We conclude that HR analysis using

Suunto’s software (MoveSense HRAnalyzer 2011a, RC1) needs further development for use in estimations of the daily TEE in free-living individuals. The authors have no conflicts to disclose. This work was funded by the Academy of Finland, the Finnish Ministry of Education, Suunto Oy, the Shanghai overseas distinguish professor award program 2011, the Shanghai Key Lab of mTOR cancer Human Performance (No. 11DZ2261100), and 2012 National Science and Technology Infrastructure Program (Grant No. 2012BAK21B00). “
“Obesity is a risk factor for several chronic diseases, including type 2 diabetes and cardiovascular disease.1 and 2 Lifestyle interventions, such as dietary weight loss and increasing physical activity (PA), are advocated for the treatment INCB018424 concentration of obesity and prevention of future chronic diseases.3 and 4 The mechanisms through which dietary weight loss and exercise training alter adipose tissue lipid metabolism and lower adiposity need to be investigated. Lipolysis is the process by which triglycerides stored in adipocytes are broken down and free fatty acids and glycerol are released. One of the important enzymes to regulate adipocyte lipolysis is hormone sensitive lipase (HSL).5

HSL and adipose triglyceride lipase (ATGL) work hierarchically to regulate complete lipolysis.6 Currently, HSL and ATGL have been considered to be the major regulators of lipolysis under catecholamine-stimulated and basal lipolysis, respectively.7 In the absence of adipose tissue HSL or ATGL, energy metabolism was altered next and exercise performance was impaired in mice.8 and 9 However, fasting, but not exercise, up-regulated ATGL expression in human adipose tissue,10 suggesting that exercise may be more effective in regulating HSL, but not ATGL in adipose tissue. The role of exercise training intensity on adipose tissue metabolism has been reported by several studies. In exercise-only studies, vigorous-intensity, but not moderate-intensity exercise,

tended to increase adipose lipolysis.11 and 12 However, it is unclear if this is due to an exercise training effect on adipose tissue HSL expression. In an animal study, exercise training increased adipose tissue HSL amount and activity.13 It is well known that an acute exercise session increases catecholamine levels and the release of catecholamines is directly related to exercise intensity.14 It is highly possible that acute and chronic exercise intensity also influences HSL, which is the key enzyme to regulate catecholamine-stimulate lipolysis. However, the effect of exercise training intensity on adipose tissue HSL has not been studied, especially in obese individuals during dietary weight loss. Identification of effective lifestyle interventions is needed for the treatment of obesity. Changes in adipose tissue metabolism by lifestyle interventions may be reflected in current or future changes in adiposity.

Thus, for a circuit consisting of N neurons, there may be of order N2 nonlinear synaptic interactions. This modeling challenge has traditionally been tackled by two highly disparate approaches. Conceptual models use strong simplifying assumptions on the forms of synaptic connectivity and neuronal responses to provide tractability in modeling complex neural circuits (Figure 1). Although such studies provide qualitative insight, the chosen assumptions limit the set of possible mechanisms explored and make close comparison

to experiment difficult. Alternatively, to make close contact with experiment, other studies have used brute-force explorations Screening Library chemical structure of the large parameter space defined by multiple intrinsic

and synaptic variables (Goldman et al., 2001, Prinz, 2007 and Prinz et al., 2004). These studies have successfully demonstrated how circuit function can be highly sensitive to changes in certain combinations of parameters but insensitive to changes in others. However, the combinatoric explosion of parameter combinations has limited such studies to exploration of approximately ten or fewer parameters at a time, a minute fraction of the total parameter space needed to fully describe a circuit. Here we describe a modeling framework in which a wide range of experimental data from cellular, network, and behavioral investigations are directly incorporated into a single coherent model, while predictions for difficult-to-measure quantities, such as synaptic connection strengths and synaptic PF-01367338 clinical trial nonlinearities, are generated by directly fitting the model Ergoloid to these data. This approach is applied to data from a well-characterized circuit exhibiting persistent neural activity, the oculomotor neural integrator of the eye movement system (Robinson, 1989). This circuit receives transient inputs that encode the desired velocity of the eyes, and stores the running total of these inputs (the desired eye position) as a pattern of persistent neuronal firing across a population of cells. Such maintenance of a running total represents the defining feature of temporal integrators or

accumulators, which are widely found in neural systems (Gold and Shadlen, 2007, Goldman et al., 2009 and Major and Tank, 2004). Previous studies of the goldfish oculomotor integrator have gathered data at each of the levels of analysis typical of studies of memory systems: intrinsic cellular properties (Aksay et al., 2001), anatomy (Aksay et al., 2000), behavior (Aksay et al., 2000), and functional circuit interactions (Aksay et al., 2003 and Aksay et al., 2007). Thus, this system provides an ideal setting in which to illustrate how data at each of these levels can be coherently combined to gain a fuller understanding of memory-guided behavior. The results described below comprise the following principal findings.

