Article

Transient Cortical Excitation at the Onset of Visual Fixation

Hungarian Academy of Sciences, Budapeŝto, Budapest, Hungary
Cerebral Cortex (Impact Factor: 8.67). 02/2008; 18(1):200-9. DOI: 10.1093/cercor/bhm046
Source: PubMed

ABSTRACT

Primates actively examine the visual world by rapidly shifting gaze (fixation) over the elements in a scene. Despite this
fact, we typically study vision by presenting stimuli with gaze held constant. To better understand the dynamics of natural
vision, we examined how the onset of visual fixation affects ongoing neuronal activity in the absence of visual stimulation.
We used multiunit activity and current source density measurements to index neuronal firing patterns and underlying synaptic
processes in macaque V1. Initial averaging of neural activity synchronized to the onset of fixation suggested that a brief
period of cortical excitation follows each fixation. Subsequent single-trial analyses revealed that 1) neuronal oscillation
phase transits from random to a highly organized state just after the fixation onset, 2) this phase concentration is accompanied
by increased spectral power in several frequency bands, and 3) visual response amplitude is enhanced at the specific oscillatory
phase associated with fixation. We hypothesize that nonvisual inputs are used by the brain to increase cortical excitability
at fixation onset, thus “priming” the system for new visual inputs generated at fixation. Despite remaining mechanistic questions,
it appears that analysis of fixation-related responses may be useful in studying natural vision.

Full-text

Available from: Peter Lakatos, May 23, 2014
Transient Cortical Excitation at the Onset
of Visual Fixation
Csaba Rajkai
1,2
, Peter Lakatos
1,2
, Chi-Ming Chen
1
, Zsuzsa
Pincze
2
, Gyorgy Karmos
2
and Charles E. Schroeder
1,3
1
Cognitive Neuroscience and Schizophrenia Program, Nathan
S. Kline Institute for Psychiatric Research, Orangeburg, NY
10962, USA,
2
Research Institute for Psychology, Hungarian
Academy of Sciences, Budapest 1394, Hungary and
3
Department of Psychiatry, Columbia University College of
Physicians and Surgeons, New York, NY 10032, USA
Primates actively examine the visual world by rapidly shifting gaze
(fixation) over the elements in a scene. Despite this fact, we
typically study vision by presenting stimuli with gaze held constant.
To better understand the dynamics of natural vision, we examined
how the onset of visual fixation affects ongoing neuronal activity in
the absence of visual stimulation. We used multiunit activity and
current source density measurements to index neuronal firing pat-
terns and underlying synaptic processes in macaque V1. Initial
averaging of neural activity synchronized to the onset of fixation
suggested that a brief period of cortical excitation follows each
fixation. Subsequent single-trial analyses revealed that 1) neuronal
oscillation phase transits from random to a highly organized state
just after the fixation onset, 2) this phase concentration is ac-
companied by increased spectral power in several frequency
bands, and 3) visual response amplitude is enhanced at the specific
oscillatory phase associated with fixation. We hypothesize that
nonvisual inputs are used by the brain to increase cortical
excitability at fixation onset, thus ‘priming’ the system for new
visual inputs generated at fixation. Despite remaining mechanistic
questions, it appears that analysis of fixation-related responses
may be useful in studying natural vision.
Keywords: current source density, excitability, fixation, macaque,
multiunit activity, neuronal oscillation phase, visual cortex
Introduction
In natural vision, information is actively acquired by directing
gaze toward or ‘fixating’ on points of interest (Yarbus 1967).
Humans and other primates typically scan a visual scene with
a large number of brief fixations, at a rate of 2--3/s, separated
by rapid ‘‘saccadic’ eye movements. At each fixation, a volley
of retinal outputs courses into the system and produces a
spatiotemporal pattern of brain activation determined by the
interaction of stimulus qualities with properties of neurons
within each of the visual pathways (Nowak and Bullier 1997;
Schmolesky et al. 1998; Schroeder et al. 1998; Chen et al. 2006).
Because of this, investigations have increasingly turned to study-
ing the influence of eye movement dynamics on visual process-
ing and perception (Gallant et al. 1998; Vinje and Gallant 2000,
2002). ‘Perisaccadic’ modulation of neuronal firing, usually sup-
pression during the saccade, followed by enhancement starting
at the onset of fixation, has been observed throughout the visual
pathways from the lateral geniculate nucleus (LGN) to pre-
frontal cortex (reviewed by Purpura et al. 2003). Interestingly,
perisaccadic modulation is also observed in total darkness and,
thus, is at least partially nonvisually mediated (Ringo et al. 1994;
Sobotka and Ringo 1997; Lee and Malpeli 1998; Nakamura and
Colby 2000; Sylvester and Rees 2005; Sylvester et al. 2005).
Although much of the recent interest in perisaccadic modu-
lation has focused on the neural basis of perceptual saccadic
suppression (Ross et al. 2001; Reppas et al. 2002; Thiele et al.
2002), we focused on the following fixation-related increase in
neuronal firing. We evaluated the hypothesis that nonvisually
mediated increase in firing, coupled with the onset of fixation,
may reflect an underlying active process that amplifies neuronal
responses to retinal inputs generated at fixation. Evaluation of
this ‘Fixation-Amplifier’’ hypothesis is of fundamental impor-
tance because, aside from a few other forms, such as pursuit
eye movements (Lisberger and Nusbaum 2000; Gardner and
Lisberger 2001), the brief, snapshot-like fixation provides the
major means of sampling the visual environment.
Data were collected with the monkeys in complete darkness
to eliminate the possibility that effects stemmed from visual
stimulation consequent to eye movements. We analyzed fixa-
tion-related changes in laminar current source density (CSD)
and multiunit activity (MUA) profiles in area V1 in awake-
behaving macaque monkeys. CSD analysis indexes the first-order
synaptic response in a neuronal population (Freeman and
Nicholson 1975; Nicholson and Freeman 1975). Multielectrode
sampling of CSD and its action potential (MUA) correlates pro-
vide an efficient method that is sensitive to subtle processes,
such as subthreshold inputs and balanced excitation/inhibition
inputs (Schroeder et al. 1998).
Our results confirm that nonvisually mediated neuronal
excitation occurs in V1 at fixation onset. We hypothesize that
this effect may reflect modulation of the local neuronal en-
semble, preparatory to the arrival of visual inputs generated
at fixation.
