different contra versus ipsi
memory trial firing rates (orange line, Figure 3C) is paralleled in
Figures 4A and 4B by an early onset in population firing rate
difference for contra versus ipsi memory trials. Similarly, the
late onset ramp in Figure 3C for nonmemory trials is paralleled
in Figures 4C and 4D.
We then turned to analyzing error trials. The activity on error
trials (shaded pink for ipsi-instructed but contra motion, and
blue for contra-instructed but ipsi motion; Figures 4A–4D)
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Cortical Basis of Memory-Guided Orienting in Rats
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B
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D
E
F
Figure 4. Predictive Coding of Contra- and Ipsilateral Choice in the FOF
(A–D) Each panel is a population PETH showing the average Z-score normalized response on correct (thick lines, mean ± SE across neurons) and error trials
(shaded, mean ± SE across neurons) where the correct response was contralateral (blue) or ipsilateral (red) to the recorded neuron. PETHs are aligned to the time
of the Go signal (center LED offset). (A) The average responses of memory trials for 50 contra-preferring neurons. Vertical axis tick marks indicate Z-score value.
The average firing rate across all cells used for Z-score normalization is shown next to the Z = 0 mark (8.2 spikes/s). This overall mean ± the across-cell average of
the PETH standard deviation are shown at the Z = ±1 marks. They indicate a typical firing rate modulation of 7.2 spikes/s. (B) The average responses of memory
trials of 39 ipsi-preferring neurons. (C) Same as (A) but for nonmemory trials. (D) Same as (B) but for nonmemory trials.
(E) Cells encode the direction of the motor response, not the identity of the cue stimulus. Scatter plots of the side-selectivity index for memory trials (orange) and
nonmemory trials (green) (n = 89).
(F) Histogram of the choice probability of neurons for trials where the rat was instructed to go in the cells’ preferred direction (n = 89). The dot and line indicate the
mean ± 95% confidence interval of the mean. Black bars indicate individually significant neurons. White bars indicate neurons that were not individually
significant.
showed that, on average across the population, cells that fire
more on correctly performed contra-instructed trials also fire
more on erroneously performed ipsi-instructed trials; that is,
these cells fire more on trials where the animal orients contralateral to the recorded side, regardless of the instruction. Similarly,
ipsi preferring cells fire more on trials where the animal orients
ipsilaterally, regardless of the instruction. This indicates that
the firing rates of FOF cells are better correlated with the
subject’s future motor response than with the instructing sensory
stimulus. We quantified this observation on a cell-by-cell basis
by generating a side-selectivity index (SSI) for each neuron
(see Experimental Procedures for details). Positive SSIs mean
that a cell fired more on contra-instructed trials. Negative SSIs
mean that a cell fired more on ipsi-instructed trials. If cells
encode the instruction we would expect SSIcorrect z SSIerror.
But if cells encode the direction of the motor response, then
we would expect SSIcorrect z "SSIerror. We first calculated the
SSI focusing on the delay period of memory trials. We found
that, over neurons, SSIcorrect correlates negatively with SSIerror
(r = "0.42, p < 10"4), confirming that on memory trials, the delay
period firing rates of FOF neurons encode the orienting choice
of the rat, not the instruction stimulus. We then repeated this
calculation for firing rates over the movement period (from Go
signal to 0.5 s after the Go signal), for both memory (SSIcorrect
and SSIerror correlation r = "0.59, p < 10"8) and nonmemory
(r = "0.78, p < 10"17) trials. These negative correlations indicate
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B
D
Figure 5. Trial-by-Trial Correlation of Neural and Behavioral Latency
(A) Head angular velocity data from left correct memory trials in a single session. Each row is a single trial, showing head angular velocity (color-coded) as
a function of time. The white dots indicate the time of the Go cue, and the green dots indicate the Response Onset time. Left panel: Trials are sorted by reaction
time (Response Onset-Go cue). Right panel: same trials, after each trial has been time-shifted to maximize the similarity between the trial’s angular velocity profile
and the average of all the other trials.
(B) Same trials as in (A), but color code here indicates firing rate of a single neuron. Left panel is before alignment. Right panel is after time-alignment to maximize
the similarity between each trial’s firing rate profile and the average of all the other trials.
(C) Correlation between the angular velocity time offsets and the neural firing rate time offsets computed in (A) and (B).
(D) Histogram of r values for 53 cells with significant delay period activity that were recorded during sessions where head-tracking was also recorded. Black bars
indicate individual cells with correlations significantly greater than zero. The dot with the line through it shows the mean ± SE of r values for the population. See
also Figure S5.
that the FOF is again encoding the motor choice of the rat.
We summarized the observations from both the delay and
movement periods by calculating the SSI for the entire period,
from "1.5 s before to 0.5 s after the Go cue. This again resulted
in negative SSIcorrect and SSIerror correlations for both memory
(r = "0.49, p < 10"5) and nonmemory (r = "0.59, p < 10"8) trials
(Figure 4E). Overall, then, the firing rates of FOF neurons encode
the orienting choice of the rat, not the instruction stimulus.
If the delay period activity in the FOF subserves the planning of
an orienting movement, then variation in that activity should lead
to variation in behavior, even when the instruction stimulus is
held constant (Riehle and Requin, 1993). One measure of trialto-trial covariation between neuronal signals and choice
behavior is choice probability (Britten et al., 1996), which quantifies the probability that an ideal observer of the neuron’s firing
rate would correctly predict the choice of the subject. We
computed the choice probability for firing rates of delay period
cells. For each cell, we focused on the last 400 ms of the delay
period, using only memory trials in which the instruction was to
orient to the cell’s preferred side. Consistent with the SSI delay
period analysis, we found that an ideal observer would, on
average, correctly predict the rat’s side port choice 64% of the
time. The cell population is strongly skewed above the chance
336 Neuron 72, 330–343, October 20, 2011 ª2011 Elsevier Inc.
prediction value of 0.5, with 75% of cells having a choice probability value above 0.5 (Figure 4F). Twenty-seven percent of cells
had choice probability values that were, individually, significantly
above chance (permutation text, p < 0.05).
We used red and blue LEDs, placed on the tetrode recording
drive headstages of the electrode-implanted rats, to perform
video tracking of the rats’ head location and orientation
(Neuralynx; MT). Two thirds of the delay period neurons (53/89)
were recorded in sessions in which head tracking data was
also obtained. Figure 5A shows an example of head angular
velocity data for left memory trials in one of the sessions, aligned
to the time of the Go signal. There is significant trial-to-trial variability in the latency of the peak angular velocity as the animal
responds to the Go signal and turns toward a side port to report
its choice. As shown in data from the example cells of Figure 3,
and an example cell in Figure 5B, many neurons with delay
period responses also fire strongly during the movement period,
and the latency of each neuron’s movement period firing rate
profile can vary significantly from trial to trial. To quantitatively
estimate latencies on each trial, we used an iterative algorithm
that finds, for each trial, the latency offset that would best align
that trial with the average over all the other trials (Figures 5A
and 5B; see Experimental Procedures for details). Firing rate
Neuron
Cortical Basis of Memory-Guided Orienting in Rats
A
B
Figure 6. Rats Plan Their Response During the Delay Period on Memory Trials
(A) Movement times (MT) are faster for memory trials than nonmemory trials. Movement times are measured as median Response time " Response Onset time for
each physiology session. The mean difference between memory and nonmemory trials is 47 ms (t test, t141 = 3.58, p < 10"5 from the five electrode implanted rats).
The dot above the histogram indicates the mean ± SE of the distribution.
(B) Average head-angle data from 84 recording sessions. Thin lines are 200 example trials randomly subsampled from all 84 sessions. The thick lines are the
average across all trials across all sessions. In our coordinate system, 4 = 0# points directly toward the center port, positive 4 corresponds to rightward
orientations, and negative 4 to leftward orientations. On memory trials one can observe a subtle but clear change in the head angle in the direction of the response
during the delay period, starting around 500 ms before the end of the fixation period. See also Figure S6 for head-direction related neural activity.
latencies and head velocity latencies were estimated independently of each other using this algorithm. We then computed,
for each neuron, the correlation between the two latency estimates (e.g., Figure 5C). We focused this analysis on correct
contralateral memory trials of delay period neurons (as in Riehle
and Requin, 1993). Of 53 delay period cells analyzed, 23 of them
(43%) showed significant trial-by-trial correlations between
neural and behavioral latency (Figure 5D). Furthermore, as a
population, the 53 cells were significantly shifted toward positive
correlations (mean ± SE, 0.36 ± 0.05, t test p < 10"8). We
concluded that a significant fraction of delay period neurons
not only have firing rates that predict the direction of motion
before it occurs (Figure 4F), but in addition, once the motion
has begun, the timing of their firing rate profile is strongly correlated with the timing of the execution of the movement.
Delay Period Firing Rates Cannot Be Explained
As Encoding Head Direction
On memory trials, the subject has many hundreds of milliseconds to plan a motor response in advance of the go signal. We
examined the behavioral data for evidence of planning, and
found it in two forms: faster reaction times on memory trials,
and head angle adjustments during the fixation period. With
respect to reaction time, we found that the time from exiting
the central port until reaching the side port was, on average,
47 ms shorter on memory trials compared to nonmemory trials
(t test,t141 = 3.58, p < 10"5; Figure 6A). This is consistent with
the idea that prepared movements take less time to initiate
and/or execute.
We then asked whether there were any consistent head direction adjustments during the fixation period that would predict
subsequent orienting motion choices. Figure 6B plots 4(t), the
head angle as a function of time aligned to the Go signal, for
both left-orienting and right-orienting trials. As can be seen
from the average 4(t) for each of these two groups, during the
delay period of memory trials, rats tended to gradually and
slightly turn their heads toward their intended motion direction,
even while keeping their nose in the center port. At the time of
the Go signal, 4(t = 0), the rats’ heads had already turned, on
average, !4# in the direction of the intended response. We
used ROC analysis at each time point t to quantify whether the
distribution of 4(t) for trials where the animal ultimately oriented
left was significantly different from the distribution for trials where
the animal ultimately oriented right. We found that, on average,
4(t) allowed a significantly above-chance prediction of the
rat’s choice 444 ± 29 ms before the Go signal (mean ± SE) on
memory trials, and 19 ± 26 ms before the Go signal on nonmemory trials. We also found that on some sessions (8/80,
10%) 4(t) was not predictive of choice at any time point before
the Go signal, even while percent correct performance and
neural delay period activity was normal in these sessions. This
showed that preliminary head movements were not performed
by all rats in all sessions, and suggested that preliminary head
movements may not be necessary for performance of the task.
Firing rates of some neurons in rat FOF have been previously
described as encoding head-direction responses (Mizumori
et al., 2005). That is, the firing rates of some FOF neurons were
a function of the allocentric orientation of the animal’s head
(Taube, 2007). Our recordings replicated this observation (Figure S6). Our data further revealed that head direction tuning in
the FOF was significantly affected by behavioral context: for
many cells the preferred direction depended on whether the
animal was engaged versus not engaged in performing the
task (Figure S6).
