, 1993; Imamura et al., 2006; Macrides et al., 1985; Orona et al., 1983). This implies that distinct subsets of granule cells mediate differential inhibitory control even within a single glomerular module, and may create layer-specific odorant response properties. These different layers would then send the varied aspects of odorant information to different higher brain center areas in different manners. This has indeed been suggested to be the case for mitral and tufted buy Everolimus cells (Fukunaga et al., 2012; Griff et al., 2008; Igarashi et al., 2012; Nagayama et al., 2010; Nagayama et al., 2004). Lateral inhibition is one of several possible neuronal mechanisms that may contribute

to the phenomenon of odor selective tuning. Another possibility may be the differential input sensitivities of each cell type, as the cells show differences in their sizes, morphologies, and distances from the glomeruli (Figures 5, S2C, and S2D). JG cells are relatively smaller and may have weaker attenuation of dendritic excitatory postsynaptic potentials due to higher input resistances, different lateral inhibitory connections, and short pathways between the excitatory inputs and the cell body.

Mitral cells may require larger excitatory postsynaptic potentials for activation than JG and tufted cells and may only deal with odor information from odorants present at relatively high concentrations. These higher thresholds for the activation of mitral cells ZD1839 mw could result in more finely tuned odorant selectivities. This idea may answer how sharpening occurs, but does not

explain why deeper neurons show differential odor selectivities in an interneuronal distance-dependent manner. Another Astemizole possible mechanism is functional compartmentalization within a glomerular formation. Previous morphological and immunohistochemical findings suggest that the axonal and dendritic arborizations within glomeruli are not evenly distributed (Hálasz and Greer, 1993; Kasowski et al., 1999). Furthermore, a functional study suggested that odorant stimulations do not evenly activate olfactory sensory nerve terminals within a glomerulus (Wachowiak et al., 2004). Because we observed that similarities in odorant selectivities were not associated with interneuronal distances in the GL (Figures 7 and S3), it is reasonable to speculate that this differential tuning effect is largely controlled in a deeper part of the OB. Therefore, although we cannot neglect the potential contributions of multiple factors, we currently favor the lateral inhibition hypothesis in which mitral-granule cell circuits drive the heterogeneous odorant selectivities of deeper layer cells. Multiple neuronal subtypes have been identified recently within the GL (Aungst et al., 2003; Kiyokage et al., 2010; Kosaka et al., 1998; Liu and Shipley, 2008) and are thought to play different functional roles (Shepherd et al.

Maybe this concern is misplaced, at least in part. If ChIs innervate the grafts, they might be able to appropriately modulate DA release. Given the movement of the transplant field toward induced pluripotent stem cells (iPSCs), it also is important that DA neurons derived from iPSCs be pushed far enough toward the terminal phenotype that they express the appropriate complement of nAChRs, enabling ChIs to modulate them. These studies also point to further questions. One is about the nature of the synchrony requirement. Why is synchronous spiking in a population of ChIs

necessary for DA release? The striatal extracellular space is full of acetylcholinesterase (AChE) that rapidly degrades ACh. It could be that synchrony is required to produce a large enough release of ACh so that this enzymatic brake is temporarily overwhelmed, allowing ACh diffusion to DA terminals. Such dynamics would keep the DA release spatially restricted. An important Apoptosis Compound Library implication

is that the effect PLX-4720 nmr of ChIs on DA release might not be uniform. AChE density, like choline acetyltransferase activity, is high in the striatal matirix and low in striosomes. It could be that ChI enhancement of DA release is most prominent in striosomes. Another question is what sort of nAChR-evoked activity triggers DA release. Cragg and colleagues found that DA release was sensitive to tetrodotoxin (TTX) (Threlfell et al., 2012). The simplest interpretation of this dependence is that propagation of spikes in the axons of ChIs was necessary. However, because the ChI terminals were in the field illuminated by the blue laser and because ChR2 is capable of evoking transmitter release in terminals, it is possible that the all TTX-sensitive event is propagation of spikes in the DA axons. This circumstance would allow a relatively focal burst of activity in ChIs to be broadcast to a large region of striatum, because the terminal fields

