Download Local Field Potential in the Visual System

Encyclopedia of Computational Neuroscience
DOI 10.1007/978-1-4614-7320-6_722-1
# Springer Science+Business Media New York 2014
Local Field Potential in the Visual System
Gregor Rainer*
Department of Medicine, University of Fribourg, Fribourg, Switzerland
Definition
Low-frequency component of the continuous time-varying electrophysiological signal recorded
using intracranial extracellular electrodes placed inside the visual cortex. It is dominated by local
cooperative activity of neural networks occurring in a volume of tissue around the tip of the
electrode.
Detailed Description
Biophysical Origin
It is thought that the main contribution to the local field potential (LFP) derives from synchronous
activation of neurons in the surrounding cortex. The LFP represents the sum over typically many
thousands of local electrical fields that are generated within individual neurons. When a neuron is
activated by the arrival of an excitatory postsynaptic potential on its dendrite, charged ions pass
through its membrane which normally acts as an electrical insulator (Eccles 1951). An inflow of
positive ions is termed current sink by convention, whereas an outflow of positive ions is termed
current source. Since inflowing positive ions must be compensated for by an appropriate outflow of
positive ions, there must be a current source corresponding to every sink. While current sinks reflect
membrane depolarization and neuronal excitation, current sources can, for example, result from
hyperpolarization or the action of ionic pumps of the neuron that restore the membrane potential to
its equilibrium value. The spatial separation between sources and sinks during neural activation
gives rise to an electrical dipole within each neuron, whose magnitude depends on the transmembrane ionic flow. The geometry of neural dendrites determines whether a measurable LFP signal is
generated from the dipoles within individual neurons. If all neurons had symmetric “closed field”
dendritic geometry or exhibited randomly oriented dendritic trees, then individual electrical dipoles
would tend to cancel each other and no LFP signal could be measured. However, pyramidal neurons
of the cortex exhibit an “open field geometry,” such that their apical dendrites are all aligned parallel
to each other and perpendicular to the cortical layer structure. This causes an alignment of the
electrical dipoles in these pyramidal cells when these are synchronously activated, which permits
their summation and in turn generates a robust LFP signal.
In addition to synaptic contributions described above, there are a number of additional factors that
can influence the LFP. For example, intrinsic properties of the membrane may exhibit resonance
such that depolarization or activation of the neuron results in a self-sustaining voltage oscillation
(Silva et al. 1991). Other neuronal events have also been shown to contribute to the LFP, including
direct electrical communication between neurons via gap junctions (Traub et al. 2004), interactions
between neurons and glial cells (Poskanzer and Yuste 2011), and potentially also dendritic calcium
spikes. Unlike the action potential, which has a clear functional role as transmitting information from
*Email: [email protected]
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Encyclopedia of Computational Neuroscience
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one neuron to another, the LFP is not thought to have a direct role in information processing or
transmission. Because of this, it has been sometimes considered to be a mere epiphenomenon.
However, in recent years interest in the LFP has accelerated, and it is now widely considered to be
a signal that provides useful information about the local processing that occurs in the cortical volume
near the electrode tip. In part, this resurgent interest stems from studies that have linked hemodynamic signals, including the functional magnetic resonance imaging (fMRI) signal that is extensively used to study human cognition to aspects of the LFP (Logothetis 2003; Niessing et al. 2005).
An interesting and hitherto largely unexplored issue is whether the LFP itself exerts any ephaptic
influence on neural processing, such that the extracellular voltage fluctuations of the LFP might be
sensed by local neurons and in turn influence, for example, their excitability. This remains an open
issue, but studies using transcranial magnetic stimulation have indeed shown that the electrical
gradients of the LFP are within a range that can affect action potential generation and cognitive
behavioral performance (Marshall et al 2006; Ozen et al 2010).
