, 2009). An analogous mechanism may be controlling NFIA expression during astro-glial development. Another key consideration in our understanding of the transcriptional mechanisms controlling the induction of NFIA is the role of epigenetics. Chromatin-modifying factors, PcG genes Ring1b and Ezh2, have been implicated in the repression of neurogenesis, a key JQ1 molecular weight process in the gliogenic switch, in the embryonic cortex, and DNA methylation has been implicated in

regulating the expression of GFAP during astrocyte differentiation ( Fan et al., 2005, Hirabayashi et al., 2009 and Takizawa et al., 2001). Future studies will be aimed at examining the link between epigenetic modifiers and NFIA induction. Biochemical studies demonstrate that NFIA and Sox9 physically associate and collaborate to induce the expression of a subset of genes just after the initiation of gliogenesis. Given that Sox9 function is associated with neural stem cell maintenance, initiation of gliogenesis, and various aspects of glial differentiation during CNS development, its interaction with NFIA GSK1210151A molecular weight may mediate

a subset of these diverse roles. Although Sox9 induction of NFIA may trigger the generation of glial fates, it does not result in a loss of neurogenic potential from these populations, as Sox9 expression is required at these stages for neurosphere formation in vitro, and NFIA is not sufficient to suppress neurogenesis (Deneen et al., 2006 and Scott et al., 2010). Therefore,

we propose a model whereby Sox9 function during the gliogenic switch evolves from maintaining neural stem cells and initiating gliogenesis (E10.5–E11.5) to promoting glial lineage progression (E11.5–E12.5) by controlling a set of genes that contribute to early gliogenesis (Figure 8). This shift in Sox9 function during glial lineage progression is facilitated by a feedforward mechanism, where Sox9 induces NFIA expression during glial initiation and subsequently associates with NFIA to drive lineage progression. Hence, Sox9 coordinates glial initiation and glial lineage progression via regulation and association with NFIA, respectively. Our rescue analysis of targets of the Sox9/NFIA complex found that these genes restore panglial second or ASP-specific identity during gliogenesis. The role of this complex in ASP formation is supported by specific defects at later developmental stages in astrocyte differentiation in both Sox9 and NFIA knockout mice (Deneen et al., 2006 and Stolt et al., 2003). That this complex appears to influence ASP development raises the question of whether it also has a specific role in oligodendrocyte precursor (OLP) development. Given that both NFIA and Sox9, and the targets we identified, are also expressed in OLPs, it is possible that a subset of their targets specifically contribute to OLP development.

GM volume is uncorrelated with preferences for altruism in the domain of disadvantageous inequality α (p = 0.551, small volume [SV] FWE corrected) or with preferences for positive reciprocity θ (p = 0.581, SV FWE corrected) or negative reciprocity δ (p = 0.629, SV FWE corrected). Finally, note that all our results are robust to the exclusion of the participant with extreme values of β and α (top left data point in Figure 2). When we repeat the analyses without the data from this participant, our main findings remain the same: using the independent ROI specified

above, β correlates significantly (r = 0.57, p = 0.0013) with TPJ GM volume, while all other parameters do not (p > 0.10). These findings suggest that GM volume in TPJ may be a crucial neuroanatomical basis for subjects’ baseline willingness to behave altruistically because BTK inhibitor the preference parameter β determines a subject’s generosity in the domain of advantageous inequality. This parameter determines, in particular, the maximal cost (denoted by w¯) a subject is willing to bear to increase the partner’s payoff by a given amount (say by one unit). The higher β, the higher the subject’s maximum willingness to pay w¯ to increase the partner’s payoff by one unit (see Figure S2). Therefore, subjects with a high β are generally willing to consider behaving altruistically for a much larger

range Thiazovivin of costly altruistic actions than those with a low value of β. In other words, if the costs of an altruistic act are relatively high, a subject with a relatively high value of β is still willing to consider behaving altruistically, while a subject with a low Thymidine kinase value of β will behave selfishly in this situation. This means that w¯ represents a subject-specific cutoff value such that if the actual cost of the altruistic act is below w¯, the subject will consider making an altruistic choice, while the subject behaves selfishly if the actual cost is above w¯. This insight about the role of β (and the

implied role of w¯), together with the known functional role of the TPJ in perspective-taking tasks (Decety and Lamm, 2007, Frith and Frith, 2007, Saxe and Kanwisher, 2003 and Young et al., 2010), can help us establish a link between GM volume in the TPJ and functional activations in TPJ during decision making in our task (in which subjects faced many different cost levels across trials). A high value of β implies a high maximum willingness to pay w¯, meaning that the correlation between GM volume in right TPJ and β should translate into a correlation between GM volume and w¯ (see Figure 4A). In addition, taking the other individual’s perspective seems particularly necessary in those cost situations in which a subject is in principle willing to behave altruistically (i.e., when the actual cost is below w¯) but in which self-interest provides a strong obstacle for altruistic acts because the cost is close to w¯.