Here, the observation of head direction tuning in the FOF,
together with the data of Figure 6B, immediately raised the question of whether delay period firing rates could predict the rat’s
choice merely by virtue of encoding the current head orientation
4 (that, as shown in Figure 6B, is itself predictive of the rat’s
choice). To address this question in a quantitative manner that
did not depend on an in-task versus out-of-task comparison or
distinction, we took advantage of existing variability in 4 during
the fixation period. We first reperformed the analysis of Figure 3A,
but now restricting it to neurons recorded in sessions where
head-tracking data was also recorded. We divided trials into
two groups, based on the sign of 4 at t = +0.6 s after the Go
Neuron 72, 330–343, October 20, 2011 ª2011 Elsevier Inc. 337
Neuron
Cortical Basis of Memory-Guided Orienting in Rats
A
B
C
Figure 7. Predictive Coding of Response Is
Not a Simple Function of Current Head
Angle
(A) Plot of head angle as a function of time relative
to the Go signal for memory trials. Thin blue lines
are from a random subsample of trials where the
head angle was >0 (oriented leftward from center
port) at time t = +0.6 s relative to the Go signal, as
indicated by the vertical dotted line. Thin red lines
are from a random subsample of trials where the
head angle was <0 at t = +0.6 s. Thick lines are the
mean head angles for each group, averaged over
all correct memory trials.
(B) ROC plot (similar to Figure 5B) for the trial
grouping defined in (A).
(C) As in (A), but with groupings defined by the sign
of the head angle at t = "0.9 s relative to the Go
signal.
(D) ROC plot for the trial grouping defined in (C).
See Figure S7 for similar analyses using angular
velocity and acceleration.
D
signal (shown in Figure 7A as traces in blue 4(0.6) > 0, and red
4(0.6) < 0). These two groups are essentially identical to the
‘‘ultimately went Left’’ and ‘‘ultimately went Right’’ groups of Figure 6B, but redefining them in terms of the sign of 4(t) will prove
convenient below. We counted the percentage of neurons that
had firing rates that significantly discriminated between these
two 4(0.6) > 0 and 4(0.6) < 0 groups. The result, essentially replicating that of Figure 3A for the subset of sessions with head
tracking data, is shown in Figure 7B. At the time of the Go signal
(t = 0), 21% of cells significantly discriminated 4(0.6) > 0 versus
4(0.6) < 0 trials. At this same time point (t = 0), the mean difference in 4 for the two groups of trials was !8# . In other words,
if FOF firing rates simply encode current head angle, an 8#
head direction signal should produce a detectable firing rate
change in !21% of cells. We then performed the same analysis,
but this time based on the sign of 4 at t = "0.9 s before the Go
signal (traces in blue for 4("0.9) > 0, and red for 4("0.9) < 0 in
Figure 7C). At t = "0.9 s, the mean difference in 4 for this new
grouping of trials was !8# , very similar to the difference at
t = 0 s for the previous grouping (compare Figures 7A and 7C).
However, only 5% of cells discriminated between the two groups
at t = "0.9 s (Figure 7D). This is in strong contrast to the 21% that
we would have expected if FOF neurons encoded head angle.
We concluded that encoding of head angle was not sufficient
to explain the FOF delay period firing rates that predict orienting
choice. We repeated this analysis with angular head velocity 40 (t)
(Figures S7A–S7D), and with angular head acceleration 400 (t)
(Figures S7E–S7H) and found that, as with head angle, neither
angular head velocity nor angular head acceleration could
explain choice-predictive delay period firing rates. We also performed a regression analysis, fitting the firing rate of each cell on
each trial, f(t), as a linear function of angular position, velocity,
and acceleration (f(t) = b1 3 4(t) + b2 3 40 (t) + b3 3 400 (t) + r(t);
338 Neuron 72, 330–343, October 20, 2011 ª2011 Elsevier Inc.
see Supplemental Experimental Procedures for details). The residuals r(t) have
had any linear effects of head angular
position, velocity, and/or acceleration
eliminated. At each time point, we used ROC analysis to test
whether the distributions of residuals r(t) for ipsilateral versus
contralateral trials were different, and as in Figure 3C, we
counted the number of neurons for which this difference was
significant. We found that only a small portion of the delay period
activity could be accounted for by a combination 4(t), 40 (t), and
400 (t) (Figure S7I).
DISCUSSION
To investigate the contribution of the rat FOF (studies centered
at +2 AP, ±1.3 ML mm from Bregma) to the preparation of orienting motions, we trained rats on a two-alternative forced-choice
memory-guided auditory discrimination task. Subjects were presented with an auditory cue that indicated which way they should
orient to obtain a reward. However, the subjects were only
allowed to make their motor act to report a choice after a delay
period had elapsed. The task thus separates the stimulus from
the response in the tradition of classic memory-guided tasks
(Mishkin and Pribram, 1955; Fuster, 1991; Goldman-Rakic
et al., 1992). We carried out unilateral reversible inactivations
of the FOF, M1, and the whiskers, recorded extracellular neural
spiking signals from the FOF, and tracked head position and
orientation, while rats were performing the task. The resulting
data provide several lines of evidence supporting the hypothesis
that the FOF plays a role in memory-guided orienting. First,
unilateral inactivation of the FOF produced an impairment of
contralateral orienting trials that was substantially greater for
memory trials as compared to nonmemory trials (Figure 2).
Control performance on both memory and nonmemory trials
was very similar (Figure 1 and related text), suggesting that the
differential impairment was not due to a difference in task difficulty, but instead reveals a memory-specific role of FOF activity
Neuron
Cortical Basis of Memory-Guided Orienting in Rats
in contralateral orienting. Second, we found robust neural firing
rates during the delay period (after the offset of the stimulus
and before the Go cue) that differentiated between trials in
which the animal ultimately responded by orienting contralaterally from those where it responded by orienting ipsilaterally
(Figure 3 and Figure 4). Third, we found trial-by-trial correlations
between neural firing and behavior, both for firing rates during
the delay period (Figure 4H) and for neural response latency
during periods that included the subjects’ choice-reporting
motion. (Figure 5). Several groups studying the neural basis of
movement preparation (Riehle and Requin, 1993; Dorris and
Munoz, 1998; Steinmetz and Moore, 2010; Curtis and Connolly,
2008) have agreed upon three operational criteria for interpreting
neural activity as being a neural substrate for movement preparation: (1), changes in neural activity must occur during the delay
period, before the Go signal; (2), the neural activity must show
response selectivity (e.g., fire more for contralateral than ipsilateral responses); (3), there must be a trial-by-trial relationship
between neural activity and some metric of behavior (usually
reaction time, but since our task was not a reaction time task
we used choice probability). Our results satisfy all three of these
criteria, so interpreting the activity in the FOF as ‘‘movement
preparation’’ is, at least, consistent with prior work. There are
several possible interpretations as to what component(s) of
response preparation FOF neurons might encode: do they represent a motor plan? A memory of the identity of the motor plan?
Attention? Intention? (Bisley and Goldberg, 2010; Glimcher,
2003; Goldman-Rakic et al., 1992; Schall, 2001; Thompson
et al., 2005; Gold and Shadlen, 2001). Our data do not discriminate between these possibilities. Nevertheless, we conclude
that, as in the primate, there exists in the rat frontal cortex a structure that is involved in the preparation and/or planning of orienting responses. An area with such a role may be conserved
across multiple species, including birds (Knudsen et al., 1995).
Since FOF delay period firing rates are better correlated with
the upcoming motor act than with the initial sensory cue (Figure 4), our data do indicate that FOF neurons are not likely to
encode a memory of the auditory stimulus itself. Furthermore,
in memory trials, some form of memory is required immediately
after the end of the auditory instruction stimulus. We did not
observe a short-latency sensory response in the FOF, but
instead observed a slow and gradual development of choicedependent activity during the delay period. This suggests that
FOF neurons do not support the early memory the task requires.
The FOF is strongly interconnected with the posterior parietal
cortex (PPC) (Reep and Corwin, 2009; Nakamura, 1999) and
with the medial prefrontal cortex (mPFC, Condé et al., 1995).
We suggest both of these areas as candidates for supporting
the early memory aspects of the task, perhaps even including
the transformation from a continuous auditory signal (clickrate) to a binary choice (plan-left/plan-right). Based on data
from an orienting task driven by olfactory stimuli, Felsen and
Mainen (2008) recently proposed that the superior colliculus
(SC) may play a broad role in sensory-guided orienting. Projections to the SC from the FOF (Leonard, 1969; Künzle et al.,
1976; Reep et al., 1987), together with our current data, suggest
that the FOF may be an important contributor to orienting-related
activity in the SC. As in the primate, orienting behavior in the
rodent is likely to be subserved by a network of interacting brain
areas. The relative roles and mutual interactions between the
FOF, PPC, mPFC, and SC (and possibly other areas, including
the basal ganglia) during orienting behaviors in the rat remain
to be elucidated.
We focused our analyses here on the response-selective delay
period activity of FOF neurons. However, we also found neurons
carrying a wide variety of other task related neural signals,
including ramping during the delay that was not responseselective (consistent with a general timing or anticipatory signal),
sustained firing rate increases or decreases during the fixation
period, and activity after the reward/error signal. Detailed
descriptions of these neural responses are outside the scope
of this manuscript and will be reported elsewhere.
If we think of visual saccades as orienting responses, the
results presented here from the rat FOF are, qualitatively
speaking, consistent with results from monkey FEF studies
of memory-guided saccades. Muscimol inactivation of FEF
strongly impairs memory-guided contralateral saccades, but
leaves visually guided and ipsilateral saccades relatively intact
(Sommer and Tehovnik, 1997; Dias and Segraves, 1999; Keller
et al., 2008). Similarly, we found that muscimol inactivation of
rat FOF strongly impaired memory-guided contralateral orienting, had a weaker effect on nonmemory contralateral orienting,
and spared ipsilateral orienting (Figure 2). However, FEF inactivation also increases reaction times of contralateral saccades and
increases the rate of premature ipsilateral responses, two results
that we failed to replicate. Recordings from monkey FEF show
robust spatially selective delay period activity in memory-guided
saccade tasks (Bruce and Goldberg, 1985; Schall and Thompson, 1999) for both ipsilateral and contralateral saccades (Lawrence et al., 2005), similar to the spatially-dependent activity we
observed in rat FOF neurons (Figures 3 and 4). In typical visualguided saccade tasks a substantial portion of FEF neurons
show responses to the onset of the stimulus (c.f. Schall et al.,
1995), which we did not observe in our auditory-stimulus task.
However, monkey FEF neurons also encode saccade vectors
preceding auditory-guided saccades (Russo and Bruce, 1994),
and show very little auditory-stimulus-driven activity. This again
is similar to our observations in rat FOF (Figures 4A and 4B).
We note that although we have focused here on similarities to
the monkey FEF, which is a particularly well-studied brain area,
we do not believe we have established a strict homology between
rat FOF and monkey FEF. Similarities to other cortical motor
structures may be greater, or it may be that the rat FOF will not
have a strict homology with any one primate cortical area.
We are aware of only one other electrophysiological study in
rats during a memory-guided orienting task in which rats stay still
during the delay period (Gage et al., 2010). In that study, Gage
et al. (2010) recorded from M1, striatum, and globus pallidus.
They found that, although a few response-selective signals in
M1 could be observed many hundreds of milliseconds before
the Go signal, maintained response selectivity in M1 neurons
arose only !180 ms before the Go signal. In contrast, once
neurons of the FOF start firing in a response-selective manner,
they usually maintain their response selectivity throughout the
rest of the delay period (Figure 3), even when their response
selectivity arises many hundreds of milliseconds before the Go
Neuron 72, 330–343, October 20, 2011 ª2011 Elsevier Inc. 339
Neuron
Cortical Basis of Memory-Guided Orienting in Rats
signal. The population count of response selective FOF cells
therefore starts rising very shortly after the end of the instruction
signal, and rises continually until the Go signal (Figure 4F;
compare to Figure 5B, top panel, of Gage et al., 2010). This
suggests that orienting preparation signals are represented
significantly earlier in the FOF than in M1. Consistent with the
much weaker electrophysiological delay period signature found
in M1, as compared to the FOF, unilateral pharmacological inactivations of M1 produced very different, and much weaker,
behavioral effects than those found in FOF (Figure S2, compare
to Figure 2). The difference is particularly strong for memory
trials. FOF inactivation reduced contralateral memory trials to
almost 50% correct performance (chance), but M1 inactivation
impaired performance on these trials only to !75% correct.
