Thus, O. armillata appears to be protected from eosinophil degranulation, but the mechanism involved for this putatively motile species may differ from that observed in nodule-forming Onchocerca spp. None of the authors declares any conflict of interest. This study was funded by a Wellcome Trust Vacation Scholarship, the British Veterinary Association (BVA) Harry Steele-Bodger Memorial fund, a British Cattle Veterinary Association (BCVA) Student Clinical Research Grant, an Intervet Student Vacation Bursary, a Pfizer Vacation Study Grant

and the University of Cambridge Veterinary School Jowett Fund. The study sponsors did not have any role in the study design; in the collection, analysis and interpretation check details of data; in the writing of the manuscript; or in the decision to submit the manuscript for publication. “
“Vaccines are an important tool in livestock production, not only as a means of maintaining find more health and freedom from clinical diseases, but also in some cases as a means of preventing zoonotic disease and thus enhancing food safety and public health. Bovine respiratory

disease (BRD) in calves and young cattle is a significant source of morbidity and mortality, and is a major contributing factor for economic losses in cattle industry (Snowder et al., 2006). BRD is a multifactorial disease (Babiuk et al., 1988); the most common bacterial pathogens associated with it are Mannheimia haemolytica, Pasteurella multocida, Mycoplasma bovis as well as various viral pathogens; bovine respiratory syncytial virus (BRSV), parainfluenza virus type 3 (PI-3), bovine viral diarrhoea virus (BVDV) and bovine herpes virus type 1 (BHV-1). Even though there is limited published information that definitively establishes the efficacy of respiratory vaccines in the field in reducing the burden of bovine respiratory disease ( Perino and Hunsaker, 1997), vaccinations against one or more of the respiratory

pathogens are commonly used. Helminth infections can influence the immune response to unrelated antigens (Kullberg et al., 1992). Furthermore, they have been shown to decrease the response to vaccinations in various host species (Elias et al., 2001 and Urban et al., 2007). Fasciola hepatica, a helminth parasite, causes fasciolosis Histidine ammonia-lyase in cattle and sheep and fasciolosis is also a zoonosis. It is a common parasite, especially in the temperate climate of the UK and Ireland, where the prevalence in cattle is as high as 84% ( McCann et al., 2010). F. hepatica has been proven to have immunoregulatory effects in mice ( Brady et al., 1999). In cattle, it can alter the response to immune-mediated diagnostic tests ( Flynn et al., 2007) but its effect on vaccine responsiveness in this species has not yet been studied. The aim of this study was to establish whether a concurrent F.

3 current is selectively impaired by CaV2.3 knockout or SNX-482 blockade, without affecting LVA Ca2+ currents. We next examined the intrinsic firing behaviors of wild-type and CaV2.3−/− RT neurons with whole-cell current clamp methods using a K+-based intracellular solution. Evoked responses were recorded from genetically labeled GFP-positive

neurons ( Lopez-Bendito et al., 2004) in anatomically distinct regions of dorsal or lateral RT nuclei ( Figure 3A) that are known to be associated BMN 673 clinical trial with visual or motor modalities, respectively ( Coleman and Mitrofanis, 1996, Jones, 1975 and Lee et al., 2007). Low-threshold (LT) bursting was evoked by a current injection (1 s duration) that ensured a hyperpolarization close to −90 mV. On average −112.89 ± 6.44 pA current

was injected, which hyperpolarized the wild-type cells by −31.86 ± 0.66 mV from the initial baseline potential of −60 mV. Similarly, a −118.84 ± 8.97 pA current injection hyperpolarized the CaV2.3−/− neurons by −29.35 ± 1.14 mV from the initial baseline potential of −60 mV. We found that similar percentages of RT neurons in both dorsal and lateral regions of Enzalutamide in vivo wild-type mice showed rhythmic burst discharges or single-burst firing only ( Figure 3B; see Table S1 available online). Approximately 60% of wild-type neurons (n = 40) showed rhythmic burst discharges, with 2–13 burst discharges, each typically containing 2–8 action potentials at 209.47 ± 9.69 Hz; about 25% (n = 17) showed only a single LT burst, and 15% (n = 10) exhibited no LT burst at all ( Figures 3B and 3C; Table S1). Next, we examined CaV2.3−/− neurons in a similar

