5–200 Hz), and a quality factor of 2 (for 3 dB bandwidths), consistent with those in previous models of human modulation

filtering (Dau et al., 1997), and broadly consistent with animal neurophysiology data (Miller et al., 2002 and Rodríguez et al., 2010). Although auditory neurons often exhibit a degree of tuning to spectral modulation as well (Depireux et al., 2001, Rodríguez et al., 2010 and Schönwiesner and Zatorre, 2009), this is typically less pronounced than their temporal modulation tuning, particularly early in the auditory system (Miller et al., 2002), and we elected not to include it in our model. Because 200 Hz was Ibrutinib concentration the Nyquist frequency, the highest frequency filter consisted only of the lower half of the half-cosine frequency response. We used a smaller set of modulation filters to compute the C1 and C2 correlations, in part because it was desirable to avoid large numbers of unnecessary statistics, and in part because the C2 correlations necessitated octave-spaced filters (see below). These filters also had frequency responses that were half-cosines on a log-scale, but were more broadly tuned ( Q=2),

with center frequencies in octave steps from 1.5625 to 100 Hz, yielding seven filters. All filtering was performed in the discrete frequency domain, and thus assumed circular boundary conditions. To avoid boundary artifacts, the statistics measured in original recordings were computed as weighted time-averages. The weighting selleck inhibitor window

fell from one to zero (half cycle of a raised cosine) over the 1 s intervals at the beginning and end of the signal (typically a 7 s segment), minimizing artifactual interactions. For see more the synthesis process, statistics were imposed with a uniform window, so that they would influence the entire signal. As a result, continuity was imposed between the beginning and end of the signal. This was not obvious from listening to the signal once, but it enabled synthesized signals to be played in a continuous loop without discontinuities. We denote the k  th cochlear subband envelope by sk  (t  ), and the windowing function by w  (t  ), with the constraint that t∑w(t)=1∑tw(t)=1. The nth modulation band of cochlear envelope sk is denoted by bk,n(t), computed via convolution with filter fn. Our texture representation includes the first four normalized moments of the envelope: Mk1=μk=t∑w(t)sk(t),M1k=μk=∑tw(t)sk(t), M2k=σk2μk2=∑tw(t)(sk(t)−μk)2μk2, M3k=∑tw(t)(sk(t)−μk)3σk3,and M4k=∑tw(t)(sk(t)−μk)4σk4k∈[1…32]ineachcase. The variance was normalized by the squared mean, so as to make it dimensionless like the skew and kurtosis. The envelope variance, skew, and kurtosis reflect subband sparsity. Sparsity is often associated with the kurtosis of a subband (Field, 1987), and preliminary versions of our model were also based on this measurement (McDermott et al., 2009).

Analysis of GFP-expressing neurons at P18 revealed a significant increase in the number of dendritic spines on CA1 pyramidal neurons in NgRTKO−/− mice relative to their triple heterozygous littermate controls ( Figures 5A and 5B). These findings are

consistent with the idea that the NgR family members function together in vivo to limit the number of excitatory synapses. To extend this analysis using an independent approach, we performed transmission electron microscopy to visualize the ultrastructural features of excitatory synapses. In micrographs from NgRTKO−/− mice, we observed asymmetric synapses of typical morphology, suggesting that the overall structure and vesicle content of Bafilomycin A1 excitatory synapses are normal in the absence of NgRs. However, quantification of the number of excitatory synapses in the apical dendritic regions of CA1 revealed that NgRTKO−/− mice had a significant

increase in the density of excitatory synapses relative to heterozygous littermate controls ( Figures 5C and 5D). Furthermore, this effect was not limited to CA1 neurons, since analysis of CA3 neurons also revealed a clear increase in the number of PSDs in NgRTKO−/− animals ( Figure 5E). Thus, analysis by confocal and electron microscopy suggests that the NgR family functions to limit the number of excitatory synapses in vivo. To address whether the observed increase in synapse number reflects an increase INCB018424 clinical trial in functional synapses, we performed whole-cell patch-clamp electrophysiology on CA1 pyramidal neurons from acute hippocampal slices obtained from NgRTKO−/− mice and control littermates to quantify the frequency and amplitude of miniature excitatory postsynaptic currents (mEPSCs). This analysis revealed a significant increase in the frequency of mEPSCs in