This was a saturated effect: doubling the dose of muscimol in
M1 did not further impair performance (Figure S2). Much further
work is required to draw and refine functional maps of the rat
cortex during awake behaviors, but we do conclude that the
role of the FOF in memory-guided orienting is not common
across frontal motor cortex.
We targeted the FOF based on previous anatomical, lesion,
and microstimulation studies that suggested a role for this
area in orienting behaviors (Leonard, 1969; Cowey and Bozek,
1974; Crowne and Pathria, 1982; Sinnamon and Galer, 1984;
Corwin and Reep, 1998). However, a different line of research,
observing whisker movements in response to intracortical
microstimulation in head-fixed, anesthetized rats, has described
the same area as whisker motor cortex (Brecht et al., 2004).
Nevertheless, the functional role of the FOF in awake animals
is not firmly established: single-unit recordings from the area
in awake animals remain very sparse (Carvell et al., 1996; Kleinfeld et al., 2002; Mizumori et al., 2005). We asked whether
whisking played a role in our memory-guided orienting task,
and found that it did not: removing the whiskers had little effect
on performance (Figures 1D and 1F and associated text), unilaterally paralyzing the whiskers did not produce a lateralized or
memory specific effect (Figures 1E and 1F), and video analysis
of regular trials did not find evidence of asymmetric or lateralized
whisking during the memory delay period. The video showed
instead that whiskers are held quite still during the delay period
(Figure S1 and Movie S2). We speculate that well-trained
animals that are highly familiar with the spatial layout of the
behavior apparatus do not use whisking to guide their movements during the task. In particular, whisking appears to play
no role in the short-term memory component of the task (Movie
S2). The lack of whisker-related effects on task performance or
task behavior contrasts with the strong pharmacological and
electrophysiological correlates with behavior that form the basis
of this report, and suggests that the FOF plays a role in orienting
that is independent from any role in control of whisking. Previous
single-unit studies of this area in awake animals, focusing on
whisker motor control, have suggested that the FOF is not primarily involved in low-level motor control of whisking, but may
instead play a more prominent role in longer timescale (!1 s
or longer) control of whisking parameters (Carvell et al., 1996).
More recent studies (D. Kleinfeld, personal communication)
have identified some of the long timescale parameters as control
of amplitude and offset angle of whisking; this last refers to the
340 Neuron 72, 330–343, October 20, 2011 ª2011 Elsevier Inc.
average orientation of the whiskers with respect to the head. Our
data, by providing evidence that the FOF participates in the
preparation of orienting movements many hundreds of milliseconds before these movements actually occur, is consistent with
this view of the FOF as a high-level motor control area.
A third line of research in this cortical area, represented so far
only by a book chapter (Mizumori et al., 2005), has described
finding head direction cells (Taube, 2007) in the FOF. Our recordings replicated this finding (Figure S6). We found no correlation
between the strength of a neuron’s head direction tuning and
the strength of its preparatory orienting signals (data not shown).
The two types of signals coexist in the FOF, but are distinct from
each other: a quantitative analysis showed that head direction
tuning could not account for the preparatory orienting signals
recorded during the delay period of memory trials (Figure 7).
We found that head direction signals in the FOF are strongly
modulated by behavioral context. That is, for many cells, tuning
while animals were performing the task was very different to
tuning while animals were not performing the task (Figure S6).
The relationship between orienting preparation signals and
head direction signals in the FOF is complex, and we will explore
it in detail in a future manuscript.
The confluence of three different types of signals (orienting,
head direction, whisking) in a single area is remarkable. Although
different, the signals are related: head direction information is
important for making orienting decisions, whisking reaps information from the environment that can then be used to guide
orienting decisions, and orienting movements themselves will
have a direct effect on both head direction and whisker position.
Having these three signals represented in a single area is consistent with the view of the FOF as an area that integrates multiple
sources of information in the service of high-level control of
spatial behavior. Elucidating the precise relationship between
these signals, both in the FOF and in other brain areas, will
require many further experiments that will bring together the
orienting, navigation, and whisking literature.
EXPERIMENTAL PROCEDURES
Subjects
Animal use procedures were approved by the Princeton University Institutional
Animal Care and Use Committee and carried out in accordance with National
Institutes of Health standards. All subjects were male Long-Evans rats
(Taconic, NY). Rats were placed on a restricted water schedule to motivate
them to work for water reward.
Behavior
Rats went through several stages of an automated training protocol before
performing the task as described in the results (see Supplemental Experimental Procedures). All data described in this study were collected from fully
trained rats. Sessions with poor performance (<70% correct overall or fewer
than 8 correct memory trials on each side without fixation violations) were
excluded from analyses. These sessions were rare (2.4% of all sessions
from trained rats) and were usually caused by problems with the hardware
(e.g., a clogged water-reward valve or a dirty IR-photodetector).
To generate psychometric curves, we collected 12 data points: the % ‘‘Went
Right’’ for each of six different click rates, separately for memory and for nonmemory trials. We then combined the data points across all sessions (total
data points per fit = 6 3 # of sessions) and used MATLAB nlinfit.m to fit
a 4-parameter sigmoid to the data. For these fits, x is the natural logarithm
of clicks/sec, y is ‘‘% Went Right,’’ and the four parameters to be fit are: x0,
Neuron
Cortical Basis of Memory-Guided Orienting in Rats
the inflection point of the sigmoid, b, the slope of the sigmoid, y0, the minimum
% Went Right, and a + y0 is the maximum % Went Right.
y = y0 +
1+e
a
"ðx " x0Þ
b
Data from memory and nonmemory trials were fit separately.
Surgery
All surgeries were done under isoflurane anesthesia (1.5%–2%) using standard stereotaxic technique (see Supplemental Experimental Procedures
for details). The target of all FOF surgeries in our Long-Evans strain rats
was +2 AP, ±1.3 ML (mm from Bregma). This location was chosen because
it was the center of the distribution of stimulation sites that resulted in contralateral orienting movement in Sinnamon and Galer (1984).
Infusions
Dose and volume of muscimol infusions into FOF was 0.5 mg/mL and 0.3 ml,
respectively. Infusions for M1 were done in two sets of experiments, first
0.5 mg/mL and 0.3 mL, then 1 mg/mL and 0.3 mL. See Supplemental Experimental Procedures for details.
Recordings
Recordings were made with platinum iridium wire (16.66 mm, California Fine
Wire, CA) twisted into tetrodes. Wires were gold-plated to 0.5–1.2 MOhm.
Spike sorting was done by hand using SpikeSort3D (Neuralynx). Cells had to
satisfy several criteria to be included in the presented analyses: 1), zero interspike intervals <1 ms; 2), signal to noise ratio >4; and 3), at least one time point
of a smoothed, response-aligned PETH had to have a firing rate of at least
3 spikes/s. We recorded 378 cells over 100 sessions that satisfied the first
two criteria. A total of 242 cells (recorded from 91 sessions) satisfied all three
criteria. Median number of cells per session was three. The maximum number
of cells recorded in a session was 11.
Neural Data Analysis
We examined a 2 s window around the Go signal ("1.5 s pre, to +0.5 s post).
Spikes from each trial were smoothed with a causal half-Gaussian kernel with
a full-width SD of 200 ms—that is, the firing rate reported at time t averages
over spikes in an !200-ms-long window preceding t. The resulting
smooth traces were sampled every 10 ms. To determine whether cells were
response-selective at any point between the stimulus and the rat’s choice,
we divided correctly performed trials into contralateral-orienting and ipsilateral-orienting groups, and used ROC analysis at each time point to ask
whether the firing rates of the two groups were significantly different for that
time point. For each cell, we randomly shuffled ipsi and contra trial labels
2000 times and recomputed ROC values. We labeled individual time bins as
significant if fewer than 1% of the shuffles produced ROC values for that
time bin that were further from chance (0.5) than the original data was (i.e.,
p < 0.01 for each time bin). We then counted the percentage of shuffles that
produced a number of significant bins greater than or equal to the number
of bins labeled significant in the original data. If this randomly produced
percentage was less than 5%, the cell as a whole was labeled significant
(i.e., an overall p < 0.05 for each cell).
To determine the time at which the population count of significant cells
became greater than chance, we used binomial statistics. These indicate
that with probability 0.999, at any given time point, an individual cell threshold
of p < 0.01 would lead to fewer than 8/242 cells being labeled significant by
chance. The population count was designated as significantly different from
chance when it went above this p < 0.001 population threshold.
In order to quantify whether neurons in FOF tended to encode the stimulus or
the response we generated a stimulus selectivity index (SSI) from Go aligned
PETHs for correct and error trials as follows:
SSItt =
0:5
P
t = "1:5
0:5
P
t = "1:5
PETHcontra;tt " PETHipsi;tt
PETHcontra;tt + PETHipsi;tt
where tt indicates trial type (correct-memory, correct-nonmemory, errormemory, and error-nonmemory). If a cell fired only on contra and not on ipsi
trials, then SSI = 1. If a cell fired on ipsi and not contra trials, then SSI = "1.
If a cell fired equally for ipsi and contra trials then SSI = 0.
For latency estimations, we used an alignment algorithm to find a relative
temporal offset for each trial as follows. Given a signal as a function of time
for each trial (either firing rate or head angular velocity), we computed the
trial-averaged signal. For each trial we then found the time of the peak of the
cross-correlation function between the signal for that trial and the trial-averaged signal. We then shifted each trial accordingly, and recomputed the
trial-averaged signal after. We iterated this process until the variance of the
trial-averaged signal converged, typically within fewer than five iterations.
The output of this alignment procedure was an offset time for each trial, which
indicated the relative latency for that trial.
Histology
In all cases, the electrode and cannula placements in FOF were within the
borders of M2 and between 2 and 3 mm anterior to Bregma (Paxinos and
Watson, 2004). In all cases the M1 placements were within the borders of M1
and between 2.5 and 3.5 mm anterior to Bregma (Paxinos and Watson, 2004).
SUPPLEMENTAL INFORMATION
Supplemental Information includes Supplemental Experimental Procedures,
seven figures, and three movies and can be found with this article online at
doi:10.1016/j.neuron.2011.07.010.
ACKNOWLEDGMENTS
We thank B.W. Brunton and J.K. Jun for contributions to software to obtain
head direction data, D.W. Tank and J.P. Rickgauer for suggestions to improve
whisker tracking, B.W. Brunton, J.K. Jun, C.D. Kopec, and T. Hanks for discussion and comments on the manuscript, A. Keller and D. Kleinfeld for discussions related to the role of the FOF in whisker control, and L. Osorio and
G. Brown for technical assistance. This work was supported by the Howard
Hughes Medical Institute.
Accepted: July 7, 2011
Published: October 19, 2011
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Neuron, Volume 71
Supplemental Information
A Cortical Substrate for Memory-Guided Orienting in the Rat
Jeffrey C. Erlich, Max Bialek, and Carlos D. Brody
Table of Contents
Page Number
Figure S1 - Spectral analysis of whisking
2
Movie S1 - Exploratory Whisking
3
Movie S2 - Delay Period Whisking
3
Movie S3 - Out of task Whisking in the Center Port
3
Figure S2 - Muscimol Inactivation of M1
4
Figure S3 - ISI histograms, waveforms, and electrode placement
7
Figure S4 - Population PETHs for frequency and location discrimination
8
Figure S5 - Latency correlations between cells
9
Figure S6 - Head Direction Tuning
10
Figure S7 - Velocity and Acceleration Control
12
Supplemental Experimental Procedures
14
1
B
Whisking data from Movie S2:
on-task, delay period center poking.
