Miura); pUAS-Dronc-FLAG (courtesy of S. Kornbluth); darkCD4 (courtesy of J.M. Abrams); debcl[E26] (Bloomington Stock Center); and dl1 (Bloomington Stock Center). The full-length Wengen cDNA RE29502 and the full-length Dcp-1 cDNA LD13945 were obtained from the Drosophila

Genomics Research Center (Indiana University, Bloomington, IN, USA) and tagged with a C-terminal fusion protein encoding the fluorescent protein Venus by cloning into the plasmid PtWV using the Gateway System (Carnegie Gefitinib mw Drosophila Gateway Collection). Transgenic flies were produced and balanced using standard procedures (BestGene). Excision lines of eiger were generated by the excision of the transposon insertion line egr-GAL4 using the delta[2-3] Compound C in vivo transposase. PCR screening with homozygous progeny was performed with primers 5′- gcaggcgcccttaagtatg-3′ and 5′- gcttgatcagccaagaaacc-3′. Sequencing of PCR products revealed that ∼1.5 kb of the genomic region was deleted in egrΔ25. Wandering third-instar larvae were dissected and stained according to standard procedures (Massaro et al., 2009). Primary antibodies were used at the following dilutions: 1:100 anti-Bruchpilot (Developmental

Studies Hybridoma Bank); 1:20 anti-Futsch (22C10; Developmental Studies Hybridoma Bank); 1:10,000 anti-Dlg (anti-Discs large); 1:500 anti-Eiger (courtesy of M. Miura); 1:400 anti-GFP (3E6; Invitrogen); and 1:20 anti-Repo (BD12; Developmental Studies Hybridoma Bank). Secondary Alexa Fluor antibodies (goat anti-mouse 488, goat anti-rabbit 555, and goat anti-rabbit 647) were obtained from Invitrogen and diluted in PBT to a final concentration of 1:500. All other secondary antibodies and Cy3- and Cy5-conjugated HRP were obtained from Jackson ImmunoResearch Laboratories, Inc., or Invitrogen and used at a 1:300 dilution. Images were digitally captured using a CoolSNAPHQ CCD camera mounted on a Zeiss Axiovert 200 M microscope and analyzed 3-mercaptopyruvate sulfurtransferase using SlideBook software (Intelligent Imaging Innovations). Individual nerves and

synapses were optically sectioned at 0.5 μm using a piezoelectric-driven z-drive controlling the position of a Zeiss Plan-Apochromat 100× oil immersion objective (NA = 1.4). Images were deconvolved with the nearest neighbor algorithm, and Z stacks were combined into a single projection image. For quantification of Brp fluorescence within nerves, the maximum fluorescence intensity of each Brp punctum within a nerve area was determined for each 2D projection image using a semiautomated procedure as described previously (Massaro et al., 2009 and Heckscher et al., 2007). Image stacks were taken at the same exposure from animals projected into a single 3D stack before masking. No alterations were made to the images before quantification. Degeneration was scored at 40× magnification with the observer being blind to the genotype.

The riparian reserves typical of current oil palm plantations may increase the foraging Alisertib solubility dmso activity of arthropods in adjacent areas of oil palm, but our results do not suggest that this corresponds to a reduction in herbivory on palm fronds under normal pest densities. However, the extent to which wider reserves may provide pest control services deserves further investigation. Our data suggest that the use of artificial pest mimics is likely to be more informative about the predatory

behaviour of birds than arthropods, and this should be taken into account by future studies using this method. Importantly, our results show that riparian reserves do not increase defoliating pest activity, and this information should be highlighted in circumstances where doubt over pest problems may prevent the protection of this habitat. We are grateful to EPU Malaysia, Sabah Biodiversity selleck chemicals llc Council and SEARRP for research permissions. The SAFE project coordinators (Dr. Ed Turner, Johnny Larenus and MinSheng Khoo), Dr. Arthur Chung, Joana Ferreira and several SAFE project research assistants provided logistical support and assistance with data collection. CLG was supported by a NERC DTG studentship.

