sCRACM relies on photostimulating axons, which can Sirolimus clinical trial be efficiently excited even when severed from their parent somata.

Therefore, sCRACM can map connections between defined neuronal populations over long length scales, not limited to circuits preserved in brain slices. sCRACM also provides an estimate of the spatial distribution of synapses made by ChR2-positive axons onto the dendritic arbors of recorded neurons. Here, we applied anatomical methods and sCRACM to map inputs from vS1 onto neurons in vM1. vM1 neurons in upper layers (L2/3 and L5A), which harbor mostly cortico-cortical neurons, receive strong input from vS1. These neurons also provide the majority of the projection back to vS1. In contrast, deep layer neurons (L5B and

L6), which include the “corticofugal” neurons that project to motor centers in the brainstem and elsewhere, received only weak input from vS1. We characterized the projections www.selleckchem.com/products/obeticholic-acid.html between vibrissal somatosensory cortex (vS1) and vibrissal motor cortex (vM1) using viral-mediated anterograde tracing (Figure 1; see Figure S1 and Movie S1 available online). vS1 was identified by the presence of large barrels. vS1 layers were defined according to well-established cytoarchitectural criteria (Bureau et al., 2006 and Groh et al., 2010). Individual layers contain distinct sets of neurons, with different projection patterns and inputs (Groh et al., 2010, Hattox and second Nelson, 2007, Sato and Svoboda, 2010 and Svoboda et al., 2010). We labeled vS1 neurons by

infection with recombinant adeno-associated viruses (AAV) (Chamberlin et al., 1998) expressing eGFP or tdTomato, and imaged the projections of the infected neurons throughout the brain using a high-resolution slide scanner (excluding most of brainstem and spinal cord). Infected neurons were distributed over several barrel columns (diameter of infection site <1.5 mm) (Figures 1A and 1B), mainly in L2/3 and L5 (Figure S1A). Axonal projections were seen in multiple cortical and subcortical targets. We quantified these projections by integrating the fluorescence intensity over the sections containing specific targets and fluorescent axons (see Supplemental Experimental Procedures).

The stimulus was turned off once a saccade was detected. The monkey was rewarded with juice for choosing the correct choice target (congruent with the motion direction at nonzero coherence levels; randomly picked for 0%-coherence trials). Eye position was monitored using a video-based system (ASL) sampled at 240 Hz. Reaction time (RT) was measured as the time from stimulus onset to saccade onset, the latter identified offline with respect to

velocity (>40°/s) and acceleration (>8,000°/s2). At the beginning of a session, we identified a caudate find more site with single- or multiunit activity modulated on the dots task. Neural activity was recorded using glass-coated tungsten electrodes (Alpha-Omega) or polyamide-coated tungsten electrodes (FHC, Inc.). The motion direction that elicited Caspase inhibitor the largest responses was determined by online visual inspection and then used to define the axis of motion for the dots task used in the remainder of the experimental session (Table S1). Unlike cortical regions such as MT and LIP, the caudate is not topographically organized, and nearby neurons do not necessarily share the same response profiles (Ding and Gold, 2010; Hikosaka et al., 1989). We thus selected microstimulation sites based on only neural activity at those sites without considering nearby neural activity.

Electrical microstimulation was delivered at the same site during motion stimulus presentation (negative-leading bipolar current pulses, 300 Hz, 50–80 μA, 250 μs pulse duration). These parameters were chosen to maximize potential effect sizes while avoiding evoked saccades (Nakamura and Hikosaka, 2006a; Watanabe and Munoz, 2010, 2011). Because higher currents are needed to activate the thinner, sparsely myelinated projection axons in the caudate nucleus compared to the thicker, more myelin-dense projection axons of the cortex, the current intensity used is expected second to have similar effective current spread to that of comparable microstimulation studies in cortex (Adinolfi and Pappas, 1968; Blatt et al., 1990; Felleman and Van Essen, 1991; Spatz and Tigges, 1972; Tehovnik,

1996; Tomasi et al., 2012). Trials with and without microstimulation were equally divided and randomly interleaved in a session. The neural responses were sorted offline (Plexon, Inc.). Each neuron’s spatial selectivity was quantified as a receiver operating characteristic (ROC) index, which is the area under the ROC curve constructed using average spike rate during motion viewing (from 200 ms after stimulus onset to 100 ms before saccade onset, all coherence levels were included; also see Figure S3). Performance was quantified with psychometric and chronometric functions (Figure 2), which describe the relationship of motion strength (signed coherence, Coh, positive for toward T1, negative for toward T2) with choice and RT, respectively.