3
2
Power (a. u.)
Power (a. u.)
A
right trials
left trials
1
0
4
Whisking data from Movie S3:
off-task, exploratory center poking.
3
2
1
0
6
8
10
Frequency (Hz)
4
6
8
10
Frequency (Hz)
Figure S1, No significant difference in whisking between right choice and left choice
trials when the animals are performing the task. Related to Figure 1.
Figure
S1, No of
significant
difference
in whisking
between
right
choice
and
left choice
Quantification
the whisking
data collected
in Movie
S2 and S3;
see
caption
to those
videostrials when
the
are performing
theand
task.
Related
to Figure
1. The initiation time of each trial in
for animals
an explanation
of the source
form
of the whisking
data.
Quantification
of the orienting
whisking task
dataiscollected
in by
Movie
S2 andRats
S3; see
caption
those
our memory-guided
controlled
the subject.
do not
initiatetotrials
at videos for an
consistentlyofidentical
intervals,
but instead
perform the
task
in bouts,
typically
explanation
the source
and form
of the whisking
data.
The
initiation
time ofperforming
each trial many
in our memorytens oforienting
trials in quick
regular
succession
call these
time
by short
guided
task is
controlled
by the(we
subject
placing
its periods
nose in“on-task”)
the centerfollowed
port. Rats
do not initiate
breaks
(typically, a few
minutes)
of grooming
and sniffing/whisking
asinthey
move
aroundperforming
the
trials
at consistently
identical
intervals,
but instead
perform the task
bouts,
typically
many
behavior
boxin(“off-task”).
(A) succession
Data from Movie
S2:these
minimal
motion,
and no
significant
tens
of trials
quick regular
(we call
timewhisker
periods
“on-task”)
followed
by short breaks
whisking a
difference
between
left trials and
trials during on-task
(typically,
few minutes)
of grooming
and right
sniffing/whisking
as theycenter
move pokes.
aroundThe
the panel
behavior box (“offshows a spectral analysis of whisking during the “nose in center” delay period from randomly
task”). (A) Data from Movie S2: minimal whisker motion, and no significant whisking difference between
selected, correctly completed left and right memory trials. (B) As in panel A, but from center
left
trialsduring
and right
trials
during(Movie
on-taskS3).
center
Theat
panel
shows
a spectral
analysis
of whisking
pokes
off-task
periods
The pokes.
clear peak
the theta
frequency
(~ 6
Hz) during
during
the
“nose
in
center”
delay
period
from
randomly
selected,
correctly
completed
left
and
these off-task center pokes demonstrates that animals are capable of producing measurable right
memory
(B) while
As in their
panelsnouts
A, butare
from
center
pokes
during
periods
(Movie
S3). The clear
whiskingtrials.
motions
in the
center
poke.
Dataoff-task
in panels
(A) and
(B) suggest
peak
at
the
theta
frequency
(~
6
Hz)
during
these
off-task
center
pokes
demonstrates
that
animals are
that during on-task center poking, rats choose to not whisk.
capable of producing measurable whisking motions while their snouts are in the center poke. Data in
panels (A) and (B) suggest that during on-task center poking, rats choose to not whisk.
2
2
Movie S1. Strong, clearly visible whisking during exploration of the behavior box at the beginning
of a session.
To facilitate visualizing the whiskers, we painted several dots of titanium dioxide onto them, as well as
affixing a small piece of aluminum foil to one of the right whiskers. At the start of each session, before
performing any trials, rats typically sniff and whisk briskly around the behavior box. The video shows a rat
during this initial exploration period, before performing any trials in the session. The video demonstrates
that the titanium dioxide and aluminum foil do not affect the rat’s ability to rapidly whisk as it performs its
exploratory motions.
Movie S2. Minimal whisking during on-task center poking
The video shows that when rats are performing the task, whiskers are held very still during delay period
center poking. Data from this video is quantified in Figure S1A. Correct left or right memory trials were
randomly selected for whisker position scoring. Trials were included if the rat’s head remained in a
relatively horizontal position for the duration of the trial, so that whiskers were visible throughout the trial
and their position could be scored. Scoring was performed blind as to whether a trial was a rightinstructed or a left-instructed trial. The video is 5x slower than real time to facilitate observation of the
whiskers during the delay period. The rat in this video was cannulated, and the cannula sites were used
as fiducial markers on the head. Two points on a right whisker were marked in every frame as fiducial
points on the whisker. The distance from the distal whisker point to the right head fiducial point is marked
with a red line. This distance (l, length of the red line) was used as a measure of whisker position. A
spectral analysis of l(t) is shown in Figure S1A.
Movie S3. Visible whisking during off-task center poking
The video shows that rats are capable of whisking even when their snouts are in the center port. Data
from this video is quantified in Figure S1B. During off-task pauses between runs of completed trials, rats
will sometimes sniff and whisk around the behavior box, including occasionally nose poking into the
center port. The video shows selected bouts of center poking that were defined as “off-task” in that no
completed trials occurred closer than 5 seconds, neither before nor after the time period shown in the
video. For example, in one of these periods the rat center poked during the inter-trial interval. In another,
the rat initiated a regular center poke during the “nose-in-center” period, but then did not complete it and
did not return to complete a trial until 5 seconds later. Scoring methods and conventions as in Movie S2.
3
M1, 0.5 mg/ml muscimol = Equal to the dose in FOF
Left M1
Inactivations, n=8
Right M1
Inactivations, n=9
p<0.01
Non−Memory
Trials
95
50
5
% Went Right
90
Memory
Trials
95
B
% Correct
A
50
70
Non-Memory Trials
Memory Trials
50
No Drug
Ipsi
Contra
Muscimol
5
20
50
Clicks/Sec
125
20
50
125
⇤⇥ ⌅⇤⇥ ⌅ ⇤⇥ ⌅ ⇤⇥ ⌅
Ipsi trials
Contra trials
Contra trials
Ipsi trials
M1, 1 mg/ml muscimol = Double the dose in FOF
Left M1
Inactivations, n=8
Right M1
Inactivations, n=8
90
Non−Memory
Trials
95
50
5
70
50
Memory
Trials
95
% Went Right
p<0.01
D
% Correct
C
50
Non-Memory Trials
Memory Trials
No Drug
Ipsi
Contra
Muscimol
5
20
50
Clicks/Sec
125
20
50
125
⇤⇥ ⌅⇤⇥ ⌅ ⇤⇥ ⌅ ⇤⇥ ⌅
Ipsi trials
Contra trials
Contra trials
Ipsi trials
4
(continued on next page)
Example FOF histology
E
G
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Figure 13
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10
+15
+10
+5
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M1 histology
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Example M1 histology
F
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AP=3.24 from Bregma
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0 Interaural
5
3
-15
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5
10
6
4
4
10
+5
0
-5
0
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5
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6
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7
1
2
3
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2
1
AP=2.52 from Bregma
Interaural 11.52 mm
7
6
5
4
3
2
1
0
1
2
3
6
28
5
19
Bregma 2.52 mm
4
5
6
4
Interaural 12.24 mm
7
6
5
4
8
4
9
5
Bregma 3.24 mm
3
2
1
0
1
2
3
4
5
6
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3
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2
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1
Interaural 11.76 mm
7
5
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Bregma 2.76 mm
4
3
2
1
0
1
2
3
4
5
6
7
Figure S2, Unilateral inactivation of neck motor cortex (M1) produces a different and much
weaker pattern of impairment than inactivation of the FOF. Related to Figure 2.
(A,C) Behavioral performance in control and muscimol-infusion days for 0.5 mg/ml (A) and 1 mg/ml (C)
muscimol. Top row: non-memory trials. Bottom row: memory trials. Left column: muscimol infusions into
left M1. Right column: muscimol infusions into right M1. Open circles, data from muscimol infusions.
Closed circles: control data from days immediately preceding infusion days. Dashed lines: sigmoidal fits
to muscimol data. Solid Lines: sigmoidal fits to control data. Error bars are standard error of the mean
across sessions (The number of sessions is indicated at the top of each plot). Error bars for control data
were smaller than the marker in most cases. Underbraces at bottom indicate the sets of trials in which
animals were instructed to orient ipsilaterally or contralaterally to the site of infusion. (B,D) Combined
data from left and right infusion sessions at 0.5 mg/ml (B) and 1mg/ml (D) of muscimol and collapsed
across all stimulus difficulty levels. The “No Drug” data come from the 20 sessions one day before
infusion sessions. The Ipsi and Contra Muscimol data are the performance on ipsilateral trials and
contralateral trials on infusion sessions (n=20). (E) Example of a nissl stained coronal section from a rat
with cannula implanted in the FOF. The section is ~2.6 mm anterior to Bregma. (F) Example of an
unstained bright field coronal section from a rat with cannula implanted in M1. The section is ~3.3 mm
anterior to Bregma. (G) FOF cannula placements. Diamonds indicate the location of the scar left from the
injection cannula for each rat. (H) M1 cannula placements. Diamonds indicate the location of the scar
left from the injection cannula for each rat.
6
4000
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3
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Figure 13
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Figure 14
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8
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7
6
Bregma 2.52 mm
5
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6
8
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1
Figure S3, Waveforms and Inter-Spike
Interval histograms for example cells
figure 3, and
Interaural 11.28 mm
Bregma in
2.28 mm
histological
placement of
electrodes.
Figure 3. for example cells in figure 3,
Figure S3, Waveforms
and
Inter-SpikeRelated
Intervaltohistograms
(A)
Waveforms
(mean
± standard
deviation) Related
and inter-spike
interval
and
histological
placement
of electrodes.
to Figure
3. histograms for the three
(A) Waveforms
(mean ±neurons
standard
deviation)
and inter-spike
interval histograms
the three
contralateral
preferring
shown
in Figure
3A. (B) Waveforms
(mean ± for
standard
deviation) and
contralateral
preferring
neuronsofshown
in Figure
3A. (B)
Waveforms
(mean
± standard
inter-spike
interval
histograms
the three
ipsilateral
preferring
neurons
shown
in Figure 3B. (C)
deviation)
inter-spiketrack
interval
histograms
of the
three
ipsilateral
neurons
in and
Example
ofand
an electrode
in FOF.
The thin
black
lines
indicatepreferring
the borders
of M2 shown
(Paxinos
Figure 3B.
(C) Example
of an tracks
electrode
FOF. Therats.
thin Green
black lines
indicateindicate
the borders
Watson,
2004).
(D) Electrode
for track
the 5 in
implanted
diamonds
the final location
M2tips
(Paxinos
and Watson,
2004).
Electrode
for the
5 implanted
rats.
Green of the tips from
ofofthe
from electrodes
in the
left (D)
FOF
and red tracks
diamonds
indicate
the final
location
diamonds indicate the final location of the tips from electrodes in the left FOF and red diamonds
electrodes in the right FOF.
indicate the final location of the tips from electrodes in the right FOF.
7
6
5
4
3
2
6
7
1
0
1
2
3
4
5
6
7
Population PETHs from rats
performing frequency discrimination
Population PSTH of
Ipsi Preferring Cells (n=21)
Population PSTH of
Contra Preferring Cells (n=23)
8.2 Hz
0
4.6 Hz
−1
1
Stimulus
Ipsilateral
Correct
−1.5
−1
−0.5
0
0.5
E
0
0
0
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Non-Memory Trials
Firing Rate (Z-Score)
Contralateral
Correct
1
Population PSTH of
Ipsi Preferring Cells (n=18)
Population PSTH of
Contra Preferring Cells (n=27)
Memory Trials
A
11.8 Hz
Population PETHs from one rat
performing spatial location discrimination
Time from Go Cue (s)
Figure S4, Comparison of electrophysiological results for frequency discrimination vs spatial
location discrimination. Related to Figure 4.