We thank the anonymous reviewers who provided valuable comments on the manuscript. “
“Cocaine is, after cannabis, the second most commonly used illicit drug in Europe. Approximately 4.1% of citizens aged between 15 and 64 years have used cocaine at least once in their lives. Swiss cities like Zurich, Geneva and Bern have been found to be among the places where cocaine consumption is highest in Europe, Oxygenase comparable to Antwerp and Amsterdam (Osterath, 2012). Cocaine use is associated with numerous medical and psychosocial consequences, including increases in risks of myocardial infarction, infectious diseases, comorbid psychiatric disorders, delinquency and violence (Compton et al., 2007, Macdonald et al., 2008, Qureshi et al., 2001 and Tyndall et

al., 2003). Currently, no pharmacological therapy has been found to be broadly effective in the treatment of cocaine dependence (for a review, see Sofuoglu and Kosten, 2006). Conversely, a large body of evidence supports the efficacy of psychosocial interventions in treating cocaine dependence. Two of the most promising interventions are contingency management (CM) and cognitive-behavioral therapy (CBT). Contingency management interventions are based on behavioral research indicating that when a behavior is reinforced, it increases in frequency. CM can help to reduce or discontinue cocaine use (Higgins, 1999). CM (for a review, see Lussier et al., 2006) and CBT (for a review, see Farronato et al., 2013 and Magill and Ray, 2009) have been proven to be efficacious for treating a variety of substance use disorders.

Genes with similar expression values between chimpanzee and macaques, but significantly different in humans, would be indicative of those changing specifically on the human lineage (hDE). Examination of hDE genes revealed

several striking findings. First, the number of hDE genes was greater in the FP than in the two other brain regions examined. For example, nearly 30% more hDE genes are detected in hFP (1,450 genes) than hCN (1,087 genes) (Figure 2D). check details This could not simply be explained by a greater number of reads in these samples, as the FP samples had fewer mapped reads on average than either CN or HP (Table S1). Moreover, the FP predominance for the lineage-specific DE genes is not observed in macaque and chimpanzee, indicating learn more that this is truly human specific. The increase in genes changing in the frontal pole is of special interest given the recent finding of an enrichment of evolutionary new genes in the human lineage specifically within the prefrontal cortex using different

methods (Zhang et al., 2011). Thus, our data identify the increasing number of genes changing specifically in the frontal cortex compared to other noncortical regions in human brain evolution. Gene ontology (GO) analyses identified enrichment of several key neurobiological processes. In the FP, genes involved in neuron maturation (FARP2,

RND1, AGRN, CLN5, GNAQ, and PICK1) and genes implicated in Walker-Warburg Rutecarpine syndrome (FKTN, LARGE, and POMT1), a disorder characterized by agyria, abnormal cortical lamination, and hydrocephalus ( Vajsar and Schachter, 2006), were enriched. Filtering the FP list for those specifically hDE in FP and not other brain regions revealed additional categories of interest including regulation of neuron projection development (e.g., MAP1B, NEFL, PLXNB1, and PLXNB2), the KEGG category for neurotrophin signaling (e.g., BAX, CSK, CALM2, and IRAK1), and the cellular component category for axon (e.g., GRIK2, LRRTM1, NCAM2, MAP1B, NEFL, and STMN2). HP hDE GO analyses uncovered enrichment of genes involved in cell adhesion (e.g., CAV2, DSG2, SDC1, SDC4, TJP2, CDH3, and NEDD9) and HP-specific analyses demonstrated enrichment for neuron differentiation (e.g., EFNB1, MAP2, NNAT, REL2, and ROBO1) and the cellular component category for synaptosome (e.g., ALS2, DLG4, SYNPR, and VAMP3). CN-specific GO analyses identified enrichment for genes involved in dendrites and dendritic shafts (e.g., CTNNB1, EXOC4, GRM7, and SLC1A2), synapse (e.g., SYNGR3, SYT6, and CHRNA3), and sensory perception of sound (e.g., SOX2, CHRNA9, USH2A, and KCNE1).

