It can be seen that these predictive patterns consist of small subregions in which activity increases and decreases with larger angles. Specifically, some voxels have

higher responses for orientations >45° (yellow), whereas other voxels show higher responses for orientations <45° (blue). We compared our multivariate results to a more conventional univariate whole-brain analysis searching for correlations between stimulus orientation and the BOLD signal in each voxel BMS-354825 in vivo by using a parametric approach (Büchel et al., 1998). This analysis did not reveal any significant voxels (p < 0.0001, uncorrected, k = 5). Furthermore, a region of interest (ROI) analysis at a more liberal threshold of p < 0.05 revealed no univariate correlations with stimulus orientation in the early visual cortex (t = 1.29, p = 0.21), the lateral parietal cortex (t = 1.34, p = 0.20), and the medial frontal gyrus (t = 0.56, p = 0.58) as identified by our multivariate analysis (see above). This suggests that the results of the multivariate analysis are above and beyond what could have been obtained through univariate approaches. Our results so far suggest that information about the physical properties of the stimulus, i.e., its orientation,

is encoded in the early visual cortex as well as in higher brain regions such as the putative LIP. However, our model suggests that the orientation of the Gabor is not used directly to make the perceptual decision. Torin 1 datasheet What is used to make the choice is the decision variable DV. Thus, activity patterns in brain regions that are directly involved in perceptual decision-making should correlate with DV. We identified such brain regions by applying the same local information mapping procedure described above, but this time searching for representations of DV rather than orientation. We found significant information (p < 0.0001, k = 20, corrected for about multiple comparisons

at the cluster level, p < 0.001) about the model-derived decision variable in the left putative LIP (BA 7 [-24, −63, 48], t = 5.98, Figure 5A), the ACC (BA 32 [-3, 45, 24], t = 9.01, Figure 5C) and the precuneus (BA 23 [0, −39, 39], t = 6.57) but not the early visual cortex (see Figure S2 and Table S2 for complete results). In these regions distributed patterns of activity can be used to make linear predictions about the decision variable derived from the reinforcement learning model ( Figures 5A and 5C, right). Again, a univariate whole-brain analysis searching for correlations with DV revealed no significant voxels (p < 0.0001, uncorrected, k = 5). Furthermore, an ROI analysis revealed no significant (p < 0.05) univariate correlations with DV in the lateral parietal cortex (t = 0.64, p = 0.53) or the ACC (t = 0.75, p = 0.46) as identified by our multivariate analysis (see above). The physical stimulus orientation is correlated with the decision variable (DV) that is used by the model to make perceptual choices.

The crossing defects of integrin and Sin1/fry/trc mutant neurons illustrate the importance

of spatial restriction for a tiling system based on homotypic repulsion. For neurons normally tiling a 2D territory, without spatial restraints, INK 128 order repulsion would drive homologous dendrites to disperse into a 3D space with potentially considerable overlap of dendritic coverage in a given receptive field. In the case of Drosophila class IV da neurons, the restricted space is a 2D sheet between the basal surface of epidermal cells and the ECM. As highlighted by our study, tiling requires high precision in this spatial restriction: a slight deviation of dendrites from this 2D space is sufficient to circumvent homotypic repulsion and cause overlap of dendritic fields. What about other tiling systems? One vertebrate tiling system that has some similarity with the Drosophila class IV da neurons is the fish somatosensory neurons, which innervate the skin with axon arbors selleck compound ( Sagasti et al., 2005). Like the dendrites of class IV da neurons, these peripheral axons exhibit contact-dependent repulsion and expansion after ablation of adjacent neurons. It will be interesting to determine whether the processes of these sensory neurons are restricted to a 2D layer within the skin and whether the interaction of those neurites with ECM or skin cells are important for their

tiling. The vertebrate retina is a classical system for studying neuronal tiling. Certain types of retinal ganglion cells (RGCs) and amacrine cells perfectly tile the retina in the x-y plane. Ablation studies suggest that dendro-dendritic repulsion exists between dendrites of neighboring RGCs (Perry and Linden, 1982). The dendrites of RGCs and amacrine cells innervate various layers of retinal inner plexiform in a neuronal type-specific manner (Dacey et al., 2003, Kolb Ketanserin et al., 1992 and Mariani, 1990). It will be intriguing to find out whether the dendrites of a given type of ganglion cells or amacrine cells that tile are indeed restricted to a molecularly defined 2D layer(s) in the inner plexiform, and, if so, whether interactions