(A-D) Population PETHs for rats performing frequency discrimination (n=44). Each panel is a
population perievent time histogram (PETH) showing the average response (across neurons) on correct
(thick lines, mean±std.err. across neurons) and error trials (shaded, mean±std.err. across neurons)
where the stimulus indicated that the correct response was contralateral (blue) or ipsilateral (red) to the
Figure S4,
Comparison
electrophysiological
recorded
neuron.
PETHsofare
aligned to the timeresults
of the for
Go frequency
cue (centerdiscrimination
LED offset). vs
(A) The average
spatial
location
discrimination.
Related
to
Figure
4.
responses of memory trials for neurons that fired more on contralateral trials. (B) The average responses
Population
PETHs for
performing
frequency
discrimination
(n=44).
panel
of(A-D)
memory
trials of neurons
thatrats
fired
more on ipsilateral
trials.
(C) Same as
A but Each
for non-memory
trials.
is aSame
population
perievent
time histogram
the PETHs
averagefor
response
(across
(D)
as B but
for non-memory
trials.(PETH)
(E-H) showing
Population
rats performing
spatial
neurons) discrimination
on correct (thick (n=45)
lines, mean±std.err.
neurons) and
errorshowing
trials (shaded,
mean response
location
Each panel across
is a population
PETH
the average
±std.err. across neurons) where the stimulus indicated that the correct response was
(across
neurons)
correct (thick
lines,
mean±std.err.
and
error
trials
contralateral
(blue)on
or ipsilateral
(red) to
the recorded
neuron.across
PETHsneurons)
are aligned
to the
time
of (shaded,
mean±std.err.
across
neurons)
where
the
stimulus
indicated
that
the
correct
response
was
contralateral
the Go cue (center LED offset). (A) The average responses of memory trials for neurons that
(blue)
or ipsilateral
(red) to the
recorded
areofaligned
to trials
the time
of the Go
fired more
on contralateral
trials.
(B) Theneuron.
average PETHs
responses
memory
of neurons
thatcue (center
LED
The average
responses
trials for neurons
thatSame
fired as
more
onfor
contralateral
firedoffset).
more on(E)
ipsilateral
trials. (C)
Same asofAmemory
but for non-memory
trials. (D)
B but
trials.
(F) The trials.
average(E-H)
responses
of memory
trialsfor
of neurons
that fired more
on ipsilateral
non-memory
Population
PETHs
rats performing
spatial
location trials. (G)
Same
as E but for
non-memory
trials.
Same asPETH
F butshowing
for non-memory
trials.
discrimination
(n=45)
Each panel
is a(H)
population
the average
response (across
neurons) on correct (thick lines, mean±std.err. across neurons) and error trials (shaded, mean
±std.err. across neurons) where the stimulus indicated that the correct response was
contralateral (blue) or ipsilateral (red) to the recorded neuron. PETHs are aligned to the time of
the Go cue (center LED offset). (E) The average responses of memory trials for neurons that
fired more on contralateral trials. (F) The average responses of memory trials of neurons that
fired more on ipsilateral trials. (G) Same as E but for non-memory trials. (H) Same as F but for
non-memory trials.
8
A
B
15
40
7% p<0.05
% of Cells
# of cell pairs
All Cells, Contra Mem Trials
20
0
−1
1
C
30
10
20
5
10
0
0
r
Onset of Selectivity
Peak of Selectivity
0
−1
0
1
−1
0
Onset of Selectivity
Time from Peak Head Velocity (s)
1
Figure S5. Neural latency is not correlated across pairs of simultaneously recorded neurons.
Related to Figure 5.
(A) Histogram of the correlations in neural latency (as computed in Figure 5) for 238 pairs of neurons.
The population is not significantly different than zero, nor is the number of individually significant neurons
(7%) significantly more than expected at p<0.05. (B) Histogram of the time difference between the onset
of differential ipsi vs. contra firing rate (as measured by a sliding ROC, as in Figure 3A,B) and the time
of peak head velocity. Each entry in the histogram is a neuron; the histogram shows all 166 neurons
recorded
during
sessions
withis
head-tracking
data.across
One hundred
forty-six (88%)recorded
of the neurons had
Figure S5.
Neural
latency
not correlated
pairs ofand
simultaneously
Related
to Figure
aneurons.
differential
firing rate
onset5.that preceded the time of peak head velocity. (C) As panel (B), but now
(A) histogram
of the correlations
in time
neural
latency
(as differential
computed ipsi
in Figure
5) for
238rate
pairs
of the time of
showing
the difference
between the
of the
largest
vs contra
firing
and
neurons.
population
is not significantly
different
than zero,
is the number
individually
peak
head The
velocity.
One hundred
and twenty-four
(75%)
of thenor
neurons
had the of
peak
of their differential
significant
neurons
(7%)
more
than
expected at p<0.05. (B) Histogram of the time
firing
rate occur
before
thesignificantly
time of peak
head
velocity.
difference between the onset of differential ipsi vs. contra firing rate (as measured by a sliding
ROC, as in Figure 3A,B) and the time of peak head velocity. Each entry in the histogram is a
neuron; the histogram shows all 166 neurons recorded during sessions with head-tracking data.
One hundred and forty-six (88%) of the neurons had a differential firing rate onset that preceded
the time of peak head velocity. (C) As panel (B), but now showing the difference between the
time of the largest differential ipsi vs contra firing rate and the time of peak head velocity. One
hundred and twenty-four (75%) of the neurons had the peak of their differential firing rate occur
before the time of peak head velocity.
9
Session 52767, Cell 1919
Session 52767, Cell 1922
0.5
10
−90
90
0
0.4
0.2
0
−200
10
−90
0.2
0.5
5
−90
0.2
0
−200
0
200
Head Angle (degrees)
Session 51252, Cell 2920
0
−200
10
0 1
0.5
5
−90
Occupancy
0.1
0
200
Head Angle (degrees)
90
0
0.2
0.1
0
−200
0
200
Head Angle (degrees)
4
90
0
0.2
3
0 1
2
0.5
1
−90
0
0.2
0.1
0
−200
0
200
Head Angle (degrees)
0
200
Head Angle (degrees)
C
Out of Task
0 1
In Task
0 1
0.5
−90
In Task − Out of Task
0 1
0.5
90
0.5
−90
90
−90
90
D
−90
0 10
0 10
5
5
90
90
0
0.4
Spikes/sec
Spikes/sec
Spikes/sec
0 1
0.5
10
15
10
0 1
90
0
0.4
0
−200
20
Session 51252, Cell 2922
15
Occupancy
0.5
5
Session 51252, Cell 2921
B
0 1
−90
0
200
Head Angle (degrees)
Spikes/sec
0 1
30
Occupancy
20
Session 52767, Cell 1925
15
Spikes/sec
In Task
Out of Task
Occupancy
Occupancy
Spikes/sec
30
Occupancy
A
0 15
10
5
−90
90
10
−90
90
90
Figure S6. Head direction tuning in FOF. Related to Figure 6
(A) Head direction tuning and occupancy histograms for three cells recorded simultaneously in a single
session. Tuning and occupancy were computed separately for in-task epochs (green; any timepoint
within 10 seconds of a poke into any of the nose ports) and for out-of-task epochs (black; all other
timepoints). Occupancy histograms indicate fraction of time spent with the head pointing in each
direction. Error bars for firing rates are bootstrapped 99% confidence intervals of the firing rate at each of
18 bins of head angle. The center port is at 0 degrees. The compass plots to the right of each tuning
histogram summarize the in-task and out-of-task tuning. Each cell’s summary vector is the weighted
average of a set of unit length vectors pointing in each of the 18 head direction bins; the weighting was
proportional to the cell’s firing rate in each direction bin. The summary vectors show how each cell
contributes to the plots in panel C. (B) Same as A for three neurons recorded during a different session.
(C) Summary of head-direction tuning for 166 cells recorded during sessions with head-tracking data.
Each cell is represented by a summary vector, shown as a single arrow. The rightmost panel is the
vector difference, for each cell, between its in-task summary vector minus its out-of-task summary
vector. (D) Polar histogram of the angles of the arrows in C. The distribution of angles for In Task - Out
of Task is significantly non-uniform (Omnibus Test, p<0.03)
11
!"’ ~ 10˚/s
0
50
100
C
% Cells Selective
40
−1 −0.5
0
0.5
Time from Go Cue (s)
30
20
10
−1.5
40
−0.5
0
0.5
D
30
20
10
I
0
−1.5
−1 −0.5
0
0.5
Time from Go Cue (s)
2
−1 −0.5
0
0.5
Time from Go Cue (s)
−1
E
−1000
F
!"’’ ~ 160˚/s2
!"’’ ~ 160˚/s2
−500
0
% Cells Selective
−1.5
0
−1.5
Angular
Head
Acceleration
s 2)
Angular
Head
Acceleration
(!ʼʼ(°, /°/s
B
!"’ ~ 10˚/s
−50
% Cells Selective
Angular
Head
Velocity
(!ʼ ,(°°/s)
Angular
Head
Velocity
/ s)
A
−100
40
20
Copy of data from Fig 3C
After regressing out
"(t), "’(t), "ʼʼ(t)
0
−1.5 −1 −0.5
0
0.5
Time from Go Signal (s)
500
1000
40
−1 −0.5
0
0.5
Time from Go Cue (s)
G
30
20
10
0
−1.5
% Cells Selective
% Cells Selective
−1.5
−1 −0.5
0
0.5
Time from Go Cue (s)
−1.5
40
−1
−0.5
0
0.5
H
30
20
10
0
−1.5
−1 −0.5
0
0.5
Time from Go Cue (s)
Figure
S7. Predictive coding of response is not a simple function of current angular head velocity or
acceleration. Related to Figure 7
(A) Angular head velocity (φ’, deg/s) as a function of time relative to the Go cue for correct memory trials.
Thin blue lines are from a random subsample of trials where the rat’s final choice was to move left; thin
red lines are a random subsample of trials where the final choice was to move right. Thick lines are the
mean φ’(t) for each group, averaged over all correct memory trials. At the time of the Go cue (t=0), the
difference in head velocity between the two groups is approximately 10 deg/s. (B) As in panel A, but with
the grouping defined by the sign of the angular head velocity at time t=−0.9 sec (yellow arrow;
φ’(t=−0.9)>0 versus φ’(t=−0.9)<0). At t=−0.78 sec, the difference in head velocity between the two
groups is approximately 10 deg/s. (C) Percentage of cells (out of 166 neurons) with significantly different
firing rates for the red vs blue groups of panel A. At each timepoint, threshold for each cell being
considered significant was p<0.01. At t=0, 23% of cells have firing rates that discriminate between the
two groups. (Continued on next page)
12
(D) As in panel C, but for the φ’(−0.9)>0 versus the φ’(−0.9)<0 groups. At t=−0.78 sec, the difference in
head velocity between the two groups is approximately 10 deg/s, but only 9% of cells discriminate
between the two groups.(E, F, G, H) As in panels A,B,C,D, but for angular head acceleration (φ’’, deg/s2).