There are several results that support this finding. When subjects make reaching movements with their two arms and have the endpoint of one arm perturbed to either side of the movement, the reflex response in the perturbed arm only will act to return the hand back to the trajectory. However, when the two arms are acting together in a reaching movement, controlling a single cursor that is displayed at the spatial average of the two hands, a physical

perturbation of a single limb elicits feedback responses in both limbs to adjust the cursor’s position (Diedrichsen, 2007). This demonstrates the flexibility of OFC. Because noise is signal dependent, the optimal response is to divide the required change in the control signal BMN 673 cell line between the actuators. Another example involved manipulating the visual environment in which subjects reached. During reaching movements a sensory discrepancy produced by a difference between the visual location and the proprioceptive location of the hand could be either task relevant or irrelevant. By probing the visuomotor reflex gain using perturbations, it was shown that the reflex gain was increased in task-relevant but not for task-irrelevant

environments (Franklin and Wolpert, 2008). Similarly it has been shown that target shape modulates the size of the visuomotor reflex response (Knill et al., 2011). Liu and MAPK inhibitor Todorov (2007) investigated another predicted feature of optimal control. The theory itself predicts that feedback should be modulated differently during a movement depending on the distance to the target. At the beginning of the movement, the feedback is less important because there is sufficient time to correct very for errors that might arise in the movement. However, near the end of the movement, errors are likely to cause the target to be

missed. This was investigated by having subjects make reaching movements to a target, and jumping the target lateral to the direction of movement at different times (Figure 1A). As predicted, the subjects responded more strongly when the target jump occurred close to the end of the movement (e.g., blue paths), producing both a change in the movement speed and lateral movement to the target (Figures 1B and 1C). Interestingly, in this case, subjects also failed to completely compensate for the target displacement. For target jumps occurring near the start of movement, no change occurred in the movement speed, and the movement trajectories slowly converged to the shifted target location over the rest of the movement. These results were explained by an OFC model of the task that was able to reproduce the characteristics of the human movements (Figures 1D–1G). The optimal control model has three time-varying feedback gains that act throughout the movement (Figure 1E).

Although the clathrin-coated pits accumulating in the DKO had a size similar to that of synaptic vesicles, they were less densely packed than synaptic vesicles (Figure 6B versus Figure 6C). Furthermore, the overall loss of synaptic vesicles was not fully accounted for by the increase in clathrin-coated pits (Figure 6F), suggesting that synaptic vesicle membranes might be partially trapped within the axonal plasma membrane outside of pits. Such a possibility agrees with the increased steady-state plasma membrane abundance of the vGlut1-pHlourin reporter in DKO neurons (Figure 4A). As a result, immunofluorescence for intrinsic membrane proteins

of synaptic vesicles (synaptophysin, synaptobrevin, synaptotagmin, and SV2), which are expected to be enriched in these pits, was less punctate in DKO Selleckchem MEK inhibitor nerve terminals than in control

nerve terminals where the puncta correspond to abundant and highly clustered synaptic vesicles (Figure 7A and data not shown). However, not surprisingly, given the loss of synaptic vesicles, the most striking change was observed for synapsin 1 and Rab3a, two peripheral proteins of synaptic vesicles (De Camilli et al., 1990 and Fischer von Mollard et al., VX-809 concentration 1990) that dissociate from the vesicle prior to, or in parallel with, exocytosis and then reassociate with newly reformed synaptic vesicles once the endocytic/recycling journey is completed (Chi et al., 2001, Giovedi et al., 2004 and Star et al., 2005). Immunoreactivities for these proteins lost their normally highly punctate enrichment within presynaptic terminals and became more diffusely spread out along the axonal length (Figure 7A). Biochemical analysis of the subcellular localization

of synaptic vesicle proteins (synaptotagmin 1 and synaptophysin) by a cell surface biotinylation-based strategy Edoxaban confirmed an increase in their plasma membrane levels (Figure 7B). The ultrastructural changes of many DKO nerve terminals described above (Figure 6) revealed a near-complete switch from a “secretion-ready mode” to an “endocytic mode,” where the large clusters of synaptic vesicles, which represent the defining morphological feature of synapses, are replaced by a massive accumulation of clathrin-coated pits. We asked whether these dramatic changes are reversible upon silencing of electrical activity (Ferguson et al., 2007). Neurons were first allowed to differentiate over 14 days in culture so that they would exhibit a strong accumulation of endocytic intermediates, and then exposed to tetrodotoxin (TTX; 1μM, overnight) to silence neuronal network activity. After TTX treatment, immunofluorescence for α-adaptin (and other clathrin coat components) became diffuse (Figures 8A–8C) and electron microscopy showed a reduction of clathrin-coated pit number (Figures 8D–8F).