Materials and Methods
Data for this study were collected during the course of experiments
examining mechanisms by which nonretinal influences modulate neu-
ronal ensemble activity in the visual and auditory systems. The effects
related to stimulus processing are reported elsewhere (Mehta et al.
2000b; Shah et al. 2004). This report concerns the eye movement--
related activity measured in V1, while the animal waited in total
darkness, during time periods between stimulus trials.
Subjects and Preparation
Complete details of the surgical procedures can be found in earlier
reports (Schroeder et al. 1998; Mehta et al. 2000a). Briefly, 2 male
macaques (Macaca fascicularis), weighing 6--9 kg, were surgically
prepared for chronic, awake intracranial recordings. All animal care and
procedures were approved by the Institutional Animal Care and Use
Committee of the Nathan Kline Institute and were in accordance with
the Principles of Laboratory Animal Care (the National Institutes of
Health Publication no. 86-23, revised 1985). Preparation of subjects for
chronic awake recording was performed using aseptic techniques,
under general anesthesia. To provide access to the brain and to promote
an orderly pattern of sampling across the surface of visual areas, matrices
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of 18-gauge stainless steel guide tubes were positioned normal to the
brain surface for orthogonal penetration of the lateral striate operculum,
targeting the foveal representation of area V1. Individual epidural guide
tubes were positioned over central and frontal sites to serve as ground
and reference electrodes. The matrices were placed within small,
appropriately shaped craniotomies to rest against the intact dura.
Together with socketed Plexiglas bars (to permit painless head fixation),
they were secured to the skull with orthopedic screws and embedded in
dental acrylic. Recovery time of 2 weeks was allowed before the
beginning of data collection.
Electrophysiological Recordings
Animals sat in a primate chair in a dark, isolated, electrically shielded,
sound-attenuated chamber with heads fixed in position. Laminar profiles
of field potentials (i.e., local electroencephalography [EEG] signals) and
concomitant MUA were obtained by recording with a linear array
multicontact electrode (14 equally spaced contacts; Fig. 1) constructed
with an intercontact spacing of 150 lm and positioned to sample all the
layers simultaneously (Barna et al. 1981). The impedance at each
contact was 0.1--0.3 MX. Each intracortical electrode referenced to an
epidural electrode at the frontal midline. Signals were amplified with
a bandpass of 1--3000 Hz. MUA were obtained by band-pass filtering the
signals (0.5--2 kHz), full-wave rectified and digitizing at 4 kHz. For field
potential recordings, the amplifier outputs were sampled at 2 kHz.
Although this procedure can cause aliasing of high-frequency signals,
analysis of power spectra showed that most of the power in the signal is
concentrated below 50 Hz and there is negligible power above 1 kHz
(Schroeder et al. 1998). Signals were processed using PC-based data
acquisition system (Neuroscan, El Paso, TX) and analyzed using custom-
made code in MATLAB (The Mathworks Inc., Natick, MA). One-
dimensional CSD profiles were calculated using a 3-point formula for
estimation of the second spatial derivative of voltage (Nicholson and
Freeman 1975). CSD analysis provides an index of the location,
direction, and density of transmembrane current flow; this is the first-
order synaptic process that in turn generates postsynaptic potentials
and the extracellular distribution of local field potentials (Schroeder
et al. 1998).
Determination of Visual Fixation Effects
Eye position was monitored using a Stoelting, Model 4100/4500 infrared
system, which tracked one eye with a resolution of 1.0 of visual angle
and a 60-Hz sampling rate. The monkeys in this study were trained on
a fixation task for the purposes of studying selective attention effects
(Mehta et al. 2000a) and mechanisms of event-related potential (ERP)
generation (Shah et al. 2004). During the training session of the
experimental paradigm, monkeys were first trained to fixate within
a 4.5 window around a light-emitting diode (LED) and then trained to
hold the gaze within the fixation window when the LED was off. The
LED was used only during the training session but not during the
experiments reported in the present study. Visual stimuli (10 ls light
flashes at 2/s) generated by a Grass PS22 Photo Stimulator projected
onto a diffuser in front of the monkey at the viewing distance of 43 cm,
subtending 11.8 of the visual field. Neither the stimulator nor the
diffuser had background luminance, and there were no other light
sources (e.g., equipment LED indicators, etc.) in the chamber during the
experiments. Further paradigmatic details are available in Mehta et al.
(2000a). However, critical to the present report is the fact that
stimulation paused whenever the monkey released the switch or broke
fixation. As the task was self-paced, the monkeys would often ‘take
a break’ by releasing the switch and/or gazing around outside of the
fixation window. Data used in the present study were acquired during
these periods. The animals were spontaneously looking all around the
chamber. There was no apparent preferred plane or directional bias of
fixation. While stimulus trains were being presented, monkeys were
holding fixation and there were no saccades prior to or after visual
stimulation for at least 500 ms.
Data Analysis
Data used in the present study were obtained from 13 experimental
sessions (8 from one monkey and 5 from the other), each entailing acute
positioning of an electrode array in V1, followed by extensive sampling
Figure 1. Laminar profile of stimulus and visual fixation-related neuronal activity in area V1 from one experimental session. On the left is a schematic of the linear array
multielectrode used to record field potential and MUA from V1. (A) Stimulus-related (foveal light flash) averaged CSD profile, with overlay of concomitant MUA traces from a subset
of the electrodes. CSD profile is color coded; current sinks (red) reflect net inward flowing transmembrane currents in the local neuronal population; current sources (blue) reflect net
outward current flow. Bottom trace shows the time of stimulation. Arrow indicates the major excitatory response to thalamic input in lamina 4C; the earlier oscillation in MUA,
unaccompanied by prominent CSD features, reflects thalamic input (see Schroeder et al. 1998). CSD profiles were calculated from the field potential profiles, and laminar boundaries
(indicated by horizontal dotted lines) were determined using functional criteria derived from prior studies (see Materials and Methods). (B) CSD and concomitant MUA profiles
associated with the onset of fixation. Bottom traces represent a subset of horizontal eye position traces. Average (n 5 100 trials) was made from single trials aligned at the onset of
fixation. Note that the CSD and MUA profiles in (B) are displayed at a larger gain than in (A). Both stimulus- and fixation-related activities were recorded during the same
experimental session with the same electrode position.