between cell adhesion molecules and the ECM contribute to the restricted dendritic distribution and tiling. Theoretically, spatial restraints should be a general prerequisite for homotypic repulsion, which may or may not be limited to 2D. Tiling of neuritic fields could conceivably be established by homotypic repulsion in restricted 3D spaces, such as columnar tiling of the transient neurites of murine retinal horizontal cells during postnatal development (Huckfeldt et al., 2009). However, besides spatial restraints, there could be additional mechanisms to enhance the effectiveness of homotypic repulsion in partitioning 3D neuritic fields. One potential mechanism is to have more homologous neurites within a given space, which would increase the possibility of neurite encounter and repulsion.

This suggests that the changes across conditions in the reliability of slow dynamics (Figures

7A and 7B) are not driven by differences in low-level properties (e.g., the audio envelope; Figure S5) of the stimuli. Slow (<0.1 Hz) fluctuations in population activity are a ubiquitous feature of neural dynamics, but their functional role is uncertain (Bullmore et al., 2001; He, 2011; He et al., 2010; Leopold et al., 2003; Nir et al., 2008; Weisskoff et al., 1993; Zarahn et al., 1997). We mapped the TRWs of human cortical regions using ECoG and tested whether regions with shorter and longer TRWs differ in their slow dynamics. Consistent with fMRI studies (Hasson et al., 2008; Lerner et al., 2011), the electrophysiological measurements revealed that TRWs increased from sensory toward higher order cortices. Notably, regions with longer TRWs exhibited relatively more slow fluctuations and greater temporal autocorrelation, even during resting fixation. Although the slow fluctuations Trametinib manufacturer were observed in the absence of any stimulus, they became time-locked to the content of audiovisual movie stimuli. Moreover, the slow timecourses were highly reliable in response to movie clips that contained long-range contextual information structure, but they were significantly less reliable in response

to movie clips that had been scrambled. The relationship between long TRWs and slow fluctuations of power was observed regardless of whether the slow fluctuations were measured during Bosutinib the intact

or scrambled movie clips (Figures 6C, 6D, 6F, and 6G) or during a fixation period (Figures 6E and 6H). In addition, the LowFq and ACW values were highly correlated across states of fixation and movie viewing (Figure S6). These data suggest that the dynamic timescale in each region is determined in part by circuit properties which shape dynamics in a similar way, regardless of the state of external stimulation. This finding is also consistent with the idea that sensory circuits, which tend to have shorter TRWs, are optimized for rapid transient responses to the environmental state, while higher order circuits, which tend to have longer TRWs, more readily maintain and accumulate information over time (Huk and Shadlen, 2005; Ogawa and Komatsu, 2010; Romo et al., 1999; Shadlen and Newsome, Methisazone 2001; Wang, 2002). Although the regional ordering of dynamic timescales was well-preserved across states of task and fixation, the dynamic timescales in individual electrodes did change across conditions. Both short TRW and long TRW regions exhibited relatively more slow fluctuations of broadband power during the intact than during the scrambled stimuli (Figures 6A and 6B). Electrodes with short TRWs responded to low-level stimulus properties such as the audio amplitude (Figure 4A), which changes more rapidly in the scrambled condition (Figure S5). Thus, the change in slow fluctuations in short TRW areas may be attributable to changes in low-level stimulus properties.

, 2010b). Axin overexpression in NPC nuclei increased the levels of proneural targets of β-catenin,

Ngn1 and NeuroD1, by 4.3 ± 0.3-fold and 0.7 ± 0.2-fold, respectively ( Figure 7D). Intriguingly, blocking the interaction between nuclear Axin and β-catenin by expressing the Axin point mutant (CIDm) that was unable to bind β-catenin in the nucleus ( Xing et al., 2003) inhibited neuronal differentiation and maintained the NPC pool ( Figures 7E and 7F), particularly IPs ( Figures S7F and S7G). To further confirm the importance of the interaction between Axin and β-catenin in the nucleus, we designed a small peptide CID based on the protein sequence of the INK1197 manufacturer β-catenin-interacting domain of Axin ( Xing et al., 2003) and tagged the peptide with an SV40 T-antigen NLS to enable specific targeting of the CID peptide into the nucleus. CID-NLS effectively blocked