The visual cortex has been described as performing receptive-field transformations that

are best computed by a series of precisely wired feedforward networks (Hubel and Wiesel, 1962), although this view has been controversial from the beginning. The hippocampus, on the other hand, has been described as a learning machine that makes associations between its complex inputs by strengthening some connections and weakening others. The details of how this learning results in the storage of specific memories are not always specified, but it is widely accepted that plasticity results in the long-term storage of information. It is ironic that the two fields, sensory processing in neocortical networks versus information storage in recurrent hippocampal networks, have had such different biases. In the network for which we have far more information about input/output transformations in vivo—information processing in neocortical Selleck DAPT networks—the

idea of functional specificity has not often been championed. Until recently, connections between cortical neurons (excitatory neurons in particular) were often presumed to be random or at most having topographic (Braitenberg and Schüz, 1998) or cell-type specificity. The inverse problem, of reading out the information stored in connections, is one that has received even less attention. In one scenario, it has been proposed that a temporal sequence in the firing of neurons can be predicted by analyzing the graph of their interconnections (Seung, 2009). Alternatively, it is likely that the MK-8776 chemical structure spatial relations in a sensory map can be inferred from the connections in a network. In the LGN, as in the cortex (Hubel and Wiesel, 1962), there is a coarse grain retinotopic map at the scale of hundreds of μm to several mm, but the most map breaks down at the scale that is smaller than 100 μm. Nonetheless, physiological information about the location of receptive fields can be examined so that nearby neurons can be placed in a precise retinotopic map (as in Alonso et al., 2001). The hope is that the wiring diagram can also be used to perform the same sorting operation to yield spatial information about receptive fields

without any functional measurements. This idea was first proposed by Cleland (1986) for the simple and highly structured wiring diagram from retina to LGN, but it is very likely to hold for other wiring diagrams based on retinotopic relations, such as Hubel and Wiesel’s model of the simple cell (Hubel and Wiesel, 1962). A major goal of functional connectomics should be to test this conjecture: to examine not only whether function can predict connectivity, but also whether connectivity can predict function. At minimum, synaptic circuit reconstruction requires several things: the ability to recognize a synapse and the ability to assign the pre- and postsynaptic neurons that form the synapse. Recently, there has been a great expansion in the tools for reconstruction of circuits in the nervous system.

, 1995, Fuhrer et al., 1997 and Moransard et al., 2003). This interaction was abolished or attenuated in muscle-specific, double, and null LRP4 mutant muscles, but not in motoneuron-specific www.selleckchem.com/products/ABT-737.html mutant muscles ( Figures S7A and S7B).

Finally, motoneuron LRP4 may be unable to generate the retrograde signals for presynaptic differentiation. Results of this study highlight distinct functions of motoneuron and muscle LRP4 for NMJ formation and maintenance (Figure 8). In a working model, motoneuron LRP4 contributes to formation of primitive AChR clusters and initial postsynaptic differentiation. This effect is probably mediated by ecto-LRP4, released by extracellular cleavage, which acts as agrin’s receptor in trans to stimulate AChR clustering. Motoneuron LRP4 is also necessary for well-being of motor axons. On the other hand, muscle BAY 73-4506 cost LRP4 plays a dominant role in NMJ formation. It instructs where nerve terminals stop and where muscle