of DA axons are twice as big as those of the ChIs (Matsuda et al., 2009). There is clearly much still to be done, but what these two beautiful studies make clear is that the interaction between DA and ACh in the striatum is not so much a feud as it is a dance. “
“The remarkably selective response properties of individual neurons in visual cortex result from specific patterns of synaptic connections that link large numbers of cortical neurons. In some species, including primates and carnivores, cortical neurons with similar response properties (e.g., similar preferred orientation) and shared connectivity are grouped together into radial columns, forming orderly maps of stimulus features (Hubel and Wiesel, 2005). In rodents, cortical neurons with different orientation preferences are intermingled in a “salt-and-pepper” fashion (Ohki et al., 2005). Nevertheless, rodents exhibit fine-scale specificity in the organization of synaptic connections (Yoshimura and Callaway, 2005 and Yoshimura et al.

An important implication of splitting visual input into ON and OFF components is that the subsequent motion detection circuit now is

confronted with nonnegative signals only. This significantly facilitates the implementation of the nonlinear operation inherent to motion detection (Poggio and http://www.selleckchem.com/products/pf-06463922.html Reichardt, 1973), as specified by the multiplication in the Reichardt Detector. Independently of the exact kind of nonlinearity actually used in motion detection, it is required to give a positive output for two positive (excitatory) as well as for two negative (inhibitory) inputs. Performing such an operation within one neuron is biophysically implausible. In contrast, splitting the inputs into nonnegative signals (ON and OFF) allows for a neural implementation of the nonlinearity that operates on two nonnegative inputs, only. This unit is replicated for the different signal components with a final stage that combines the outputs.

Nonetheless, splitting of the input does not answer the question of what exact kind of nonlinearity is used, and many ideas have been put forward in the literature to this end (Grzywacz and Koch, 1987, Gabbiani et al., 2002, Hausselt et al., 2007 and Enciso et al., 2010). One possibility of approximating a multiplicative interaction is the so-called log-exp-transform, where the two factors are preprocessed by a saturating, e.g., logarithmic function, and their sum is fed through an exponential nonlinearity. This mechanism has been experimentally confirmed in an identified neuron of the locust involved in collision SB431542 nmr avoidance (Gabbiani et al., 2002). Another possibility consists of a tonic voltage gradient along the dendrite together with a high voltage-activated calcium current, giving rise to a supra-linear relationship between any two inputs along the dendrite, which has been tested in the starburst amacrine cells of the rabbit retina (Hausselt et al., 2007). What exact mechanism is implemented in the neurons presynaptic to

the fly lobula plate tangential cells can only be answered by experimental investigation of the respective through cells. A further interesting question concerns the separation of the input into its ON and OFF components. In their dendrites, both L1 and L2 depolarize in response to OFF stimulation and hyperpolarize in response to ON stimulation. Expressing a genetically encoded calcium indicator in L2 neurons, Reiff et al. (2010) have shown that the extraction of the OFF component occurs in the axon terminals of L2. Given that blocking synaptic output of L1 removes lobula plate tangential cell responses to moving ON edges, which are encoded by L1 dendritic hyperpolarizations, we suggest that the ON component is extracted via a tonically active, inhibitory synapse from L1 onto downstream neurons.

Moreover, forced attachment of dendrites to the ECM by integrin overexpression in these tiling mutants rescued both isoneuronal and heteroneuronal dendritic crossing defects. Our results demonstrate the importance of

2D distribution in the tiling of see more class IV da neurons and reveal that the TORC2/Trc pathway plays a major role in ensuring tiling by confining dendrites to a 2D space rather than by mediating homotypic repulsion. The Drosophila larval body wall is evenly covered by class IV da dendrites ( Figure 1A). To understand how dendrites and epidermal cells are arranged relative to one another in a 3D space, we simultaneously imaged class IV da dendrites and the ECM of the epidermis in live third instar larvae by labeling dendrites with an improved class IV-specific membrane marker ppk-CD4-tdTom ( Han et al., 2011; see Experimental Procedures) and the ECM with viking-GFP (vkg-GFP). Since the epidermis is comprised of a thin layer of epithelial cells, a detailed cross-section view of the epidermis requires 3D reconstruction of an image stack with a high resolution on the z axis (i.e., the apical-basal axis of epidermal cells, Figure 1B). To maximize the resolving power on the z axis, we adopted a high-resolution confocal imaging protocol combined with deconvolution