the interaction between Axin and β-catenin ( Figure S7H) and significantly inhibited neuronal differentiation in Doxorubicin manufacturer the mouse neocortex ( Figures 7G and 7H). These observations collectively indicate that nuclear Axin promotes neuronal differentiation in a β-catenin-dependent manner. The fate decision of NPCs between amplification and differentiation controls the number of neurons produced during brain development and ultimately determines brain

size. However, it is unclear how the NPCs make this fundamental choice. Here, we show that the subcellular localization of a signaling scaffold protein, 4-Aminobutyrate aminotransferase Axin, defines the activation of specific signaling networks in NPCs, thereby determining the amplification or neuronal differentiation of NPCs during embryonic development (Figure 8). Cytoplasmic Axin in NPCs enhances IP generation, which ultimately leads to increased neuron production, whereas nuclear Axin in IPs promotes neuronal differentiation. Intriguingly, the Cdk5-dependent phosphorylation of Axin facilitates the nuclear accumulation of the protein, thereby functioning as a “brake” to prevent the overproduction of IPs and induce neuronal differentiation. The drastic increase in the size of the cerebral cortex in the human brain, which is thought to underpin our unique higher-cognitive functions, is associated with a disproportionate expansion of cortical neurons, especially the upper-layer neurons. The expansion of cortical surface may result from increased numbers of neuroepithelial (NE) cells and RGs (Rakic, 2009) or from an amplified IP pool (Pontious et al., 2008). NE/RG augmentation evidently controls the global enlargement of cortical surface (Chenn and Walsh, 2002 and Vaccarino et al., 1999).

Archaeorhodopsin-3 (Arch) and

halorhodopsin (NpHR) are members of the opsin family used to silence neuronal activity (Chow et al., 2010 and Zhang et al., 2007). Illumination of Arch, a proton pump, for an extended period of time could result in intra- and Tariquidar solubility dmso extracellular pH disturbance, which could negatively impact on cell health (Han, 2012 and Okazaki et al., 2012). Activation of the chloride pump NpHR leads to accumulation of intracellular chloride ions and can compromise GABAA-receptor-mediated inhibition (Raimondo et al., 2012). In addition, continuous activation of Arch or NpHR is limited by its inactivation and potential photo damage, which is not ideal for studies, such as those researching epilepsy, in which it is important to maintain membrane hyperpolarization for a long period of time (Kokaia et al., 2013). Selleckchem Doxorubicin In contrast, PIRK is based on Kir2.1, an inward rectifying potassium channel whose native function is to regulate neuronal excitability (Bichet et al., 2003, Hibino et al., 2010 and Nichols and Lopatin, 1997). Through a small amount of outward K+ current, Kir2.1 can directly silence the electrical activity of neurons. In fact, ectopic expression of Kir channels has been used previously over the last decade to investigate the effect of neuronal excitability on circuit function (Burrone et al., 2002, Johns

et al., 1999, Nadeau et al., 2000 and Yu et al., 2004). By endowing Kir2.1 with photoresponsiveness in PIRK, we have provided

the ability to temporally control through light precision the activation of Kir2 channels. Another advantage PD184352 (CI-1040) of PIRK is that it functions like a binary switch, whereby a single light pulse can induce the lasting silencing effect on target neurons. Without the need to continuously deliver light through the optical fiber, this binary switch feature of PIRK is convenient for animal studies to mitigate potential interference of light or light devices on animal behavior and could, therefore, be useful for studying or treating intractable epilepsy, intractable pain, or muscle spasms. Moreover, PIRK channels may be utilized for studying a variety of physiological processes and diseases that directly involve Kir2.1 channels. For example, Kir2.1 function has been implicated in Andersen syndrome (Plaster et al., 2001), cardiac short QT syndrome (Priori et al., 2005), and osteoblastogenesis (Zaddam et al., 2012). PIRK is designed with a photoreleasable pore-blocking group. This “block-and-release” strategy may be generally applicable to other channels and receptors. For instance, G protein-gated Kir channels (Kir3 family), α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors, and N-Methyl-D-aspartic acid receptors share similar pore topology with Kir2.1. By incorporating Cmn into pore residues in these proteins, one should be able to similarly install in light responsiveness to them for highly disciplined study of channel/receptor physiology.