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of both ambient- and stimulus-related activity. Each experiment cor-
responded to one multielectrode penetration. Saccade onset and end
points were defined based on the velocity of eye movement. The
maximum eye velocity was determined for each saccade that exceeded
40/s threshold. Saccade onset was defined as the time when eye speed
reached 10% of the maximum velocity. Fixation onset was defined as the
time when eye velocity had decreased to 10% of the maximum. Saccade
duration was considered as the interval between these 2 points. Only
epochs with fixations maintained for at least 500 ms after the saccade
were involved in the analyses. Trials containing blink or muscle artifact
were discarded.
In the first part of the data analysis, averages were made from data
epoched from
500 to
+
500 ms, with time zero set as 1) the onset and 2)
end of saccade (i.e., onset of fixation) and 3) to the onset of visual
stimulation, respectively. These zero points allowed us to look for
saccade-related, fixation-related, and stimulus-evoked effects, respec-
tively. CSD profiles were calculated from averaged field potential
profiles, and the laminar assignment of the CSD channels was defined
as supragranular, granular, and infragranular according to functional
criteria established by earlier studies (Schroeder et al. 1991, 1998; Givre
et al. 1995). To make the extensive single-trial analysis manageable, data
reduction was necessary (see also Shah et al. 2004). We full-wave
rectified the signals from all channels in the CSD profile for each of the
13 experiments and selected one channel from each laminar division
(supragranular, granular, and infragranular), that is, the channel with
the largest integral area of the full-wave--rectified CSD. Summary
Averaged REtified Current (sAVREC) and summary Averaged MultiUnit
Activity (sAVMUA) values were calculated by averaging together the
rectified CSD and MUA signals for each single trial (note that this signal
contains both phase-locked and nonphase-locked activity).
In the second part of the analysis, phase distribution across trials was
determined by assigning a phase to each time point of the data by using
Hilbert Transform for each single trial. Phase was determined with
Hilbert Transform in broadband without narrow band filtering of the
signal. This capitalizes on high time resolution that is not ensured with
narrow band filtering the data or with wavelet transforms, which also
can generate artifacts during phase assignment (Netoff and Schiff 2002;
Kruglikov and Schiff 2003). Hilbert Transform is a powerful tool to
assign phase to raw signals like EEG/CSD. This method has been used
extensively in EEG studies to analyze such signals (Netoff and Schiff
2002; Kruglikov and Schiff 2003). Its advantage is that phase for
a broadband signal can be found. The Hilbert Transform extracts the
phase of the dominant cortical cycle regardless what dominant cycle is
present (e.g., alpha, theta, etc.). Even if the signal is broadband, the phase
denotes the position of a peak or trough in the signal ‘‘and can be used to
characterize the oscillation’s dynamics.’ Phase is also physiologically
relevant because neuronal excitability is regulated according to the
phase of ongoing local oscillations (Bishop 1933; Kruglikov and Schiff
2003; Lakatos et al. 2005, 2007). To avoid being biased by large transient
fluctuations in the raw signal, we used the Rayleigh test for statistical
evaluation of the results of the Hilbert Transform. This determined if
the distribution of the phase of CSD was significantly different from a
uniform (random) distribution for each data point from
500 ms pre-
fixation onset to
+
500 ms postfixation onset, using an analysis window
1 data point (0.5 ms) wide. Also, in order to more directly relate the
results of the Hilbert Transform analysis to neuronal oscillatory activity,
we took the additional step of analyzing oscillation amplitude and
phase concentration of prefixation and postfixation (
300 and 77.5 ms,
respectively) in 3 EEG bands (i.e., 3--8 Hz for delta/theta band, 8--20 Hz
for alpha/beta band, and 20--58 for beta/gamma band) in supragranular,
granular, and infragranular layer. In each layer, we first filtered the
signals into 3 bands and ran the Hilbert Transform in each band. After
the transform, we got phase and amplitude information of each band.
Second, we used Reyleigh tests for determining phase concentration
(i.e., calculating Rayleigh statistics parameter R, intertrial coherence,
and their P values) in each band. Third, paired t-tests were used to
determine significant prefixation to postfixation amplitude increase.
In the final part of the analysis, we calculated visual-evoked response
amplitudes as a function of prestimulus phase by first sorting the phase
values gained by Hilbert Transform from
p to p radians. Then the
permutation vector obtained during the phase sorting procedure was
applied to the event-related response amplitude values (Lakatos et al.
2005). A whole cycle was divided into 6 even parts, and response
amplitudes falling into the same phase division were averaged together
(Fig. 5). By sorting the visual-evoked response amplitudes and then
applying the permutation vector to the phase values, the phase asso-
ciated with the largest response amplitude (called ‘‘ideal’ phase) can be
determined. Similarly, the phase associated with the smallest response
amplitude (called ‘‘worst’ phase) was assessed. CSD amplitude differ-
ence between ideal and worse phase was determined by subtracting the
amplitude values obtained in ideal and worst phase. This CSD amplitude
difference was determined for the prestimulus period (from
170 to
0 ms relative to stimulus onset) and also for the poststimulus period
(from 30 to 200 ms relative to stimulus onset). Amplitude and frequency
values were statistically analyzed by Student’s t-test. Phase was analyzed
by circular statistics. To test the difference between 2 phase distribu-
tions, Watson U
2
test was used. The Rayleigh statistic was used as a test
of uniformity of phase distribution (Fig. 3).
Results
Effect of Fixation on Neural Activity
Figure 1A displays the averaged laminar CSD profile, time locked
to the onset of a foveal light flash, obtained from a site in the
foveal representation of V1. On the extreme left is a schematic
of the electrode positioned with respect the laminar expanse of
V1 (depicted with the cytochrome oxidase section). In the CSD
profile, extracellular current sinks (representing net inward
flowing current) are colored red--yellow and sources (repre-
senting net outward flowing current) are colored blue. Super-
imposed on the CSD plot are selected MUA recordings from the
supragranular, granular, and infragranular laminae, acquired
simultaneously with the CSD profile. This profile is typical of
the V1 response to this stimulus, as established by earlier studies
(Schroeder et al. 1991, 1998; Givre et al. 1995; Mehta et al. 2000a,
2000b). The initial major postsynaptic response, a current sink
with a concomitant increase in action potentials (arrow), occurs
in lamina 4C and is followed by a smaller activation in the
supragranular laminae; this granular to supragranular activa-
tion sequence signifies feedforward activation by afferents from
LGN (Schroeder et al. 1991, 1998; Givre et al. 1995).