(see Experimental Procedures), which greatly improves the visualization of the spatial relationship BMS-387032 in vivo between dendrites and the ECM. Each abdominal hemisegment of the Drosophila larva contains three class IV da neurons, the dorsal ddaC, the ventro-lateral v′ada, and the ventral vdaB ( Figure 1A). We examined the dendrite positioning of all three neurons along the z axis. Most of the dendrites were found to directly contact the ECM (green dendrites in Figures 1C′, 1D′, and 1E′; also see Movie S1 available online), while a small percentage of dendrites are detached from the ECM Isotretinoin in the apical direction (magenta dendrites in

Figures 1C′, 1D′, and 1E′). Since epidermal cells are located immediately apical to the ECM ( Figure 1B), the dendrites detached from the ECM are enclosed within the epidermal layer (referred to as enclosed dendrites hereafter). We noticed several characteristics of these enclosed dendrites. First, they can be either segments in the middle of stabilized branches (small panel 1 in Figures 1C′, 1D′, and 1E′), or parts of terminal dendrites (small panel 2 in Figures 1C′, 1D′, and 1E′). Second, the enclosed dendrites may appear to cross other branches attached to the ECM in z axis projections, even though the overlapping dendrites are located at different depths in the epidermis and are not in direct contact (small panel 3 in Figures 1C′, 1D′, 1E′, and Figures 1F–1F″). We call this type of dendritic overlap noncontacting crossing.

Interestingly, strong modulation at one timescale is sometimes accompanied by weak modulation on the other (e.g., units 1 and 2 in Figure 4). In general, the firing rates of most units in vM1 cortex are significantly modulated by at least one slow or fast parameter of whisking (Table

1). Approximately 65% of all units were modulated by either amplitude or midpoint (Kolmogorov-Smirnov test, p < 0.05). The firing rates of significantly modulated units typically showed a monotonic dependence on amplitude or midpoint. A compilation of the change in rate, i.e., maximum minus minimum rate, is plotted against the average rate for all units (Figures 5A and 5C). The firing rate could increase or decrease with either signal such that the HER2 inhibitor average tuning curve across all units was nearly flat (Figures 5B and 5D). The mean rate of neurons that encoded amplitude was significantly related to the slope of the rate versus amplitude curve, with an average mean firing rate of 15 Hz for cells that increased their firing rate with amplitude and 4.6 Hz for cells that decreased their firing rate with amplitude (Figure 5A) (Kolmogorov-Smirnov test, p < 0.05). Nonetheless, the fidelity of reporting amplitude BI 2536 price and midpoint cannot be increased by a simple summation of spikes across the population

of neurons. Lastly, we found no obvious correlation between the modulation by amplitude and by midpoint in single units. In contrast to the high yield of units modulated by slow parameters, only 22% of all units showed a firing rate that was significantly modulated by phase in the whisk cycle (Kuiper test, p < 0.05). The tuning curve for λ(ϕ) is parameterized in terms of its peak at the preferred phase in the whisk cycle, denoted ϕo. There was no significant bias in the distribution of preferred phases. The relative modulation appears large through in many cases because the baseline rate was quite small for many of these cells. Most of these phase-sensitive units were also modulated on the slow timescale (Table 1).

Lastly, while fast spiking units make up about 20% of recorded cells (Figure S3), they are 50% more likely to show significant modulation with phase or one of the slow variables. We tested for the possibility that the coding properties of units in vM1 cortex were affected by head fixation. The above analysis was repeated using data from free-ranging animals, for which the |∇EMG| of the intrinsic muscles served as a surrogate of vibrissa position. We found that the modulation of the envelope of the |∇EMG| with the whisk amplitude was similar to that using videographic data with head-fixed animals (Figure S4). Further, the reliability of the phase variable was unchanged using data from the |∇EMG| versus videographic data (Figure S4). Our past studies focused on coding of motion in vS1 cortex, in which past work emphasized the role of phase coding.