The higher correlations among neurons that are not tuned for the attended location

Selleckchem GDC-0199 or feature may be a hallmark of neuronal populations that are not well-driven or engaged in a task. A recent study found that trial-to-trial variability in individual neurons in seven cortical areas is higher when the cells are not well-driven, even after correcting for the expected effects of a lower rate of firing (Churchland et al., 2010). This effect may be a signature of a network in which stimulus drive suppresses correlated ongoing activity (Rajan et al., 2010). At low frequencies, both spike-field coherence and cortical oscillations in the local field potential are higher for populations encoding unattended stimuli (Fries et al., 2001, Gregoriou et al., 2009 and Womelsdorf et al., 2007). In humans, withdrawing attention increases MEK inhibitor low frequency oscillations in MEG signals (Siegel et al., 2008), and functional connectivity (and therefore

variability) is often higher during the spontaneous “resting state” than when neural populations are well-driven (for review see van den Heuvel and Hulshoff Pol, 2010). In contrast, attention increases spike-field coherence and oscillations at high frequencies (Fries et al., 2001, Gregoriou et al., 2009 and Womelsdorf and Fries, 2007). These increases have been hypothesized to improve communication between sensory neurons and downstream cells by improving the probability that synchronous spikes

tuclazepam will drive a post-synaptic cell above threshold (for review see Womelsdorf and Fries, 2007); but see also (Ray and Maunsell, 2010). Attentional increases in high frequency correlations are not inconsistent with the reductions in low frequency correlations we and others have reported. In principle, the two could work in concert to remove correlations on long timescales while improving neural communication on short timescales. The observation that even in a controlled experimental setting, both spatial and feature attention vary substantially (Figure 5) suggests that all aspects of a subject’s internal state vary from moment to moment and that it is impossible to measure any particular cognitive factor in isolation. The spatial and feature attention axes we defined, which measure differences in the amount of attention allocated to two particular locations and two seemingly nonopposed features, are by no means the only aspects of attention that could vary. The animal may allocate attention to locations other than these two stimuli (e.g., the fixation point or the door to the room) and to features other than orientation or spatial frequency, or other sensory modalities.

Regions associated

with reward maximization (i.e., returning less than expectations) no longer survived cluster correction after controlling for forgone financial rewards, presumably as a consequence of high multicollinearity (see Figure S3 and Table S4). These data support the intriguing possibility suggested by our model that distinct networks may be processing competing motivations to either increase reward or decrease one’s anticipated guilt. To examine this hypothesis further, we employed an individual differences approach in which we explored the relationship between differences in self-reported counterfactual guilt, assessed independently of the game, and our regions of interest across participants (see Figures 4C and S2; Experimental Procedures). Results from a robust regression (one-tailed) indicated that increased guilt sensitivity is positively related to increased BMS-354825 cell line activity in the insula and SMA (b = 106.92, se = 50.44, p = 0.05 and b = 99.64, se = 46.49, p = 0.02, respectively). That is, participants who reported that they would have felt more guilt had they returned less money showed increased insula and SMA activity when they matched expectations. In contrast, we observed a negative relationship between guilt sensitivity and the NAcc