manner. The most conspicuous finding was a dramatic suppression of rhythmic burst discharges check details in the majority of CaV2.3−/− neurons; only 10% (5 of 49) exhibited rhythmic burst discharges, whereas 67% (33 of 49) exhibited a single LT burst, and 23% (11 of 49) showed no LT burst at all ( Figures 3B and 3C; Table S1). The onset of LT burst, assessed by comparing the time points between end of hyperpolarization and the first action potential, was significantly delayed in CaV2.3−/− neurons (205.74 ± 24.55 ms) compared to wild-type neurons (134.58 ± 9.12 ms; p = 0.002). The total number of burst events was also significantly reduced in CaV2.3−/− neurons (1.16 ± 0.08) compared to wild-type neurons (6.16 ± 0.55; p = 0.0001; Figure 3D), as were the number of spikes in a burst (3.16 ± 0.31 in CaV2.3−/− versus 4.77 ± 0.30 in wild-type; p = 0.001; Figure 3E) and the intraburst spike frequency (126.67 ± 10.38 Hz in CaV2.3−/− versus 209.47 ± 9.69 Hz in wild-type, p = 0.0003). On the other hand, the characteristic accelerating-decelerating pattern of intraburst spikes ( Llinas and Steriade, 2006 and Steriade et al., 1986) remained unchanged in the mutant in the majority of neurons tested ( Figure S1A). Notably, the amplitude of slow AHP following the initial LT burst was significantly reduced in CaV2.3−/− neurons (−3.59 ± 0.

, 2009). The hippocampal and prefrontal cortex (PFC) appear to play special roles as “hubs” of interstructure communication. As such, both of these structures are capable of orchestrating the activity of many cortical and subcortical areas subserving cognitive

functions such as working memory, memory acquisition and consolidation, and decision making (see Benchenane et al., 2011 and Schwindel and McNaughton, 2011, for reviews). Both structures receive converging input from the higher sensory/associative areas. Furthermore, the PFC is one of the few neocortical areas that receives direct input from the hippocampus itself. Consistent with this link, activity oscillations in PFC and the hippocampus are coherent (see e.g., Sirota et al., 2008), and the degree of coherence covaries with working memory (Jones and Wilson, 2005) and decision making (Benchenane et al., 2010) Selleckchem MK-2206 demands. In this issue of Neuron, Fujisawa and Buzsáki (2011) aim to extend our understanding of oscillatory coherence in the context of working memory by studying the simultaneous activity of the rat PFC, the hippocampal CA1 subfield and the VTA, a basal ganglia nucleus containing dopaminergic (DA) cells, which sends neuromodulatory signals to much of the brain. The authors analyzed the activity of ensembles of single neurons and local field potentials (which reflect local

averages of membrane currents) in these areas while rats performed a working memory task on a T-maze. The animals were Screening Library trained to choose either the left or right target arm, based either on association PtdIns(3,4)P2 with an odor sampled at the departure point, or to alternate between arms. In both cases, while the rat was in transit to the choice-point, information about the arm to be chosen (or which arm was most recently chosen) has to be maintained in working memory, a function that, in rats, has been shown to require the integrity of the hippocampal-PFC network ( Floresco et al.,

1997). In analyzing the spectral coherence between PFC and VTA, the authors indentify a novel slow rhythm centered at 4 Hz. In this frequency range, both regions engage in coherent oscillations that are modulated by behavior, where the strongest and most coherent oscillations are observed on the central arm, or “choice-point,” of the T-maze (i.e., where working memory is necessary for correct decision-making). Those oscillations were not present during performance of a forced-choice control task that did not require working memory (Figure 1). Concurrently, oscillatory coherence at theta frequencies (∼8 Hz) was observed between the PFC and the hippocampus, as previously shown during working memory maintenance (Jones and Wilson, 2005), and surprisingly between the hippocampus and VTA as well.