Figure 1B depicts the averaged laminar CSD and MUA profiles
phase locked to the onset of fixation, at the same recording site;
the conventions are identical to Figure 1A , though the ampli-
tude calibrations are different. Like the local stimulus-evoked
response, this activity appears excitatory, but the fixation-
related excitation appears smaller (see MUA and CSD amplitude
calibrations). Comparison of the peak amplitudes of fixation-
related and stimulus-evoked excitatory responses across all 13
experiments (see Materials and Methods) revealed that this
difference is significant (paired t-test, P
<
0.05 for CSD; P
<
0.01
for MUA). Also in contrast to the stimulus-evoked response
(described above), fixation-related response does not fit the
simple feedforward (granular followed by extragranular excita-
tion; Schroeder et al. 1998) pattern, based on either the timing
or laminar distribution of activity. We did not attempt to quan-
titatively analyze the laminar timing pattern of the fixation-
evoked response, given the relatively coarse (~17 ms) temporal
resolution of the eye tracker. Finally, prior to fixation onset
(during the saccade), there is mild suppression of local neuronal
firing, whereas no such effect is visible in the stimulus-evoked
profile.
To describe fixation effects more quantitatively, we computed
(Fig. 2) the grand mean of the normalized single-trial--rectified
CSD (sAVREC) and grand mean of the corresponding MUA
202 Visual Fixation Increases Neuronal Excitability
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measure (sAVMUA). This analysis indicates that prior to fixation,
there is actually CSD suppression accompanying MUA suppres-
sion. This is a subtle effect compared with the subsequent
fixation-related enhancement, but whenever saccade-related
MUA suppression was detected, it was accompanied by CSD
suppression. No such ‘prior’ suppression has ever been observed
in similar analyses of the stimulus-evoked response in V1 (Givre
et al. 1994, 1995; Schroeder et al. 1998; Mehta et al. 2000a, 2000b;
Shah et al. 2004). Significantly, the quantification also shows that
neuronal suppression peaking during the saccade and followed
by enhancement at fixation onset is typical of the entire data set.
The prefixation suppression peaked in both AVREC and AVMUA
around the time of saccade onset (saccade duration: 84.0 ms
[mean]
± 33.6 [standard deviation {SD}]). The postfixation
enhancement peaked at 77.5 and 51.0 ms (relative to the fixation
onset) in AVREC and AVMUA, respectively. Neuronal activity
increase after fixation was significant at P
<
0.01 for AVREC and
P
<
0.05 for AVMUA (paired t-tests). These effects were similar
when the averaging of single trials was synchronized to saccade
onset (not shown). However, comparison of fixation-related and
saccade-related excitatory response peak amplitudes across all
13 experiments showed that the fixation-related response is
significantly larger (paired t-test, P
<
0.05 for CSD; P
<
0.05 for
MUA).
Physiological Mechanisms of Visual Fixation Effects
The foregoing analysis indicates that in the absence of visual
stimulation of any sort, there is neuronal excitation immediately
following the onset of fixation. The second goal of this study
was to evaluate the underlying mechanisms. An obvious
possibility is that the effect is a local excitation, akin to an
‘evoked’ response, triggered by some form of input, for
example, an efferent copy of the eye movement command.
Another possibility is that the effect is generated by reorgani-
zation of ongoing activity without addition of energy to the
system. This type of effect is referred to as ‘‘phase resetting’ or
‘phase modulation’ (Makeig et al. 2004; Shah et al. 2004; Lakatos
et al. 2005, 2007). Both types of mechanism predict a shift from
a random distribution of oscillatory phase prior to the onset of
fixation, to an organized or ‘phase concentrated’’ state with
fixation. However, in an evoked response, phase concentration
would accompany an increase in spectral power. To assess
phase concentration, we examined the uniformity of phase
distribution by using the Rayleigh test for each single time point
over the interval from
500 ms prefixation onset to
+
500 ms
postfixation onset (Fig. 3A). Initial analyses revealed that the
phase distribution pattern was similar in the supragranular,
granular, and infragranular laminar divisions; therefore, the data
are collapsed across the layers. In the prefixation period
(including the time frame of the saccade), the phase distribu-
tion did not differ significantly from a random (uniform)
distribution (Rayleigh tests, P
>
0.05). However, beginning at
fixation onset and continuing for about 200 ms postfixation,
phase distributions were significantly different from uniform
(P
<
0.001). This time frame brackets the arrival time of retinal
inputs to V1 (Maunsell and Gibson 1992; Schmolesky et al. 1998;
Schroeder et al. 1998; Chen et al. 2006), and thus, these findings
are in line with our hypothesis that oscillatory phase concen-
tration reflects modulation of the local neuronal ensemble,
preparatory to the arrival of visual inputs generated at fixation.
To further characterize this effect, we chose 2 time points,
fixation onset (the beginning of phase concentration) and 300
ms prefixation onset (just before the beginning of saccade-
related influences—see Fig. 2). We then calculated the phase
distribution of single trials at
300 and 0 ms, for the entire data
set, collapsing across experiments and cortical layers (Fig. 3B).
At the time of fixation the grand mean (n
= 13) phase (/) was
1.85 rad and the pooled angular deviation (s) was 0.91. Pooled
(grand mean) phase distribution for a particular time point tells
us how uniform the distribution is for the whole data set, as well
as the most common phase at that time point. Pooled angular
deviation (s) gives information about the variability from session
to session at the time of fixation.
These findings raise 2 additional questions. First, is this phase
concentration indicative of ‘pure’ phase resetting? According
to earlier formulations (Shah et al. 2004), pure phase resetting
produces event-related responses without attendant increase in
power at relevant frequencies. Second, given that the Hilbert
Transform extracts phase without reference to oscillation
frequency, how exactly are we to interpret these findings
with respect to the well-known EEG bands? In order to address
these issues, we examined the degree to which fixation-related
phase concentration (indexed by Rayleigh test) is accompanied
Figure 2. Grand mean of normalized single-trial--based CSDs (sAVREC) and grand mean of the corresponding MUA measure (sAVMUA) relative to the fixation onset time.
