On average the Vm was most hyperpolarized during quiet waking (Q; mean ± standard deviation [SD] −60.5 ± 7.5 mV; median −60.7 mV; range −73.8 to −44.7 mV), depolarized during free whisking (W; mean ± SD

−58.4 ± Transmembrane Transporters activator 8.3 mV; median −57.7 mV; range −73.7 to −36.9 mV), and was significantly more depolarized during active touch (T; mean ± SD −55.4 ± 7.7 mV; median −57.2 mV; range −70.6 to −37.0 mV) (Figure 2A and Table S2). Compared to free whisking, during an active touch sequence the Vm of layer 2/3 neurons on average depolarized by 3.0 ± 2.9 mV (median 3.0 mV; range −2.2 to +6.9 mV). Vm variance was significantly lower during free whisking than during quiet wakefulness or active touch (Figure 2B). The low Vm variance during free whisking (when there is no incoming

touch information) may help provide a reduced noise background enhancing the detection of sensory-evoked signals during active touch. The mean action potential firing rates (Figure 2C and Table S2) indicate that spike rates increased during active touch (1.7 ± 5.0 Hz; median 0.2 Hz; range 0.0 to 20.8 Hz) as compared to quiet wakefulness (0.2 ± 0.2 Hz; median 0.1 Hz; range 0.0 to 0.5 Hz) and free whisking (0.3 ± 0.9 Hz; median 0.04 Hz; range 0.0 to 3.9 Hz). For most neurons the firing rate of layer 2/3 pyramidal cells remained low Bafilomycin A1 datasheet in all conditions, in good agreement with recent awake extracellular recordings of identified layer 2/3 pyramidal cells (de Kock and Sakmann, 2009 and Sakata and Harris, 2009)

and awake two-photon calcium imaging in layer 2/3 (Greenberg et al., 2008 and O’Connor et al., 2010). Low-frequency Vm dynamics dominated the Fast Fourier Transform (FFT) during all behavioral periods, with a near linear decrease at higher frequencies when plotted on log-log scale axes (Figure 2D) similar to observations from EEG recordings (Buzsáki and Draguhn, 2004). Slow Vm fluctuations (1–5 Hz) were significantly more prominent during quiet wakefulness than during free whisking (Crochet and Petersen, 2006, Poulet and Petersen, 2008 and Gentet et al., 2010) or active touch (Figures 2D and 2E). High-frequency Vm changes (30 to 100 Hz) were significantly increased during active touch compared to quiet wakefulness or free whisking (Figures 2D and 2E). These higher-frequency PDK4 Vm dynamics are likely to be driven by the rapid and large-amplitude depolarizations evoked by individual touch responses. Analysis on the millisecond timescale revealed further important correlations between the C2 whisker-related behavior and neuronal Vm. We averaged the Vm across many individual whisking cycles aligned to the peak of protraction during free whisking and found small-amplitude phase-locked Vm fluctuations, which weakly influenced action potential firing (Figure 3A and Figure S1) (Fee et al., 1997, Crochet and Petersen, 2006, Poulet and Petersen, 2008, Curtis and Kleinfeld, 2009 and de Kock and Sakmann, 2009).

This use of the μECoG method is an innovative and potentially important approach, which raises a number of implications as well as underscoring important open questions. Methodologically, the paper showcases the strengths of μECoG in providing a wide-range

view of functional organization in a large cortical network including core auditory cortices, A1 and the rostral area (R), as well as more anterior regions. As pointed out by the authors, the view they provide is on a scale comparable to that provided by previous fMRI studies in both human and nonhuman primates. Critically, μECoG yields this view with high-temporal resolution, http://www.selleckchem.com/products/nutlin-3a.html utilizing amplitude fluctuations in a specific range of the neuronal activity spectrum, high gamma (60–250 Hz). The amplitude of neuronal activity in the high-gamma frequency range provides a relatively uncomplicated index of massed firing click here in neuronal ensembles underlying the electrodes, as well as a relatively direct linkage to studies using this exact measurement for studying brain activity in