, 2009), and thus should generate a measurable Ca 3-MA cell line transient in the postsynaptic compartment (Goldberg et al., 2003b). We recorded from fast-spiking interneurons (see Experimental Procedures) with patch pipettes containing the fluorescent Ca indicator Oregon Green BAPTA-1 (150 μM) in a slice preparation that preserves much of the thalamocortical fiber bundle (Agmon and Connors,

1991 and Porter et al., 2001), allowing a stimulation electrode to be placed in this pathway slightly ventral to the fimbria. We simultaneously imaged the dendritic arbor of the recorded neuron (Figure 1D) and recorded the electrophysiological response in voltage clamp to stimulation of thalamic afferents. We used three stimulation protocols: (1) bulk stimulation, in which multiple thalamic afferents are recruited; (2) threshold single fiber stimulation, in which a single afferent impinging on the recorded neuron or imaged dendrite is stimulated just at threshold, leading selleck chemicals llc to fluctuation between recruitment successes and failures; and (3) single fiber stimulation, in which a single fiber impinging on the recorded neuron or imaged dendrite is recruited reliably,

without failures, by the stimulation electrode (see Experimental Procedures). Bulk stimulation of thalamic afferents elicited a pattern of Ca hotspots—localized, transient postsynaptic increases in Ca concentration—decorating the dendritic arbor of cortical interneurons (Figure 1E). Addition of the AMPA-R antagonist NBQX reduced hotspot intensity by 58% ± 5% (n = 5), while further addition of the NMDA-R antagonist R-CPP eliminated hotspots entirely (to −1% ± 1%, n = 3; Figure 1F). Similarly, application of R-CPP reduced hotspot intensity by 59% ± 4% (n = 5; see Figure S1 available online), suggesting that Ca-permeable however AMPA-R and NMDA-R contribute about equally to the postsynaptic Ca signal under our recording conditions. Do hotspots mark the location

of a synaptic contact? In the absence of perfect voltage clamp, hotspots could in principle result from the activation of voltage-gated Ca channels (VGCCs) not necessarily colocalized with the synaptic contact. To test this possibility, we monitored the spatial distribution of Ca transients in response to depolarizing voltage steps (Goldberg et al., 2003b). The resulting Ca transient was spread throughout the dendritic arbor (Figure 1G), indicating that VGCCs are distributed broadly and therefore unlikely to produce hotspots at significant distance from the site of synaptic contact. Thus, thalamic stimulation generates a spatial pattern of Ca transients that corresponds to the location of glutamate receptor-mediated thalamic inputs. To ascertain whether an individual hotspot corresponded to the input of a single thalamic fiber, we used a threshold single fiber stimulation paradigm.

This suggests that the low-energy state in the delay period is also stable across time. Importantly, this velocity metric is sensitive to changes in the state of the network, even if the overall energy of the system remains constant. Therefore, multidimensional velocity provides a richer measure of the population dynamics than overall change in activity levels (shown in Figure 2E, bottom), which reveals only a single dominant peak at around 85 ms corresponding to the initial increase in firing at stimulus onset, followed by a second smaller increase in energy

change at around 250–300 ms that tracks the gradual decrease in firing rate observed across the population. Overall, these initial analyses show that the transient increase

in neural firing triggered by the instruction cue is associated with a rapid configuration C646 of activity in state space that differentiates trial type. Activity then settles into a relatively low-energy stable state toward the offset of the cue and into the delay period. Although separation by trial type becomes less distinct during this more quiescent phase, the population response remains statistically separable. To explore the dynamic evolution of activity states discriminating different trial types, we exploited a cross-temporal selleck products variant of pattern classification (see schematic in Figure 3A). First, we demonstrate that the general classification approach is able to decode information content from the pattern of activity observed after the cue presentation. This time-resolved pattern analysis demonstrates significant coding of the cue at around 100 ms (Figure 3B), corresponding to the time of rapid divergence observed in the distance metric (Figure 2B). Pattern classification also peaks at around 230 ms and remains relatively uniform into the delay period. To directly assess the time stability of the activity state differentiating trial types, we decoupled the temporal windows used