The stimuli were generated digitally (200 kHz sampling rate, 24 bit D/A) by the RX6 Multi Function Processor (Tucker Davis Technology Inc., FL). The sound stimuli were presented on a calibrated free-field speaker (Reveal 501A, Tannoy, Scotland, UK) located 50 cm directly in front of the animal’s head. The stimuli were tone bursts (100 ms duration, 2 ms cosine rise/fall). In total, 180 different pure-tone stimuli were used (30 frequencies from 100 Hz to 20 kHz equally spaced logarithmically, each presented at six equally spaced intensity levels from 52–87 dB). We presented these stimuli in pseudorandom order with an interstimulus interval of 1 s. Each stimulus was presented www.selleckchem.com/products/Fulvestrant.html 60 times to monkey

M and 40 times to monkey B. The auditory evoked potential from each channel of the μECoG array was band-passed between 2 and 500 Hz, digitally sampled with a sampling rate of 1500 Hz, and stored on hard-disk drives. For recording spontaneous neural activity, no auditory stimulus was presented, and the monkey’s ears were covered by an ear muff (premium ear muff 1440, 3M Inc., MN) to minimize acoustic stimulation from noise. We also monitored and video-recorded the monkey’s behavior. The monkeys mostly sat quietly and never vocalized. We excluded epochs of the recording during which the monkey moved suddenly or there was any substantial noise. The total duration of spontaneous-activity recording included

in the analyses was 49 min for monkey M and 61 min for monkey B). Matlab (The Mathworks Inc., MA) was used for offline analyses of the field potential data. Since there BMN673 was little significant auditory evoked power above 250 Hz, we low-pass filtered and resampled the data at 500 Hz to speed up the calculations and reduce the amount of memory necessary for the analysis. The field potential data from each site was

re-referenced by subtracting the average of all sites within the same array (Kellis et al., 2010). For the analysis of frequency tuning, the field potential was band-pass filtered in the following conventionally defined frequency ranges: theta (4–8 Hz), alpha (8–13 Hz), beta (13–30 Hz), low gamma (30–60 Hz), and high gamma (60–200 Hz) (Leopold et al., 2003 and Edwards et al., 2005). We filtered the field potential with a butterworth filter. We achieved a zero-phase too shift by processing the data in both forward and reverse direction (“filtfilt” function in Matlab). We then computed power in each frequency band in time windows of 150 ms. The power was computed by squaring band-passed voltage values at each point in time and averaging them for all the points in the 150 ms time window (Figure 2B). To judge whether the power of the evoked potential from each site significantly discriminated the frequency of the stimulus, we used a two-way ANOVA where the two independent variables were the frequency and intensity of the tone stimulus.

Note that this procedure extracts the time-varying envelope amplitude of each band-pass-filtered signal. Next, the BLP signals were further filtered into this website slow (<0.1 Hz) fluctuations (two other frequency bands [0.1–1 Hz and >1 Hz] were also computed for comparison) using a second-order, zero-phase Butterworth band-pass filter. We calculated Pearson’s correlation coefficients between all possible pairs of ROIs (1) over the entire time course of the filtered BLP signals (“long epochs”) and (2) over the stable-eye epochs (“short epochs”; 135 ± 69 epochs per recording session; a total of 58 sessions). The significance of correlations was assessed using one-sample

t tests on Fisher Z-transformed coefficients. Coherence Analysis. find more We used multitaper methods