surgical epilepsy patients (Canolty and Knight, 2010). Fukushima et al. (2012) were able to use μECoG to detail a relationship of spontaneous activity to functional architecture. Specifically, they verified that the high-gamma fraction of the stimulus-evoked response can be used to outline tonotopic maps within core and more rostral areas and the mirror-symmetric reversals at area boundaries as demonstrated by a host of earlier studies. They then cross-registered tonotopic maps with maps derived from spontaneous activity Org 27569 using the same high-gamma measure. This is a large step forward, as it begins to bridge the gap between a reasonably well-evolved understanding of how auditory cortex responds to stimulus input with the deeper issues surrounding ongoing activity and all the neuronal activities that compete and/or collaborate in this period, as discussed above. The fact that the rules governing ongoing neuronal activity are—at least to some extent—determined by the structural and functional organization

of a given brain region highlights the need for a better understanding of the underlying neuronal circuitry. Fukushima et al. (2012) relate their findings to several current questions in systems neuroscience, two of which we highlight here. One key issue that they discuss is the impact of ongoing activity on stimulus processing; a variety of findings indicate that ongoing fluctuations of activity have a large impact on the parameters of stimulus-evoked responses, stimulus detection, and the efficiency of behavioral responding. To be clear, these “activity fluctuations” usually reflect synchronous, rhythmic excitability variations (oscillations) in interconnected ensembles of local neurons (Jensen et al., 2012 and Schroeder and Lakatos, 2009). We will elaborate on this theme below.

Furthermore, pre-culture cells from the second and third products demonstrated a progressively increased antigen-specific T cell proliferation and memory response (interferon gamma enzyme-linked immunospot [IFNγ ELISPOT]) [17]. This pattern of activation is consistent with the concept that the first infusion primes the immune system and subsequent GDC-941 infusions boost the response. Of note, CD54 up-regulation and

enhanced T cell-associated cytokine responses were not observed when aliquots of pre-culture cells were incubated with GM-CSF in the absence of PA2024 [18], indicating the GM-CSF is not solely responsible for the observed response following incubation with PA2024. Longer-term measures of immune function obtained in a subset of subjects in the Phase

3 IMPACT trial (6, 14, and 26 weeks after the start of treatment) demonstrated that sipuleucel-T Docetaxel chemical structure generates a robust immune response. A positive antibody response at any post-baseline time point (antibody titer >400 by ELISA) to PA2024 was observed in 66.2% of subjects treated with sipuleucel-T (vs. 2.9% of control patients), and a positive antibody response to PAP was observed in 28.5% of subjects treated with sipuleucel-T (vs. 1.4% of control subjects) [7]. Overall survival was significantly correlated with a positive antibody response to PA2024 (P < 0.001), and the data suggested an association between overall survival and a positive Cell press antibody response to PAP (P = 0.08; [7]). Significant increases in T cell proliferative responses and antigen-specific (PA2024) (IFNγ ELISPOT) responses were observed 2 weeks after the final sipuleucel-T infusion [7] and [13]. Thus, both product parameters and longer-term measures demonstrated that sipuleucel-T treatment produces a robust immune response that includes a progressive and persistent increase

in antigen-specific cellular and humoral immune responses. Treatment with sipuleucel-T improves overall survival in subjects with asymptomatic or minimally symptomatic mCRPC; adverse events are generally mild-to-moderate and of short duration. The pattern of activation with sipuleucel-T is consistent with a mechanism of priming by the first infusion and boosting by the second and third infusions, which results in long-lasting antigen-specific cellular and humoral immune responses to the recombinant fusion protein (PA2024) and, to a lesser extent, the self-antigen PAP. Evidence from other active immunotherapies suggests that the initial immune response to the targeted antigen may subsequently evolve to include additional tumor antigens [19], [20], [21] and [22]. In sipuleucel-T trials, both APC activation and humoral responses have been shown to correlate with overall survival [7] and [14]. It is believed that the treatment-induced immune response prolongs survival by slowing the tumor growth rate in patients with mCRPC [19] and [21].