fibers form AChR clusters, is essential for NMJ maturation, and regulates presynaptic differentiation. These observations may have an implication for understanding how LRP4-like “receptors” work in other contexts including CNS synapse formation and plasticity. Chemicals were purchased from Sigma-Aldrich Company unless otherwise indicated. Alexa Fluor 350 phalloidin (A22281; 1:200 for staining) and Alexa Fluor 594 α-bungarotoxin (BTX) (B-13423; 1:3,000 for staining) were purchased from Invitrogen. Matrix metalloproteinase (MMP) inhibitor N-[(2R)-2(hydroxamideocarbonylmethyl)-4-methylpantanoyl]-L-tryptophan Sodium butyrate methylamide (GM6001) (364206) and β-Secretase inhibitor IV (565788) were purchased from Calbiochem (EMD Millipore). Information of antibodies was as follows: neurofilament (Millipore) (AB1991; 1:1,000 for staining); synaptophysin (Dako) (A0010; 1:2,000 for staining); LRP4 (ECD) clone N207/27 (UC Davis/NIH NeuroMab Facility) (75-221; 1:1,000 for western

blotting); Flag (M2) (Sigma-Aldrich) (F3165; 1:2,000 for western blotting); 4G10 (Millipore) (05-1050X; 1:2,000 for western blotting); GFP (Abcam) (ab6556; 1:2,000 for western blotting); β-III-Tubulin (Covance) (MMS-435P; 1:1,000 for immunostaining); HB9 antibodies (C-terminal 307–403, gift from Dr. Samuel Pfaff; 1:4,000 for staining) (Thaler et al., 1999); synapsin (sc-20780; 1:500 for immunostaining); SV2 (Developmental Studies Hybridoma Bank) (1:1,000 for immunostaining); α-Tubulin (sc-23948; 1:3,000 for western blotting); β-actin (Novus) (NB600-501; 1:3,000 for western blotting); and Alexa Fluor 488 goat anti-rabbit IgG (Invitrogen) (A-11034, 1:1,000 for staining). Polyclonal horseradish peroxidase (HRP)-conjugated goat anti-rabbit IgG (32260), goat anti-mouse IgG (32230), and goat anti-rat IgG (31470) secondary antibodies were purchased from Pierce (Thermo Scientific) (1:3,000 for western blotting). Anti-MuSK, anti-rapsyn, and anti-AChRα antibodies were described previously (1:1,000 for western blotting) (Luo et al., 2002).

, 2007). The correlations between activation in the two left prefrontal regions and the HC may suggest that these effects take place at the level of the hippocampal memory traces. Critically, either of these accounts predicts

that the effectiveness of thought substitution as an approach to forgetting depends on the relatedness of the substitute to the unwanted memory. That is, if the two memories are coded by overlapping neuronal populations, it would not be possible to completely weaken the avoided memory while strengthening the substitute trace (Norman et al., 2007; Goodmon and Anderson, 2011). In such cases, it might be more effective to engage a more systemic direct suppression mechanism. In line with this proposal, direct suppression can sometimes induce selleck cue-independent forgetting in situations in which thought substitution fails to do so (Bergström et al.,

2009). An important avenue for Selleck GSK1210151A future research is to characterize the conditions determining the efficacy of the two mechanisms. To conclude, there seem to be at least two routes that can lead to voluntary forgetting: a direct suppression mechanism that systemically disrupts retrieval processes and a thought substitution mechanism that impairs retention by resolving competition at the level of conflicting, individual memories. Both of these mechanisms limit momentary awareness of unwanted memories—one by suppressing representations needed to achieve awareness of a memory and

the other by activating representations through that occupy the limited capacity of awareness. Both ways of controlling awareness also induced, in the present study, behaviorally indistinguishable forgetting. Strikingly, despite these functional similarities, the data reported here indicate that these mechanisms are mediated by distinct neural networks that achieve their functions in very different ways. Whereas direct suppression appears to reflect hippocampal suppression originating from the DLPFC, thought substitution seems to reflect the resolution of competition mediated by cPFC-mid-VLPFC coupling and possible interactions with hippocampal retrieval processes. Appreciation of these distinct systems underlying the control of unwanted memories may help in the development of treatments that remediate mental health problems associated with a deficient regulation of memories, such as might occur in the aftermath of trauma (Dunn et al., 2009; Brewin, 2011). Forty right-handed volunteers participated. They all reported no history of psychiatric or neurological disorder and gave written informed consent as approved by the local research ethics committee. Four participants were excluded either due to excessive movement (two) or falling asleep in the scanner (two). Thus, data from 36 participants are reported, with half performing thought substitution (six males; mean age: 23.

e., regional distribution) and subsequently determine their precise layering within the radial axis (i.e., laminar distribution).