To ensure we were only recording monosynaptic currents from THVTA::ChR2 fibers, we added a Na+-channel blocker (1 μM TTX) and a K+-channel blocker (1 mM 4-AP) to the bath as previously described ( Cruikshank Selleck PCI32765 et al., 2010). Voltage-clamp recordings from LHb neurons revealed that light pulses that selectively stimulated THVTA::ChR2 fibers in the LHb (THVTA-LHb::ChR2), produced

light-evoked currents that were blocked by 10 μM of the GABAA receptor antagonist gabazine ( Figures 5A–5C). Of the neurons we recorded from in the LHb, 82% (45/55) received a direct monosynaptic inhibitory input from THVTA neurons. Dopaminergic terminals in the dorsal striatum release GABA that is dependent on Vmat2 activity ( Tritsch et al., 2012). However, we observed no changes in inhibitory currents in LHb slices from THVTA::ChR2 mice treated with the Vmat2 inhibitor reserpine, compared to untreated slices ( Figure 5D). This same reserpine protocol was sufficient to inhibit electrically-evoked NLG919 order dopamine release in the NAc ( Figure S3), demonstrating that this treatment was capable of inhibiting Vmat2 and depleting evoked dopamine. These data demonstrate that THVTA-LHb neurons do not require Vmat2 function to release GABA in

the LHb. Additionally, we observed a small (−7.2 ± 2.2 pA) excitatory current in some of the recorded neurons (5/10), consistent with a previous study demonstrating that Vglut2-expressing VTA neurons (some of which could be dopaminergic) innervate the LHb ( Hnasko et al.,

2012). To determine whether activating THVTA-LHb::ChR2 terminals would affect the spontaneous firing rate of postsynaptic LHb neurons, mafosfamide we performed cell-attached recordings from LHb neurons and found that the average spontaneous firing rate of these neurons was 8.0 ± 2.2 Hz. When we delivered a 1 s 20 Hz optical pulse-train to optically stimulate THVTA-LHb::ChR2 terminals, we observed that the firing rate of LHb neurons significantly decreased ( Figures 5E–5G), demonstrating that the net effect of THVTA-LHb::ChR2 terminal stimulation was to suppress the firing of LHb neurons. To determine whether this suppression of firing was due to GABA or dopamine release, we added a D1/D2 receptor antagonist cocktail (10 μM SCH23390 and 10 μM raclopride) to the bath, followed by a GABAA receptor antagonist (10 μM gabazine). The D1/D2 receptor antagonist did not modify the decrease in firing in response to optical stimulation, but the GABAA receptor antagonist blocked this decrease, leading us to conclude that the inhibition of spontaneous firing following activation of THVTA-LHb::ChR2 terminals is due to activation of GABAA receptors. We performed electron microscopy to provide anatomical support for the electrophysiological findings. Accordingly, we collected images of THVTA-LHb::ChR2 synapses (as defined by electron-dense DAB reaction product or silver-enhanced nanogold after pre-embedding immunostaining for eYFP).

To investigate in detail how location and behavioral variables influenced firing patterns, we first examined whether the rats developed

stereotyped behavioral sequences, often observed during periods that precede a reward (Skinner, 1948). Behavior was indeed partially stereotyped such that during the first second of the selleck chemicals delay, rats typically ran directly to the end of the delay zone, then retreated back toward the beginning. Subsequently, they typically reared against one wall and occasionally changed location (Figure S1), thus permitting an analysis of the extent to which time and other factors influenced firing rate during these mediating behaviors. We first computed the neuron’s firing rate with reference to the rat’s position during the entire delay using traditional occupancy-normalized firing rate histograms

and also created spatial firing rate maps for each successive 1 s segment of the delay (Figure 5 illustrates the results from 15 simultaneously monitored neurons; see Experimental Procedures). PF-01367338 manufacturer This analysis revealed that most of the space occupied by the rat during the first second of the delay is not occupied again. However, there was substantial overlap among the positions that were occupied from 1 s until the end of the delay, allowing an examination of how firing patterns changed over the remainder of the delay. Remarkably, each of these Fossariinae neurons fired only when the rat was at one place, but its firing rate varied across time. Thus, for each neuron shown in Figure 5, one can see that the cell fired maximally, or only, within some of the time segments,

even though the rat occupied the same places in other time segments. ANOVAs indicated that 87 out of the 167 delay neurons (52%) varied in firing rate over time independent of position (significant main effect of time; p ≤ 0.05). Thus, confirming the results of the GLM analyses described above, the firing rates of most hippocampal neurons signaled a combination of time and space. These convergent results indicate that, in addition to their well-known spatial coding, temporal coding is a robust property of hippocampal neurons. We also conducted the same analysis on the influences of head direction and running speed during the delay (Figure S2). ANOVAs revealed a main effect of time in relation to head direction and running speed for 73% (122/167) and 79% (132167) of delay neurons, respectively. Both of these proportions were higher than that observed for position, indicating that the firing rates of these cells were more influenced by time than by head direction or running speed (χ2 test, both p values <0.001). In addition for 77 out of these 167 delay neurons (46%), the firing rate in relation to location, head direction, and running speed depended on the passage of time during the delay.