(three Slepian tapers, providing an effective taper smoothing of ± 4 Hz; Mitra and Pesaran, 1999) to calculate the coherence Cxy(f): Cxy(f)=|Sxy¯(f)|Sx¯(f)Sy¯(f),where Sx(f) and Sy(f) are the spectra of LFP time series, and Sxy(f) is the cross-spectrum. Coherence values range from zero to one, where zero coherence means that the LFPs are unrelated, and a coherence of one means that the LFPs have a constant phase relationship. We Fisher transformed coherence values and accounted for the different number of stable-eye epochs in each resting-state session according to: Cxy_t(f)=tanh−1(Cxy(f))−12m−2,where Cxy_t is the transformed coherence, and m is the product of K and the number of stable-eye epochs ( Bokil et al., 2007). We rejected the null hypothesis of no significant coherence between two ROIs only when the coherence was above zero (based on jackknife estimates of the variance) across a frequency range greater than the bandwidth (i.e., 8 Hz), to account

for multiple comparisons ( Bokil et al., 2007). Cross-Frequency Coupling. We measured cross-frequency coupling between low-frequency oscillations and gamma power using the SI ( Cohen, 2008). There were two reasons for using this measure: (1) the SI can be reliably computed on the short stable-eye Dichloromethane dehalogenase epochs examined in our study; and (2) the SI can capture dynamic changes in cross-frequency coupling. There were three processing steps to calculate the SI. First, we extracted gamma power time series for given frequency bands whose central frequency ranged from 30 to 100 Hz, stepped in 5 Hz increments, with a bandwidth of ± 5 Hz. Second, for each of the theta, alpha, low-beta (13–20 Hz), and high-beta (20–30 Hz) bands, we identified the low frequency with which the gamma power time series might synchronize. (The aim here was to identify the dominant frequency at which the gamma power time series oscillated.) Third, we identified the peak of the power of the gamma frequency envelope time series, extracted the phase time series from both the gamma- and low-frequency bands (low-frequency bandwidth ± 1.

NMDARu currents were always accompanied by an increase in [Ca2+]i (Figure 5Di). The kinetics of the NMDARu currents were rapid, mean time

to peak of 1.36 ± 0.29 ms (n = 6; Figure 5Dii). Importantly, we only observe NMDARu currents and their associated increase in [Ca2+]i when photolysis is directed at boutons. Directing the photolytic spot at points along the collateral failed to generate either. This is illustrated in Figures 5Ei and 5Eii, where an NMDARu current and increase in [Ca2+]i are seen at the bouton, whereas there http://www.selleckchem.com/products/DAPT-GSI-IX.html is no response when the spot is moved 2 μm away from the bouton. Because both voltage-dependent relief of the Mg2+ block and glutamate binding are requisite steps for the activation of the NMDAR (Mayer et al., 1984 and Nowak et al., 1984), we used these features to explore the mechanism by which presynaptic NMDARs generate large Ca2+ transients. Initially, we increased the level of extracellular Mg2+ to 10 mM. Superfusion of 10 mM Mg2+ (Figures 6Aii and 6Aiii) significantly reduced the probability of observing a large event (ACSF

θ = 0.185 ± 0.075; 10 mM Mg2+ θ = 0.009 ± 0.018; n = 5; Figures 6Aii–6Aiv), whereas the amplitude of these events remains unchanged (Figure 6Av). In contrast, the absence of Mg2+ from the extracellular solution did not change the probability of observing a large Ca2+ event (ACSF θ = 0.19 ± 0.079; Mg2+-free θ = 0.197 ± 0.078; n =

5; Figure 6Biv) but did increase the amplitude of both large and small events (Figure 6Bv). We manipulated the release of glutamate NVP-BKM120 from the boutons by modifying the duration of the AP. This was achieved by lowering the extracellular concentration of K+ ions to 0.1 mM, thereby reducing the duration of the AP, or by applying Tryptophan synthase 4-aminopyridine (4-AP, 40 μM) to increase AP duration (Qian and Saggau, 1999). As expected, low K+ conditions significantly decreased the width of the AP (ACSF: τ [ms] = 2.35 ± 0.01; 0.1 mM K+: τ = 1.65 ± 0.01; n = 4; p < 0.0001). With the duration of the AP reduced, the probability of observing large Ca2+ events was significantly decreased compared to control (ACSF θ = 0.178 ± 0.075; 0.1 mM K+ θ = 0.134 ± 0.043; n = 4; Figure 7Aiv). In contrast, 4-AP enhanced spike duration (ACSF: τ [ms] = 2.14 ± 0.07; 40 μM 4-AP: τ = 14.36 ± 2.7; n = 5; p < 0.0001) and significantly increased the probability of observing a large event (ACSF θ = 0.196 ± 0.063; 40 μM 4-AP θ = 0.006 ± 0.013; n = 5; Figure 7Biv). These results indicate that in normal K+ conditions, the depolarization arising from a single AP invading the bouton is adequate to relieve the Mg2+ block of the NMDAR, but this is not the case when the AP duration is curtailed. Enhancing the duration of the AP increases Ca2+ influx and consequently transmitter release (Mintz et al.