We examined click here the turning response of flies using an array of motion stimuli rotating about the animal (Figure 5). L1 and L2 are required redundantly for responses

to rotating gratings (Clark et al., 2011, Rister et al., 2007 and Joesch et al., 2010). Flies lacking L1 function have specific deficits in turning responses to rotating light edges (a transition from darker to brighter), while flies lacking L2 function have strong deficits in turning to moving dark edges (a transition from brighter to darker) (Figures 5A, 5B, 5D, 5E, and S5, compare blue traces to both control traces; Clark et al., 2011). These results were substantiated by an opposing edge stimulus, in which light and dark edges move in

opposite directions, which evokes little turning response in wild-type flies, as the motion circuits tuned to light and dark edges cancel one another. L1-silenced flies turn in the direction of the dark edge motion (as the motion circuitry that normally responds to moving light edges is inactivated), whereas L2-silenced flies turn with the direction of light edge motion (as the motion circuitry that normally responds to moving dark edges is inactivated) (Figure S5; Clark et al., 2011). We next tested the behavioral contribution of L3 to motion detection. When we silenced L3 neurons using the L30595-Gal4 line, we detected no significant deficits when presented with rotating square wave gratings, single edges of either polarity, Epacadostat price or opposing edges ( Figures 5G–5I, blue traces; Figure S5). Likewise, flies in which the highly specific splitL4-Gal4 line was used to silence L4, also responded nearly normally to rotational stimuli ( Figures 5J–5L and S5). Similar results were obtained using the L40987-Gal4 driver ( Figure S5). Thus, neither L3 nor L4 are individually required to guide turning responses to rotational visual motion under the conditions tested. We below next examined whether these single cell type inactivation experiments might mask redundant functions

among input pathways. Interestingly, although L2 silencing alone reduced responses to rotating dark edges and caused turning in the direction of light edge motion in an opposing edges stimulus, some dark edge response remained (Figure 5E). Since L3’s physiological properties make it preferentially sensitive to contrast decrements, we tested whether L3 acts redundantly with L2. When both L2 and L3 were silenced, flies displayed turning responses to light edges (Figure 6A). However, they displayed no turning at all in response to a rotating dark edge stimulus and turned more strongly in the direction of light edge motion in an opposing edge stimulus than flies in which L2 was silenced alone (Figures 6B and S6). Thus, double silencing experiments uncovered a redundant role for L3 in the detection of moving dark edges.

, 1989) and potassium channels ( Liman et al., 1991 and Papazian et al., 1991), leading to massive shifts in the voltage dependence of gating. Case closed? Well not quite. These studies also found that substitutions of Trichostatin A arginine with lysine produced similar shifts, despite preserving the charge. Moreover, so did hydrophobic for hydrophobic mutations in the residues between the arginines ( Lopez et al., 1991). Clearly, another approach was needed to test the contribution of

the arginines to gating charge. If only one could measure the amount of gating charge per channel directly, determine whether S4 is really a transmembrane segment, and, if so, see whether it moves in and out through the membrane. In 1996, two groups measured the total gating charge in a cell expressing wild-type or arginine-neutralized

potassium channels and divided the value by the number of channels on the cell membrane determined using either a radio-labeled blocking toxin or noise analysis (Aggarwal and MacKinnon, 1996 and Seoh et al., 1996). The results FRAX597 datasheet closely agreed: each of the four subunits of the channel had three to four gating charges, corresponding to the first four arginines of S4. At about the same time, cysteine accessibility analysis in both KVs and NaVs showed that S4 does indeed span the membrane and that it moves outward with membrane depolarization by an amount that displaces the same first four arginines through a narrow passage, thereby accounting for the

transfer of about three charges per subunit (Larsson GPX6 et al., 1996, Yang et al., 1996 and Yang and Horn, 1995). The agreement between the studies was remarkable. But one was left hankering for a real-time measure of S4 motion. Voltage-clamp fluorometry made this possible, showing that the voltage dependence and kinetics of S4 displacement precisely match the displacement of gating charge (Cha and Bezanilla, 1997, Larsson et al., 1996, Mannuzzu et al., 1996, Yang et al., 1996 and Yang and Horn, 1995). One still had some explaining to do. How does one accommodate charged arginines in a hydrophobic membrane? Conserved negatively charged residues in the S2 and S3 membrane segments were shown to electrostatically interact with S4 arginines (Papazian et al., 1995) and these could accommodate the two arginines at a time that entered the inaccessible pathway in the span of the membrane (Baker et al., 1998). Moreover, evidence was obtained that suggested that S4 does actually turn when it moves outward (Cha and Bezanilla, 1997 and Glauner et al., 1999), supporting the helical screw model.