Amplitudes were normalized by the largest peak-to-peak response amplitude per session. Dashed lines represent the ±2.5 standard error of the mean. Vertical dotted line shows
the mean time of the saccade onset. Horizontal lines depict thresholds for statistically significant deviations from baseline (P \ 0.01 for AVREC and P \ 0.05 for AVMUA).
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by change in amplitude (indexed by Hilbert Transform) in 3
bands (3--8 Hz for delta/theta band, 8--20 Hz for alpha/beta
band, and 20--58 for beta/gamma band). We keyed the analysis
to the prefixation (
300 ms) and postfixation (77.5 ms, the
postfixation enhancement peak in AVREC) values. This analysis
(Fig. 4) reveals significant prefixation to postfixation phase
concentration in the 3- to 8-Hz ‘delta/theta’’ band, with no
significant effects in the other bands (Fig. 4A). Accompanying
this pattern of effects, significant prefixation to postfixation
amplitude increase is largely confined to the delta/theta band
(Fig. 4B).
Effects of Fixation-Biased Phase on Stimulus Processing
Data presented in Figures 1 and 2 reveal increases in ambient
CSD and MUA amplitudes following fixation onset, and these
increases are associated with oscillatory phase concentration
(Figs 3 and 4) and a general increase in spectral power (Fig. 4).
Do these effects reflect an increase in local cortical excitability?
Excitability could not be directly addressed here, as the
foregoing analyses are based on analyses of time periods
between stimulus presentation trials. However, we could
address this question indirectly using the accompanying trials
in which stimuli were presented, by examining the relationship
between the phase of the ongoing EEG at the time a stimulus is
delivered and stimulus-evoked response amplitude in that trial.
Therefore, we asked whether the observed phase distribution at
the time of fixation is associated with enhanced excitability.
To isolate the effects of oscillation phase from those of
fixation onset, we analyzed only the responses to visual stimuli
presented during periods of stable fixation (see Fig. 5). Mean
rectified CSD and MUA amplitudes were calculated for the
30- to 200-ms poststimulus interval of each single trial; 30 ms is
Figure 3. Pooled perisaccadic phase concentration and phase histograms from 13
experimental sessions; each entailed one multielectrode penetration. (A) Rayleigh
statistics parameter R as a function of time (fixation onset 5 0), indicating phase
uniformity prior to and after fixation. Dotted line depicts the threshold for significant
(P 5 0.01) deviation from a uniform (random) phase distribution. The Rayleigh statistic
was applied to each data point from --500 ms prefixation onset to þ500 ms postfixation
onset; thus, the analysis window is 0.5 ms wide, and there is no interdependence
between prefixation and postfixation phase estimates. (B) Pooled (across all experi-
ments in both subjects) phase distribution histograms of single time points collapsed
across layers. Phases are derived by Hilbert transform at 300 ms (left column) and
0 ms (right column) relative to fixation. The 300 ms time point was chosen as it is prior
to the period of saccade-related effects (Fig. 2). Phase histogram at 0 ms displays the
phase distribution at the onset of fixation (i.e., at fixation onset). Rayleigh-test
parameter R and P values are shown for each distribution. Significant difference from
uniform (random) phase distribution is indicated by P value with asterisk.
Figure 4. Intertrial coherence and amplitude of prefixation and postfixation in 3
frequency bands in 3 layers. (A) Rayleigh statistics parameter R, intertrial coherence,
at prefixation time point, 300 ms, and at postfixation time point, 77.5 ms, in 3 bands
(i.e., 3--8 Hz for delta/theta band indicated by dark box plots, 8--20 Hz for alpha/beta
band indicated by gray box plots, and 20--58 for beta/gamma band indicated by white
box plots). Each numerator in a fraction above a box plot denotes how many
experimental sessions were significantly deviated from a random phase distribution
out of total of 13 experimental sessions, the denominator. (B) Amplitude box plots at
prefixation and postfixation time points (300 and 77.5 ms, respectively) in 3 bands
were illustrated in 3 layers. Brackets indicate the significant paired t-test comparisons
of amplitudes between prefixation and postfixation time points in certain frequency
bands.
204 Visual Fixation Increases Neuronal Excitability
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the approximate onset latency in V1 under these stimulation
conditions (Givre et al. 1995). To determine if there is a sys-
tematic relationship between these single-trial response ampli-
tude values and the phase of the ongoing activity, we sorted the
amplitudes (CSD and MUA values, respectively) as a function of
prestimulus phase. The systematic relationship between pres-
timulation oscillatory phase and visual response amplitude is
depicted in a polar plot in Figure 5A. The results are quantified
as a pooled amplitude distribution as a function of prestimulus
phase across sessions in Figure 5B. These data show that the
phase of the ongoing local neuronal oscillation is consistently
related to the amplitude of the stimulus-evoked response.
Following Lakatos et al. (2005), we designated the phase
associated with the largest response amplitudes the ideal (/
mean: 1.92; s : 1.13) and that related to the smallest response am-
plitudes the worst phase (/ mean:
1.73; s : 0.98). As described
above in relation to Figure 3, at the time of fixation, the grand
mean (n
= 13) phase (/) was 1.85 rad and the pooled angular
deviation (s) was 0.91. The mean phase at visual fixation onset
did not differ significantly from the ideal excitability phase
identified in this way (Watson U
2
test, P
>
0.05).
To determine whether the enhanced evoked response ampli-
tude reflects a simple linear summation of the ongoing oscilla-
tion with the evoked response as opposed to an interaction
between input and local oscillation phase (i.e., interaction
between stimulation and excitability state), we compared the
amplitude differences between ideal and worst phase of
the ongoing oscillation in the prestimulus period with that of
the poststimulus interval. A significant change in the maximal
(ideal--worst phase) amplitude difference from the prestimulus
to poststimulus interval would indicate the presence of an
interaction. The prestimulus (from
170 to 0 ms relative to
stimulus onset) CSD amplitude difference between ideal and
worst phase was 0.082 mV/mm
2
(mean) ± 0.063 (SD) and the
poststimulus (from 30 to 200 ms relative to stimulus onset) CSD
amplitude difference was 0.114 mV/mm
2
(mean) ± 0.071 (SD).