Characteristics
The LFP often exhibits oscillations, and these are traditionally classified into separate frequency
bands on the basis of characteristic behavioral states associated with each band. These bands,
together with the approximate frequency range, are delta (0–4 Hz), theta (4–8 Hz), alpha
(8–12 Hz), beta (12–20 Hz), and gamma (20–80 Hz). These bands are also used for analysis of
the electroencephalographic (EEG) signal, and the LFP is in fact closely related to the EEG but has
superior signal-to-noise ratio and spatial specificity, since it is not subject to the filtering of electrical
signals by the skull. The LFP, in similarity to all neurophysiological brain signals, is stochastic, and
each repetition of a particular experimental condition yields a distinct time series. During a typical
neurophysiological study, about ten repetitions are conducted per experimental condition. Averaging of all repetitions for a given condition yields the visual evoked potential (VEP), which generally
consists of a number of positive and negative deflections in a time range between about 30 and
400 ms after the presentation of a visual stimulus. The time of the first observable VEP deflection is
called visual response latency and represents the time of arrival of visual signals from the eye via the
visual pathway. Subsequent VEP deflections are related to local processing of the visual signals
within the neural circuits of the visual cortex. To further characterize the VEP, the approach of
current source density (CSD) estimation has been introduced (Mitzdorf 1985) and is commonly
applied in the analysis of VEP signals. CSD estimation requires VEP signals to be recorded from
multiple layers of the cortex, which can be achieved by successively advancing a recording electrode
through the layers or by employing a multi-contact depth electrode capable of simultaneous
recording of LFP signals from the six layers of the cortex. The CSD of a small volume element of
the cortex can be written as the divergence of the current flow from that surface element under the
assumption of an ohmic medium:
CSDðx; y; zÞ ¼ ∇ðs∇Fðx; y; zÞÞ,
where s is the conductivity tensor and F(x,y,z) is the field potential.
Under the assumptions of dendrites being elongated in the vertical (z) direction and therefore
current flowing mainly along this dimension, as well as homogenous conductivity medium, this
reduces to
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Encyclopedia of Computational Neuroscience
DOI 10.1007/978-1-4614-7320-6_722-1
# Springer Science+Business Media New York 2014
CSD ¼ sz
@2 F
@2z
Note that homogeneity of conductivity is only approximately true, since the cerebrospinal fluid
(CSF) surrounding the brain exhibits about five times higher conductivity than cortical gray matter.
This of course means that the above solution is distorted particularly if large reservoirs of CSF are
nearby, as is the case in brain structures close to the ventricles.
Using a three-point approximation of the second derivative, this results in the following equation
for the CSD:
CSDðz; t Þ @2 Fðz; t Þ Fðz þ h, tÞ 2Fðz; t Þ þ Fðz h, tÞ
@2z
h2
Thus, the CSD value at cortical depth z and time t can be written as a sum of the experimentally
determined VEP value F at that depth z and at cortical depths above (z h) and below (z + h).
Under the assumptions described above, the CSD provides information about in which layer of the
cortex outward positive ion flow or sources occurs, as well as about the laminar location of
extracellular sinks.
The VEP and CSD estimates consider only those portions of the LFP that are time-locked to the
onset of a visual stimulus, whereas LFP deflections whose timing varies from one repetition to the
next are lost in the averaging process. To faithfully estimate these components, the LFP is first bandpass filtered in an appropriate frequency band, and then the estimates of the power in this band on all
repetitions of a particular condition are averaged. LFPs are generally measured against a ground
position that is far away from the visual cortex, for example, a metal skull screw over the frontal
cortex, and are typically in the range of about 100 mV.
Primary Visual Cortex (V1) Visual Evoked Potential (VEP)
The primary visual cortex is the cortical area that receives visual information from the retina via the
lateral geniculate nucleus of the thalamus, with most projections arriving in the thalamocortical
recipient layer 4 and some also in layer 6. From layer 4, which is often referred to as the granular
layer due to the presence of granular cells in this layer, visual signals are sent to the supragranular
layers 2 and 3 where they are processed further by cortical circuits (Douglas and Martin 2007).
These supragranular layers are the main site of cortico-cortical information exchange and are thus
involved both in sending and reception of signals from higher-order cortical areas. In the case of
primary visual cortex, strong reciprocal projections exist, for example, with extrastriate visual
cortical areas such as V2 or V4. The highly recurrent nature of cortical function generally complicates the interpretation of VEP signals, since these are influenced not just by bottom-up inputs from
the ascending visual pathways but also by recurrent feedback from higher visual areas. The earliest
VEP deflections are however thought to be determined by the ascending inputs. For example, in
macaque monkey V1, the VEP displays a number of features that are related to these anatomical
structural characteristics (Schroeder et al. 1998). Accordingly, strongest responses, in terms of VEP
deflections and CSD values, occur in thalamo-recipient layer 4 of cortex, and both layer 4 and layers
2/3 exhibit an initial current sinks consistent with neural activation by the feed-forward pathway. An
interesting aspect of the V1 VEP is a prominent reversal of polarity in the early prominent VEP
deflection occurring around 50 ms between layer 4 and layers 5/6 that can be used as a marker to
estimate the border between granular and infragranular recording sites during neurophysiological
experiments. Using the V1 VEP as a measure of response latency in terms of the earliest detectable
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change of the VEP signal yields values of about 30 ms, considerably larger than corresponding
values of about 15 ms observed in the visual thalamus.
