, 2006, Carrillo et al., 2010, Matthes et al., 1995 and Winberg et al., 1998a), whereas Sema-2b™ GOF in muscle-12 had no affect

on ISNb RP5 formation ( Figure 4L, arrowheads and Figure S4D). However, Sema-2b™ overexpression in peripheral muscle-12 did have a pronounced Bortezomib supplier effect on the lateral branches of the SNa pathway, which were observed to retain ectopic contact with muscle-12 in ∼30% of hemisegments, a phenotype never observed in wild-type embryos ( Figure 4L, arrows, and Figures S4D and S4E). Overexpression of Sema-2a™ from the same muscle had no effect on SNa motor axons ( Figure 4K, arrows, and Figure S4D), further demonstrating that Sema-2a and Sema-2b mediate distinct guidance functions. Taken together, these GOF experiments demonstrate that Sema-2a and Sema-2b function differently in both CNS longitudinal connectives and motor axons: Sema-2b functions to promote axonal attraction, whereas Sema-2a functions as a repellent. To understand how PlexB mediates secreted semaphorin signaling during selleck chemicals llc CNS development, we first examined its requirement for 2b-τMyc pathway formation. In PlexB−/−

mutant embryos the 2b-τMyc pathway formation is severely disrupted; 2b-τMyc longitudinally projecting axons are often defasciculated, and individual axons are diverted both medially and laterally ( Figures 5A and 5H). This phenotype is a combination of both the Sema-2a−/− and Sema-2b−/− mutant phenotypes ( Figures 5G and 5H). Using the elav-GAL4 driver to express PlexB in all neurons in the PlexB−/− mutant, we observed full rescue of the 2b-τMyc pathway ( Figure 5B) and also full rescue, as previously reported ( Ayoob et al., 2006), of the adjacent

1D4-i tract ( Figure 5C). These data further support PlexB functioning to integrate both Sema-2a-mediated repulsion and Sema-2b-mediated attraction, resulting in proper organization of select CNS longitudinal tracts. PlexB is enriched in the intermediate and lateral regions of the CNS scaffold (Figures S5A–S5C). To determine in which neurons PlexB functions, we next assessed the requirement for PlexB in distinct neuronal populations. In a wild-type background, pan-neuronal overexpression, using the elav-GAL4 driver of a modified PlexB receptor lacking its cytoplasmic domain (PlexBEcTM) leads to the Dipeptidyl peptidase disorganization of both the 2b-τMyc pathway and the 1D4-i tract ( Figure 5D), phenocopying the PlexB−/− null mutant and showing that PlexBEcTM functions as a dominant-negative receptor. The MP1 neurons, which can be genetically labeled by the sim-GAL4 driver ( Hulsmeier et al., 2007), serve as pioneer axons for the 1D4-i tract ( Figures S5D–S5F) ( Hidalgo and Brand, 1997). The MP1 longitudinal pathway resides in the same intermediate region as the 2b-τMyc pathway and lies directly adjacent to it ( Figures S5G–S5I). Expressing PlexBEcTM selectively in these neurons disrupts 1D4-i tract formation; however, the 2b-τMyc pathway remains intact ( Figure 5E and Figures S5J–S5L).

By contrast, comparable injections of manganese spread widely and quickly (Figures

S6B and S6D). The increased spatial specificity of the GdDOTA-CTB injections was evident in both horizontal (i.e., along the dorsolateral surface of the cortex) and vertical (i.e., across cortical layers) directions. As shown in Figures S6A and S6C, the pattern of signal enhancement at 50 hr after injection was near-identical to that measured 170 hr after injection, with a half-amplitude at half-maximum (HAHM) of Ibrutinib in vivo 1–1.8 mm, measured away from the midline, in all layers. This remarkable spatial specificity was found in all animals studied (Figure 7A, left panels, and 7D, top middle panel). As expected, the center of the injection core was not enhanced, reflecting signal dropout due to the T2 shortening effect at high concentrations of the contrast agent. In comparison, enhancement due to manganese at the

injection sites spread rapidly, in both axes. As early as 1–2 hr after injection (i.e., the earliest possible data acquisition point), manganese enhancement at the injection site (involving learn more the supragranular layers) was quite extensive (HAHM = 4–6 mm) in the horizontal direction within the supragranular layers, nearly triple that of the GdDOTA-CTB extent (Figure S6, top panels of B and D, and Figure 7B, top left panel). By 10 hr postinjection, the MR enhancement spanned all cortical layers in the vertical dimension, and also increased in the horizontal dimension (Figure S6, lower panels of B and D). Existing evidence suggests that these rapid changes in manganese enhancement at the injection site reflect a combination of diffusion and continued uptake. The diffusion may be mediated via the CSF, at least in part (Liu et al., 2004 and Chuang and Koretsky, 2009), due to the small molecular weight of the manganese. Therefore, the growing size of the injection site likely reflects manganese transport by the neurons at the site of diffusion, followed by further diffusion, uptake, and transport, and so on.