One example is the successor representation (Dayan, 1993). Further, there are suggestions that there are multiple model-based controllers, i.e., a mixture model (Doya et al., 2002), in which the selection between them can have model-based or potentially model-free components. Finally, there is a rich panoply of other formulations of the dichotomies between model-free and model-based control and of model-based control itself (Dayan, 2009, Kahneman, 2011 and Stanovich and Baf-A1 West, 2002). We have already seen some variants, with the issue of instruction versus experience (as in Wunderlich et al., 2012a) but

there are many others too, including declarative versus procedural, spatial/geometric versus abstract, interpreted versus compiled, prior- versus data-bound (Dayan, 2009), and even episodic versus semantic control (Lengyel and Dayan, 2008). Teasing these various aspects apart, and understanding what properties and substrates they share, is critical. Compound C purchase For example, iterations of reflective control as captured by ideas such as model based, declarative, and goal directed are almost certainly not fully commensurable. So far, we have concentrated on instrumental control, i.e., the choice of actions based on their past or current

contingencies. Another, even more influential source of control is Pavlovian, in which predictions of future valenced outcomes lead automatically to a choice of action (such as approach for appetitive outcomes and inhibition PIK-5 or withdrawal for aversive ones) irrespective of the benefit of that action (Dayan et al., 2006 and Williams and Williams, 1969). One way to conceive of these Pavlovian systems is in terms of an evolutionarily specified

prior, serving to facilitate performance by alleviating the computational costs that come with instrumental conditioning’s increased flexibility in being able to learn to emit arbitrary actions. There is good evidence for Pavlovian predictions of actual outcomes, which what we argue underpins instrumental model-based control, and this seems to account for behavioral phenomena such as specific forms of Pavlovian instrumental transfer (PIT) (Ostlund and Maidment, 2012 and Kruse et al., 1983). However, there are two key additional aspects to Pavlovian conditioning. First is the idea that Pavlovian control might influence instrumental model-based calculations. For instance, we noted above that building and evaluating the tree might be considered in terms of a set of internal actions (Dayan, 2012). Those actions might also be susceptible to Pavlovian biases.

Furthermore, we find that the axonal boutons

of these interneurons also show a baseline XAV-939 chemical structure level of turnover. Following removal of sensory input by focal retinal lesions, we observed a rapid loss of both dendritic spines and axonal boutons of inhibitory neurons. This effect is not spatially limited to the silenced cortical region, but gradually decreases with increasing distance from the border of the LPZ, and appears to be driven to a large degree, by reduced cortical activity levels. Because the changes in inhibitory structures precede increases in excitatory spine turnover (Keck et al., 2008), these data suggest that inhibitory structural plasticity may be the first step in cortical reorganization after sensory

deprivation. Most studies of synaptic structural plasticity in vivo thus far have focused on excitatory synapses, particularly postsynaptic dendritic spines. Here, we report that a subset of inhibitory neurons (mostly NPY positive cells) in adult mouse visual cortex bears dendritic spines. We have observed these spines under very different experimental conditions: in fixed tissue sections, in vivo and in acute cortical brain slices. Many, if not all, Everolimus purchase of these spines carry functional excitatory synapses, as revealed by immunohistochemistry and their response to glutamate uncaging. As has been observed for excitatory cells (Hofer et al., 2009, Holtmaat et al., 2006, Keck et al., 2008, Majewska et al., 2006, Trachtenberg et al., 2002 and Zuo et al., 2005), inhibitory cell spines demonstrate a baseline level of turnover in naive adult animals over a period of days. Following sensory deprivation, changes to excitatory cell spines occur on the time scale of days (Hofer et al., 2009, Holtmaat et al., 2006, Keck et al., 2008, Trachtenberg et al., 2002 and Zuo et al., 2005), typically in the form of increased dynamics, lasting for weeks to months. Here, we observe that spines on inhibitory neurons