A previous study demonstrated the sensitivity of these connections

to alterations of the visual input (Levin et al., 2010). Visual pathways white matter analysis was performed in two steps: identifying the fiber bundles and evaluating their properties. Using a new probabilistic algorithm (Sherbondy et al., 2008a, 2008b), we could clearly identify the optic tract and the optic radiation composing the input fibers to the visual cortex as well as the output fibers from each hemisphere, which cross at the corpus callosum ( Figure 3A). Following fiber identification, we studied white matter integrity using directional diffusivity measures. By measuring diffusivity in multiple directions we obtained estimates of the principal diffusion direction (longitudinal) as well as the perpendicular direction (radial). The ratio of these two EGFR inhibitor values is similar to the fractional anisotropy (FA). We found that the properties of the achiasmic subject’s CHIR-99021 solubility dmso (AC2) visual pathways were within the range of 30 normally sighted control subjects

( Figure 3B). Finally, the cross-sectional area of the occipital fibers that connect right and left visual cortex was assessed ( Figure 3C). In normal sighted controls, there is a correlation between the cross-sectional area of this tract and the cross-sectional area of the entire callosum. The cross-sectional area of the achiasmic subject’s occipital callosal fiber group was smaller than that of controls, yet the overall size of his corpus callosum was small too ( Figure 3D). These results highlight that the white matter integrity at the resolution of our neuroimaging measurements is comparable to control subjects. Our results highlight both differences and similarities of the achiasmic compared to the typical human visual system. In achiasma, we found a highly atypical organization of the visual cortex consisting of overlapping visual hemifield maps with bilateral pRFs. In contrast, pRF sizes were in the

normal range as were the properties of all major visual pathways, in particular the geniculate-cortical and occipital-callosal fibers. Moreover, normal pRF sizes across early visual cortex in conjunction with the persistence of bilateral pRFs imply relatively unaltered cortico-cortical connections (Harvey isothipendyl and Dumoulin, 2011). Our results can be explained by conservative developmental mechanisms in human achiasma that largely preserve the normal visual pathways beyond the LGN. Both retinotopic and pRF mapping demonstrated an overlay of orderly retinotopic maps from opposing hemifields in the visual cortex, such that each cortical location represents two separate visual field locations, namely one in each hemifield. This intermixed representation could result from individual neurons with bilateral receptive fields, but also from the interdigitation of two different neural populations representing the contra- and ipsilateral visual field at the current fMRI resolution.

, 2002) were bred with an Olig1-Cre line, in which

Cre recombinase is produced in the oligodendrocyte lineage ( Xin et al., 2005 and Ye et al., 2009) ( Figure 2A). We observed that all resulting mutant Sip1flox/flox;Olig1Cre+/− mice (referred to as Sip1cKO), but not their control littermates, developed generalized tremors, hindlimb paralysis, and seizures from postnatal week 2 ( Figure 2B, upper panel), although they were born at a normal Mendelian ratio. Sip1cKO mice exhibited the phenotypes reminiscent of myelin-deficient mice ( Nave, 1994) and died around postnatal week 3, in contrast to the normal lifespan of wild-type (WT) and Sip1 conditional heterozygous selleck products control (Sip1flox/+;Olig1Cre+/−) mice ( Figure 2C). The optic nerve, a well-characterized CNS white matter tract, from Sip1cKO mice was translucent compared to the control ( Figure 2B, lower panels), which is a sign of severe deficiency in myelin formation. To confirm the myelin-deficient phenotypes, we examined myelin gene expression in

Sip1cKO mice. In contrast to robust expression in control mice, expression of myelin genes such as Mbp (myelin basic protein) and Plp1 (proteolipid protein) is essentially undetectable in the forebrain, spinal cord, and cerebellum of mutant mice at P14 (Figures 2D and 2F). In light of our data demonstrating that expression of mature oligodendrocyte markers was absent in Sip1cKO mice, we further examined myelin sheath assembly in the CNS of these mutants by electron microscopy. In contrast to a large number of myelinated axons GSI-IX ic50 that are observed in control mice at P14 (Figures 2G and 2H, upper panels), they were completely absent in the optic nerve

and spinal cord of Sip1cKO mutants (Figures 2G and 2H, lower panels), indicating that myelin ensheathment has not begun in these animals. These results suggest that Sip1 is required for myelinogenesis in the CNS. Despite the deficiency in myelin gene expression, the OPC marker PDGFRα was detected in the brain much and the spinal cord in the mutant mice (Figures 3A and 3B). The number of OPCs and their proliferation rate (percentage of Ki67+ proliferating OPCs) in Sip1 mutants were comparable to control mice ( Figures 3C and 3D). We did not detect any significant cell death in the brain and spinal cord of Sip1cKO mice at P7 and P14 based on TUNEL assay and staining for the active form of caspase-3 (n = 3; data not shown). In addition, oligodendrocyte lineage-specific Sip1 inactivation did not lead to obvious alterations of astrocytes and neurons marked by GFAP and NeuN, respectively, in the brain of Sip1cKO mice ( Figure S2). Our data indicate that OPCs are able to form in the CNS of Sip1cKO mice. To investigate whether the differentiation capacity of OPCs in the absence of Sip1 in vitro is blocked, we carried out Cre-mediated Sip1 excision in cultures of purified OPCs.