, 2004 and Pan et al., 2006). In order to map the evolution of the AIS, Hill et al. (2008) made an elegant comparative study of the gene sequences of Na+ and Kv7 channel anchoring motifs in chordates, nonchordates, and vertebrates. Their results show that while anchoring motifs in Na+ channels are highly conserved and found as early as the chordates, the first immunohistological observations of Na+ channel clustering in axons occurs only with the appearance of the vertebrates, such as

the lamprey. In contrast, the anchoring motif in Kv7 channels developed 50 to 100 million years later, at the same time Compound Library order as the appearance of axon myelination (Hartline and Colman, 2007). This suggests that the formation of the AIS preceded the evolution of myelination and coincided with the appearance of complex sensory systems in vertebrates. Furthermore, these studies suggest there are parallels

selleck compound in the molecular evolution of the AIS and the transition to a single site for AP initiation in neurons. We next address the issue of what types of proteins are specifically expressed in the AIS and their role in excitability. Na+ channels provide the main transient inward current responsible for the rapid depolarizing phase of the AP (Hodgkin and Huxley, 1952). Early computational modeling studies predicted that initiation of APs in the AIS required a high concentration of Na+ channels (Dodge and Cooley, 1973). Consistent with this, initial binding studies indicated that the density of Na+ channels in the AIS of cultured spinal cord neurons and retinal ganglion cells is indeed high (Catterall, 1981 and Wollner and Catterall, 1986). We now know that of the four Na+ channel α-subunits expressed in the brain (Nav1.1, Nav1.2, Nav1.3, and Nav1.6), three subtypes (Nav1.1, Nav1.2, and Nav1.6) are localized to the AIS with developmental, regional, and cell-type-specific diversity (see Table 1). Immunocytochemical studies indicate that Cediranib (AZD2171) the main Na+ channel isoform found in the AIS of neurons in the adult CNS is Nav1.6 (Figure 2A). The Nav1.1 subtype is also found in the

AIS of GABAergic interneurons, retinal ganglion cells, and spinal cord neurons (Duflocq et al., 2008, Lorincz and Nusser, 2008, Lorincz and Nusser, 2010, Ogiwara et al., 2007 and Van Wart et al., 2007). Nav1.2 is primarily expressed in the AIS early in development and in adults in unmyelinated axons (Boiko et al., 2003 and Jarnot and Corbett, 1995), but has also been reported in the proximal part of the AIS of pyramidal neurons from the cortex and hippocampus (Hu et al., 2009). While these immunocytochemical studies provided strong evidence for a high Na+ channel density in the AIS, initial functional experiments using patch-clamp recording surprisingly reported that the Na+ current density in the AIS was similar to that at the soma (Colbert and Johnston, 1996 and Colbert and Pan, 2002).

We computed figure-ground

measure for population response (FG-m, Equation 1; see Figure 3Ai). FG-m was defined by subtracting the population response (average over pixels) in the background from the circle for the contour and noncontour conditions and then taking the difference between the two conditions. This index indicated how well the “figure” (circle area) is differentiated from the “ground” (background area). FG-m was calculated for each recording session separately. equation(Equation 1) FG-m=(Pc−Pb)cont−(Pc−Pb)non−cont,FG-m=(Pc−Pb)cont−(Pc−Pb)non−cont,where Pc and Pb are the population responses in the circle and background areas, respectively, cont and non-cont are the contour and noncontour conditions, respectively. The subtraction of the noncontour from the contour condition also enabled us to eliminate any response differences PD0325901 price in space due to uneven staining. We also computed the differential selleck compound (contour minus noncontour) circle or background response ( Equations 2 and 3). Figure 3Bi depicts the circle differential response (Pcdiff) and background differential response (Pbdiff) as function of time. equation(Equation 2) Pcdiff=Pccont−Pcnon−contPcdiff=Pccont−Pcnon−cont equation(Equation 3) Pbdiff=Pbcont−Pbnon−contPbdiff=Pbcont−Pbnon−cont To study

the behavioral performance in the contour saliency experiments, we computed the probability of contour detection. This was normalized to the contour and noncontour conditions by setting the probability of contour detection to 1 in the contour condition, 0 in the noncontour condition and varying accordingly the probability for the jittering orientation conditions (Figure 5B). The purpose of this