NgRTKO−/− mice relative to littermate controls ( Figure 5F and S5C), suggesting that the NgR family restricts the development of functional excitatory synapses. Interestingly, there was a small but significant Urease decrease in the amplitude of mEPSCs ( Figure 5G and S5D), consistent with the immature spine types observed in NgR1 knockouts ( Lee et al., 2008 and Zagrebelsky et al., 2010). Thus, reducing the expression of the NgR family results in an increase in functional synapses that are slightly reduced in strength. The question remained as to how NgRs work at a mechanistic level to restrict excitatory synapse number. One possibility was that NgRs limit the formation of new synapses in part by inhibiting dendritic growth, thereby reducing the possibility of contact between axons and dendrites. Therefore, we asked whether loss of NgR family members affects dendritic branching.

While this is probably an oversimplification given the complex dynamics of the brain, there is no reason to think that there would be complex differences in dependencies across SWRs before correct and incorrect trials that would result in illusory significance values for our analyses. We also chose to use the number of cell pairs, individual cells, or trials as the N in our statistical analyses, as is standard in the field. We note here that our results are highly significant and consistent

across individual animals and across tracks. We also carried out a complementary analysis to determine whether we could predict the outcomes on individual trials. Our goal here was to use a measure that allowed us to combine multiple run sessions from multiple

EGFR inhibitor animals together, and as each run session was associated with a different number of recorded place cells, we measured the proportion of possible cell pairs that were active before each trial. We calculated, for each run session, the total number of possible coactive cell pairs, which is (number place cells recorded) × (number place cells recorded − 1)/2. We then determined, for each trial, the number of those cell pairs that were coactive within an SWR preceding that trial and then divided that number by the total to get a proportion. Given that measure for each correct INCB018424 purchase or incorrect trial, we then used logistic regression to relate the proportion of coactive cells to the trial outcome (correct or incorrect). The model was estimated

based on half of the total data, subsampled to include an equal number of correct and incorrect trials from each run session. The specific correct and incorrect trials were chosen at random. We then tested the model prediction on the other half of the data, once again subsampled to include an equal number of correct and incorrect trials from each run session. We repeated that estimation and testing process 1,000 times with different sets of correct and incorrect trials to produce a distribution of predictions and heptaminol compared that distribution across performance categories and to chance performance of 50% correct. We also examined the content of individual SWRs. We used our previously developed decoding approach (Karlsson and Frank, 2009) to translate the activity of neurons active during the SWR to a trajectory through space. Briefly, for all SWRs with at least two active place cells, we divided the SWR into 15 ms bins and for each bin used the place fields of neurons active in that bin to derive a probability distribution function over distance from the end of the center arm. For each bin, that pdf represented where we would expect the animal to be on the track given that those cells had fired the observed numbers of spikes. To determine whether a given decoded trajectory was best described as inbound or outbound, we fit a line to samples from the sequence of pdfs plotted versus time.

In DIII, a hydrophilic Thr is also found next to the second outermost S4-positive residue (R2). The two hydrophilic residues are not unique to Nav1.4 as they are remarkably well conserved

Pifithrin-�� price across eukaryotic phyla (Figure S2). To evaluate the role of these amino acids, we sought to create a “slow Nav channel” by substituting the hydrophilic residues in DI–DIII of Nav1.4 for Ile (S2) and Val (S4) (Figure 2B). Because two hydrophilic residues are present in the S4 in DIII (S4-DIII), S1120 next to K1, and T1123 next to R2 (Figures 2A and S2), we constructed three mutants: NaSlo1 (which conserves T1123), NaSlo2 (which conserves S1120), and NaSlo3 (which replaces both residues by Val). Then, we engineered a “fast Kv channel” by substituting the homologous hydrophobic residues in the slow VSs of the Shaker FRAX597 purchase Kv channel (I287 in S2 and V363 in S4) by Thr. We named it Shaker-I287T/V363T (Figure 2B). All NaSlo mutants produced a significant slowing down of the time constant of activating sodium currents (see Figure S1 for the fitting procedure) upon moderate depolarizing pulses from −45 mV to −20 mV (Figures 2C and 2D). The activation kinetics (τ) of the VS movement in the NaSlo1 and Shaker-I287T/V363T mutants were further determined using