We found that the maximal amplitude difference between the 2
phases (i.e., ideal and worst) is significantly greater poststimulus
than prestimulus (paired t-test, P
= 0.0015).
In sum, the oscillatory phase associated with the onset of
fixation (in the absence of stimulation) is not discriminably
different from the ideal phase, that at which maximal visual-
evoked responses occur. The ideal phase does appear to reflect
a high excitability state in local cortical neurons. Overall, these
findings are consistent with the hypothesis that the onset of
fixation is associated with an increase in cortical excitability.
Discussion
The results of this study clearly demonstrate that neuronal mod-
ulation at the onset of fixation in the dark reflects an underlying
nonvisual process that produces local neuronal excitation in V1.
Additional analyses suggest that this effect stems from a tran-
sient increase in local neuronal excitability. We propose that in
a lighted environment, this increased excitability will amplify
responses to visual inputs generated at fixation. Consistent with
this proposition, the effect persists until about 150 ms post-
fixation, which allows ample time for even the slowest retinal
outputs to transit through V1 into the higher order visual areas
including inferotemporal cortex (see e.g., Schmolesky et al.
1998; Schroeder et al. 1998; Chen et al. 2006).
Our single-trial analyses reveal that fixation onset is associ-
ated with significant oscillatory phase concentration, which is
consistent with the possibility that the fixation effect operates
through phase modulation of ongoing neuronal oscillations in
visual cortex. The idea is that fixation-related phase resetting of
an ongoing neuronal oscillation can place new retinal input in
an ideal (optimal excitability) phase, so that it is amplified
relative to inputs that are not synchronized to fixation. This is an
appealing mechanism as it makes elegant use of the large
amounts of energy present in neuronal oscillations. Moreover,
recent evidence suggests that oscillatory phase modulation may
be a general mechanism of predictive, as well as adaptive
cortical operation (Lakatos et al. 2007). However, the accom-
panying power increase rules out a pure phase resetting
interpretation according to current formulations of how such
a mechanism operates (Makeig et al. 2004; Shah et al. 2004). We
will continue to investigate this hypothesis.
Figure 5. Effect of the phase of ongoing activity on stimulus processing. (A) Event-
related CSD (left) and MUA (right) amplitudes of trials with different prestimulus phase
in one experimental session. The prestimulus phase of the intracortical EEG is
represented by polar angle in the plot, and the normalized amplitude of the stimulus-
related response is represented as the distance from the origin of the circle, with the
center of the circle representing the least amount of amplitude. The radius of the circle
is 1 unit. Phase is measured at the time of stimulus presentation (0 ms) on
supragranular electrode. Amplitude value is the mean of the rectified event-related
CSD and MUA of the 30- to 200-ms poststimulus time interval measured on the same
electrode. (B) Pooled (n 5 13) amplitudes (collapsed across layers) of stimulus-evoked
CSD (left) and MUA (right) as a function of prestimulus phase. Error bars denote
standard errors of the mean. The phase of the ongoing activity, which results in
maximal event-related CSD and MUA amplitudes named ‘ideal phase’ (filled arrows),
whereas ‘‘worst phase’ (open arrows) means that if prestimulus EEG is in this phase,
the event-related response will be smaller. Amplitudes were normalized by the biggest
response amplitude per session. Note that during stimulus presentation, monkeys
were holding fixation and there were no saccades immediately before or after visual
stimulation. The time between the onset of fixation and the delivery of a stimulus
varied over intervals from 1 to 2 s; thus, stimuli occurred in a random relationship to the
phase of the local oscillatory activity.
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‘Are these effects perceptually relevant?’ Because fixations
provide the major means of visual data collection in humans and
other primates (Yarbus 1967), it is logical to suppose that the
system might operate to enhance the perceptual impact of
visual inputs occurring just after fixation, and there is evidence
for this idea. The perceptual impulse response accelerates
immediately following eye movements (i.e., at fixation; Ikeda
1986; Burr and Morrone 1996). Also, perceptual sensitivity,
particularly for colored patterns, is enhanced at fixation (Burr
et al. 1994).
Mechanisms of Phase Modulation
One important mechanistic issue concerns the underlying brain
circuitry. There are numerous potential anatomical sources of
both direct and indirect saccade/fixation-related inputs to V1.
These include cortical feedback projections from parietal
cortex (Barash et al. 1991a, 1991b; Goldberg et al. 2002) and
frontal/prefrontal sites (Funahashi et al. 1991; Schall 1991;
Stanton et al. 1995), as well as subcortical feedforward inputs
from lateral pulvinar (Benevento and Rezak 1976), intralaminar
thalamic nuclei (Schlag-Rey and Schlag 1984), and brainstem
(Doty et al. 1973; McCormick and Pape 1988; Lu et al. 1993).
Distinguishing these alternatives will require additional exper-
imentation.
Whatever the input route is, the local physiology of fixation
effects is an important question. Excitatory and inhibitory
mechanisms are both possible. Although long distance afferents,
such as cortical feedback projections, have generally excitatory
(glutamatergic) effects, they can also operate locally by activat-
ing c-aminobutyric acidergic (GABAergic) inhibition (Gonchar
and Burkhalter 1999). Direct enhancement of local cortical
excitability could account for both increase in spectral power
and any accompanying phase modulation. Detracting slightly
from this possibility is the idea that both the feedback and
feedforward input sources that appear likely (above) would
tend to operate as ‘‘modulatory,’ as opposed to ‘‘driving inputs’’
(Sherman and Guillery 2002). The nontrivial increases in
spectral power and MUA amplitude that occur at fixation fit
better with the description of a driving input that with that of
a modulatory input. A more complex mechanism, operating
through local GABA
a
-mediated (silent or shunting) inhibition
(e.g., Nicoll et al. 1990) during the prior saccade could also
account for our observations. Because the reversal potential for
chloride ions is near typical membrane resting potential values,
activation of GABA
a
receptors should not result in large trans-
membrane currents. Thus, the reduction in ongoing MUA
during the saccade would have little or no correlate in the
concomitant CSD profile, which is what we observe. However,
the underlying phasic inhibition could itself contribute to the
effects we note at fixation onset. That is, with release of local
inhibition, ongoing activity in inputs from outside of the
immediate region, trigger a resumption of ambient activity,
with a phase tied to the offset of inhibition. A similar logic
appears implicit in the use of the term ‘‘pause rebound’ to
describe neuronal activity related to generation of eye move-
ment potentials (e.g., Purpura et al. 2003).