As local circuit neurons, interneurons could be potentially incorporated in any cortical region. The question is whether interneurons are specified to migrate to precise locations or they just colonize the cerebral cortex without being targeted to specific coordinates. In other words, is there a correlation between their site of origin within the subpallium and their distribution along the rostrocaudal and mediolateral dimensions of the cortex? Multiple lines of evidence suggest that the different classes of cortical interneurons are born in specific regions of the subpallium (Gelman and Marín, Selleckchem Bortezomib 2010 and Wonders

and Anderson, 2006) (Figure 1). In brief, the embryonic subpallium has five major proliferative regions: the lateral, medial, and caudal Adriamycin mw ganglionic eminences (LGE, MGE, and CGE, respectively), the preoptic area (POA), and the septum. The large majority of PV+ and SST+ interneurons derive from the MGE (Butt et al., 2005, Flames et al., 2007, Fogarty et al., 2007, Inan et al., 2012, Taniguchi et al., 2013, Wichterle et al., 2001, Xu et al., 2004 and Xu et al., 2008). In turn, the CGE gives rise to most of the remaining interneurons, including bipolar VIP+ interneurons, most neurogliaform neurons, and NPY+ multipolar interneurons (Butt et al., 2005, Miyoshi et al., 2010, Nery et al., 2002 and Xu et al., 2004). Finally, the POA generates a small, but diverse, contingent of PV+, SST+, and NPY+ interneurons (Gelman et al., 2009 and Gelman et al., 2011). Although the vast majority of cortical

interneurons originate in the embryonic subpallium and migrate as postmitotic cells toward the cortex, postnatal sources of cortical interneurons seem to exist. One of these has been identified most in the dorsal white matter and comprises what seems to be an expanding pool of progenitor cells possibly derived from the LGE and/or CGE (Riccio et al., 2012 and Wu et al., 2011). Interestingly, these interneurons appear to follow a unique specification program and differentiate later than interneurons born in the embryo. Interneurons from this source populate primarily the lower layers of the anterior cingulate cortex. In addition, the adult subventricular zone (SVZ), the main postnatal source of olfactory bulb interneurons, also seems to give rise to some interneurons that populate forebrain structures other than the olfactory bulb, including the neocortex, caudoputamen nucleus, and nucleus accumbens (Inta et al., 2008). Intriguingly, some of the SVZ-derived interneurons that populate the deep layers of the frontal cortex share some morphological and functional features with olfactory bulb interneurons.

, 2010 and Wu et al., 2011). Tubulovesicular structures carrying APP and BACE-1 moved bidirectionally selleck chemical within the dendritic shaft and the movement was microtubule dependent, resembling the movement of motor-driven vesicular cargoes (Figures S1E and S1F; Movie S1; Table S1; also see Tang et al., 2012). As shown in the kymographs (Figure 1B), localization of both stationary and mobile APP/BACE-1 particles were largely nonoverlapping (Figure 1B, bar graph). Subtle differences were also seen in the transport kinetics of APP and BACE-1 vesicles (Figure 1C), further suggesting that these two cargoes were largely conveyed in distinct

organelles. Next, to examine the biogenesis of APP/BACE-1 cargoes, we focused on the distribution of these proteins within the neuronal soma. As both

APP and BACE-1 are transmembrane proteins, they would be expected to traffic via the ER→Golgi (biosynthetic) pathway. In accordance with this, we found significant colocalization of APP and BACE-1 in the perinuclear region (Figure 1D)—a distribution reminiscent of the ER-Golgi network in these neurons (Dresbach et al., 2006; also see overlap with Golgi marker galactosyl-transferase [GalT] below). However, we also saw BACE-1 particles Paclitaxel chemical structure (Figure 1D, arrowheads) that did not colocalize with APP, suggesting that BACE-1 may be sorted into a distinct compartment after trafficking via the ER→Golgi pathway. If the latter was true, conditions inhibiting the emergence of Golgi-derived vesicles would be expected to “trap” APP/BACE-1 within the Golgi network.