change much more rapidly—in the first 6 hr after deprivation—mainly via increased Org 27569 spine loss resulting in a decrease in spine density. This increase in dynamics occurs through the first 72 hr after deprivation, but not afterward, suggesting that inhibitory cell spine plasticity ends well before changes in excitatory spines subside. Axonal boutons in the naive cortex have been reported to demonstrate a baseline turnover in excitatory cells, the rate of which depends largely on cell type (De Paola et al., 2006 and Stettler et al., 2006). Previous studies using chronic two-photon imaging of PV positive inhibitory neurons (Kuhlman and Huang, 2008), GABA positive inhibitory neurons (Chen et al., 2011) or GAD65 positive inhibitory neurons (Marik et al., 2010) demonstrated a baseline turnover of axonal boutons in adult cortex.

Bilateral DLPFC in turn had a significant inhibitory influence on the rAI. In addition, dACC and posterior cingulate cortex (PCC) had significant inhibitory influence, while preSMA and temporal pole had significant excitatory influence on the rAI. These results are shown in Figure 1 and Table S2. Two-sample t tests buy NVP-BGJ398 revealed significant differences between patients and controls in the “causal” outflow from

the rAI to the rDLPFC. In controls, the rAI exerted a significant excitatory influence on right DLPFC (t(34) = 7.42, corrected p < 0.001), while in the patients, this influence was weak (t(37) = 2.06, uncorrected p = 0.047). In addition, there was a group difference in the effect of rAI on precuneus at an uncorrected threshold (p < 0.001, k = 30), where the controls exhibited an excitatory influence (t(34) = 3.14, uncorrected p = 0.004), while the patients exhibited an inhibitory influence (t(37) = −2.18, uncorrected p = 0.036). Patients also showed a significant reduction in the “causal” influence from bilateral visual cortex and right hippocampal formation to the insula when compared to controls. These group differences are shown in Figure 2 and Table 1. In order to investigate the effects of influences of the rDLPFC

on the rest of the brain, we performed voxelwise GCA using a 6 mm spherical region of high throughput screening interest (ROI) placed in the rDLPFC node showing the significant group difference. The SN was the primary site of dysfunctional “causal” influence on the rDLPFC in patients. Patients had a significantly reduced excitatory PR-171 chemical structure effect from the bilateral (more ventral) insula and the dACC to the rDLPFC in addition to a significant

loss of inhibitory effect of the rDLPFC on the bilateral anterior insula and dorsal ACC (Figure 2; Table 2). The results of the one-sample t tests of GCA based on the rDLPFC seed are presented in Figure 3 and Table S3. None of the x-to-y or y-to-x path coefficients from the rAI or the DLPFC seed regions showed significant correlations with antipsychotic dose equivalents (all p > 0.2). The GCA analysis using a homologous left anterior insula seed revealed that the salience-execution loop disturbances are predominantly right lateralized in schizophrenia (further details are presented in the Supplemental Information and in Figures S4 and S5 and Tables S5 and S6). To relate the illness severity to GCA coefficients in patients, we conducted three principal component analyses to extract an illness severity factor, a factor representing the integrity of “causal” interactions within the salience-execution loop (rAI, rDLPFC, and dACC), and a factor representing visual inflow to rAI. A multiple regression analysis was then conducted as described in the Experimental Procedures section. The model had a significant fit (F[3,34] = 4.03, R2 = 0.26, p = 0.015).

Warren et al. (2010) revisited singing-driven gene regulation in area X and found 474 known genes (represented by 807 probes) that were regulated over the course of 0.5–7 hr of singing. Three hundred of these genes were in our network, with subsets enriched in the three song modules (blue: 71 genes, with, e.g., SHC3, SMEK2, and NTRK2 having the highest GS.motifs.X, p < 4e-28; orange: 17 genes, e.g., CSRNP3, SCN3B,