Similar differences were also observed in the thalamic transport sites, in comparisons between these two tracers. After a relatively short period of time, manganese enhancement appeared in multiple subfields and nuclei, including some that new are not confirmed by classical neuroanatomical tracer data. As early as 12 hr following manganese injection into S1, regions such as the subthalamic nuclei and sustantia nigra are prominently enhanced (data not shown, but see Tucciarone et al., 2009, Figure 2A)—even though these regions do not have direct connections with S1 (Fujimoto and Kita, 1993; see also Paxinos, 2004 for review). We found that once GdDOTA-CTB is transported to its target zones, the enhancements remain at the same location and at a constant size (Figure 7A, right panels, Figure 7C, middle panel, and Figure 7D, lower middle panel).

, 2011) but not in those from human. DNDI-VL-2098 was found to be 94–98% bound to plasma proteins, but this extent of protein binding does not limit its efficacy. Taken together, the data suggest that the in vivo anti-parasitic activity of DNDI-VL-2098 is related to circulating levels of parent drug, and that during further toxicological and clinical development

quantification of the parent compound DNDI-VL-2098 will suffice. The oral absorption properties of DNDI-VL-2098 were generally very good. The compound has a low aqueous solubility (about 10 μM at pH 7.4) and a high permeability (226 nm/s in Caco-2 cells). Its total polar surface area (tPSA) is 91 (⩽140 Å2) another feature consistent with its good permeability characteristics (Veber E7080 purchase SCH 900776 manufacturer et al., 2002). It showed excellent bioavailability at low oral doses in three rodent species (80–100%) consistent with its high permeability and metabolic stability. Moreover, even at high toxicologically relevant oral doses, oral suspension exposure in rats increased linearly with dose over a 100-fold dose range (5 mg/kg to 500 mg/kg) (Harisudhan et al., 2011). Taken together with its low aqueous solubility and high permeability, these data suggest that the high permeability

of DNDI-VL-2098 overrides its poor aqueous solubility and enables high oral bioavailability in rodents. In dogs, oral bioavailability appears slightly lower (39–79%) although providing adequate exposure. For a 100-fold increase in dose from 5 mg/kg to 500 mg/kg, a 37-fold increase in exposure was observed. The corn oil formulation was tested as a mean

to enhance exposure and QD and BID dosing were assessed. Corn oil is also an accepted vehicle for early toxicity assessment. Following 500 mg/kg BID dosing in corn oil (1000 mg/kg/day), there was a 50% increase in exposure compared to a 1250 mg/kg QD dose. These data indicate that the less than dose-proportional increase in exposure in dogs can be circumvented by using appropriate formulation and dosing frequency for toxicology studies. Importantly, these proof-of-principle data with corn oil in dog suggest that, if needed, other alternative formulation Cell press approaches with DNDI-VL-2098 are likely to be similarly successful for human. Overall the safety impact of any possible drug–drug interactions with DNDI-VL-2098 appears acceptable. DNDI-VL-2098 did not inhibit CYPs 1A2, 2C9, 2D6 and 3A4/3A5 in vitro and is unlikely to cause drug–drug interactions mediated by these isozymes. DNDI-VL-2098 did inhibit CYP2C19, for which substrates are comparatively limited as compared to the other major CYPs. They include the proton pump inhibitors lansoprazole and omeprazole; anti-epileptics such as diazepam, phenytoin, and phenobarbitone; the tricyclic antidepressants amitriptyline and clomipramine; and the nitrogen mustard alkylating agent cyclophosphamide.