, 2005; Doya, 1999; Redgrave et al., 2010; Wunderlich et al., 2012). Model-free RL learns the course of action leading to maximum long-run reward through a temporal difference (TD) prediction error teaching signal (Montague et al., 1996). Integrase inhibitor By comparison, model-based choice involves forward planning, in which an agent searches a cognitive model of the environment to find the same optimal actions (Dickinson

and Balleine, 2002). An unresolved question is whether neuromodulatory systems implicated in value-based decision making, specifically dopamine, impact on the degree to which one or the other controller is dominant in choice behavior. Phasic firing of dopaminergic INCB024360 concentration VTA neurons encodes reward prediction errors in reinforcement learning (Hollerman and Schultz, 1998; Schultz et al., 1997). In humans, drugs enhancing dopaminergic

function (e.g., L-DOPA) augment a striatal signal that expresses reward prediction errors during instrumental learning and, in so doing, increases the likelihood of choosing stimuli associated with greater monetary gains (Bódi et al., 2009; Frank et al., 2004; Pessiglione et al., 2006). While previous research has focused on the role of dopamine in model-free learning, and value updating via reward prediction errors, its role in model-based choice remains poorly understood. For example, it is unknown if and how dopamine impacts on performance in model-based decisions and on the arbitration between model-based and model-free controllers. This is the question we address in the present study, in which we formally test whether dopamine influences the degree to which behavior is governed by either control system. We studied 18 subjects on a two-stage Markov decision task after being treated with Madopar (150 mg

L-DOPA plus 37.5 mg benserazide) or a placebo in a double-blind, fully counterbalanced, repeated-measures design. We used a task previously shown to distinguish model-based and model-free Parvulin components of human behavior and in which subjects’ choices pertain to a mixture of both systems (Daw et al., 2011). These properties render this task optimally suited to test the influence of a pharmacological manipulation on the degree to which choice performance expresses model-based or model-free control. In each trial, subjects made an initial choice between two fractal stimuli, leading to either of two second-stage states in which they made another choice between two different stimuli (see Figures 1A and 1B). Each of the four second-stage stimuli was associated with probabilistic monetary reward. To incentivize subjects to continue learning throughout the task, we changed these probabilities slowly and independently according to Gaussian random walks.

, 2005, Jaworski and Burden, 2006 and Li et al., 2008). Moreover, when β-catenin is ablated in muscle cells using HSA-Cre, mutant mice die immediately after birth without functional NMJs (Li et al., 2008). We further generated double knockout mice—HSA-Cre;HB9-Cre;LRP4f/f (HSA/HB9-LRP4−/−)—in which the LRP4 gene is ablated in both motoneurons and muscles. Remarkably, HSA/HB9-LRP4−/− pups died soon after birth with cyanosis. AChR cluster formation was severely

impaired (Figure 4) as clusters were almost undetectable in HSA/HB9-LRP4−/− diaphragms, except occasional smaller, weak clusters. Their CH5424802 cost size was only 9.6% of that in LRP4loxP/+ control and 34.9% of that in HSA-LRP4−/− (Table S1). Quantitatively, the number of AChR clusters in double KO pups was reduced by 95.5%, compared to LRP4loxP/+ controls, and by 96.2%, compared to HSA-LRP4−/− pups (588 ± 95.9 per mm2 in controls, 707 ± 89.2 per 3-Methyladenine mouse mm2 in HSA-LRP4−/−, and 26.7 ± 15.8 per mm2 in HSA/HB9-LRP4−/− pups) (Table S1). These results demonstrate that the ablation of LRP4 in motoneurons further impairs AChR clustering in HSA-LRP4−/− mice and identifies a role of motoneuron LRP4 in AChR clustering (Figure 4F). As observed in LRP4mitt null mice, aneural AChR clusters were almost undetectable in diaphragms of E13.5 HSA/HB9-LRP4−/− embryos, indicating impaired prepatterning of muscle fibers (Figure S4A). The presynaptic deficits in HSA/HB9-LRP4−/−

mice also resemble those in LRP4mitt null as the number and length of secondary and tertiary branches of these two genotypes were similar (Table S1) (Figures 4A and 4B).