Higher Visual Cortex Visual Evoked Potential (VEP)
VEPs invariably accompany spiking activity in response to visually presented stimuli, not only in
V1 but also for other brain regions that are part of the visual system, including the extrastriate visual
cortex, the inferior temporal and parietal cortices, as well as certain areas of the prefrontal cortex
where visual information is used for guiding action. Since synaptic activity is the major determinant
of the LFP, this signal provides information about the source of these synaptic inputs. For a cortical
neuron, the input source comes mostly from the same cortical column by way of recurrent local
connectivity, as well as to a lesser degree from brain regions that send projections to the neuron
under study. Conversely, local action potential activity is related mainly to local recurrent processing
but also carries the information that is sent from the region under study to distant targets. Following
this logic, comparison of timing and task-related information present in LFP and spiking activity can
be used to infer the location of neural computations. For example, if task-related signals are present
in spiking activity before they are observable in the LFP, this strongly argues for a local computation
of this information in the brain region under study (Monosov et al. 2008; Nielsen et al. 2006).
Visual Cortex LFP: Spatial Specificity
A pertinent question related to LFP signals is their spatial specificity, reflecting the degree to which
they represent local activation within a region of cortex rather than mirroring activation that actually
occurs at a distant site. Electrical signals observed locally may thus be due to transmitted potentials
from distant sources, in a process that is termed volume conduction. The degree to which LFP
signals in the visual cortex are of local origin is a subject of current debate. On the one hand, various
evidence related to EEG recordings suggests that volume conduction can occur, and electrical
signals can be picked up in scalp recordings from sources that are known to be far away from the
recording location (Cobb and Sears 1960; Jewett and Williston 1971). Similarly, LFP recordings in
the cortex have also shown that signals generated in one area of the cortex can spread to regions
a centimeter or more away. For example, LFP signals related to auditory stimuli were greatest in
amplitude within the primary auditory cortex itself, and the amplitude declined approximately
linearly with increasing distance such that 12 mm away a signal of about 5 % of the peak magnitude
could be picked up (Kajikawa and Schroeder 2011). Notably, the LFP spread in this study was
measured in the z-axis, parallel to the apical dendrites and perpendicular to the cortical layer
structure. These results were obtained in the auditory cortex, but it is highly likely that similar
volume conduction occurs also in the visual cortex but is probably more difficult to observe there
due to the close proximity of multiple visual areas in the occipital lobe of the brain that each generate
VEPs in response to visual input. Other studies have estimated LFP spread to be up to two orders of
magnitude lower, with values between 250 and 600 mm. In one study, a high-resolution map of
orientation selectivity of a V1 patch was obtained (Katzner et al. 2009) using voltage-sensitive
dye-based optical imaging, by presenting a visual-oriented grating stimuli large enough to activate
the entire cortex being imaged in pseudorandom order. The resulting map was then used to predict
LFP orientation selectivity at particular grid nodes of the map using different two-dimensional
Gaussian spread standard deviation values. When these predicted LFP orientation selectivity values
were compared to LFP measurements, the optimal fit occurred for a standard deviation of about
100 mm, which translates to over 95 % of the LFP being generated within a radius of 250 mm. In
another study, action potential activity, so-called multiunit activity (MUA), was recorded simultaneously with LFPs in the visual cortex during the presentation of a sparse noise visual stimulus,
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Encyclopedia of Computational Neuroscience
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where small spots of light on a rectangular grid are successively and individually illuminated in
a pseudorandom order (Xing et al. 2009). This allows the determination of the spatial spread of both
MUA and LFP activity, yielding an average value of 250 mm. Interestingly, spatial spread was lowest
for the thalamocortical input layer 4 (150 mm) and relatively highest in the supragranular layers 2/3
(280 mm). Notably, for both cited studies, the LFP spread of 250 mm was estimated in the horizontal
(x–y) direction, parallel to the cortical layer structure, which represents a difference compared to the
above spread estimate of 1 cm or larger that is based on measurements in the z-direction. Another
possible explanation is that LFP integration has been shown to be dependent on the contrast of the
stimulus, which is related to the strength of the sensory input. For low contrast stimuli of relatively
weak strength, LFPs tended to be correlated across relatively large distances of 1 mm or more,
whereas for high contrast stimuli they were more localized (Nauhaus et al. 2009). It is however
unclear whether these factors really are the source of the discrepancy in the literature regarding the
spatial selectivity of LFP signals. Other potential sources include differences in sensory stimulus
characteristics, details concerning data analysis procedures and experimental data acquisition such
as location of the ground or hardware filtering, as well as potential capacitive cross-talk between
channels on multichannel recording probes.