activation gating current recordings (Figure 2E) and plotted as a function of the membrane potential (Figure 2F). The τ versus voltage (V) (τ-V) curve for the NaSlo1 channel is displaced toward more negative voltages compared to the other tested channels (Figure 2F, blue triangles), presumably because the NaSlo1 mutations produce a negative shift of the charge (Q) versus V (Q-V) curve (Figure S3A,

blue symbols). Nevertheless, the slowest value for the time constant of gating currents (τmax) is similar between NaSlo1 and wild-type (WT) Shaker and between the Shaker-I287T/V363T and WT Nav1.4, respectively (Figures 2F and 2G). There was no statistical difference of the mean values of τmax between the Shaker-I287T/V363T and WT Nav1.4 (p value = 0.32597, n = 6) and between NaSlo1 and WT Shaker (p value = 0.90888, n = 6). Interestingly, in the NaSlo1 channel, the presence of the β1 subunit speeds up τmax (about 2.5-fold) quite similarly to WT Nav1.4 channel (about Bumetanide 2-fold), thus suggesting that the speed-control residues do not functionally interact with the β1 subunit. In Shaker channels, the mutations I287T-V363T also accelerate ionic current kinetics but they do so more effectively for pore closure than for pore opening (Figures S3C and S3D). This difference could be due to the fact that the I287T-V363T mutations may have little or no effect on the late concerted VS transition that rate limits pore opening (Smith-Maxwell et al., 1998) but does not rate limit pore closure (Labro et al., 2012).

Gratings moving at two opposite directions were first averaged to obtain the orientation response. The Rayleigh test (Fisher, 1993) was used to test the significance of a neuron’s direction selectivity. The Rayleigh test compares the circular data against a uniform distribution, where a rejection to the null hypothesis indicated a significantly deviation from uniformity. Neurons with p < 0.05 in the Rayleigh test were considered to be direction selective. We thank Dr. Anna W. Roe for valuable comments. We also thank Jingwei Pan, Junjie Cai, Cheng Xu, Zhongchao

Tan, and Jie Lu for technical assistance. This work was supported by grants from National Basic Research Program selleck screening library in China (973 Program 2011CBA00400); and the Hundred Talent Program of the Chinese Academy of Sciences. “
“The perceptual grouping of similarly oriented, discrete elements into a continuous contour is known as “contour integration” (Field et al., 1993). In this process, the salient contour can be detected even when embedded in a noisy background.

Previous psychophysical studies have explored the local interactions see more between collinear elements comprising contour paths (Field et al., 1993; Kapadia et al., 1995; Polat and Sagi, 1994) and showed that decreased contour saliency resulted in decreased contour detection (Braun, 1999; Hess et al., 2003; Li and Gilbert, 2002). Recent electrophysiological, imaging, and other studies have suggested that the primary visual cortex (V1) plays an important role in contour integration (Bauer and Heinze, 2002; Kapadia et al., 1995; Ko et al., 2011; Kourtzi et al., 2003; Li et al., 2006; Polat et al., 1998). The main observation

was enhanced neuronal activity for collinear elements not or a contour, and this activity enhancement was dependent on contour saliency. Additional studies have suggested that visual binding is encoded by response amplitude, e.g., increased firing rate (Barlow, 1972; Roelfsema, 2006) of neurons encoding features of the same contour relative to neurons encoding features belonging to a different contour or background. Despite recent progress, the neuronal mechanisms underlying contour integration are not fully understood. Specifically, the spatiotemporal patterns of population response in the contour and background areas, their relation to contour saliency, and contour detection remain unclear, in particular, at the single-trial level. To address these issues, we trained two monkeys on a contour-detection task and recorded the population responses in V1 using voltage-sensitive dye imaging (VSDI) at high spatial and temporal resolution (Shoham et al., 1999; Slovin et al., 2002). This allowed us to investigate and directly visualize the spatiotemporal patterns of population responses evolving in contour integration.