Consolidation and Extension of Prior Findings
Our results are consistent with the hypothesis that the in-
creased neuronal activation following fixation (Lee and Malpeli
1998; Park and Lee 2000; Reppas et al. 2002) reflects an actual
enhancement of neuronal excitability (i.e., increased neuronal
sensitivity to stimulation). Also, the CSD profiles associated with
this effect index the neuronal generators of local field potentials
(Schroeder et al. 1995), thus providing physiological under-
pinnings for eye movement--related potentials previously ob-
served within V1 and extrastriate cortex (Purpura et al. 2003),
as well as effects found with scalp ERPs (Evans 1953; Marton
et al. 1983; Skrandies and Laschke 1997) and functional mag-
netic resonance imaging (Sylvester and Rees 2005; Sylvester
et al. 2005). The fixation effects we report would likely contrib-
ute to previously hypothesized functions such as synchronizing
retinal inflow with ongoing functions in higher order regions
(Sobotka et al. 1997) and synchronizing activation onset across
areas (Purpura et al. 2003). For optimal results, fixation-induced
phase resetting (and amplification) should be coordinated
throughout the visual pathways. This prediction is consistent
with the distribution of perisaccadic modulation effects across
the visual pathways from lateral geniculate nucleus (Lee and
Malpeli 1998; Reppas et al. 2002), through V1 (Purpura et al.
2003), V3a (Nakamura and Colby 2000), middle temporal (MT;
Bair and O’Keefe 1998), inferotemporal cortex (Ringo et al.
1994; Sobotka et al. 1997; Purpura et al. 2003), medial temporal
cortex/hippocampus (Ringo et al. 1994; Sobotka and Ringo
1997; Sobotka et al. 1997), and frontal eye fields (Dejardin et al.
1998). Although it is not certain that the same process is
operating across all of these stages, preliminary findings in our
laboratory (Rajkai et al. 2005) suggest that this is the case.
Relationship to Attention
Strong links have been established between the brain circuits
controlling gaze and those controlling spatial selective attention
(e.g., Goldberg et al. 2002; Bisley and Goldberg 2003). It is pos-
sible, particularly with saccades during voluntary visual search,
that the effects we report reflect some modulation due to
attention. On the other hand, it is clear that attentional allo-
cation can be divorced from eye position (Harter et al. 1982;
Hillyard and Munte 1984; Moran and Desimone 1985; Treue and
Maunsell 1996). The present study was not designed to examine
attentional involvement per se. Because we analyzed data
gathered during periods of unconstrained eye movements in
the dark, the involvement of attention in the effects we describe
is an open question. It will be very interesting to learn if fixation-
related amplification reflects the influence of attention or if it
reflects a more automatic sensory-motor effect (e.g., a corollary
discharge) that could be utilized by attention. In any case, it
merits emphasis that although precise determination of the
moment-to-moment locus of attention in unconstrained nat-
ural viewing may remain problematic, eye position is a robust
dependent measure whose spatiotemporal dynamics can be
precisely described and related to the internal cortical state.
Implications
Our findings bridge the gap between 2 emerging themes in
systems neuroscience. On one hand, studies in a variety of
disciplines including optical imaging, single-unit recording, and
field potential/EEG recording have dramatically underscored the
effects of ambient activity on sensory processing (Pfurtscheller
1976; Rahn and Basar 1993a, 1993b; Steriade et al. 1993; Arieli
et al. 1996; Contreras et al. 1996; Polich 1997; Kisley and Gerstein
1999; Sanches-Vives and McCormick 2000; Truccolo et al. 2002;
Kruglikov and Schiff 2003; Fiser et al. 2004; Lakatos et al. 2005).
206 Visual Fixation Increases Neuronal Excitability
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For natural vision in particular, it appears that any incoming
visual stimulus will have a very small impact on the system
unless it falls into an ideal excitability phase of the ongoing
activity (Fiser et al. 2004). Findings like these led (Arieli et al.
1995) to conclude that ongoing oscillatory activity forms the
‘context’ for the processing of new sensory ‘‘content.’ On the
other hand, there is rapidly growing interest in the use complex
natural stimuli to study brain mechanisms of vision (Kayser et al.
2003; Long and Purves 2003; Kayser and Konig 2004; Lesica and
Stanley 2004; Salazar et al. 2004; Yang and Purves 2004; Howe
and Purves 2005), coupled with a recognition that adequate
understanding of natural vision requires incorporation of eye
movement dynamics (Gallant et al. 1998; Reppas et al. 2002;
Vinje and Gallant 2002). The integration of these themes is both
technical and conceptual.
On a purely technical level, it will be of interest to explore the
use of fixation triggered averaging of EEG as a novel paradigm
for recording visual event-related responses. Fixation-related
responses to visual stimuli offer the advantage of observing
visual processing under conditions that closely approximate
natural vision. Moreover, our findings suggest that because of
the amplifying effects of fixation, these responses will have
a higher signal-to-noise ratio than traditional measures.
On a more fundamental scientific level, our findings reinforce
the view that the sensory and motor systems of the brain work
in close coordination. Fixation effects reflect the ability of the
brain’s gaze control systems to ‘‘prime’ or otherwise predic-
tively prepare the visual system for a temporal pattern of visual
input that is a straightforward consequence of the way in which
the eyes are used to actively sample the visual environment.
This ability is exploited to amplify the processing of visual
stimuli that become the targets of visual fixation.
Notes
Supported in part by NIMH grant MH060358. We thank Tammy
McGinnis, Monica O’Connell, and Aimee Mills for their technical
support and Drs Ashesh D. Mehta and Istva
´
n Ulbert for their crucial
role in data collection. We also thank Drs Ankoor S. Shah and Joseph
Isler for helpful comments on an earlier version of the manuscript.
Conflict of Interest: None declared.
Address correspondence to Charles E. Schroeder, PhD, Cognitive
Neuroscience and Schizophrenia Program, Nathan S. Kline Institute for
Psychiatric Research, 140 Old Orangeburg Road, Orangeburg, NY
10962, USA. Email: schrod@nki.rfmh.org.