To test this idea, we incubated cultured neurons at 20°C for 2 hr—conditions expected to block the exit of Golgi-derived proteins (Dresbach et al., 2006). Indeed, we found that the perinuclear colocalization of APP/BACE-1 was significantly increased under these conditions (Figure 1D, inset), further suggesting that the subset of somatic BACE-1 that failed to colocalize with APP at 37°C Bay 11-7085 was a consequence of post-Golgi processing. Increased colocalization of BACE-1 with GalT at 20°C also supports this overall model (Figure 1D). Moreover, APP/BACE-1 colocalization decreased over 6–36 hr posttransfection (Figure 1D, bottom), further suggesting a differential sorting after biogenesis. The spatial segregation of APP and BACE-1 was also evident in sucrose density gradients of P100 (“vesicle pellet”; DeBoer et al., 2008; see fractionation strategy in Figure S1G) mouse brain fractions in vivo, in which endogenous holo-APP/BACE-1 were largely localized to distinct fractions (Figure 1E). Next, we sought to determine the specific organelles carrying BACE-1 and APP in neurons.

All extrastriate areas investigated, with the exception of PM, encode faster TFs than V1, suggesting a role for these higher areas in the processing of visual motion. For a subset of areas, AL, RL, and AM, this role is further supported by a significant increase in direction selectivity across each population. Another subset of areas, LI and PM, prefer high

SFs, suggesting a role in the processing of structural detail in an image. Nearly all higher visual areas improve orientation selectivity compared to V1. Every visual area could be distinguished from every other visual area statistically by comparing scores on multiple tuning metrics (and AL from RL based on fraction of responsive neurons), indicating functional specialization of spatiotemporal information processing PS-341 in vivo across mouse visual areas. The combination of distinct retinotopic representations and functionally specialized neuronal populations establish selleck that mouse visual cortex is composed of several

discrete visual areas that each encode unique combinations of visual features. These findings reveal that the mouse visual system shares fundamental organizational principles with other species and is more highly developed than expected from previous work focusing almost exclusively on V1. Future studies examining selectivity for more complex stimuli under different behavioral conditions may reveal additional specializations of each visual area. Striking similarities are evident among subsets of extrastriate areas along specific feature dimensions. These complex relationships likely reflect underlying rules of connectivity that link processing between certain areas, and may relate to the grouping of areas into hierarchically organized parallel pathways. Areas AL, RL, and AM are all highly direction selective and respond to high TFs and low SFs. These properties have served as hallmarks of the dorsal pathway in other

species (Maunsell and Newsome, 1987, Nassi and Callaway, 2009 and Van Essen and Gallant, 1994) and suggest that AL, RL, and AM perform computations related to the analysis of visual motion. This role is further supported crotamiton by the anatomical position of these areas in the posterior parietal cortex, which corresponds to the location of dorsal stream areas in other species and is closely related to neural systems for spatial navigation and motor output (Kaas et al., 2011, Kravitz et al., 2011 and Ungerleider and Mishkin, 1982). In contrast, areas LI and PM respond to high SFs, and PM is highly orientation selective, suggesting a role in the analysis of structural detail and form in an image (Desimone et al., 1985 and Maunsell and Newsome, 1987).

, 43% of Strongyloides sp., this website 8% of Trichostrongylus sp. and 1% of Oesophagostomum sp. The native pasture paddocks were

predominantly made up of Sida cordifolia, Croton sonderianus, Pithecolobium dumosum, Cheilanthes bauhinia, Combretum leprosum, Mimosa tenuiflora, Desmodium sp., Firmulus phaseolus, Senna sp., Zornia sp. and Pilosocereus pachycladus. Were used 21 permanent male goats, with a mean age of 6 months, crossbred Boer × Saanen. Fifteen days before the beginning of the experiment, the animals received the oral anthelmintic Levamisole Hydrochloride (5 mg/kg l. w.) for three consecutive days. Seven days after the first de-worming were counted the eggs per gram of feces (EPG), by the technique of Gordon and Whitlock (1939), three tests from the same sample, where all animals were negative. The animals were randomly divided into three groups: D. flagrans group, each animal received 3 g of pellets (0.6 g of mycelium) containing D. flagrans (AC001) for each 10 kg l. w., twice a week for 6 months; Moxidectin