3 mM Na-GTP, pH 7.35 with KOH). Liquid junction potential correction was performed off-line. Paired-pulse ratio (PPR) experiments were carried out to estimate release probability. The peak amplitude of excitatory postsynaptic currents (EPSCs) evoked by two identical electrical stimuli separated by 100 ms was measured. PPR was calculated as the

ratio of the peak amplitude of EPSC2/EPSC1. Stimulus artifacts from the paired-pulse traces have been deleted. To measure evoked EPSCs, we performed whole-cell voltage clamp recording (at −60 mV) in presence of 100 μM picrotoxin (added to aCSF). A stimulating electrode was placed near the VMH (300–500 μm from the recording electrode) as mentioned above. The internal recording solution contained (in mM): CsCH3SO3 125; CsCl 10; NaCl 5; MgCl2 2; EGTA 1; HEPES 10; (Mg)ATP 5; (Na)GTP 0.3; 10 lidocaine N-ethyl bromide (QX-314) (pH 7.35 with NaOH). Cre-dependent selleck adeno-associated viral vector AAV-DIO-mCherry was constructed by modifying the AAV-DIO-ChR2(H134R)-mCherry vector kindly provided by Dr. Karl Deisseroth (http://www.stanford.edu/group/dlab/optogenetics/sequence_info.html).

Briefly, Asc1 and Nhe1 restriction sites were used to replace ChR2-mCherry fusion with mCherry alone (detailed methods in A.S. and B.L.S., unpublished data). The AAV-DIO-mCherry vector was then packaged into serotype 8 through the University of North Carolina Vector Core. AAV-mCherry virus (100 nl) at 1.5 × 1012 viral mol/ml was then stereotaxically injected into the arcuate of AgRP-ires-Cre or POMC-Cre related animals at 4 weeks of age (as described above). 3 weeks later, animals were perfused

and the brains were MS 275 coronally sectioned at 30 μm thickness. Rolziracetam Immunostaining against mCherry with rabbit anti-DsRed primary antibody (Clonetech; 1:2,500) and with Alex-594-anti-rabbit secondary antibody (Invitrogen) was then performed as previously described (Kong et al., 2010). Serial images of proximal dendritic structure labeled with mCherry immunoreactivity were taken under Zeiss confocal microscope (oil objective, 63×). In the coronal sections, primary dendrites or major secondary dendrites within a distance of ∼150 μm from the soma that they originated from were imaged. These images were stacked using ImageJ software (1.44i version) for further analysis. The length of dendrites and diameter of spines were calculated according to the scale bars. Spine density was calculated by dividing total spine number to the length of the targeted dendrites. Each day, for 12 days prior to sacrifice, the mice were acclimated to handling. For immunohistochemical detection of c-Fos and GFP in Npy-GFP mice and in Agrp-ires-Cre, Grin1lox/lox, Npy-GFP mice, animals were sacrificed at 10 AM in either the ad lib fed state or after 24 hr of fasting (food removed at 10 AM on the previous day). The mice were perfused and brains were sectioned as described above. Assessment of c-Fos induction was performed using a previously developed method ( Fuller et al.

For example, a red vertical

stimulus is incongruent, requiring a rightward PR-171 price saccade under the color rule and a leftward saccade under the orientation rule. In contrast, a red horizontal stimulus requires a rightward saccade for both rules. The majority (70%) of trials were incongruent, ensuring the animal always followed the rule. After the animal made the correct saccade, a juice reward was delivered via a juice tube. There was an intertrial interval of approximately 100 ms before the next trial began. Although the rule was cued on each trial, the rule in effect was blocked into groups of trials. Each block consisted of a minimum of 20 trials of the same rule. After 20 trials, the rule switched randomly—with Selleckchem Dactolisib a 5% or 10% chance of switching rules on each trial for monkey ISA and CC, respectively. The average block consisted of 39 trials of the same rule for ISA and 30 for CC. A generalized linear model