(b = −89.17, se = 44.28, p = 0.03), indicating that participants who reported that they would have experienced no change in guilt had they returned less Epacadostat mouse money demonstrated increased activity in the NAcc when making a decision to maximize their financial reward. This effect is anatomically specific to these regions, as there were no significant relationships observed between guilt sensitivity and the right DLPFC, left DLPFC, VMPFC, or DMPFC. While we have primarily focused on disentangling the neural systems

associated with the motivations underlying decision behavior, we also observed a network of regions that have previously been associated with an executive control system (e.g., DLPFC, parietal regions, and SMA) (Miller and Cohen, 2001) when participants matched expectations. Consistent with work that has suggested that the insula and SMA may comprise a distinct network which signals the need for executive control (Sridharan et al., 2008), we observed positive relationships between the insula and SMA across subjects (r(16) = 0.64, p < 0.01) and also between bilateral DLPFC and about the SMA (r(16) = 0.74, p < 0.001), but no relationship between the insula and DLPFC (Pearson correlations, two-tailed). These relationships are concordant with previous conceptualizations of PFC functioning (Miller and Cohen, 2001) and suggest that the insula may recruit the dlPFC for increased self-control via the SMA. Finally, we also observed a significant negative relationship between activity in the insula and the NAcc across subjects (r(16) = −0.56, p = 0.02), hinting at a possible reciprocal relationship between these two systems, a relationship also predicted by our model.

, 2005), and decision-making epochs are characterized by high-frequency oscillations in the gamma range (30–50 Hz). Robust, burst-like activation of the PFC reliably www.selleckchem.com/products/BI6727-Volasertib.html produces up states in VS MSNs (Gruber and O’Donnell, 2009). Furthermore, during behavioral epochs marked by high-frequency oscillations and burst firing in the PFC, the synchrony typically observed between the VS and the HP as coherent theta oscillations is lost in favor of a period of VS entrainment to the PFC (Gruber et al., 2009a). These findings suggest that the PFC is capable of disengaging

the VS from the HP; thus, one excitatory projection can somewhat paradoxically reduce the efficacy of another glutamatergic input in VS MSNs. Although input integration is typically additive for excitatory projections, competition among converging inputs can also occur. For example, in hippocampal slices, one set of inputs to CA1 neurons may reduce the

efficacy of another (Alger et al., 1978; Lynch et al., 1977), and in the PFC, similar interactions between cortical and thalamic inputs have been reported (Fuentealba et al., 2004). Here, we tested whether brief, robust PFC activation disengages the VS from ongoing HP activity by way of heterosynaptic suppression in VS MSNs using in vivo intracellular recordings. We performed in vivo intracellular recordings in 47 Palbociclib neurons from 36 adult male rats using standard recording conditions and 22 neurons from 15 rats using electrodes containing the GABAA antagonist picrotoxin. A subset of these cells (n = 10) were processed for Neurobiotin labeling and were morphologically identified as MSNs (Figure 1A). All neurons included in this study were located within the striatal region receiving afferents from the medial PFC and HP (Voorn et al., 2004), including the nucleus accumbens core and the ventral aspect of the dorsomedial striatum (Figure 1B). All recorded cells exhibited spontaneous transitions between negative resting membrane potentials (down states; −84.1 ± 8.1 mV, mean ± SD) and depolarized up states (−70.9 ± 7.2 mV) closer to action potential threshold (Figure 1C). Up states Cytidine deaminase occurred at a frequency of 0.6 ± 0.2 Hz with a duration

of 521.8 ± 180.8 ms. The majority of recorded neurons were silent (29/47; 62%), but spontaneous firing was detected in the remaining 18 neurons at 0.96 ± 1.4 Hz (range, 0.01–5.2 Hz). Action potentials (spontaneous or evoked) in all neurons had an amplitude of 52.8 ± 7.9 mV from threshold. Input resistance in the down state was 54.5 ± 17.4 MΩ. These properties are similar to what has been previously reported in VS MSNs (Brady and O’Donnell, 2004; Goto and O’Donnell, 2001a, 2001b; O’Donnell and Grace, 1995). To assess whether robust PFC activation suppresses MSN responses to HP afferents, stimulating electrodes were targeted to the medial PFC and the fimbria-fornix, the fiber bundle carrying HP inputs to the VS (n = 21 neurons; Figure 1D).