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    • "Visual and auditory stimuli predictive of rewards can both drive frequency-specific phase resets in their respective cortices (Kayser et al., 2008; Lakatos et al., 2008; Schroeder and Lakatos, 2009), as well as the anterior cingulate cortex (Voloh et al., 2015), and the hippocampus (Mormann et al., 2005). Endogenous signals representing exploratory saccades (Hoffman et al., 2013; Jutras et al., 2013), fixations (Rajkai et al., 2008), and erroneous expectations of rewards (Hyman et al., 2011) can also serve as ''internal cues'' that can reset phase. In recent year, many notable reviews on oscillatory coordination have detailed its neurophysiological underpinnings (Buzsáki, 2006; Wang, 2010; Lisman and Jensen, 2013; Womelsdorf et al., 2014b), implications for coding (Panzeri et al., 2010; Akam and Kullmann, 2014; Fries, 2015), emergence when attention, decision making and choice demands increase (Womelsdorf et al., 2010b; Siegel et al., 2012; Gregoriou et al., 2015; Watrous et al., 2015b), as well as its clinical relevance (Thut et al., 2011; Voytek and Knight, 2015). "
    [Show abstract] [Hide abstract] ABSTRACT: Short periods of oscillatory activation are ubiquitous signatures of neural circuits. A broad range of studies documents not only their circuit origins, but also a fundamental role for oscillatory activity in coordinating information transfer during goal directed behavior. Recent studies suggest that resetting the phase of ongoing oscillatory activity to endogenous or exogenous cues facilitates coordinated information transfer within circuits and between distributed brain areas. Here, we review evidence that pinpoints phase resetting as a critical marker of dynamic state changes of functional networks. Phase resets (1) set a “neural context” in terms of narrow band frequencies that uniquely characterizes the activated circuits, (2) impose coherent low frequency phases to which high frequency activations can synchronize, identifiable as cross-frequency correlations across large anatomical distances, (3) are critical for neural coding models that depend on phase, increasing the informational content of neural representations, and (4) likely originate from the dynamics of canonical E-I circuits that are anatomically ubiquitous. These multiple signatures of phase resets are directly linked to enhanced information transfer and behavioral success. We survey how phase resets re-organize oscillations in diverse task contexts, including sensory perception, attentional stimulus selection, cross-modal integration, Pavlovian conditioning, and spatial navigation. The evidence we consider suggests that phase-resets can drive changes in neural excitability, ensemble organization, functional networks, and ultimately, overt behavior.
    Full-text · Article · Apr 2016 · Frontiers in Systems Neuroscience
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    • "Inasmuch as the rats in our task moved only a few cm as they reached forward and sampled the stimulus, locomotion is not the best explanation for the increase in theta power and frequency. It has been suggested that the triggering of phase reset of hippocampal theta band by stimulus onset414243 , may ensure that sensory input is integrated at an optimal phase of the oscillation . This may have important implications for memory-related mechanisms that are associated with a specific theta phase [42,444546). "
    [Show abstract] [Hide abstract] ABSTRACT: Rhythms with time scales of multiple cycles per second permeate the mammalian brain, yet neuroscientists are not certain of their functional roles. One leading idea is that coherent oscillation between two brain regions facilitates the exchange of information between them. In rats, the hippocampus and the vibrissal sensorimotor system both are characterized by rhythmic oscillation in the theta range, 5-12 Hz. Previous work has been divided as to whether the two rhythms are independent or coherent. To resolve this question, we acquired three measures from rats-whisker motion, hippocampal local field potential (LFP), and barrel cortex unit firing-during a whisker-mediated texture discrimination task and during control conditions (not engaged in a whisker-mediated memory task). Compared to control conditions, the theta band of hippocampal LFP showed a marked increase in power as the rats approached and then palpated the texture. Phase synchronization between whisking and hippocampal LFP increased by almost 50% during approach and texture palpation. In addition, a greater proportion of barrel cortex neurons showed firing that was phase-locked to hippocampal theta while rats were engaged in the discrimination task. Consistent with a behavioral consequence of phase synchronization, the rats identified the texture more rapidly and with lower error likelihood on trials in which there was an increase in theta-whisking coherence at the moment of texture palpation. These results suggest that coherence between the whisking rhythm, barrel cortex firing, and hippocampal LFP is augmented selectively during epochs in which the rat collects sensory information and that such coherence enhances the efficiency of integration of stimulus information into memory and decision-making centers.
    Full-text · Article · Feb 2016 · PLoS Biology
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    • "retinal image during saccades could activate visual gain control mechanisms that transiently enhance visual sensitivity . Previous experiments have demonstrated that post - saccadic enhancement of spontaneous neural activity occurs even in complete darkness ( cat : Lee and Malpeli , 1998 ; monkey : Reppas et al . , 2002 ; Ibbotson et al . , 2008 ; Rajkai et al . , 2008 ) . While this is good evidence for an internal mechanism , modulation of spontaneous rate may not be linked directly to changes in sensitivity to visual stimulation . Therefore , before a mechanism can be identified a major question remains : does visual input during saccades have any influence on post - saccadic enhancement or does th"
    [Show abstract] [Hide abstract] ABSTRACT: Primates use saccadic eye movements to make gaze changes. In many visual areas, including the dorsal medial superior temporal area (MSTd) of macaques, neural responses to visual stimuli are reduced during saccades but enhanced afterwards. How does this enhancement arise-from an internal mechanism associated with saccade generation or through visual mechanisms activated by the saccade sweeping the image of the visual scene across the retina? Spontaneous activity in MSTd is elevated even after saccades made in darkness, suggesting a central mechanism for post-saccadic enhancement. However, based on the timing of this effect, it may arise from a different mechanism than occurs in normal vision. Like neural responses in MSTd, initial ocular following eye speed is enhanced after saccades, with evidence suggesting both internal and visually mediated mechanisms. Here we recorded from visual neurons in MSTd and measured responses to motion stimuli presented soon after saccades and soon after simulated saccades-saccade-like displacements of the background image during fixation. We found that neural responses in MSTd were enhanced when preceded by real saccades but not when preceded by simulated saccades. Furthermore, we also observed enhancement following real saccades made across a blank screen that generated no motion signal within the recorded neurons' receptive fields. We conclude that in MSTd the mechanism leading to post-saccadic enhancement has internal origins.
    Full-text · Article · Sep 2015 · Frontiers in Systems Neuroscience
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