, 1994; Wheeler et al., 1995; Aly et al., 2011; Thompson-Schill et al., 1998). Neuroimaging studies have contributed specificity, highlighting different frontal systems in support of separate control processes that contribute to these demanding retrieval tasks (e.g., Badre et al., 2005; Badre and Wagner, 2007; Buckner, 1996; Buckner et al., 1998; Poldrack et al., 1999; Anderson et al., 2004; Kuhl et al., 2007; Yonelinas et al., 2005; Gallo et al., 2010; Long et al., 2010). Importantly, similar lines of neuroimaging and neuropsychological evidence also implicate the striatum in the cognitive control

C59 wnt cost of declarative memory retrieval. Within the episodic retrieval domain, source memory tasks place explicit demands on cognitive control. In a source memory experiment, participants are required to verify a specific detail from a prior encoding event, such as indicating what type of task was performed with the item. In these tasks, the retrieval goal is explicit and highly specific, and so retrieval must be directed to successful recovery of only the task-relevant “source” Selleckchem Ivacaftor detail to exclusion of other competing details. Thus, source memory decisions involve greater demands on cognitive control mechanisms than do simple item recognition

decisions. Contrasts between source and item recognition memory consistently locate activation in a network of frontal and parietal regions that include the striatum. In their meta-analysis, Spaniol et al. (2009) reported consistent source memory effects (i.e., “objective recollection”) in left dorsal caudate, overlapping with the left dorsal striatal focus observed for retrieval success (Figure 2). In our reanalysis and recoding of these data, we found that the effects in caudate were evident both

for studies contrasting correct source versus correct item decisions and those contrasting correct versus incorrect source decisions. Thus, the preferential effects of source memory in caudate were neither simply due to performing the more difficult source task nor merely PD184352 (CI-1040) to successful retrieval, irrespective of whether it was goal directed or not. Importantly, the association of striatum with source memory relative to item decisions is not necessarily reflective of the contribution of recollection versus familiarity in these two types of tasks. Studies that have distinguished between spontaneous recollection versus familiarity during item recognition (such as is assessed by using the remember/know procedure) have not consistently located activation in the striatum when participants merely experienced recollection relative to familiarity. Direct contrast of source retrieval versus recollection during item recognition indicated that left caudate was more consistently observed across studies of source memory (Spaniol et al., 2009).

, 2008). Our HITS-CLIP data indeed confirmed binding to two of the three nElavl target sequences reported in these studies ( Figure S2B). Our analysis of nElavl RNA targets revealed a reduction in levels of glutamate neurotransmitter in the brains of Elavl3−/−;Elavl4−/− mice which corresponded to a decrease in Gls mRNA and protein levels. Currently, we do not exactly understand the mechanistic details of how nElavl proteins regulate the AS and mRNA stability of Gls mRNA isoforms. While mechanisms of post-transcriptional regulation of Gls-s and Gls-l mRNA are largely unknown in neurons, an mRNA stabilizing role for selleck inhibitor Elavl1 (HuA/R)

binding to an AU-rich pH-responsive element located in the 3′UTR of Gls-l during metabolic acidosis in kidney cells is demonstrated ( Ibrahim et al., 2008). It is also likely that nElavl proteins enhance the translation of at least the Gls-s

isoform, since its mRNA levels click here are unaffected but proteins levels are significantly reduced in the Elavl3−/−;Elavl4−/− brain tissue. The Gls is the major glutamate synthesizing enzyme in neurons. Elavl3−/−;Elavl4−/− mice display some similarity to Gls1−/− mice, as both appear and behave normally at birth but die suddenly thereafter; in Gls−/− mice early postnatal death has been attributed to a deficiency in brain circuits controlling respiration ( Masson et al., 2006). Glutamate is the major excitatory neurotransmitter and impacts inhibitory signaling in two ways: it is both the biochemical precursor for the major inhibitory neurotransmitter GABA in the mammalian forebrain ( Martin and Rimvall, 1993), and synaptically activates inhibitory neuronal feedback loops ( McBain and Fisahn, 2001). While the molecular lesion due to aberrant AS in this model is complex, imbalance of these key mediators of fast those synaptic signaling in the Elavl3−/− brain is a well established mechanism for neuronal hypersynchrony and epilepsy ( Noebels, 2003). The

finding of abnormal hypersynchronization in both Elavl3+/− and Elav3−/− mice suggests that fine tuning of the stoichiometry of individual RNA isoforms can regulate cortical excitability and synchronization. On the behavioral level, we observe attenuation of cerebellum-dependent motor function based on reduced rotarod assay performance in Elavl3−/− mice. Whether or not this behavioral defect results from reduced glutamatergic signaling and an imbalance in excitation/inhibition in the cerebellum are of great interest as future research questions. Gls mRNA is alternatively spliced to generate two mRNA and protein isoforms, and the longer Gls-l isoform is dramatically reduced in both mRNA and protein levels in Elavl3−/−;Elavl4−/− brain. Gls-s and Gls-l isoforms differ in their 3′UTR sequences and also C-terminal domains of their protein products. Both protein isoforms encode a glutaminase superfamily domain involved in deamination of glutamine to glutamate.