Both of our experiments employed

event-related designs, where events were determined jointly by the actions of both the human and computer opponent. The use of competitive games in which the optimal (i.e., Nash-equilibrium) strategy was to choose the two (matching pennies) or three (RPS) Venetoclax supplier alternative options with equal probabilities, and in which all outcomes were almost equally likely, tended to equalize the frequencies of event sequences (e.g., tails choice with a win followed by heads with a loss). Nevertheless, event sequences were still not completely balanced in the data. To avoid confounds in the analysis, we balanced training and transfer sets by removing random trials for each subject to ensure that the results did not depend on a learned bias Bcl-2 expression of the classifier. We then decoded choices and outcomes (separately) for trial N based on the four fixation volumes following that trial and immediately preceding trial N+1. The factors that were balanced for the primary analysis of

Experiment 1 were the outcome and computer’s choices for trial N. Four classes were equalized: Win-Heads, Win-Tails, Lose-Heads, and Lose-Tails (this also balanced human choice). Thus, significant decoding of wins/losses could not be attributed to decoding of computer or human choice, and vice versa. All six CYTH4 cross-validation training sets were balanced independently to prevent bias acquisition. The transfer set

was balanced as a whole to ensure that high accuracy was not due to the expression of a bias in the classifier. Due to these strict balancing constraints, training sets in each cross-validation cut contained an average total of 189 trials (min = 124), while on average, the total transfer set contained 230 trials (min = 160). For Experiment 2, we balanced across nine bins (win-rock, lose-rock, tie-rock, win-scissors, and so on). This imposed even more severe constraints. Training sets contained an average total of 136 trials (minimum subject average = 38). On average, transfer sets were composed of 169 trials (min = 45). MVPA was implemented using PyMVPA (Hanke et al., 2009), and a support vector machine (SVM) algorithm. In all cases, we used a linear kernel and penalty parameter (C) of 1. Linear SVM treats a pattern as a vector in a high-dimensional space, and tries to find a linear hyperplane that optimally separates the two trained categories, by maximizing the accuracy of the split in the training data as well as maximizing the margin between the hyperplane and the nearest samples (referred to as support vectors). For Experiment 1, we evaluated statistical significance of MVPA by calculating the accuracy of the classifier within a given ROI or particular searchlight for each subject.

Accordingly, our findings agree with models that explain the formation of perceptual decisions based on weighted evidence originating from opponent

neural populations, in our case one with convex- and another with concave-selective neurons, that directly or indirectly influence each other’s input into the decision stage, via e.g., lateral or feed-forward inhibition (Ditterich et al., 2003). We observed clusters of IT neurons preferring either convex or concave 3D structures. Most likely, not all neurons within these 3D-structure-selective clusters represented exactly the kind of 3D structures that we employed in this study (i.e., Gaussian radial basis surfaces). Indeed, a previous study has shown Akt activation that IT neurons can Ku-0059436 also encode more complex 3D structures than the ones used in our study (Yamane et al., 2008). Therefore, it seems likely

that the neurons within each 3D-structure-selective cluster encode for different (complex) 3D structures but at the same time share some preference for convex or concave 3D structures. This suggests that IT neurons with specific 3D-structure preferences could not only join forces to subserve categorization of a global (nonaccidental) 3D-structure characteristic (i.e., convex or concave) but potentially also underlie more specific 3D-structure identification. Such a proposal implies a flexible readout of IT neuronal activity according to the demands implied by the task at hand. In agreement with this proposal, previous TCL studies have suggested that the activity of IT neurons can be read out to perform visual object categorization at various levels. For example, IT neurons could underlie categorization at the basic or ordinate level (e.g., faces versus cars) but could also provide information in support of finer categorizations, that is, subordinate classifications (e.g., differentiating between different faces, cars or dogs) (Hung et al., 2005, Kiani et al., 2007, Logothetis and Sheinberg, 1996,

Riesenhuber and Poggio, 1999 and Thomas et al., 2001). A previous study showed that microstimulation in clusters of face-selective IT neurons can affect a monkey’s behavioral choice when categorizing images of faces versus nonface images (Afraz et al., 2006). Our findings demonstrate that neurons in IT can also subserve finer classifications, since microstimulation in IT strongly affected visual categorization at the subordinate level, i.e., for object surfaces that differed only in the sign of their curvature. Moreover, in view of the strong stimulation effects and its high position within the cortical hierarchy, this IT region might be one of the final regions where disparity-defined 3D-structure characteristics such as the sign of the curvature are processed before being read out by decision-related areas.