(GLM) was used to quantify the effect of multiple task-related covariates on the animals’ behavioral reaction time. A gamma distribution was used in the model, as it is ideal for fitting strictly positive data with a constant coefficient of variation, such as reaction times (McCullagh and Nelder, 1989). The link function, which defines a nonlinear transformation between the linear predictors and the mean of the observations, was chosen to be the log function to enforce the requirement that reaction times be strictly positive. A complete model was developed, fitting the reaction time secondly with all task-related covariates: the rule (color/orientation), preparatory period, congruency of stimulus-response association across rules, monkeys, time in session, and whether it was a switch trial (see Supplemental Information for details). A bias-corrected percent explained variance statistic (ωPEV) was used to evaluate neural selectivity.

ωPEV determines the portion of variance of a neuron’s firing rate explained by a particular task variable (e.g., the current rule) but is analytically corrected for upward bias in percent explained variance with limited observations. Significance was determined by a permutation procedure (see Supplemental Information for details). The LFP was transformed into the time-frequency domain using Morlet wavelets. Synchrony was estimated by computing the spectral coherence between pairs of electrodes. Significant differences in coherence between the two rules were determined with a permutation test. The null hypothesis is that no significant difference exists between rules, therefore a null distribution was generated by permuting color and orientation trials and recalculating the coherence (this process was repeated at least 100 times for each pair of electrodes).

, 2001; Morris et al., 2011; Sydow et al., 2011). Now, it appears that its role may be more dynamic, possibly participating Selleckchem RG-7204 in intracellular signal transduction, among other roles. Importantly, tau is highly susceptible to hyperphosphorylation and the formation of intracellular NFTs, both of which are hallmark neuropathologies that critically promote the damage observed in AD and TBI (McKee et al., 2009; Morris et al., 2011). ApoE4 has been shown to directly

alter microtubule structure and to stimulate tau hyperphosphorylation and NFT formation (Huang, 2010; Huang and Mucke, 2012). Exposure of neuronal cultures to exogenous apoE4-lipid complexes caused significant cytoskeletal disruption and impaired neurite outgrowth compared with apoE3-containing complexes (Bellosta et al., 1995; Nathan et al., 1994; Nathan et al., 1995). ApoE4-treated Neuro-2a cells also had fewer intracellular microtubules, as identified by immunocytochemical localization of β-tubulin and by electron microscopy (Nathan et al., 1995), and these effects were associated with

impaired neurite outgrowth in apoE4-treated cells. These results were replicated in apoE3- or apoE4-transfected Neuro-2a cells expressing nanogram quantities of apoE (Bellosta et al., 1995). Increased tau Trichostatin A research buy phosphorylation has been observed in transgenic mice expressing apoE4 in neurons, but not in those expressing apoE4 in astrocytes, suggesting a cellular source-dependent effect of apoE4 on tau phosphorylation (Harris et al., 2003; Tesseur et al., 2000), occurring in parallel with the generation

of apoE fragments in neurons whatever (Andrews-Zwilling et al., 2010; Harris et al., 2003). There is evidence that apoE4 stimulates tau phosphorylation by activating the extracellular signal-regulated protein kinase pathway in the hippocampus (Harris et al., 2004a), although other signaling pathways may also be involved. Intraneuronal phospho-tau inclusions are prominent in the hippocampus and form NFT-like structures composed of apoE4, phospho-tau, and neurofilaments; by electron microscopy, these inclusions are visualized as tightly packed, straight filaments that closely associate with mitochondria (Harris et al., 2003). In addition to such insights into the cell biological impacts of apoE fragments, transgenic animal experiments are uncovering how this neurotoxicity at the cellular level corresponds to neuronal function and behavior. The expression of truncated apoE4 in transgenic mice provides insights into how and where the fragments cause neurotoxicity. Transgenic mice expressing a variant of apoE4 that lacks the C-terminal 27 amino acids (apoE4[1–272]), driven by the Thy1.2 promoter, had significant hippocampal neurodegeneration and neuronal loss (Harris et al., 2003). However, not every fragment of apoE is toxic, because expression of apoE4(1–240)—the form lacking the lipid-binding region—was not found to trigger hippocampal neurodegeneration.