(E) 200 ms after the time of the Go cue (t=0.2), the difference in φ’’ between the two groups is
approximately 160 deg/s2. (F) As in panel E, but with the grouping defined by the sign of φ’’ at time
t=−0.9 sec (φ'’(t=−0.9)>0 versus φ'’(t=−0.9)<0). At t=−0.9 sec, the difference in head velocity between
the two groups is approximately 160 deg/s2. (G) Percentage of cells (out of 166 neurons) with
significantly different firing rates for the red vs blue groups of panel E. At each timepoint, threshold for
each cell being considered significant was p<0.01. At t=0.2, 23% of cells have firing rates that
discriminate between the two groups. (H) As in panel F, but for the φ'’(−0.9)>0 versus the φ’'(−0.9)<0
groups. At t=−0.9 sec, the difference in head acceleration between the two groups is approximately 160
deg/s2, but only 9% of cells discriminate between the two groups. (I) Development of choice-dependent
activity over the course of the trial after accounting for the effects of head angle variables. The orange
line is copied from Figure 3C. The black line represents, at each timepoint, the percentage of cells (out
of n=166) with significantly different ipsi vs contra residual firing rates. Residual firing rates are obtained
after performing a linear regression to eliminate each cell’s firing rate dependencies on φ(t), φ’(t), and
φ’’(t) (see Supplementary Experimental Procedures). The black and orange horizontal lines at the top of
the panel indicate timepoints for which the percentage of significant cells is more than expected by
chance.
13
SUPPLEMENTAL EXPERIMENTAL PROCEDURES
Subjects
Animal use procedures were approved by the Princeton University Institutional Animal Care and Use
Committee and carried out in accordance with National Institutes of Health standards. All subjects were
male Long-Evans rats (Taconic, NY). Rats were pair-housed during initial behavioral training and then
single housed after being implanted with electrodes or cannula. Rats were kept on a reverse 12-hour
light dark cycle and trained in their dark cycle, when they were more active. Rats were placed on a
restricted water schedule to motivate them to work for water reward.
Behavior
Behavior took place in a custom training box (Island Motion, NY) inside a sound and light attenuating
chamber (H10-24A, Coulbourn Instruments, PA). In each box there were three “nose ports”, conical
openings into which the rats could poke their snouts. The three nose ports were arranged side-by-side
along a curved wall. Each nose port had an infra-red (IR) beam across the front (used to detect when the
rat’s nose was in the port), a visible white light emitting diode (LED), and a sipper tube connected to a
water supply that was controlled by a computer controlled solenoid. In addition, there were two speakers
mounted above each of the left and right nose pokes. All aspects of the task were computer controlled.
Behavioral events were timestamped with greater than 1ms accuracy using a custom open-source
software (Island Motion, NY; open-source code at http://code.google.com/p/rt-fsm/) on a computer
running a realtime linux operating system. Rats were placed in and removed from the behavior box by
technicians that were blind to the task.
Rats were trained using an automated training protocol. First, rats were trained using a classical
conditioning paradigm. On each trial a sound played out of one of the speakers and then water was
delivered from the corresponding port. The goal of this stage was simply to teach the rat that the ports
delivered water that was contingent on sounds. The trials during this training stage had an inter-trial
interval of about 2 minutes. The side of the sound and water delivery was alternated each day. This
stage lasted 8 days. The next stage involved the rats learning to poke in the center port in order to
initiate a trial. A light would come on in the center port and when the rat poked that would trigger the
presentation of sound. The sound was a click train, and the rats had to learn an association between the
rate of the clicks and the associated side for reward. For example, 100 clicks/sec meant reward was on
the left and 25 clicks/sec meant the reward was on the right. At first, trials were presented in blocks of
right and left trials. As the rat showed that he had learned the basic task structure we moved to
randomly interleaved right and left trials (about 1 week).
The next stage of training was growing the 'fixation period'. At first, the rat could essentially just poke
in the center and then respond in a side port. We incrementally grew the time that the rat had to stay in
the center port before making his response. The end of the fixation period was indicated by
extinguishing the center light. Once rats could maintain fixation for one second we moved to the next
training stage. Rats were highly variable as to how long it took them to pass this stage (1-4 weeks). Until
now the sound played for the entire duration of the trial. That is, there was no memory requirement. The
next stage slowly shortened the duration of the stimulus to 300 ms. This stage took 2-5 weeks
depending on the performance of the rat. At the end of this stage the rat was doing randomly
interleaved, left and right memory trials. The next stage introduced no-memory trials where the stimulus
was played at the end of the fixation period (Figure 1A). The final stage introduced intermediate stimuli
in order to produce psychometric discrimination data. Rats learned that click trains < 50 clicks/sec meant
reward on the right and > 50 clicks/sec meant reward on the left. In each session 6 discrete sounds
were played (E.g. 100, 80, 60, 40, 30, 20 clicks/sec) and the values of the sounds were adjusted at the
end of each session to better sample the psychometric curve. So, a rat with very good discrimination
might end up with 100, 60, 52, 48, 40, 20 clicks/sec as the set of stimuli. The rats in the M1 muscimol
experiment learned the opposite rule: click trains > 50 clicks/sec meant reward on the right and < 50
clicks/sec meant reward on the left.
All data described in this paper was collected from rats in the final training stage. Sessions with poor
performance (<70% overall or fewer than 8 correct memory trials without fixation violations per side)
14
were excluded from analyses. These sessions were rare (2.4% of all “final stage” sessions) and were
likely caused by problems with the hardware (e.g. a clogged water-valve or a dirty IR-photodetector).
Surgery
All surgeries were done under isoflurane anesthesia (1.5-2%) using standard stereotaxic technique. Rats
were given an injection of ketamine (10 mg) and buprenorphine (0.006 mg) to assist induction and
provide analgesia. Five minutes later they were placed in an isoflurane induction chamber. We slowly
increased the concentration of isolflurane from 0.5% to 4% over the course of 4 minutes. After
induction, rats were moved to a stereotax (Kopf Instruments; CA) and their noses placed in a cone which
provided 1.5-2% continuous isoflurane flow. After verifying surgical levels of anesthesia with pinch tests
and eye blink tests, rats were secured in non-rupture ear bars (Kopf Instruments; CA). The scalp was
shaved and cleaned with ethanol, betadine and ethanol and then a midline incision was made with a
scalpel. A spatula was used to clean the skull of all overlying tissue before craniotomies were made with
a dental drill over the FOF (AP +2, ML ±1.3 mm from Bregma). This location was chosen because it was
the center of the distribution of stimulation cites that resulted in contralateral orienting orienting
movement in Sinnamon & Galer (1984). Durotomies were then performed. After the durotomies were
finished, saline soaked Gelfoam (Pfizer Injectables; NY) was placed in the craniotomies to protect the
brain for the next step. Then a thin coat of C&B Metabond (Parkell, Inc; NY) was painted all over the
skull. The Gelfoam was then removed from the craniotomies and the implant (either electrodes or
cannula) was lowered onto the brain surface. For electrode implants only the tetrodes entered the
cortex. For cannula implants (Plastics One, VA) the outer cannula was placed at brain surface and the
injector (inserted only during infusions) extended 1.5 mm past the end of the guide. DuraLay (Reliance
Dental, IL) cement was used to secure the implant to the Metabond coated skull. Rats were given
buprenorphine and ketofen 24 and 48 hours post-operative and were allowed to recover on ad lib water
for 5 days before returning to water restriction and behavioral training.
Subsequent histological verification and comparison to Paxinos and Watson’s atlas, which is
based on Wistar strain rats (Paxinos and Watson, 2004), indicated that our implants were at a position
matching +2.4 AP, ±1.3 ML in the atlas. The target of the neck M1 surgeries was +3.5 AP, +3.5 ML
(Gage et al., 2010).
Infusions
Five rats were used for the infusion experiments in FOF and 4 rats were used for the infusion
experiments in M1. Rats were placed under a light non-surgical 1-1.5 % isoflurane anesthesia for
infusions. The cap and dummy cannula were removed and replaced with an injector that extended 1.5
mm beyond the end of the guide cannula. For the FOF dose and volume of muscimol was 0.5 mg/mL
and 0.3 uL respectively. For M1 we first used the same dose and volume as in the FOF (0.3 uL of 0.5
mg/mL muscimol) and we followed up that experiment with double the dose (0.3 uL of 1 mg/mL
muscimol). The rate of infusion was 0.2 uL/min and we waited 4 minutes after infusions before removing
injectors to allow the drug to spread. Rats were allowed to recover for 30 minutes from the anesthesia
before beginning their training sessions for that day. Rats were placed in and removed from the
behavior box by technicians that were blind to the task and the side of the infusion. Each infusion day
was followed by a minimum of 3 days of running with no infusions. Left and right infusions were
alternated. Each rat received at least two right infusions and two left infusions.
For analysis of the effects of unilateral muscimol infusions we compared infusion days with the days
immediately preceding the infusion. For example, if we did infusion on the 10th and 20th of the month,
then we would use data from the 9th and the 19th as control days. We did not use saline infusions as a
control because we are not specifically interested in the mechanism of the effect of muscimol, but rather
whether perturbing unilateral function of the FOF has a lateralized effect on performance. In addition, we
argue that the right and left infusions act as a control for each other, since in both cases the animals
experience the same procedure, the only difference being the side of the infusion. Thus, any behavioral
difference between left and right infusions would be due to the drug infusion and not some other aspect
of the infusion procedure (a change in handling or the isoflurane).
15
Whisker Experiments
In order to determine the contribution of whisking to memory-guided orienting we conducted three
experiments: Unilateral lidocaine injections into the whisker pad; video analysis of the whiskers during
the task; and removal of the whiskers. For the unilateral lidocaine experiments rats were anesthetized
with isoflurane and then 0.2 cc of 1% lidocaine was injected subcutaneously either at the right or left
whisker pad. The front and rear claws were blunted with a nail file to prevent self-injury from scratching
at the anesthetized site. The effect of lidocaine was visually confirmed by unilateral absence of whisking
on the injected side before training and every hour thereafter. In every case the effect of lidocaine lasted
for at least 140 minutes. In some cases the whiskers were marked with titanium dioxide paint (SigmaAldrich, Product # 224227 mixed with clear nail polish) to facilitate visualization of the whiskers on video.
The injections were performed on alternate sides with no-injection days in between each experiment: for
example, a right injection on the 10th, a left injection on the 12th and a right injection on the 14th.
For video analysis of whisking during memory guided orienting we used small pieces of aluminum foil
glued to the whiskers as well as beads of titanium dioxide paint to make the whiskers more visible on
video. Video was collected at 29.97 frames / sec using a Hamamatsu CCD (XC-77). Illumination was
provided by an infra-red led 30Hz strobe light (custom made) with a 30% duty cycle to reduce motion
blur. Then the position of the head and whiskers were marked by hand on each frame (assisted by a
custom algorithm based on the local cross-correlation between subsequent frames). We used the
distance of the whisker marker to the head marker as a measure of whisker position. We then used a
multi-taper method (pmtm, MATLAB) with a WM (time-bandwidth product) of 2 to determine the spectral
content of the whisker movements separately for exploratory whisking and delay period whisking on left
versus right correct memory trials (Figure S1).
Recordings
Five rats were used to collect single-unit electrophysiology data. Recordings were made with
platinum iridium wire (16.66 µm, California Fine Wire, CA) twisted into tetrodes. Each tetrode was
threaded into an polyimide tube (34 AWG triple wall) which was part of a movable bundle of eight tubes.