0.2% group received 0.2 mg/kg of Moxidectin 0.2% orally, every 30 days, for 6 months; Control group, each animal received 3 g of pellets without fungi per 10 kg l. w., twice a week for 6 months. Each group was kept in a paddock at a stocking rate of 0.3 animal Alectinib unit per hectare. Every day, all animals were supplemented with protein-energy concentrate at a concentration of 0.75% l. w., with balanced mineral salt and water ad libitum. To prevent deaths, salvage anthelmintic treatments were performed individually when the animal’s packed cell volume (PCV) was less than 16%, being used Levamisole Hydrochloride (5 mg/kg l. w.). Each month, three Boer × Saanen male tracer goats, mean age of 8 months, without gastrointestinal nematodes by treatment with Levamisole

Hydrochloride (5 mg/kg l. w.), were placed in the permanent herd, one in each paddock, for 30 all days without receiving any treatment. After this period, the animals were removed from the paddocks and remained in individual boxes for 14 days, and fed with Leucaena leucocephala hay (13%), corn (47%), Pennisetum purpureum (17%), and balanced mineral salt (3%). Then, the animals were sacrificed and necropsied, according to international standards set by the World Association for the Advancement of Veterinary Parasitology (WAAVP), described by Vercruyse et al. (2002). The animals’ abomasums were opened at their greatest curvature and the contents were stored in a container, where the total volume was kept in formaline 5%. The abomasum was submerged in a saline solution at 39 °C for 6 h, being collected the sediment, which was preserved in formaline 5%. Similar procedures were performed for the small and large intestines. The counting and identification of recovered helminths were performed according to Ueno and Gonçalves (1998).

These evoked currents were blocked by NBQX (12.9 ± 4.5% of control, n = 5) but had unusual properties including slow kinetics (10%–90% rise time 6.7 ± 0.9 ms, decay τ 36.3 ± 1.1 ms, n = 19), virtually no trial-to-trial amplitude variability (coefficient of variation 0.05 ± 0.01, n = 19), and little sensitivity to membrane potential (7.5 ± 2.7% reduction in amplitude from −80 mV to −40 mV, n = 7) (Figure S1 available

online). These responses were also observed in cells in which the primary apical dendrite was severed (n = 3). Although we cannot rule out the possibility that these NVP-BKM120 molecular weight small responses reflect synaptic contacts that only occur onto electrotonically remote regions of lateral dendrites or axons, they could also reflect glutamate spillover from cortical fibers onto distal processes, intracellular detection of local field potentials, or gap junctional coupling with cells receiving

direct synaptic input. Regardless of their exact origin, these small currents did not have an obvious effect on mitral cell excitability since they caused only weak membrane depolarization (0.3 ± 0.1 mV at rest, n = 9) and never elicited APs. Granule cells are thought to be the major target of direct excitation from cortical feedback projections (Strowbridge, 2009). Indeed, brief light UMI-77 research buy flashes evoked EPSCs in GCs (Figure 3A1) with fast kinetics (10%–90% rise time: 0.76 ± 0.06 ms, decay τ: 1.49 ± 0.08 ms, amplitude range:13 to 587 pA, n = 20) and little jitter in their onset times (SD = 0.23 ± 0.02 ms, n = 20). Light-evoked EPSCs in GCs were abolished by tetrodotoxin (TTX, 1 μM, n = 6) but were partially recovered following subsequent application of the K+ channel blocker 4-aminopyridine (4-AP, unless 1 mM, n = 5; Figure 3A2). Consistent with previous studies (Petreanu et al., 2009), the synaptic response elicited in the presence of TTX and 4-AP indicates that we could trigger transmission via direct ChR2-mediated depolarization of boutons, however, the responses we observe under normal conditions reflect AP-mediated transmitter release from cortical fibers. Membrane depolarization (Vm = +40 mV)

in the presence of picrotoxin (100 μM) revealed a slow NMDAR component to cortically-driven EPSCs that was abolished by APV (n = 4), while the fast EPSCs were blocked by NBQX (n = 7, Figure 3A3). The current-voltage relationship of the isolated AMPAR response was linear (n = 5; Figure 3A4), indicating that AMPARs at cortical synapses on GCs are Ca2+-impermeable (Hollmann and Heinemann, 1994). We think it likely that GCs are a major source of cortically-evoked disynaptic inhibition onto mitral cells. Cell-attached recordings of GCs revealed that cortical input is sufficient to drive GCs to spike threshold (n = 5; Figure 3B1). Furthermore, simultaneous whole-cell recordings indicated that the onset of evoked mitral cell IPSCs followed EPSCs in GCs with a disynaptic latency (3.2 ± 0.4 ms, n = 7; Figure 3B2).