Visual Cortex LFP Oscillations: Locking to External Stimulus
The visual cortical LFP, similar to spiking activity, reflects temporal characteristics of the visual
input up to frequencies approaching 90 Hz. Visual displays with deterministic timing characteristics,
such as cathode ray tube and some LED displays but not most TFT monitors, can be operated at
different refresh rates. For refresh rates up to about 90 Hz, visual LFP signals are highly entrained to
this refresh rate and display oscillatory power in the corresponding frequency band during periodic
visual stimulation (Veit et al. 2011; Williams et al. 2004). The appearance of the corresponding
peaks at the refresh rate and higher harmonics in the LFP spectral analysis is thus not due to intrinsic
cortical mechanisms but is driven by the external stimulus and must be interpreted as such. These
oscillations are visible in the average VEP only if timing is monitored with millisecond precision
which generally requires additional verification using a light-sensitive diode. For refresh rates above
90 Hz, the neural response is no longer strongly phase locked to the visual display refresh and
approximates the response to a real-world object that emits continuous rather than pulsed light.
Visual Cortex LFP Oscillations: Gamma Band
In addition to these oscillations that result from the visual display procedures, the visual LFP also
exhibits various oscillations in different frequency bands that are generated intrinsically by the brain
and can be linked more or less closely to perceptual or cognitive phenomena. Oscillations in the
gamma (20–100 Hz) range occur in the visual cortex during sensory stimulation, as is demonstrated
either by enhanced broadband gamma activity or by the appearance of specific spectral peaks in the
gamma range when one compares periods of visual activation to spontaneous activity (Eckhorn
et al. 1988; Gray et al. 1989). Of the different LFP frequency bands, the gamma band is most closely
linked to locally generated action potentials. Elevated spiking is thus often accompanied by the
appearance of gamma oscillations in the LFP. Several studies have provided evidence linking
gamma oscillations to perceptual processes such as, for example, temporal expectation and perceptual grouping (Lima et al. 2010, 2011) which is essential for object vision. Gamma oscillations have
also been described in higher-order visual cortices (Grothe et al. 2012; Taylor et al. 2005), generally
with an involvement in similar cognitive and perceptual functions as have been associated with V1
gamma. In addition, the phase of the gamma cycle is related to the exact spike timing, such that
action potentials tend to appear preferentially during the downward slope and trough of the gamma
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cycle, with the precise phase relationship varying from neuron to neuron. It has been suggested that
this phase locking of spike timing to the gamma cycle may provide a mechanism for optimizing
information transmission from primary visual cortex to high visual centers, because spikes arriving
simultaneously at postsynaptic targets will generate summating excitatory postsynaptic potentials
and thus increase the probability of activating the postsynaptic target neuron compared to a case
where inputs arrive uniformly distributed on a gamma cycle. Indeed, gamma oscillations in
extrastriate area V4 have been linked to attention, such that a patch of cortex that is processing an
attended visual stimulus exhibits enhanced gamma oscillations compared to a similar patch of cortex
that is processing an identical unattended stimulus (Fries et al. 2001). In the inferior temporal cortex,
gamma has also been described and implicated, for example, in perceptual awareness, albeit using
recordings from the surface of the cortex (Fisch et al. 2009). Gamma oscillations can thus be
considered to be an extremely widespread phenomenon in the visual system and may serve different
functions in a brain-region-dependent manner.
Gamma oscillations are thought to originate locally in the cortex through coordinated interaction
between excitation and inhibition (Buzsaki and Wang 2012) and generally require thalamocortical
activation by a sensory stimulus. Evidence for local generation comes, for example, from the
observation that gamma enhancements vary substantially as a function of cortical layer, with the
thalamocortical input layer exhibiting much weaker gamma compared to the supragranular layers
2/3 (Xing et al. 2012). The generation of visual cortical gamma oscillations can be influenced by
long-range projections from other brain areas that send axons to the visual cortex. One example is
the basal forebrain, which provides a source of cholinergic, GABAergic, as well as glutamatergic
inputs to the visual cortex. Thus, electrical stimulation of the basal forebrain generates gamma
oscillations in V1, and both spectral peaks as well as broadband enhancements of gamma activity
have been observed (Bhattacharyya et al. 2013). While local in nature, gamma oscillations are under
the control of nonvisual brain structures, which can thus modulate the transmission of information
about the visual environment to higher cortical centers.