normalization was to overcome the slight variation in behavioral performance due to the animal’s motivation. We verified that the nonnormalized and normalized psychometric curves showed similar results (Figure S4A). To study the effects of contour saliency on the population response, the neurometric curve was computed by calculating the FG-m as a function of orientation jitter (FG-mjitt; Equation 4). equation(Equation 4) FG−mjitt=(Pc−Pb)jitt−(Pc−Pb)non−cont,FG−mjitt=(Pc−Pb)jitt−(Pc−Pb)non−cont,where Pc and Pb are the population responses in the circle and background Terminal deoxynucleotidyl transferase areas, respectively, and jitt and non-cont are the different jitter conditions and noncontour condition, respectively (the contour condition is defined by jitter = 0). The neurometric curve values were normalized to maximal and minimal values in each recording session (to overcome the variable staining quality across recording sessions; Figure 5C). We verified that the nonnormalized and normalized curves showed similar results (Figure S4B). The population response for each pixel (VSDI amplitude, normalized as in the previous section) was computed as function of the orientation jitter condition. This yielded the neurometric curve for each pixel, which was then computed for each time frame.

These studies showed that adult unc-55 mutant VD neurons lacked ventral axonal varicosities and ventral GFP-tagged synaptobrevin (SNB-1) puncta, consistent with the idea that ventral VD synapses in unc-55 had been eliminated due to ectopic expression of the DD neuron remodeling Selleckchem Trametinib program ( Shan et al., 2005, Walthall and Plunkett, 1995 and Zhou and Walthall, 1998) ( Figure 1A). To confirm these results, we analyzed VD synapses in adult unc-55 mutants by both imaging and electrophysiology.

To image these synapses, we expressed two GFP-tagged pre-synaptic proteins (UNC-57 endophilin and SNB-1 synaptobrevin) in the D neurons (using the unc-25 GAD promoter). In wild-type adults, both UNC-57

and SNB-1 were expressed in a punctate pattern in the nerve cords, and these puncta were closely apposed to post-synaptic sites in body muscles (labeled with mCherry-tagged UNC-49 GABAA receptors) ( Figure S1A available online and data not shown). These ventral cord puncta likely correspond to VD NMJs, because the VDs are FK228 the only neurons that form ventral GABAergic synapses in adults ( White et al., 1986). In unc-55 adults, the density of UNC-57 puncta in the ventral cord was significantly reduced compared to wild-type controls ( Figures 1B and 1C). By contrast, presynaptic (UNC-57) and postsynaptic (UNC-49 GABAA) puncta densities were significantly increased in the dorsal cord of unc-55 adults ( Figures 1D and 1E and Figures S1B and S1C). To assay the function of GABAergic synapses, we recorded inhibitory postsynaptic currents (IPSCs) from adult ventral and dorsal body muscles. In unc-55 mutants, ventral IPSC isothipendyl rates were significantly reduced (33 Hz wild-type, 0.1 Hz unc-55, p < 0.0001), whereas dorsal IPSC rates were significantly increased (33 Hz

wild-type, 65 Hz unc-55, p < 0.0001 Student’s t test) ( Figures 1F–1I). Thus, inactivation of unc-55 shifts GABAergic NMJs from ventral to dorsal muscles, as assessed by both imaging and electrophysiology. The rates and amplitudes of excitatory post-synaptic currents (EPSCs) were indistinguishable in wild-type and unc-55 ventral body muscles ( Figures S1D–S1F), suggesting that cholinergic transmission was unaltered. Consequently, the loss of ventral synapses in unc-55 mutants was specific for GABAergic (i.e., VD) synapses. The absence of ventral GABAergic NMJs in unc-55 adults could result from decreased formation or decreased retention of ventral NMJs. To assay ventral synapse formation, we imaged ventral GABAergic synapses in L2 larvae. We observed similar patterns of closely apposed pre-synaptic (UNC-57) and post-synaptic (UNC-49 GABAA receptor) puncta in the ventral cord of unc-55 and wild-type L2 larvae, indicating that inactivation of unc-55 did not disrupt ventral synapse formation by VD neurons ( Figures S1G–S1J).