The influence of VEGFD expression on synaptic transmission in hippocampal neurons in culture was directly assessed by recording miniature excitatory postsynaptic currents (mEPSCs)

in the presence of TTX and the GABAA receptor blocker, gabazine. Neurons transfected with pAAV-shVEGFD or infected with rAAV-shVEGFD showed longer mEPSC interevent intervals (IEIs, 1/frequency) and smaller mEPSC amplitudes than their respective shSCR-expressing controls ( Figures 7E and 7F). The reduced mEPSC frequency in transfected hippocampal neurons suggests that the effect was not mediated by a reduced release probability presynaptically because the low transfection rate ensures that the majority of synaptic input to shRNA-expressing learn more cells comes from non-shRNA-expressing cells. The reduced mEPSC frequency is thus most likely indicative of fewer AMPA receptor-containing synapses per cell. The 21%–24% reduction in mEPSC amplitude also suggests a lower density of AMPA receptors at synapses in shVEGFD-expressing cells. mEPSCs of hippocampal neurons expressing shVEGFD also showed faster rise and decay time constants than their respective shSCR-expressing controls (

Figure 7C and Table S1), most likely due to reduced filtering of mEPSCs in their more compact dendritic trees. Alternatively, a synaptic NMDA receptor-mediated slow component of the mEPSC may have been reduced in shVEGFD-expressing neurons, although significant NMDA currents are unlikely in our Paclitaxel recording conditions (−71 mV holding potential, 1.3 mM Mg2+). Responses were also recorded to bath-applied AMPA, which produced a peak within 30 s whose amplitude was used as an indication of the total number of functional AMPA receptors per cell PDK4 ( Figure 7D). AMPA response amplitudes were smaller in hippocampal neurons expressing shVEGFD

( Figures 7D and 7G), indicative of a reduced total number of surface-expressed AMPA receptors per cell. Taken together, our patch-clamp analysis has identified a reduced plasma membrane surface area, as well as a reduced number of AMPA receptor-containing synapses, a reduced number of AMPA receptors per synapse, and a reduced total number of AMPA receptors in shVEGFD-expressing cells. These results are consistent with the reduced dendritic morphology identified by morphometric analyses. We next investigated the role of VEGFD in vivo. rAAV-shVEGFD or the appropriate control rAAVs were stereotaxically delivered to the dorsal hippocampus of 2-month-old C57BL/6 male mice. Infected neurons were readily identified by analysis of the mCherry fluorescence ( Figure S4). The morphology of neurons in the CA1 area of the hippocampus was assessed by manually tracing the basal dendrites of Golgi-stained brain slices obtained from animals 2.5 weeks after viral gene delivery.

Biologists can benefit from enhanced appreciation of the intellectual potency of simultaneously click here examining all problems of a given category, an approach that has yielded many technologies that form the bedrock of modern biological research practice and infrastructure. In the

coming years, neuroscientists and engineers will need (and want) to work more closely together than ever before, making “cross-cultural” exchange of ideas and working modes increasingly important for, and part of, the natural fabric of neuroscience. K.D. acknowledges support from the Wiegers Family Fund, NIMH, NIDA, NSF, the DARPA REPAIR Program, and the Gatsby Charitable Foundation. M.J.S. acknowledges support from NIMH, NSF, the Paul Allen Family Foundation, DARPA, the Ellison Foundation, the Keck Foundation, NIDA, and NIBIB. M.J.S. is a cofounder and consults scientifically for Inscopix Inc., which has commercialized the miniature integrated microscope technology of Figure 1. K.D. is a cofounder and consults for Circuit Therapeutics Inc., which is using optogenetics to screen for medications and build devices for treating diseases in the peripheral nervous system; optogenetics tools, training, and

protocols are freely available C59 research buy (http://www.optogenetics.org). “
“Genomes encode the key macromolecular building blocks of our cells, RNA, and proteins. In concert with intracellular and extracellular signals, our genomes regulate the times, places, quantities, and cell-type-specific patterns of expression of