LDN-193189 supplier and PLCB1, p < 3e-6; dark green: 38 genes, e.g., BSDC1, VLDLR, and RORA, p < 5e-5; Fisher's exact test; Table S2) and in one other module (yellow: 104 genes, p < 5e-7; Table S2). Compared to the rest of the network, probes for all 300 genes had greater expression increases (p = 1.9e-12, Kruskal-Wallis test; 882 probes total), higher GS.motifs.X (p = 7.8e-11), and higher GS.singing.X (p = 2.7e-11; Table S2). These genes were also more interconnected in their respective modules throughout the network (kIN.X, p = 4.2e-4), especially in the blue song LBH589 supplier module (p = 3.8e-14). A separate aspect of the study revealed enrichment for the functional annotation term “ion channel activity” in 49 genes posited to have undergone positive selection in zebra finches, which are also suppressed in the auditory forebrain during song perception. Of these, 42/49 were in our network (114 probes; Table S2),

with six in the orange song module (p < 3.3e-4, Fisher's exact test). One of the ion channel genes, TRPV1 (dark green/salmon modules), was highly connected and strongly suppressed by singing in our data, and thus selected for validation in area X in vivo (see below and Table S2). We previously showed that FoxP2 mRNA and protein are lower in area X following 2 hr of undirected singing compared to nonsinging, with the magnitude of downregulation correlated to singing (Miller et al., 2008, Teramitsu and White, 2006 and Teramitsu et al., 2010). This finding was reproduced here; expression levels for all 12 FOXP2 probes in the network were negatively correlated with the number of motifs sung ( Figure S5). Although our study

used an indirect approach, i.e., a behavioral paradigm in which the birds’ natural singing behavior significantly alters FoxP2 levels within area X ( Miller GABA Receptor et al., 2008, Teramitsu and White, 2006 and Teramitsu et al., 2010), we predicted that this paradigm coupled with WGCNA would reveal FoxP2 transcriptional targets in area X singing-related modules. To test this, we screened the network for direct FOXP2 targets previously identified by three studies. Of 175 targets found in human fetal basal ganglia ( Spiteri et al., 2007), 56 were in our network (149 probes total; Table S2). These had relatively high MM in the orange song module (p = 0.05, Kruskal-Wallis; Table S2), which contained genes that were downregulated with continued singing, including 9/12 probes for FOXP2. Of 302 targets found by a second study in SY5Y cells ( Vernes et al.

For some cells, rectification was incomplete, as seen by the shallow, but nonzero slope of the obtained nonlinearities for nonpreferred signals (Figure 3B). Iso-rate curves (Figures 3A–3C, blue lines) displayed more variable shapes than iso-latency curves. selleck For some cells, the iso-rate curve had approximately the same shape as the cell’s

iso-latency curve (Figures 3A and 3B), also indicating a nonlinearity of stimulus integration that is approximately threshold-quadratic or sometimes close to threshold-linear (insets in Figures 3A and 3B, blue lines). For other ganglion cells, however, the iso-rate curves displayed a notably different shape (Figure 3C), characterized by a notch along the lower-left diagonal. This notch gave the curves a distinctive nonconvex shape. It showed that relatively little contrast was required for these cells to achieve the predefined spike count when both receptive field halves were stimulated with similar (negative) contrast. Stimulation

of only one receptive field half, on the other hand, required much larger contrast values. Thus, when considering the spike count, these ganglion cells displayed exceptional sensitivity to spatially homogeneous stimulation of the receptive field, and in the following we will therefore refer to these cells as homogeneity detectors. The classification of iso-rate curves into convex and nonconvex curves did not depend on the chosen target spike count. Convex iso-rate curves appeared to be largely scaled versions of each selleck products other if measured for the same cell at different spike counts (Figure 3D), whereas iso-rate curves of homogeneity detectors displayed the characteristic

nonconvex shape over a range of different spike counts (Figure 3E). However, the notch in the iso-rate curve became more pronounced with higher target spike counts, a fact find more to which we will return when discussing the underlying mechanisms. In addition, the nonconvex shape of homogeneity detectors did not depend on the exact stimulus layout; it proved robust to changes in stimulation radius or insertion of a gap between the two stimulus areas (Figure 3F). To quantify the degree to which individual iso-response curves were convex or nonconvex, we defined a form factor that compares the radial distance of the curve along the lower-left diagonal to its linear prediction obtained from the intersections of the curve with the two axes of the plot (see Experimental Procedures for details). In particular, this form factor is smaller than unity for a nonconvex iso-rate curve as in Figure 3C and larger than unity for the iso-response curves of Figures 3A and 3B. Calculating the form factor for all measured iso-response curves confirmed that iso-latency curves always had similar convex shapes (Figure 3G). In fact, their form factors clustered around their average value of 1.38 (standard deviation: 0.08), close to the value of 2≈1.41, which is expected from quadratic integration of preferred stimuli.