Two rats were implanted with a movable bundle of 8 tetrodes on each of the right and left FOF (16
tetrodes per rat). Three rats were implanted with an 8-tetrode bundle unilaterally. Implants were targeted
to +2 AP ±1.3 ML (mm relative to Bregma). Within each bundle, tetrodes were spaced ~250 µm from
each other; we estimate that neurons were sampled from within a radius of ~0.5 mm. Wires were goldplated to 0.5-1.2 MOhm. The tetrodes could be advanced by turning a nut against a spring on a 0-80
threaded rod so that a 1/8 turn drove the tetrodes down about 40 µm. The tetrodes were advanced at
the end of sessions so that the brain tissue had time to stabilize before recording the next day.
We used two electrophysiology systems to collect the data presented here. In both cases the
reference selection, analog to digital conversion, time-stamping, filtering (600-9000Hz, FIR filter) and
video tracking was done using a Digital Cheetah System (Neuralynx; MT). For some recording we used
Neuralynx unity gain headstages (HS-36, Neuralynx). For other recordings we used 4 x 31 channel (124
channel total) time-division multiplexing headstages (Triangle BioSystems Inc, NC) with a 10 channel
commutator (Dragonfly, WV), and then the signals were demultiplexed and then used as input to the
Digital Cheetah system.
Spike sorting was done by hand using SpikeSort3D (Neuralynx). Cells had to satisfy several criteria
to be included in the presented analyses: 1) No inter-spike intervals < 1 ms; 2) Signal to noise ratio >4; 3)
At least one bin of the perivent time histogram (PETH, aligned to the Response Onset time), had to have
a firing rate of at least 3 spikes/sec. The PETH was a 2 second window (-1.5s before Response Onset
to 0.5s after) with 10 ms bins and a causal half-gaussian smoothing window with a s.d. of 200 ms
(effective smoothing was 100 ms since only half the gaussian was used). 378 cells recorded over 100
sessions satisfied the first two criteria (i.e. Well-isolated single units). 242 cells satisfied all three criteria.
Median number of cells per session was three. The maximum number of cells recorded in a session was
eleven.
16
Behavior
Analysis
Behavior
Analysis
To generate
the psychometric
curves curves
(Figure(Figure
1b,c) we
analyzed
all sessions
from the
finalthe
training
To generate
the psychometric
1b,c)
we analyzed
all sessions
from
final
stage for
each
rat.
Each
session
generates
12
behavioral
data
points:
the
%
“Went
Right”
for
each
training stage for each rat. Each session generates 12 behavioral data points: the % “Went
frequency
(n=6)
both
memory (n=6)
and non-memory
trials.and
Wenon-memory
then combined
theWe
data
across
sessions
Right”
for for
each
frequency
for both memory
trials.
then
combined
the
(separately
memory
and non-memory
trials)
and fit
that
data with a trials)
4-parameter
sigmoid
data for
across
sessions
(separately for
memory
and
non-memory
and fit that
datawhere
with ay,
4-“%
parameter
sigmoid
y, “% went-right”
is a function of x, the log(click frequency):
went-right”
is a function
of where
x, the log(click
frequency):
y = y0 +
a
1+e
(x
x0)
b
The
four parameters
lower asymptotic
performance;
y0+a
, theasymptotic
upper asymptotic
0 , theasymptotic
The four
parameters
are: y0 ,are:
the ylower
performance;
y0+a , the
upper
performance;
in log(frequency)
of the inflection
of the curve;
b , theofslope
0 , the location
performance;
x0 , thexlocation
in log(frequency)
of the inflection
point ofpoint
the curve;
b , the slope
the
If a
rat ran 100
sessions
would fit 4toparameters
to 600fordata
pointstrials
for memory
curve. of
If athe
ratcurve.
ran 100
sessions
we would
fit 4we
parameters
600 data points
memory
and
and another
4 parameters
600
data points trials.
for non-memory
was done
with
anothertrials
4 parameters
to 600
data pointstofor
non-memory
Fitting was trials.
done Fitting
with Matlab’s
nlinfit.
ForMatlab’s
analysis nlinfit.
of the effects of whisker trimming we included data from the day of trimming and the two
For analysis
the effects
offor
whisker
trimming
weback
included
data
from the
day ofWe
trimming
following days.
(It takes of
several
weeks
whiskers
to grow
to their
normal
length.)
generated
and
the
two
following
days.
(It
takes
several
weeks
for
whiskers
to
grow
back
to
their
normal
psychometric fits to the whisker trimming data in the same way as we did for the behavioral data
in
length.) We generated psychometric fits to the whisker trimming data in the same way as we
Figure 1b,c. Since the whisker trimming experiment immediately followed the lidocaine experiments we
did for the behavioral data in Figure 1b,c. Since the whisker trimming experiment immediately
used the same sessions as controls for the lidocaine and the whisker trimming sessions.
followed the lidocaine experiments we used the same sessions as controls for the lidocaine and
Wethe
generated
psychometric
fits to the muscimol and control data in the same way as for Figure
whiskerthe
trimming
sessions.
1. To generate
statistics
for
the
effect
of muscimol
wemuscimol
combinedand
left control
and right
infusion
We generated the psychometric
fits to the
data
in the days
sameacross
way asrats
for
and labeled
trials
asgenerate
contra- orstatistics
ipsi-muscimol.
For example,
if wewe
infused
into the
trials
Figure
1. To
for the effect
of muscimol
combined
leftleft
andFOF
rightthen
infusion
where the
correct
toward
the
would
be labeled as contra-muscimol
for infused
that session.
days
acrossresponse
rats andwas
labeled
trials
asright
contraor ipsi-muscimol.
For example, if we
into
To determine
statistical
significance
we
bootstrapped
the
distribution
for
the
mean
of
the
differences
the left FOF then trials where the correct response was toward the right would be labeled as
between
conditions. Wefor
didthat
8 tests:
memory
vs. non-memory
forsignificance
ipsi-muscimol
memory vs.
contra-muscimol
session.
To determine
statistical
wetrials;
bootstrapped
the nonmemorydistribution
for contra-muscimol
trials;
ipsi-muscimol
vs.
contra-musicmol
for
non-memory
trials
and
ipsifor the mean of the differences between conditions. We did 8 tests: memory vs.
muscimol
vs.
contra-muscimol
for
memory
trial;
ipsi-muscimol
vs.
control
for
non-memory
trials
and
non-memory for ipsi-muscimol trials; memory vs. non-memory for contra-muscimol trials; ipsi-ipsimuscimol
vs. contra-musicmol
non-memory trials
and ipsi-muscimol
vs. contra-muscimol
for
muscimol
vs. control
for memory trials;for
contra-muscimol
vs. control
for non-memory
trials and contramemory
trial; ipsi-muscimol
vs. control
for non-memory
trialsthe
and
ipsi-muscimol
vs.mean
control for
muscimol
vs. control
for memory trials.
The p-values
reported are
probability
that the
memory
trials;
contra-muscimol
control
for non-memory
andM1
contra-muscimol
vs.
difference
is zero
for each
comparison. vs.
The
analyses
and statisticstrials
for the
muscimol experiments
control to
forthe
memory
trials. The p-values reported are the probability that the mean difference is
were identical
FOF experiments.
zero for each comparison. The analyses and statistics for the M1 muscimol experiments were
to the FOF experiments.
Neural identical
Data Analysis
To determine whether cells had upcoming-choice-dependent firing rates during the delay period on
Analysis the firing rate of the cells during the delay by counting the number of
memoryNeural
trials, Data
we determined
To
determine
whether
hadstimulus
upcoming
choice-dependent
firing
spikes fired after the offset
of the cells
auditory
and
before the Go cue;
werates
then during
dividedthe
by delay
the
period on memory trials, we determined the firing rate of the cells during the delay by counting
duration of the delay period on that trial to obtain the firing rate. We sorted the trials into correct left and
the number of spikes fired after the offset of the auditory stimulus and before the Go cue; we
correct right trials. We then computed the area under the receiver operator characteristic curve (AUC)
then divided by the duration of the delay period on that trial to obtain the firing rate. We sorted
for the distribution
ofcorrect
firing rates
on left
vs. the
distribution
firingcomputed
rates on right
trials.
To determine
the trials into
left and
correct
right
trials. Weofthen
the area
under
the receiver
statistical
confidence
on the AUC
value
we for
randomly
relabeledofthe
trials
as left
or right
(keeping
the #of
of
operator
characteristic
curve
(AUC)
the distribution
firing
rates
on left
vs. the
distribution
left andfiring
right rates
trials on
theright
same)
and
computed
the
AUC
of
the
shuffled
trials.
We
did
this
2000
times.
A
trials. To determine statistical confidence on the AUC value we randomly cell
was considered
significantly
selective
if the AUC
thesame)
95% confidence
relabeled the trials asside
left or
right (keeping
the #ofofthe
leftdata
andwas
rightoutside
trials the
and computed
intervals
of
the
shuffled
data.
the AUC of the shuffled trials. We did this 2000 times. A cell was considered significantly side
To determine
which
upcoming-choice-dependent
firingofrates,
we first data.
selective ifat
the
AUCtimepoints
of the datacells
washad
outside
the 95% confidence intervals
the shuffled
generated To
single
trial
firing
rate
traces
by
convolving
the
spike
trains
on
each
trial
with
a
halfdetermine at which timepoints cells had upcoming choice-dependent firing causal
rates, we
first
Gaussian
(s.d. of 200
ms)
smoothing
At each
timepoint,
running
in 10
bins
from
seconds
generated
single
trial
firing ratekernel.
traces by
convolving
the spike
trains
onms
each
trial
with1.5
a causal
half-Gaussian
(s.d.seconds
of 200 ms)
kernel.
At each
timepoint,
running
in distribution
10 ms bins of
from
before the
Go cue to 0.5
aftersmoothing
the Go cue,
we then
computed
the AUC
of the
left
seconds
cue tothe
0.5significance
seconds after
Gocell
cue,
then
computed
the AUC of
vs. right1.5
correct
firingbefore
rates.the
To Go
compute
forthe
each
at we
each
time
bin we randomly
thethe
distribution
of left vs.and
right
correct firing
rates.forTothe
compute
significance
for each
relabeled
trials as left/right
computed
the AUC
shuffledthe
data
1000 times.
If the cell
AUCatof
binneuron
we randomly
relabeled
thewas
trials
as left/right
andconfidence
computedinterval
the AUC
the dataeach
for atime
given
at a given
time bin
outside
the 99%
offor
thethe
shuffled
shuffled
databin,
1000
If the
of the
for a given
at a given time
bin was
data then
that time
fortimes.
that cell,
wasAUC
labelled
asdata
significant.
Weneuron
then determined,
for each
cell, the
largest number of time bins labeled as significant in the shuffled data (n) that would result in 5% or more
of the shuffled trials having n or more significant time bins. (The larger that n is, the fewer the shuffled
16
17
outside the 99% confidence interval of the shuffled data then that time bin, for that cell, was
labelled as significant. We then determined, for each cell, the largest number of time bins
labeled as significant in the shuffled data (n) that would result in 5% or more of the shuffled trials
trials that are labeled as significant.) If the original data for the cell had more than n time bins labeled as
having n or more significant time bins. (The larger that n is, the fewer the shuffled trials that are
significant,
then
cell as a whole
was labeled
as the
significant
p<0.05.
labeled
asthe
significant.)
If the original
data for
cell hadatmore
than n time bins labeled as
Insignificant,
order to quantify
the
timing
of
the
recruitment
of
the
FOF
population
over
theused
course
of the trial we
then the cell as a whole was labeled as significant at p<0.05
and
in further
used the
same
sliding
ROC
analysis
of
left
vs.
right
correct
memory
trials
with
activity
aligned
tofor
the time
time-dependent analysis (e.g., Figure 3C). Cells labeled as not significant were not used
of the time-dependent
Go cue as above.