for train and test (see schematic in Figure 3A; see also Crowe et al., 2010; Meyers et al., 2008). If accurate generalization is observed across time (train at time t, test at time t+n), we can infer that the population code that differentiates trial type at time t is significantly similar to the coding scheme at time t+n. At the extreme, if the coding schemes were completely Tryptophan synthase time stable, pattern classification should not be sensitive to which time points are used for test or train—by definition a stationary code does not vary across time. Conversely, if classifiers trained at time t are unable to decode patterns observed at time t+n, then we can conclude that population coding is time specific. Cross-temporal classification results for trial type are presented in Figure 4. Different color traces represent classification performance for classifiers trained on data from corresponding shaded time windows and tested throughout the cue and delay epochs.

Very little is known about the role of peripheral glia contacting axons or the nerve terminal. In mammals, the proinflammatory cytokine TNF-α is expressed in Schwann cells and has been implicated in the mechanisms of demyelination during multiple sclerosis (Qin et al., 2008). However, the involvement of TNF-α in ALS remains controversial. TNF-α knockout mice are viable, and elimination of TNF-α did not protect motoneurons from degeneration following overexpression of mutant SOD1 in mouse motoneurons (Gowing et al., 2006). It is worth noting that a compensatory upregulation of related proinflammatory cytokines, IL-1-β and TLR-2, was

observed, and this could reasonably account for the failure of the TNF-α knockout to protect against SOD1 mediated motoneuron degeneration Z-VAD-FMK nmr (Gowing et al., 2006). We previously established a system to study motoneuron degeneration in Drosophila. In Drosophila, genetic lesions in the dynein dynactin complex ( Eaton et al.,

2002) and the spectrin/ankyrin skeleton ( Pielage et al., 2005, Pielage et al., 2008, Pielage et al., 2011 and Massaro et al., 2009) disrupt axonal transport and cause degeneration of the neuromuscular junction (NMJ) and motor axons. Motoneuron degeneration in Drosophila shares many of the cellular hallmarks of degeneration in mammalian neurons, observed at the light level, ultrastructurally and electrophysiologically. Genetic lesions in dynactin and the spectrin/ankyrin skeleton cause ALS and spinal cerebellar ataxia type 5 (SCA5) in humans ( Puls et al., 2003 and Ikeda et al., 2006). Mouse and Drosophila models of these diseases employing similar genetic lesions have UMI-77 nmr been developed ( LaMonte et al., 2002 and Lorenzo et al., 2010). Taken together, these data imply that common cellular stresses are able to initiate motoneuron Chlormezanone degeneration in insects and mammals. Furthermore, motoneuron

degeneration in Drosophila can be suppressed by expression of a wallerian degeneration slow (WldS) transgene, implying the existence of common degenerative signaling pathways in mammalian and fly neuromuscular systems ( Massaro et al., 2009). Taking advantage of an in vivo model system for motoneuron degeneration in Drosophila, we now provide evidence for a prodegenerative-signaling pathway that originates within the motoneuron and passages through the peripheral glia that are in close proximity to the motoneuron axon. We present evidence that TNF-α, expressed in a subset of peripheral glia, acts via a conserved TNF-α receptor (TNFR), expressed in motoneurons, to initiate prodegenerative signaling within the motor axon. The prodegenerative-signaling pathway is genetically independent of c-Jun N-terminal kinase (JNK) and NFκβ, two prominent pathways that reside downstream of the TNFR. Instead, we show that the prodegenerative process requires the Drosophila effector caspase, Dcp-1, which we demonstrate is both necessary and sufficient for motoneuron degeneration.

, 2013a and Polgár et al., 2013b). We previously found that ∼50% of inhibitory cells in laminae I-II express sst2A, and these can be further subdivided into subpopulations that contain galanin and/or nNOS (which constitute ∼60% of the sst2A-expressing cells and therefore approximately one-third of all the inhibitory neurons). The galanin cells coexpress PPD and are the major source of dynorphin in the superficial laminae (Bröhl et al., 2008 and Sardella et al., 2011).