Since these functional properties arise from different molecular mechanisms in flies and vertebrates, these similarities seem unlikely to result from a common ancestral source. Rather, we propose that these parallels reflect Panobinostat cost convergence on a common processing strategy driven by similar biological constraints and natural input statistics. We speculate that analogous parallels will be found in many other aspects of visual processing.

The Gal4 drivers 21D-Gal4 ( Rister et al., 2007) and Rh1-Gal4 (Bloomington Drosophila Stock Center) were used to express a multicopy insert of UAS-TN-XXL ( Mank et al., 2008; as in Clark et al., 2011) and GABAAR and GABABR RNAis (GABAAR-RNAi from VDRC ABT-737 [KK100429] and GABABR2-RNAi from Root et al., 2008). Two-photon imaging was performed using a Leica TSC SP5 II microscope (Leica) equipped with a precompensated Chameleon femtosecond

laser (Coherent). Triggering functions provided by the LAS AF Live Data Mode software (Leica) enabled simultaneous initialization and temporal alignment of imaging and visual stimulation. Visual stimulation was applied as described in Clark et al. (2011), except that the stimulus was passed through a 40-nm-wide band-pass spectral filter centered around 562 nm and projected on a back-projection screen situated in front of the fly. All data were acquired at a frame rate of 10.6 Hz. Imaging experiments lasted no more than 2 hr per fly. The authors would like to thank Stephen Baccus, Saskia DeVries, Daryl Gohl, Marion Silles, Tina Schwab, Jennifer Esch, and Helen Yang for helpful comments on the manuscript. We would also like to thank Daryl Gohl and Xiaojing Gao (Luo laboratory) for providing fly stocks. This work was supported by a Fulbright Science and Technology Fellowship and a Bio-X Bruce and Elizabeth Dunlevie Stanford Interdisciplinary Graduate Fellowship (L.F.), a Jane Coffin Child’s Postdoctoral fellowship (D.A.C.), and a NIH Director’s Pioneer Award

DP1 OD003530 (T.R.C.) and NIH R01EY022638 (T.R.C.). “
“Numerous studies have shown that the hippocampus plays a crucial role in Levetiracetam episodic memory in both humans and animals, and a fundamental characteristic of episodic memory is the temporal organization of sequential events that compose a particular experience. Recent research has suggested that sequential organization of episodic memories may be supported by “time cells,” temporally tuned patterns of neuronal activity in the hippocampus (Gill et al., 2011; MacDonald et al., 2011; Manns et al., 2007; Pastalkova et al., 2008). However, it remains unclear what mechanisms are driving the apparent temporal tuning of hippocampal neurons. In experiments where time cells have been observed, the animals either run continuously in place (in a running wheel) (Pastalkova et al., 2008) or can move on a small platform (Gill et al., 2011) or in a chamber (MacDonald et al.

68, p = 0.016) (Figure 5G). Hence, in contrast IPI-145 supplier to dendritic calcium spikes evoked by strong PF stimulations that are initiated in the stimulated distal dendrite and propagate toward the soma (Llinás et al., 1969), CF-evoked calcium spikes are initiated in proximal dendrites. To examine whether dendritic calcium spikes were triggered directly by somatic sodium spikes within the complex spike, we determined the time

of occurrence of unitary fluorescence transients in individual traces by interpolation of their half-rise point. The latencies of the first and second unitary fluorescence transients from the peak of the 1st sodium spike were 1.87 ± 0.44 ms and 4.81 ± 0.69 ms (±SD; n = 8 cells). The first Abiraterone nmr unitary transient was more tightly time locked to the complex spike (jitter = SD of the latency = 379 ± 75 μs; ±SD) than the second one (jitter 550 ± 155 μs; ±SD). Cross-correlograms of the time of occurrence of somatic sodium spikes within the complex spike and of dendritic unitary calcium transients were computed (Figure S4). The correlation was not found to be significantly different (2 SD) from random correlation in four of five cells, as assessed by shuffling

spikes between episodes. Hence, high-threshold calcium spikes are initiated in the proximal dendrites independently of somatic sodium spikes. It has been proposed that the fast repolarization of spikes by Kv3 channels decreases their capacity to propagate in dendrites (Martina et al., 2003 and Stuart and Häusser, 1994). We tested whether dendritic calcium Vasopressin Receptor spike propagation was impeded by high-threshold Kv3 potassium channels by blocking these channels with low concentrations of 4-AP (Figures S5A and S5B and Supplemental Information). The shape of the somatic complex spike was modified by 4-AP (Figures S5C–S5H), and large