Nonetheless, the fluorescence in split Venus-PH-GRP1 larvae that express p85 in the absence of rapamycin is still significantly lower than fluorescence measured in the presence of rapamycin (Figure 1H, compare light and dark blue). Thus, the split Venus-PH-GRP1 probe is a reliable in vivo reporter that recognizes PI(3,4,5)P3. Specialized zones for exo- and endocytosis or periactive zones have been defined within the plasma membrane of NMJ boutons. To determine NVP-BGJ398 concentration whether PI(3,4,5)P3

is restricted to specific synaptic membrane domains, we resorted to photobleaching microscopy with nonlinear processing (PiMP) that allows for superresolution imaging beyond the diffraction limit and

has been used at Drosophila neuromuscular junctions to visualize presynaptic densities ( Munck et al., 2012). PiMP imaging of the split Venus-PH-GRP1 in the presynaptic membrane indicates that the probe concentrates in patches ( Figures 1I–1K). These split Venus-PH-GRP1 patches extensively colocalize with Bruchpilot (anti-BRPNC82) and with RIM binding protein (anti-RBP), which both label aspects of presynaptic release sites ( Kittel et al., 2006; Liu et al., 2011) ( Figures 1I and 1J). Sixty-eight percent of the presynaptic GSI-IX chemical structure densities marked by BRPNC82 harbor a split Venus-PH-GRP1 patch. Conversely, split Venus-PH-GRP1 is largely excluded from regions labeled by anti-FasiclinII that concentrates in periactive zones ( Sun et al., 2000) ( Figure 1K). Thus, our data indicate that at Drosophila third-instar larval boutons, PI(3,4,5)P3 resident in the plasma membrane concentrates at presynaptic densities where neurotransmitters are released. and Expression of the PLCδ1-PH probe shields available PI(4,5)P2 (Field et al., 2005; Raucher et al., 2000) and reduced levels or availability of PI(4,5)P2 by expressing PLCδ1-PH or RNAi to PI4P5Kinase both result in reduced levels of boutonic Alpha-adaptin,

a PI(4,5)P2 binding protein (Figures 2A and 2C, green) (González-Gaitán and Jäckle, 1997; Khuong et al., 2010; Verstreken et al., 2009; Zoncu et al., 2007). Similarly, to determine whether synaptic PI(3,4,5)P3 is required for the localization of Alpha-adaptin, we expressed the PH-GRP1 to shield PI(3,4,5)P3 and we used RNAi to PI3Kinase92E, a PI(3,4,5)P3-producing enzyme. However, the abundance of Alpha-adaptin is not altered when expressing PH-GRP1 or when knocking down PI3Kinase92E (Figures 2A and 2B, green, and Figure S2A). These data suggest that synaptic PI(4,5)P2 availability is not majorly affected when lowering PI(3,4,5)P3 levels and that boutonic Alpha-adaptin localization is less sensitive to alterations in PI(3,4,5)P3 availability.

, 1978). In bulb-cortex slices, extracellular stimulation of PCx produced excitatory postsynaptic currents (EPSCs) in GCs and cortical input that drives GC action potentials (APs) is proposed to enhance M/T cell dendrodendritic self- and lateral inhibition (Balu et al., 2007; Halabisky and Strowbridge, 2003). This bulbo-cortical loop is also thought to contribute

to oscillatory dynamics in the OB and cortex (Neville and Haberly, 2003) and proximal (presumptive cortical) inputs on GCs express long-term potentiation (LTP), suggesting they may play a role in olfactory learning (Gao and Strowbridge, 2009; Nissant et al., 2009). Furthermore, recordings in awake, behaving rodents show that M/T cell activity can be modulated by contextual information suggesting that higher cortical regions can influence odor processing in the OB (Kay and Laurent, 1999). Despite the potential PLX4032 importance of cortical feedback in the regulation of OB circuits, the functional properties of these long-range projections are unclear. In large part, this reflects the challenge of selectively manipulating this feedback pathway using conventional

extracellular electrical stimulation since cortical fibers are intermingled with the axons and dendrites LY294002 in vivo of bulbar neurons. In this study, we express channelrhodopsin-2 (ChR2) selectively in olfactory cortex pyramidal cells and examine the impact of cortical feedback on circuits in OB slices and its actions on odor-evoked activity in vivo. We took advantage of a transgenic mouse line (Ntsr1-creGN209 from the GENSAT