While gamma oscillations and action potential spiking activity are closely related, this should not
be taken to mean that these signals provide identical information. When spiking and LFP activity are
directly compared in terms of their selectivity for orientation, one can observe that single-unit as well
as multiunit spiking activity at a given site is generally much better tuned for orientation than the
gamma-band LFP (Berens et al. 2008). In addition, the orientation preference of gamma activity at
a given recording site does not accurately predict the orientation preference of local action potential
spiking activity. This observation is generally consistent with the above findings related to the spatial
spread of the LFP, because adjacent neurons in the visual cortex can have drastically different
orientation.
Visual Cortex LFP Oscillations: Other Bands
There are relatively few reports of beta oscillations in the visual system, and to date no clear
functional role in visual processing appears to have emerged for LFP oscillations in this frequency
range. In contrast, beta LFP activity plays an important role in the motor cortex and has been linked
closely to spiking activity for motor control processes (Witham et al. 2007). Alpha oscillations are
classically associated with cortical idling and appear, for example, in the visual cortex when the eyes
are closed in the absence of a behavioral task. In contrast, the theta band appears to play an important
role in short-term memory and structuring cortical communication over long distances. For example,
intermediate extrastriate visual cortical area V4 exhibits characteristic oscillations in the theta
frequency band that are linked to short-term memory and thus appear during behavioral tasks that
include a delay period during which a previously presented visual stimulus needs to be maintained in
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memory (Lee et al. 2005; Raghavachari et al. 2006). The phase of the theta – like
gamma – oscillations is related to action potential timing, such that theta can structure information
transfer with higher-order visual cortices. Indeed, simultaneous recordings have confirmed that
during short-term memory, there is in fact enhanced coupling between visual and prefrontal cortices,
suggesting an important role of theta oscillations in maintenance of short-term memories (Liebe
et al. 2012).
References
Berens P, Keliris GA, Ecker AS, Logothetis NK, Tolias AS (2008) Comparing the feature selectivity
of the gamma-band of the local field potential and the underlying spiking activity in primate visual
cortex. Front Syst Neurosci 2:2
Bhattacharyya A, Veit J, Kretz R, Bondar I, Rainer G (2013) Basal forebrain activation controls
contrast sensitivity in primary visual cortex. BMC Neurosci 14:55
Buzsaki G, Wang XJ (2012) Mechanisms of gamma oscillations. Annu Rev Neurosci 35:203–225
Cobb W, Sears TA (1960) A study of the transmission of potentials after hemispherectomy.
Electroencephalogr Clin Neurophysiol 12:371–383
Douglas RJ, Martin KA (2007) Recurrent neuronal circuits in the neocortex. Curr Biol 17:
R496–R500
Eccles JC (1951) Interpretation of action potentials evoked in the cerebral cortex. Electroencephalogr Clin Neurophysiol 3:449–464
Eckhorn R, Bauer R, Jordan W, Brosch M, Kruse W et al (1988) Coherent oscillations: a mechanism
of feature linking in the visual cortex? Multiple electrode and correlation analyses in the cat. Biol
Cybern 60:121–130
Fisch L, Privman E, Ramot M, Harel M, Nir Y et al (2009) Neural “ignition”: enhanced activation
linked to perceptual awareness in human ventral stream visual cortex. Neuron 64:562–574
Fries P, Reynolds JH, Rorie AE, Desimone R (2001) Modulation of oscillatory neuronal synchronization by selective visual attention. Science 291:1560–1563
Gray CM, Konig P, Engel AK, Singer W (1989) Oscillatory responses in cat visual cortex exhibit
inter-columnar synchronization which reflects global stimulus properties. Nature 338:334–337
Grothe I, Neitzel SD, Mandon S, Kreiter AK (2012) Switching neuronal inputs by differential
modulations of gamma-band phase-coherence. J Neurosci 32:16172–16180
Jewett DL, Williston JS (1971) Auditory-evoked far fields averaged from the scalp of humans. Brain