According to binomial statistics, using a threshold of p<0.01, we would expect
analysis.
less than 8/242
cells
to be significant
byof
chance
99.9% of the
time.
In order
to quantify
the timing
the recruitment
of the
FOF population over the course of
Tothe
perform
population
of firing
rates,
we first
normalized
the perievent
time
histograms
trial we
used theanalyses
same sliding
ROC
analysis
of left
vs. right correct
memory
trials
with
(PETHs)
of each
cell by
computing
mean
deviation to
(over
time and
over trial
classes)
of
activity
aligned
to the
time of the Go
cueand
as standard
above. According
binomial
statistics,
using
a
the cell’s
PETHs,ofand
then we
subtracted
that mean
and divided
by that
standard
deviation.
The 99.9%
resulting
threshold
p<0.01,
would expect
less than
8/242 cells
to be
significant
by chance
of ztime. were then averaged across cells to obtain z-scored population PETHs.
scoredthe
PETHs
In
order
to quantify
whether
neurons
FOF tended
to encode
the stimulus
or the response
In order to
quantify
whether
neurons
in FOFintended
to encode
the stimulus
or the response
we
we
generated
a
Stimulus
Selectivity
Index
(SSI)
from
Go
aligned
PETHs
for
correct
generated a Stimulus Selectivity Index (SSI) from Go aligned PETHs for correct and error and
trialserror
as
trials as follows:
follows:
P0.5
P ET Hipsi,tt
1.5 P ET Hcontra,tt
SSItt = Pt=
0.5
t= 1.5 P ET Hcontra,tt + P ET Hipsi,tt
If a cell fired only on contra and not on ipsi trials, then SSI=1. If a cell fired on ipsi and not contra trials,
If a cell fired only on contra and not on ipsi trials, then SSI=1. If a cell fired on ipsi and not
then SSI=-1.
If a cell
fired
equallyIf for
ipsifired
and equally
contra trials
then
SSI=0.
computed
an SSI
contra trials,
then
SSI=-1.
a cell
for ipsi
and
contraWe
trials
then SSI=0.
Wefor the
following
four
trial-types
(tt):correct-memory,
correct-non-memory,
error-memory,
and
error-non-memory.
computed an SSI for the following four trial-types (tt):correct-memory, correct-non-memory,
The contra/ipsi
label referred
to the instructedThe
response
for that
trial.
So fortoerror
trials the PETH
ipsi was
error-memory,
and error-non-memory.
contra/ipsi
label
referred
the instructed
response
constructed
where
the
rat was
instructed
to
go
ipsi
but
instead
went
contra.
We
also
for thatfrom
trial.trials
So for
error
trials
the PETH
was
constructed
from
trials
where
the
rat
was
ipsi
calculated
delay to
interval
to 0 secwent
relative
to Go]
response
interval
to 0.5 sec
relative
to Go]
instructed
go ipsi[-1.5
but instead
contra.
Weand
also
calculated
delay[0interval
[-1.5
to 0 sec
SSIs. relative to Go] and response interval [0 to 0.5 sec relative to Go] SSIs.
We computed
the choice
probability
of neurons
in order
quantify
the covariance
of neural
activity
We computed
the choice
probability
of neurons
in to
order
to quantify
the covariance
of neural
duringactivity
the delay
to variability
counted spikes
in a 400ms
ending
with the
Go cue
on
during
the delaytotobehavior.
variabilityWe
to behavior.
We counted
spikes
in a 400ms
ending
with
the
Go
cue
on
memory
trials
where
the
rat
was
instructed
to
respond
towards
the
neurons
memory trials where the rat was instructed to respond towards the neurons preferred side. We used
preferredtoside.
We used
analysis
determine
how well
the firing
in that
window
ROC analysis
determine
howROC
well the
firing to
rate
in that window
predicted
therate
future
choice
of the rat.
predicted the future choice of the rat.
Latency Analysis
WeLatency
used anAnalysis
alignment algorithm to find a relative temporal offset for the neural and behavior data on
Wefollows.
used anSingle-trial
alignmentPETHs
algorithm
to find
a relative
temporal offset
for the Then
neuralaand
behavior
each trial as
were
generated
as described
previously.
trial-averaged
data
on
each
trial
as
follows.
Single-trial
PETHs
were
generated
as
described
previously.
Then
PETH was generated for each cell. For each trial we found the time of the peak of the cross-correlation
a
trial-averaged
PETH
was
generated
for
each
cell.
For
each
trial
we
found
the
time
of
peak
function between the PETH for that trial and the trial-averaged PETH. We then shifted each trial of
the cross-correlation function between the PETH for that trial and the trial-averaged PETH. We
accordingly and iterated this process until the variance of the trial-averaged PETH converged. Usually
then shifted each trial accordingly and iterated this process until the variance of the trialthis process required fewer than 5 iterations. The output of this alignment procedure was an offset time
averaged PETH converged. Usually this process required fewer than 5 iterations. The output
for each
trial,alignment
which indicated
the relative
latency
for that
trial.
We indicated
performedthe
therelative
same alignment
of this
procedure
was an neural
offset time
for each
trial,
which
neural
procedure
on
head-velocity
data
acquired
with
the
video-tracking
system,
which
produced
relative
latency for that trial. We performed the same alignment procedure on head-velocity a
data
behavioral
latency
each
trial. We then
testedwhich
whether
the neural
latency
was correlated
the
acquired
withfor
the
video-tracking
system,
produced
a relative
behavioral
latencywith
for each
behavioral
whether
for the
the average
correlation
significantly
different
trial. latency
We thenand
tested
whether
the population
neural latency
was correlated
with was
the behavioral
latency
andthan
zero (Bootstrapped
confidence
of thecorrelation
mean). We
also
compared,different
in the same
way, the neural
whether for the
populationintervals
the average
was
significantly
than zero
latencies
of pairs of simultaneously
recorded
neurons.
(Bootstrapped
confidence intervals
of the
mean). We also compared, in the same way, the
neural latencies of pairs of simultaneously recorded neurons.
Head Direction Tuning Analysis
Direction
Analysis
WeHead
determined
theTuning
relationship
between firing rate and head-direction in and out of the task in the
We
determined
the
relationship
between
firing
andsession
head-direction
and out of the task
following way. “In-task” epochs were defined
as any
timerate
in the
within 10inseconds
rat
in
the
following
way.
“In-task”
epochs
were
defined
as
any
time
in
the
session
10was not inpoking in any nose port. “Out-of-task” epochs were defined by exclusion as the timeswithin
the rat
seconds
of the rat poking
in epoch
any nose
“Out-of-task”
epochs
were defined
by exclusion
task. The
head-direction
for each
wasport.
divided
into eighteen
20 degree
bins, with
the centeras
port
the
times
the
rat
was
not
in-task.
The
head-direction
for
each
epoch
was
divided
into
aligned to 0 degrees. Each contiguous stretch of time, in which the head direction lay within eighteen
a single
direction bin, was taken as a single data point. Firing rate for that data point was simply the number of
spikes fired divided by time duration. Average firing rate
17 for each direction bin was obtained by averaging
the firing rates for all data points for that bin; error bars are the bootstrapped 99% confidence intervals of
the mean. Occupancy was defined as the number of data points that fell within each bin. Having thus
18
rate for that data point was simply the number of spikes fired divided by time duration. Average
firing rate for each direction bin was obtained by averaging the firing rates for all data points for
that bin; error bars are the bootstrapped 99% confidence intervals of the mean. Occupancy was
defined as the number of data points that fell within each bin. Having thus obtained firing rate as
obtained
firing rate
as a function
ofwe
head
direction,
we athen
computed
a summary
v for cell’s
each cell.
a function
of head
direction,
then
computed
summary
vector
v for eachvector
cell. Each
summary
vectorvector
was the
average
of a set
unitoflength
vectors,
each each
of which
pointed
Each cell’s
summary
wasweighted
the weighted
average
of aofset
unit length
vectors,
of which
head
The
weighting
was
proportional
toto
the
cell’s
pointedinineach
eachofofthe
theeighteen
eighteendifferent
different
headdirection
directionbins.
bins.
The
weighting
was
proportional
the
cell’s
firing
each direction
bin.the
Thus
the vector
v forcell
each
cell was.
firing rate
inrate
eachindirection
bin. Thus
vector
v for each
was
v=
1
b fb
b=18
⇥
ûb = [cos( b ) sin( b )]
fb ûb
b=1
where fb is the firing rate for direction bin b, and θb is the angle that corresponds to direction bin b. For
where f is the firing rate for direction bin b, and ! is the angle that corresponds to direction
example, if a cell bfired equally at every head direction, then bv would be a vector with zero length. If a cell
bin b. For example, if a cell fired equally at every head direction, then v would be a vector with
fired only at 40˚ and nowhere else then v would be a vector with length 1 and an angle of 40˚.
zero length. If a cell fired only at 40˚ and nowhere else then v would be a vector with length 1
and an angle of 40˚.
Regression Analysis
ToRegression
asses the extent
to which delay period activity was simply a function of movement parameters, we
Analysis
modeled the
firing
rate
of
eachtocell
fromdelay
[-1.5speriod
to 0.5s]
relative
the Goacue
on correct
memory trials as
To asses the extent
which
activity
wastosimply
function
of movement
a linearparameters,
function of we
head
angle, angular
acceleration:
modeled
the firingvelocity
rate of and
eachangular
cell from
[-1.5s to 0.5s] relative to the Go cue on
f(t) =function
β1⋅φ(t) of
+ βhead
+ β3⋅φ’’(t)
+ β4+r(t)
2⋅φ’(t)angle,
correct memory trials as a linear
angular
velocity and angular
Whereacceleration:
f(t) is the PETH of each cell. If the cell’s firing rate were a linear function of movement parameters
then the residuals r(t), would on average
zero.
if there
firing rate encoded the future
f(t) = !be
+ !2However,
⋅"’(t) + !3⋅"’’(t)
+ !the
1⋅"(t)
4+r(t)
choiceWhere
of the f(t)
rat is
onthe
a trial,
separately
from Ifthe
parameters,
thenfunction
the linear
model would
PETH
of each cell.
thehead
cell’sdirection
firing rate
were a linear
of movement
parameters
the residuals
r(t),about
would
average
be would
zero. remain
However,
if there
firing rate
not fully
account forthen
f(t) and
information
theonfuture
choice
in r(t).
We the
performed
ROC
encoded
the
future
choice
of
the
rat
on
a
trial,
separately
from
the
head
direction
parameters,
analysis on the residuals r(t), at each timepoint for left and right trials in the same way as the analysis on
thenforthe
linear3C.
model
account
for S7I.
f(t) and information about the future choice
firing rate
Figure
The would
results not
are fully
shown
in Figure
would remain in r(t). We performed ROC analysis on the residuals r(t), at each timepoint for left
and right trials in the same way as the analysis on firing rate for Figure 3C. The results are
Histology
shown in
Figure
S7I. on the cannula implanted rats (Figure S2E-H) and electrode implanted rats
Histology
was
performed
(Figure S3C,D) once all experiments were completed. In all cases the FOF placements were within the
Histology
borders
of M2 and between 2 and 3 mm anterior to Bregma. In all cases the M1 placements were within
Histology was performed on the the cannula implanted rats (Figure S2E,F) and electrode
the borders of M1 and between 2.5 and 3.5 mm anterior to Bregma (Paxinos and Watson, 2004).
implanted rats (Figure S3C,D) once all experiments were completed. In all cases the FOF
placements were within the borders of M2 and between 2 and 3 mm anterior to Bregma. In all
cases the M1 placements were within the borders of M1 and between 2.5 and 3.5 mm anterior
to Bregma (Paxinos and Watson, 2004).
18
19