project) that expresses Cre recombinase in olfactory cortex pyramidal cells, but not in pyramidal cells of other cortical regions or in inhibitory interneurons (Experimental Procedures) (Stokes and Isaacson, 2010). We injected the anterior PCx of neonatal mice with an adeno-associated virus (AAV-double floxed-ChR2-mCherry) to drive Cre-dependent coexpression of the light-activated channel ChR2 (Atasoy et al., 2008; Petreanu et al., 2009) and the fluorescent protein mCherry. We chose this conditional strategy since injections of unconditional AAV-ChR2 could reach below the lateral ventricle, leading to ChR2 expression in OB interneurons of wild-type mice (not shown). With this conditional approach, unilateral injections labeled layer 2/3 pyramidal cells in PCx and fibers that projected rostrally (Figures 1A1 and 1A2). Consistent with anatomical studies of the axonal projections of PCx pyramidal cells (Matsutani, 2010; Shipley and Adamek, 1984), expression of ChR2-mCherry was present in the ipsi- but not contralateral OB with the densest labeling in the GC layer and lesser expression in the glomerular layer (Figures 1A3 and 1A4). Two-photon imaging of the GC layer confirmed that ChR2 was present only in fibers and axonal varicosities (Figure 1B) rather than cell bodies of OB neurons.

, 2009, Bo and Seidler, 2009, Kennerley et al.,

2004, Verwey and Eikelboom, 2003 and Sakai et al., 2003). The temporal pattern commonly observed is the production of one slow key press that is followed by several key presses produced in quick succession (Sakai et al., 2003 and Verwey and Eikelboom, 2003). Recent studies suggest that individuals will spontaneously segment sequences into a set of subject-specific chunks (Verwey et al., 2009, Bo and Seidler, 2009, Kennerley et al., 2004, Sakai et al., 2003 and Verwey and Eikelboom, 2003). The benefit of such segmentation is that it reduces memory load during ongoing performance PLX4032 (Bo and Seidler, 2009 and Ericsson et al., 1980). With extended practice, short chunk segments can be concatenated into longer segments (Sakai et al., 2003 and Verwey, 1996), suggesting that concatenation can operate on pairs of individual motor elements or between two sets of motor elements. The aforementioned findings suggest that two chunking processes are at play during sequence learning. One process concatenates adjacent motor elements so that sequences can be expressed as

a unified action, and the other Nutlin-3a ic50 process parses sequences into shorter groups. Both processes could lead to the pattern observed in chunking. In concert, they impart competing strategies for enhancing performance in the production of long motor sequences, presumably driven by the formation of motor-motor associations and the strategic control over sequence segmentation (e.g., Verwey, 2001). Evidence suggests that the basal ganglia support the concatenation of multiple motor elements of a sequence. Studies from individuals with Parkinson’s disease (Tremblay et al., 2010) and stroke patients (Boyd et al., 2009) found that damage to the basal ganglia impairs one’s ability

to integrate until motor elements into chunks. Further support comes from rodent and nonhuman primate research (Graybiel, 2008 and Yin and Knowlton, 2006). As rats learn to navigate a T-maze for reward, neurons in the nigrostriatal circuit gradually represent motor sequences as chunks by firing preferentially at the beginning and end of action sequences, yielding concurrent improvements in performance (Thorn et al., 2010; Barnes at al., 2005). The disruption of this phasic nigrostriatal activity also leads to the impairment of sequence learning in mice (Jin and Costa, 2010). Similarly, subcutaneous injections of raclopride, a dopamine antagonist of the D2 receptor, disrupt sequence consolidation and chunking behavior in cebus monkeys (Levesque et al., 2007), which can be reversed by administration of a dopamine agonist (Tremblay et al., 2009). Several recent studies have argued that a frontoparietal network is critical for the segmentation of long sequences into multiple chunks (Pammi et al., 2012; Verwey et al.