In addition, we identified two other nonoverlapping groups among inhibitory interneurons that lack sst2A (NPY- and parvalbumin-expressing selleck inhibitor cells). There is already evidence that these neurochemical classes differ, both in their responses to noxious stimuli and in their postsynaptic targets (Todd, 2010, Hughes et al., 2012 and Polgár et al., 2013b). The present results provide further evidence in support of this classification scheme, since Bhlhb5−/− mice show a loss of inhibitory interneurons that is apparently restricted to neurochemically defined populations. We find that B5-I neurons correspond to two (mostly nonoverlapping) subpopulations—those that coexpress galanin and

dynorphin and those that express nNOS. The subpopulation of B5-I neurons that expresses galanin/dynorphin likely uses Dolutegravir GABA as its fast transmitter (Simmons et al., 1995), whereas TCL the B5-I neurons that express nNOS are thought to release GABA and glycine (Spike et al., 1993). Since relief of itch by counterstimuli begins almost instantaneously, we favor the idea that this component is mediated by fast-acting inhibitory transmitters. In contrast, dynorphin, which modulates neuronal

activity via G protein-coupled receptors, may underlie prolonged suppression of itch. A key finding from our study is that the loss of B5-I neurons (which results in an almost complete absence of dynorphin in the spinal cord) has a different phenotypic outcome than loss of dynorphin alone. Thus, Bhlhb5−/− mice show dramatically elevated itch, whereas PPD−/− mice display normal itch sensitivity. This distinction implies that an organism can compensate for the loss of dynorphin, but not for the loss of dynorphin-expressing neurons in the dorsal horn. We speculate that neuromodulatory mechanisms may be particularly amenable to homeostatic compensation ( Doi and Ramirez, 2010). In keeping with this idea, mice lacking either enkephalin or the mu opioid receptor have subtle pain phenotypes ( König et al., 1996 and Matthes et al., 1996), despite the fact that mu opioids are among the most effective analgesics. Adaptation also occurs in response to chronic opioid overexposure, as shown by the tolerance observed in humans and animal models following long-term treatment with opioid analgesics ( Morgan and Christie, 2011 and Williams et al., 2013).

In the earlier stages, shape is encoded primarily through local orientation in V1 (Hubel and Wiesel, 1959, 1965, 1968) and combinations of orientations in V2 (Anzai et al., 2007; Tao et al., 2012). At the final stages in IT,

cells have been shown to be selective for complex objects like faces (Desimone et al., 1984; learn more Tanaka et al., 1991; Tsao et al., 2006). How this transformation is achieved remains largely unknown. In addition, the selectivity to complex features becomes more invariant to simple stimulus transformations such as size or spatial position as one traverses the ventral cortical hierarchy (Rust and Dicarlo, 2010). To understand how contours of objects are integrated into coherent percepts in the later stages, a detailed understanding of shape processing in intermediate stages like V4 is critical. Previous studies (Pasupathy and Connor, 1999, 2001) demonstrate that neurons in monkey visual area V4 are involved in the processing of shapes of intermediate complexity and are sensitive to curvature. These studies showed that V4 neurons responded more strongly to a preferred stimulus, as compared to a null stimulus,

throughout the receptive field (RF)—a form of translation invariance. However, little is known about the mechanisms that underlie shape tuning of neurons in area V4 or about the degree to which NLG919 manufacturer translation invariance depends on stimulus complexity. Using a dense mapping procedure, we sought to understand the detailed structure of shape selectivity within V4 RFs. We analyzed responses from 93 isolated neurons in area V4 of two awake-behaving male macaques

(see Experimental Procedures). The stimuli consisted of oriented bars presented alone or linked end to end to form curves or in the most tightly curved conditions: “C” shapes (Figure 1A). Bars were presented at eight orientations. Composite shapes were composed of three bars linked together to yield five categories of shapes: straight, low curvature, medium curvature, high curvature, and C shaped. Stimuli were presented in fast reverse correlation sequences (16 ms duration, exponential distributed delay between stimuli with a mean delay of 16 ms) at various found locations within the RF of peripheral V4 neurons (2°–12° eccentricity) while the monkeys maintained fixation for 3 s. The composite shapes were presented on a 5 × 5 location grid centered on the RF, while the oriented bars were presented on a finer 15 × 15 location grid. The grid of locations and the size of visual stimuli were scaled with RF eccentricity to maintain the same proportions as shown in Figure 1A. A pseudorandom sequence from the combined stimulus sets was presented in each trial. We found that the majority of neurons in our population were significantly selective to the composite contours.