In addition, when subjects were grouped by strike type, individuals who used FFS and MFS landings (all but one of which was minimally shod) had significantly more plantarflexed ankles, flexed knees, and flexed hips at the moment of foot strike than individuals who use RFS landings. These kinematic differences were no longer evident click here by midstance. These data corroborate previous reports that barefoot and minimally shod runners tend to differ from habitually shod runners in a number of aspects of running form.19 The data also highlight the general kinematic similarity between forefoot

and midfoot striking, both of which differ from rearfoot striking in a number of respects, perhaps the most important being less overstride in which the ankle lands nearly below the knee at the moment of strike, providing more limb compliance at the ankle and knee. Previous studies have found that shod runners are more likely to FFS at higher speeds,47 and one study found an increased use of FFS at higher speeds among the Daasenech, a habitually barefoot population from northern Kenya that does not run very much, and which lives in a very sandy habitat.48 In contrast, this study found no effect of

speed on strike type. One explanation for this result could be that the range of speeds employed was not great (2.3–4.8 m/s), first with most runners choosing approximately selleck chemical 3.3–3.9 m/s. In addition, many of the runners already used FFS and MFS landings. It was not possible to test for the effect of sex on strike type, but there was no effect of age, body mass, preferred step frequency on strike type variation. Future research is therefore needed to understand when and why runners who are minimally shod or barefoot adopt different strike types. In this regard, additional variables to consider are how strong the runner is, especially in terms of the triceps surae and the foot muscles, the effects of distance and fatigue, and the influence of variations in the hardness,

roughness, or slipperiness of the substrate. Since minimal shoes such as huaraches presumably allow less proprioception than being barefoot but considerably more than standard running shoes (a hypothesis that merits careful testing), it is reasonable to hypothesize that substrate characteristics have less of an effect on strike type choice among minimally shod than barefoot runners, but that other factors related to the skill of running long distances such as overstride, cadence, and posture remain just as important, perhaps even more so when running very long distances. Although the focus of this study was on strike type, the results presented here also provide evidence for an effect of footwear on arch morphology and function.

If the synaptic input to the neurons in the vicinity of a recording electrode always had been uncorrelated, we could have reported the following simple rule of thumb: almost all of the LFP signal measured by an electrode comes from neurons within a lateral distance of about 200 μm. This estimate is in accordance with recent results by Katzner et al. (2009) and Xing et al. (2009). The independence of the spatial reach, i.e., the size of the region generating the LFP, from the morphology of the neurons in the population and the spatial distribution Trichostatin A of the synapses

may be at odds with common thinking on the origin of the LFP emphasizing the distinction between open-field (pyramidal) and closed-field (stellate) neurons ( Lorente de No, 1947 and Johnston and Wu, 1995), and this highlights the importance of a thorough quantitative investigation of the origin of LFP. The situation when the synaptic input to the neuronal population is correlated is, however, more in line with common thinking regarding the dominant contributions from pyramidal neurons, but only when the input is spatially asymmetric, i.e., solely onto either the basal or apical dendritic branches. In this case correlated http://www.selleckchem.com/Wnt.html synaptic inputs

were found to give correlated neuronal LFP sources and consequently an amplified LFP signal. With homogeneous inputs onto pyramidal neurons, this correlation transfer is observed to be very weak, resulting in very little such correlation amplification. For the stellate layer 4 neurons until with very symmetric dendritic branching, the LFP contributions from individual neurons were found to be essentially uncorrelated, independent of the level of synaptic input correlations. With spike-train correlations present in the synaptic input, as in our laminar network example in Figure 6, one might thus expect pyramidal neurons to give larger LFP contributions than the stellate neurons. Given the observed strong dependence on input correlations and spatial distribution

of the synaptic inputs, our model study thus suggests several possible explanations for the significant variation for the reach of the LFP seen in various experimental studies (Kreiman et al., 2006, Liu and Newsome, 2006, Berens et al., 2008a, Katzner et al., 2009 and Xing et al., 2009). As the level of synaptic input correlations depends on the state of cortical network, it follows that the LFP reach in general will not be a static fixed quantity, even for a particular fixed electrode in a particular experiment. We also find the population LFP to depend strongly on the depth position of the recording electrode. With the electrode placed above or below the dendrites of the generating population, as, e.g., for recordings done in L4, L5 or L6 with an active L3 population depicted in Figure 3, the reach is much larger than for recordings done in the soma layer (L2/3). However, the LFP amplitudes recorded in these lower layers are tiny in comparison.