messenger RNAs (mRNAs) that give rise to proteins and of RNAs with independent functions. These macromolecules, in turn, direct the synthesis and trafficking of essentially all other molecules within cells. Analysis of the completed genome sequences of many Isotretinoin organisms, together with biochemistry, physiology, and other disciplines, have made it possible to identify many if not essentially all of the genes that encode components of receptors, ion channels, synaptic proteins, and other molecular complexes of central interest to neurobiology. Increasingly powerful technologies, grounded in genetics and molecular biology, permit neuroscientists to manipulate the genomes of cells and model organisms to understand both normal function of the nervous system and disease processes (Cong et al., 2013, Fenno et al., 2011 and Wang et al., 2013). Currently, information derived from genes and genomes provides neuroscientists with molecular clues to the properties of the many thousands of neuronal and glial cell types in the brain, to functional properties of brain circuits, and ultimately to important aspects of cognition, emotion, and behavior.

The properties of stimulus categorization exhibited by neurons in the owl OTid account well for behavioral deficits in monkeys following the inactivation of the intermediate and deep layers of the superior colliculus (Lovejoy and Krauzlis, 2010, McPeek and Keller, 2004 and Nummela and Krauzlis, 2010). In monkeys performing stimulus selection tasks, focal inactivation of the portion of the superior colliculus representing the target stimulus causes an impairment in their ability to select

an oddball target or a spatially cued target among distracters, an impairment that increases dramatically as the distracting stimuli become more similar to the target stimulus. These studies indicate that the midbrain network performs computations that are essential for reliable find more competitive stimulus selection, especially

when competing stimuli are of similar strength. A neural computation that is fundamental to stimulus competition in the OTid is the suppression of responses to an RF stimulus by stimuli located outside the RF. Such “surround suppression” is observed in many brain areas across many species (Allman et al., 1985). Unlike interactions that occur among stimuli within the RF (such as crossorientation suppression in the visual cortex; Freeman et al., 2002), surround suppression is thought to be mediated by lateral inhibition and, often, by feedforward lateral inhibition (Blakemore and Tobin, 1972, Bolzon et al., 2009, Cisek and Kalaska, 2010, Hartline et al., 1956, Kuffler, 1953, Olsen et al., 2010 and Yang and Wu, 1991). click here Anatomical evidence from the avian midbrain network supports lateral inhibition as underlying global suppression in the OTid as well (Figure 1; Wang et al., 2004). Specifically, a

midbrain GABAergic nucleus, the nucleus isthmi pars magnocellularis (Imc), receives focal input from neurons with dendrites in the retinorecipient layers of the optic tectum and sends broad projections to neurons in the multimodal and motor layers of the optic tectum, the OTid. Through the use of this basic feedforward lateral inhibitory circuit as a starting point, we employ a first principles approach to address enough neural computations that underlie flexible categorization in the OTid. We show that feedforward lateral inhibition, a circuit motif at the heart of most models of selection for attention or action (Cisek and Kalaska, 2010 and Lee et al., 1999), cannot account for categorization that is flexible. However, a simple modification—introducing reciprocal inhibition between feedforward lateral inhibitory channels—successfully achieves flexible categorization. The key additional computation that achieves adaptive boundary flexibility in categorization is lateral inhibition that is dependent on relative stimulus strength.

To couple the initial linear-nonlinear system to the kinetics block, the output of the nonlinearity, u(t), scales one or two rate constants. Although this means that the transition rate is proportional to the nonlinearity output, a higher-order dependence—such as the dependence of vesicle release on a higher power of the calcium concentration—can be captured in the nonlinearity itself.