regenerative calcium events (Figure S5I) could be imaged in distal dendrites (Figures 5H and 5I). However, multiple spikes were never evoked (n = 10 cells; 17 branchlets) even at the most depolarized potentials, in contrast with the bursts occurring after mGluR1 activation. Furthermore, propagation of this single spike at distal sites (Figure S5J) remained regulated by the somatic membrane potential (Figures 5J and S5K). These results indicate that Kv3 channels, while involved in dendritic calcium spike repolarization, are not key in the mGluR1-mediated modulation of dendritic calcium electrogenesis. We looked for the molecular substrate of mGluR1 modulation and voltage-dependent spike unlocking. Because DHPG appears to regulate dendritic calcium spike initiation, it must act on voltage-gated channels activated rapidly below spike threshold. A-type potassium channels, because of their voltage-dependent inactivation, are the best candidates to modulate dendritic excitability. Two components of A-type conductances were described in Purkinje cells from young animals (Sacco and Tempia, 2002).

L.C. performed the experiments and analyzed the data. G.R. wrote the

sounds delivery software and helped with the technical design of the experiments. L.C. and A.M. wrote the paper. L.C. is supported by a fellowship from the Edmond and Lily Safra Center for Brain Sciences. This work was supported by a European Research Council grant to A.M. (grant #203994). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. “
“Parkinson’s disease (PD) is a highly debilitating and prevalent neurodegenerative disorder characterized by both motor and nonmotor symptoms (van Rooden et al., 2011), with the former mainly including muscle rigidity, 4–7 Hz Ceritinib mw rest tremor and akinesia (Zaidel et al., 2009). Human buy FG-4592 patients with advanced PD are often treated by DBS, which can alleviate the disease’s motor symptoms (Benabid et al., 2009, Bronstein et al., 2011 and Weaver et al., 2009). This procedure consists of implanting a multicontact macroelectrode, typically in either the internal segment of the globus pallidum (GPi) or the subthalamic nucleus (STN; Follett et al., 2010 and Moro et al., 2010), and the application of constant high-frequency (approximately 130 Hz) stimulation. The stimulation parameters

(e.g., frequency, pulse width, and intensity) are determined by a highly trained clinician and the initial programming can take up to 6 months before obtaining optimal results (Bronstein et al., 2011 and Volkmann et al., 2006). Subsequently, the stimulation parameters are adjusted intermittently every 3–12 months during the patient’s visits to the neurology clinic (Deuschl et al., 2006). The goal of the stimulator programming

is to adjust the DBS parameters in order to achieve an updated optimal trade-off between maximization of clinical improvement and minimization of stimulation-induced side effects. The parameters usually remain unchanged between clinical adjustments and the resulting stimulation is thus poorly suited to cope with the dynamic nature of PD. Indeed, both the neuronal discharge of the BG in PD patients and MPTP-treated primates and the parkinsonian motor symptoms display considerably faster dynamics than those provided by the adjustments of DBS therapy (Brown, 2003, Deuschl et al., 2006, Hammond et al., 2007, Moro et al., 2006 and Raz et al., PAK6 2000). Additionally, more frequent parameter adjustments have been shown to improve DBS efficacy (Frankemolle et al., 2010, Lee et al., 2010 and Moro et al., 2006). This highlights the need for an automatic and dynamic system that can continually adjust the stimulus to the ongoing neuronal discharge. In recent years, the role of pathological discharge patterns in the parkinsonian brain has emerged as pivotal in the disease pathophysiology (Eusebio and Brown, 2007, Hammond et al., 2007, Kühn et al., 2009, Tass et al., 2010, Vitek, 2008, Weinberger et al., 2009, Wichmann and DeLong, 2006 and Zaidel et al., 2009).