94:681–696
Kajikawa Y, Schroeder CE (2011) How local is the local field potential? Neuron 72:847–858
Katzner S, Nauhaus I, Benucci A, Bonin V, Ringach DL, Carandini M (2009) Local origin of field
potentials in visual cortex. Neuron 61:35–41
Lee H, Simpson GV, Logothetis NK, Rainer G (2005) Phase locking of single neuron activity to
theta oscillations during working memory in monkey extrastriate visual cortex. Neuron
45:147–156
Liebe S, Hoerzer GM, Logothetis NK, Rainer G (2012) Theta coupling between V4 and prefrontal
cortex predicts visual short-term memory performance. Nat Neurosci 15:456–462, S1–S2
Lima B, Singer W, Chen NH, Neuenschwander S (2010) Synchronization dynamics in response to
plaid stimuli in monkey V1. Cereb Cortex 20:1556–1573
Lima B, Singer W, Neuenschwander S (2011) Gamma responses correlate with temporal expectation in monkey primary visual cortex. J Neurosci 31:15919–15931
Page 7 of 9
Encyclopedia of Computational Neuroscience
DOI 10.1007/978-1-4614-7320-6_722-1
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Logothetis NK (2003) The underpinnings of the BOLD functional magnetic resonance imaging
signal. J Neurosci 23:3963–3971
Marshall L, Helgadottir H, Molle M, Born J (2006) Boosting slow oscillations during sleep
potentiates memory. Nature 444:610–613
Mitzdorf U (1985) Current source-density method and application in cat cerebral cortex: investigation of evoked potentials and EEG phenomena. Physiol Rev 65:37–100
Monosov IE, Trageser JC, Thompson KG (2008) Measurements of simultaneously recorded spiking
activity and local field potentials suggest that spatial selection emerges in the frontal eye field.
Neuron 57:614–625
Nauhaus I, Busse L, Carandini M, Ringach DL (2009) Stimulus contrast modulates functional
connectivity in visual cortex. Nat Neurosci 12:70–76
Nielsen KJ, Logothetis NK, Rainer G (2006) Dissociation between local field potentials and spiking
activity in macaque inferior temporal cortex reveals diagnosticity-based encoding of complex
objects. J Neurosci 26:9639–9645
Niessing J, Ebisch B, Schmidt KE, Niessing M, Singer W, Galuske RA (2005) Hemodynamic
signals correlate tightly with synchronized gamma oscillations. Science 309:948–951
Ozen S, Sirota A, Belluscio MA, Anastassiou CA, Stark E et al (2010) Transcranial electric
stimulation entrains cortical neuronal populations in rats. J Neurosci 30:11476–11485
Poskanzer KE, Yuste R (2011) Astrocytic regulation of cortical UP states. Proc Natl Acad Sci U S
A 108:18453–18458
Raghavachari S, Lisman JE, Tully M, Madsen JR, Bromfield EB, Kahana MJ (2006) Theta
oscillations in human cortex during a working-memory task: evidence for local generators.
J Neurophysiol 95:1630–1638
Schroeder CE, Mehta AD, Givre SJ (1998) A spatiotemporal profile of visual system activation
revealed by current source density analysis in the awake macaque. Cereb Cortex 8:575–592
Silva LR, Amitai Y, Connors BW (1991) Intrinsic oscillations of neocortex generated by layer
5 pyramidal neurons. Science 251:432–435
Taylor K, Mandon S, Freiwald WA, Kreiter AK (2005) Coherent oscillatory activity in monkey area
v4 predicts successful allocation of attention. Cereb Cortex 15:1424–1437
Traub RD, Bibbig A, LeBeau FE, Buhl EH, Whittington MA (2004) Cellular mechanisms of
neuronal population oscillations in the hippocampus in vitro. Annu Rev Neurosci 27:247–278
Veit J, Bhattacharyya A, Kretz R, Rainer G (2011) Neural response dynamics of spiking and local
field potential activity depend on CRT monitor refresh rate in the tree shrew primary visual cortex.
J Neurophysiol 106:2303–2313
Williams PE, Mechler F, Gordon J, Shapley R, Hawken MJ (2004) Entrainment to video displays in
primary visual cortex of macaque and humans. J Neurosci 24:8278–8288
Witham CL, Wang M, Baker SN (2007) Cells in somatosensory areas show synchrony with beta
oscillations in monkey motor cortex. Eur J Neurosci 26:2677–2686
Xing D, Yeh CI, Shapley RM (2009) Spatial spread of the local field potential and its laminar
variation in visual cortex. J Neurosci 29:11540–11549
Xing D, Yeh CI, Burns S, Shapley RM (2012) Laminar analysis of visually evoked activity in the
primary visual cortex. Proc Natl Acad Sci U S A 109:13871–13876
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