We fit LNK models using a constrained optimization algorithm (see Experimental Procedures). The filter and nonlinearity were reduced to a set of 20 parameters, and the kinetics block contributed 5 parameters. The activation rate ka was scaled by the input, and most other rate constants were fixed. In addition, to capture the contrast dependence of the rate of slow adaptation, the input scaled the rate Selleck Tyrosine Kinase Inhibitor Library of slow recovery ksr. The motivation for scaling of the slow rate constant by the input is discussed further below. We compared the LNK model output to the cell’s membrane potential response across the entire recording (300 s). The model accurately captured the response at all times,

including contrast transitions at both decreases and increases in contrast (Figure 2C, Figure S1). The correlation coefficient between the model and the response was 88 ± 4% (90 ± 2% for bipolar cells [n = 5], 89 ± 4% for selleck chemicals amacrine cells [n = 9], and 86 ± 4% for ganglion cells [n = 7]), mean ± SEM. We then compared these values to the intrinsic variability of each cell by repeating a stimulus sequence two to three times. The accuracy of the model was nearly that of the variability

between repeats of the Rolziracetam stimulus, which was 90 ± 5% (92 ±2% for bipolar cells, 92 ± 4% for amacrine cells, and 89 ± 6% for ganglion cells) (Figures 2D and 2E). Thus, the LNK model accurately captured the membrane potential response to changing contrast for inner retinal neurons. We then assessed how well the LNK model captured adaptive properties by fitting LN models to both the data and to the LNK model. Examining the temporal filters of these LN models, the LNK model captured the fast change in temporal processing between low and high contrast (Figure 3A). In addition, the LNK model captured fast changes in sensitivity between low and high contrast as well as fast and slow changes in baseline membrane potential (Figure 3B). Across a population of cells, the LNK model closely matched the temporal filtering and average overall sensitivity of the cell’s response across the full range of contrasts (Figures 3C and 3D). After a contrast step, the LNK model matched the fast change in average membrane potential of a cell across a range of contrast transitions (Figure 3E). Finally, the LNK model matched slow changes in baseline as the model matched the near steady-state average membrane potential value of a cell at the end of 20 s of constant contrast (Figure 3F).

Infusion of CA1 neurons with pep-OPHN1Endo had no effect on basal synaptic transmission (Figure 6B). These findings indicate that the actions of pep-OPHN1Endo are rapid and corroborate our results obtained with the OPHN1Endo mutant. When pep-OPHN1Hom was included in the pipette, mGluR-LTD and baseline EPSC amplitudes were comparable to those of the control peptide (Figures

6C and 6D). Bafilomycin A1 cell line Of note, the lack of an effect on basal synaptic transmission upon short-term disruption of the OPHN1/Homer 1b/c interaction with pep-OPHN1Hom is consistent with previous findings that prolonged, but not short-term, knockdown of OPHN1 reduces basal synaptic transmission (Nadif Kasri et al., 2009). Together, our data indicate that OPHN1 plays a crucial role in mediating mGluR-LTD, and that OPHN1′s interaction with Endo2/3, but not Homer 1b/c proteins, is critical for this event. Previous studies have shown that activation of group I mGluRs leads to persistent decreases in surface AMPAR expression levels that mediate LTD (Snyder et al., 2001 and Waung et al., 2008). Since the OPHN1-Endo2/3 interaction is critical for mGluR-LTD, we directly tested whether it is important for mGluR-induced changes in surface AMPAR expression www.selleckchem.com/products/INCB18424.html and endocytosis. To quantify

AMPAR surface levels and the degree of AMPAR internalization, we employed a biochemical method to crosslink surface-only AMPAR subunits. Acute slices of hippocampal area CA1 were preincubated with no peptide, pep-contEndo or pep-OPHN1Endo. The CA1 slices were then treated with DHPG or control vehicle (for 10 min), and 50 min later incubated with the membrane-impermeant cross-linking reagent bis (sulfosuccinimidyl) suberate (BS3). Western blotting with an anti-GluR1 antibody revealed a decrease in cell-surface GluR1 expression and an increase in internalized GluR1 levels

1 hr after DHPG treatment in the no peptide and control peptide preincubated CA1 slices (Figures S7A and S7B). The DHPG-induced decrease in cell-surface GluR1 expression and increase in Rolziracetam internal GluR1 levels were, however, significantly attenuated in CA1 slices that were preincubated with pep-OPHN1Endo (Figures S7A and S7B). Of note, the pep-OPHN1Endo peptide did not affect basal levels of surface GluR1 (Figures S7A and S7B). Similar results were obtained for the GluR2 AMPAR subunit (data not shown). To corroborate these findings, we undertook an immunofluorescence approach to measure AMPAR surface levels. Cultured hippocampal neurons, preincubated with no peptide, pep-contEndo or pep-OPHN1Endo, were treated with DHPG or control vehicle (for 10 min), and 1 hr after treatment labeled with an N-terminal directed anti-GluR1 antibody.