Indeed, one reason for the difficulty in identifying reliable brain sex differences is probably

because behavioral sex differences themselves are mostly modest in magnitude (Hyde, 2005). For example, verbal abilities differ between females and males by just 0.1 standard deviation, so it is not surprising that sporadic findings of sex differences in language-related neural activation have failed to hold up to replication. Even for one of the largest sex differences in cognition, visuospatial ability, it has been challenging to identify consistent differences in fMRI activation patterns between males and females (Clements-Stephens et al., 2009). Of course, sex differences are also found at cellular and molecular see more levels of the central nervous system (Cosgrove et al., 2007). But whether it involves gene expression, neuronal signaling, gross structure, or regional blood flow, every

brain-related sex difference is not necessarily behaviorally relevant. As Geert De Vries (2004) has shown, sex differences in neural circuitry or neurochemistry often reflect compensation for genetic and hormonal differences and Selleckchem Roxadustat actually end up making male and female behavior more similar than different. McCarthy and Arnold (2011) reinforce this point in a recent review on the complexity of brain sexual medroxyprogesterone differentiation, in which they importantly note that neuroscientists have yet to identify distinct “male” and “female” neural circuits underlying any sexually differentiated behavior, in spite of widespread belief in such circuits. Unfortunately, this message is not getting through to the public. Beyond the errors and extrapolations in popular accounts of brain sex differences lies an even deeper misperception, typified in the book A Gendered Choice, by single-sex school advocate David Chadwell. As rationale for sex-segregated teaching methods, Chadwell (2010) asks teachers to consider “biological brain differences,

otherwise referred to as hard wiring” (p. 8). The notion that sex differences in the brain, because they are biological, are necessarily innate or fixed is perhaps the most insidious of the many public misunderstandings on this topic. Neuroscientists know that, in the absence of proof of genetic or hormonal influence, any sex difference in adult neural structure or function could be shaped through experience, practice, and neural plasticity. But even some neuroscientists overlook such possibilities, limiting the Discussion sections of their papers to speculation about evolution and gonadal hormones and neglecting to mention the lifetime of gender-differentiated experience that may shape male-female differences in brain function or microstructure.

As originally defined by Sporns et al. (2005), it is “a comprehensive structural description of the network of elements and connections forming the human brain,” which could be considered either at a “macroscale [of] brain regions and pathways” or a “microscale [of] single neurons and synapses.” On the one hand, there is the network of brain areas, as in the Human Brain Connectome Project, in which MRI is used to trace projection pathways (Van Essen et al., 2012). But there is also the field of synaptic networks between individual neurons, which is typified by the use of large-scale electron

microscopy selleck compound (EM) to study local networks (Lichtman and Sanes, 2008). Another potential source of confusion is that the word itself implies comprehensiveness, but it has also been used to describe studies of networks that are only sparsely reconstructed (Seung, 2011). It would therefore be useful to have a word that denotes the less exalted study of neural connectivity with modern tools. But in modern biology, very few “-ologies” are being coined, Androgen Receptor antagonist while a new “-omics” appears almost every month. So we are left with the

term connectomics, a term that exemplifies the long-term aspirations of a field but that for now can also refer to rapidly improving anatomical methods for studying neural connections. Functional connectomics is a more specific term that describes studies of neuronal networks in which physiological measurements help us understand connections and vice versa (Seung, 2011). As such, it captures the ideas in the following quote from Hubel and Wiesel (1962): “At present we have no direct evidence on how the cortex transforms the incoming visual information. Ideally, one should determine the properties of a cortical cell, and then examine one by one the receptive fields of all the afferents projecting upon that cell. In the lateral geniculate, where one can, in effect, record simultaneously from a cell and one of its afferents, a beginning has already been made in this direction (Hubel and Wiesel,

mafosfamide 1961)” (Hubel and Wiesel, 1962). But in 1962, to study the cortex in this manner was virtually unimaginable, due to technical limitations. “In a structure as complex as the cortex the techniques available would seem hopelessly inadequate for such an approach. Here we must rely on less direct evidence to suggest possible mechanisms for explaining the transformations that we find” (Hubel and Wiesel, 1962). Fortunately, in the ensuing 50 years, the techniques for measuring neural activity and for tracing synaptic connections have advanced considerably. From work over the past 25 years, primarily from cortical slices in vitro, we now have a detailed understanding of the overall architecture of cortical circuits: cell types and their laminar organization, dendritic and axonal morphology, and the outlines of a wiring diagram.