However, compared to HSA-LRP4−/−, secondary branches were significantly longer in HSA/HB9-LRP4−/− and LRP4mitt null mice, indicating a role of motoneuron LRP4 in nerve terminal differentiation. This notion was supported by increased number of tertiary and quaternary branches in HSA/HB9-LRP4−/− and LRP4mitt mice (Figure 4E). Moreover, motor nerve terminals appeared to be fragmented in diaphragms of both HSA/HB9-LRP4−/− mice and LRP4mitt null mice. In contrast, such discontinuous intumescence of nerve terminals was not observed in HSA-LRP4−/− muscles (Figures S4B and S4C). LRP4 null mutation or conditional mutation (in muscles or in both muscles and motoneurons) had little during effect on the number and distribution of motoneurons (Figures S4D and S4E), suggesting that LRP4 controls neuron differentiation, but not survival. To investigate how muscle LRP4 regulates presynaptic differentiation, we tested whether LRP4 could be synaptogenic using an established coculture assay (Biederer et al., 2002, Fogel et al., 2011, Graf et al., 2004 and Scheiffele et al., 2000). HEK293 were transfected with EGFP alone (control) or together with LRP4 and cocultured with cortical neurons. Cells were stained for synapsin and SV2, both markers for presynaptic differentiation.

, 1976 and Williams et al., 2009). Conventionally, identification of Eimeria spp. is based on morphological features of the sporulated this website oocyst, sporulation

time and location/scoring of pathological lesions in the intestine but the procedures involved require specialist expertise and have serious limitations due to their subjective nature and overlapping characteristics among different species ( Long and Joyner, 1984). Mixed infections also pose a problem for the precise discrimination of species using morphological methods. Alternative species-specific diagnostics are required to inform routine animal husbandry, veterinary intervention and epidemiological investigation. One such alternative is Eimeria species-specific polymerase chain reaction (PCR). Over the last 20 years several PCR assays have been developed that target genomic regions of one or more Eimeria species including the E. tenella 5S or small subunit rRNAs ( Stucki et al., 1993 and Tsuji et al., 1999), the first and second internal transcribed spacer regions (ITS-1 and -2) ( Gasser et al., 2001, Lew et al., 2003, Schnitzler et al., 1998, Su et al., 2003 and Woods et al., 2000) and gene-specific targets including sporozoite antigen gene EASZ240/160 ( Molloy et al., 1998). In one

of the most comprehensive Panobinostat studies Fernandez et al. (2003) designed species-specific primers for Eimeria spp. from a group of SCAR (Sequence-Characterized Amplified Region) markers and used them to develop a multiplex PCR for the simultaneous discrimination of different Eimeria spp. in a single reaction. Importantly, many of these assays have been shown to be capable of detecting genomic DNA representing

as few as 0.4–8 oocyst-equivalents ( Fernandez et al., 2003 and Haug et al., 2007), or as few as 10–20 oocysts ( Carvalho et al., 2011a and Frölich et al., 2013). Nonetheless, routine application with field samples remains complicated Bay 11-7085 by factors including DNA extraction from within the tough oocyst wall and faecal PCR inhibition ( Raj et al., 2013). Broader uptake of PCR-based Eimeria diagnostics can be significantly enhanced by establishment of an optimised protocol. Similarly, identification of the most sensitive and robust primers from the large number of Eimeria-specific PCR assays that are available is an essential step towards standardised epidemiological analyses appropriate for international comparison. Validation of collection, purification and PCR amplification protocols across different labs, in multiple countries, is a key step in the establishment of optimal sampling strategies as we seek to improve understanding of parasite field biology. Beyond PCR other approaches to species-specific identification of Eimeria include quantitative PCR (qPCR) ( Morgan et al.

, 2006). Intriguingly, overexpression of PDK1 and Akt Selleckchem Ceritinib also increases synapse number ( Martín-Peña et al., 2006, Knox et al., 2007 and Howlett et al., 2008; L.C. and G.D., unpublished data), and, as for PI3K, these manipulations also increase

ethanol sensitivity. Conversely, overexpression of PI3KDN decreases synapse number ( Martín-Peña et al., 2006) as well as ethanol sensitivity. Since aru is required for the PI3K/Akt pathway’s effects on ethanol sensitivity, we speculate that aru might be a downstream effector of PI3K/Akt pathway-mediated regulation of synapse number. Regardless of the precise genetic mechanism, we propose that genetic pathways that alter the number of synaptic terminals also affect the flies’ sensitivity to the sedating effects of ethanol. In support of this, we show that Rheb overexpression, which activates the TORC1 pathway independently of Akt in Drosophila KRX 0401 ( Teleman, 2010), increases synapse number ( Knox et al., 2007) and dramatically enhances ethanol sensitivity. Second, a mutation in amnesiac, a neuropeptide that activates the PKA pathway ( Feany and Quinn, 1995), both increases ethanol sensitivity ( LaFerriere et al., 2008) and synapse number (this work). It is therefore likely that correct regulation of synapse number is a principal mechanism

that ensures normal ethanol sensitivity of adult Drosophila. Manipulations of aru, PI3K, and Rheb in the PDF neurons all increase ethanol sensitivity; these neurons also function to regulate cocaine sensitivity ( Tsai et al.,