Both confocal and two-photon microscopy use point illumination, which narrows planar focus. In confocal microscopy, the emitted signal is spatially filtered via a pinhole aperture. The light emitted from a single plane creates http://www.selleckchem.com/products/PD-98059.html an image, and a progression of images is captured through the thickness of the tissue, resulting in optical sectioning of the specimen (Wilson, 1989). Thus, confocal microscopy eliminates the need for resectioning thick slices typically employed in electrophysiological recording preparations. Furthermore, confocal microscopy provides high spatial resolution, which is particularly important for 3D reconstructions. The temporal stability of the sample is especially relevant

to neuronal reconstructions. Since fluorescently labeled specimens have a limited viability period, it is often necessary to collect image stacks for later

offline tracing of the arbor structure. In two-photon microscopy, fluorophores are excited by the simultaneous absorption of two photons (Denk et al., 1990). The two photons converge simultaneously only at the focal point, yielding sharper images with less background noise. Such a specific illumination removes the need for spatial filtering. Additionally, since the fluorophores outside the focal point are not excited, the specimen undergoes less photobleaching and photodamage (Denk and Svoboda, 1997). Multiphoton microscopy, an extension of two-photon microscopy, uses more than two photons to

of excite the fluorophores, resulting Ulixertinib in a narrower emission region and even less out-of-focus noise. Since the stimulation energy is split between two (or more) photons, a drawback of two-photon (or multiphoton) microscopy is its lower spatial resolution relative to confocal, due to the longer excitation wavelength. Moreover, the necessity to scan the specimen one point at a time greatly increases the time necessary to capture the same field of view (Lemmens et al., 2010). Even with the higher resolution afforded by confocal and two-photon microscopy, subcellular details such as synaptic contacts remain elusive and, until recently, neuronal ultrastructure remained the purview of electron microscopy. However, the advent of superresolution fluorescence microscopy addresses this limit. Two main approaches exist to enable superresolution: one involves the sequential and stochastic switching on and off of fluorophores; the other uses patterned illumination to modulate fluorophore emission. The former includes stochastic optical reconstruction microscopy (STORM; Rust et al., 2006), and (fluorescence) photo-activated localization microscopy (PALM; Betzig et al., 2006; or FPALM; Hess et al., 2006). In this class of techniques, only a subset of fluorophores is illuminated in each imaging cycle and localized to be imaged and reconstructed, and the process is repeated to capture the full distribution of fluorophores.

In the random wiring model, neurons receive multiple independent inputs that are anterior DS, posterior DS, or non-DS. The random wiring model is constrained by the previous experimental observation PD0325901 in vitro that mouse dLGN neurons receive one to three strong inputs from the retina (with probabilities: one input [p1], two inputs [p2], and three inputs [p3 = 1 − p1 + p2]), from which they derive their stimulus selectivity ( Cleland et al., 1971a, 1971b; Mastronarde, 1987, 1992; Usrey et al., 1999; Chen and Regehr, 2000). Importantly, the basic results of the model are robust

against the addition of dLGN neurons that receive more than three strong retinal inputs. The model assumes that input from DSRGCs must be nearly pure to generate a DSLGN or ASLGN, since linear summation of inputs only produces direction or axis selectivity (i.e., 0.5 DSI/ASI)

if over 90% of the inputs to a cell are of the required type(s). In the model, random wiring is defined such that the probability of Epigenetics inhibitor input to a dLGN neuron from a given type of RGC is equal to the total proportion of input to superficial dLGN belonging to that RGC type (f). We assume that the fractions of input to superficial dLGN of either anterior or posterior DSRGCs are equal and that upward and downward DSRGCs do not project to the superficial region, yielding 2f for the total fraction of DS input. Together, these assumptions define a set of equations for the probability of each possible type of cell (Table S1). The sum of probabilities for observing DSLGNs with one, two, or three inputs in the model is equal to the total fraction of DSLGNs, p(DS). Similar reasoning applies to ASLGNs with two or three inputs, yielding Rutecarpine p(AS) (see Supplemental Experimental Procedures for a full derivation). In the model, not all values for p(DS) and p(AS) are possible given

random wiring; however, the range of possibilities is large (Figure 4B, light gray region). Cleland et al. (1971a) performed paired RGC-LGN recordings in cats and found that very few dLGN neurons (8.8%, 5/57) had a single RGC input that accounted for all of its recorded spikes. This provides bounds on the likely fraction of dLGN neurons receiving only one driving RGC input (p1 = 0.038–0.19, 95% confidence interval [CI] using the Wilson interval for binomial variables with 5/57 single input LGN cells). Applying these bounds to p1 limits the possible solutions for fractions of ASLGNs and DSLGNs, which are consistent with the random wiring model (dark gray region of Figure 4B). The experimentally observed fractions of ASLGNs (p(AS) = 0.043, binomial 95% CI 0.026–0.069) and DSLGNs (p(DS) = 0.051, binomial 95% CI 0.033–0.