2004). In addition, PDF neurons appear particularly sensitive to environmental influences. Phosphatidylinositol diacylglycerol-lyase In particular, the number of PDF synaptic terminals is decreased by social isolation ( Donlea et al., 2009). In further support of the strong correlation between synapse number and ethanol sensitivity, we find that (1) aru mutants have an increased number of PDF synaptic terminals, (2) social isolation (which decreases PDF synapse number) decreases ethanol sensitivity, and (3) social isolation restores normal ethanol sensitivity and PDF synapse number to the aru8.128 mutant. As this restoration occurs in the absence of aru in the nervous system, the regulation of synapse number by social isolation must occur by an unknown parallel pathway. Taken together, these data point to a causal relationship between synapse number and ethanol sensitivity. We doubt that this relationship directly involves Egfr, as overexpression of Egfr decreases ethanol sensitivity ( Corl et al., 2009), whereas social isolation downregulates Egfr expression ( Donlea et al., 2009) and decreases ethanol sensitivity (this work). Interestingly, C. elegans reared in isolation show reduced sensory responses and altered synapses ( Rose et al., 2005). Moreover, social isolation in rodents, starting shortly after weaning, increases ethanol preference ( Sanna et al., 2011).

We performed additional analyses to check for possible biases imposed by thresholding (>6× standard deviation of the baseline): First, we computed input across a 3 × 3 grid around the soma (Figures S6D–S6F). Second, we generated a mask by averaging the responses across cells within a group. The mask was defined by significant responses (>5× standard deviation). The mask was then used to compute input from the original maps (Figures S6G–S6I). Third, we also computed the mean pixel value over the entire map without thresholding (data not shown). These three analysis methods yielded consistent results. Since the time between stimulus and the beginning of the baseline period for the next trial was

fairly short (300 ms), we corrected for bleedthrough across trials (baseline drift). Because the grid size buy MK-1775 for stimulation was always larger than the dendritic

arbors of the recorded cells (for example, Figure 3B), we estimated the baseline drift from the traces far outside the cell’s dendritic arbor (these traces were “blanks” that could not have contained true responses; they thus represent pure baseline drift). We then subtracted the baseline selleck chemical drift from the mean value of all other traces. Paired comparisons used the nonparametric Wilcoxon signed-rank test (Figure 6 and Figure 7, S6, S7, and S9). This work was funded by the Howard Hughes Medical Institute. new We thank Gordon Shepherd for advice and extensive discussions; Asaf Keller for advice on electrical microstimulation in vM1; Tim O’Connor for programming; Brenda Shields, Amy Hu, Alma Arnold, and Kevin McGowan for technical support; Takashi Sato and Haining Zhong for help with experiments and analysis; Stefanie Kaech Petrie for help with the blind retrograde beads counting; and Diego Gutnisky and Zengcai Guo for comments on the manuscript. “
“The olfactory bulb is the first processing center of information about odorants. In mammals, the olfactory system is the

only sensory system in which peripheral information is sent directly to the cortex, bypassing the sensory thalamus. Therefore, it has been proposed that the bulb combines the function of peripheral sensory system and the thalamus (Kay and Sherman, 2007). Consistent with this proposal, several studies have demonstrated that activity in the olfactory bulb reflects not only sensory information but also the animal’s internal state (Adrian, 1950 and Rinberg et al., 2006) and task-dependent variables (Doucette and Restrepo, 2008, Fuentes et al., 2008 and Kay and Laurent, 1999). The relative simplicity of the anatomy of the olfactory bulb and the combination of both sensory- and state-dependent activity in a single network make it an attractive model for the study of principles of sensory information processing. The surface of the olfactory bulb is covered by ≈≈ 2000 glomeruli.