, 2005) and in the songbird forebrain (Nagel and Doupe, 2006) when the temporal contrast of more complex stimuli is altered. Such gain changes improve the efficiency with which neurons encode frequently presented levels (Dean et al., 2005). Other studies have found that mean firing rates of IC neurons can have nonmonotonic dependencies on spectrotemporal contrast, while retaining their spectrotemporal preferences

(Escabí et al., 2003). Similar tuning of mean firing rate to spectral contrast (measured 17-AAG in vitro across frequency, but not across time) has been reported in auditory cortex (Barbour and Wang, 2003). These findings suggest a division-of-labor strategy. However, such effects are also compatible with contrast gain control, so long as gain changes are slow (compared to spike generation) or do not completely compensate for changes in contrast. In this study, we ask whether the mammalian auditory

cortex adjusts neural gain according to the spectrotemporal contrast of recent stimulation. One possibility is that neurons’ responses are invariant to the statistics of recent stimulation, suggesting that the problem is ignored. Alternatively, neurons may be informative only about stimuli with a particular contrast, suggesting a division-of-labor strategy. Finally, they may undergo more complex changes in their spectrotemporal tuning as contrast varies, suggesting a reallocation of resources in the auditory

system. Tuning of auditory cortical neurons second has been shown to depend on stimulus context, such as tone density (Blake and Merzenich, Z-VAD-FMK cell line 2002), stimulus bandwidth (Gourévitch et al., 2009), and the history of recent stimulation (Ahrens et al., 2008). To distinguish between these hypotheses, we designed a set of stimuli where the statistics of level variations could be controlled within individual frequency bands. This allowed us to measure the spiking responses of neurons in the auditory cortex to sounds with different means and contrasts, from which we estimated spectrotemporal receptive fields (STRFs), using both linear (deCharms et al., 1998 and Schnupp et al., 2001) and linear-nonlinear (LN) (Chichilnisky, 2001, Simoncelli et al., 2004 and Dahmen et al., 2010) models. We also sought to quantify which combination of stimulus statistics might inform cortical gain control. This requires a formal definition of the contrast of a sound. In the visual system, the contrast of a simple stimulus is defined as the ratio of the intensity difference to the mean intensity (c=ΔI/Ic=ΔI/I); this definition can be generalized to complex stimuli as the ratio of the standard deviation to the mean (c=σI/μIc=σI/μI). In principle, the same definitions can be applied directly in the auditory system. However, it is normal to describe sounds using sound pressure level (SPL), L=20log10(p/pREF), rather than (RMS) pressure, p, itself.

A 3D gradient-echo, EPI sequence with a 64 × 64 × 32 matrix was run with the following parameters: effective echo time (TE) 16 ms, repetition time (TR) 1.5 s (effective TR 46.875 ms), bandwidth 170 kHz, flip angle 12°, FOV see more 1.92 × 1.92 × 0.96 cm. A two-block design stimulation paradigm was applied in this study. For the simultaneous forepaw and whisker pad stimulation experiment, the paradigm consisted of 320 dummy scans to reach steady state, followed by 20 scans prestimulation, 20 scans during electrical stimulation, and 20 scans post-stimulation, which was repeated 3 times (140 scans were acquired overall). Six to eight multiple trials were acquired for each rat. For whisker-pad

stimulation at different intensities (1.0–3.0 mA), the paradigm consisted of 320 http://www.selleckchem.com/products/ch5424802.html dummy scans to reach steady state, followed by 20 scans prestimulation, 10 scans during electrical stimulation, and 20 scans post-stimulation, which was repeated 3 times (110 scans were acquired overall). Three to five multiple trials were repeated in a random order at different stimulation intensities with a total of 15–20 trials acquired for each rat. For the Mn-tracing study, a magnetization prepared rapid gradient echo (MP-RAGE) sequence (Mugler and Brookeman, 1990) was used. Sixteen coronal slices with FOV = 1.92 × 1.44 cm, matrix 192 ×

144, thickness = 0.5 mm (TR = 4000 ms, Echo TR/TE = 15/5 ms, TI = 1000 ms, number of segments = 4, averages = 10) were used to cover the area of interest at 100 μm in-plane resolution with total imaging time 40 min. To measure intensity in the thalamus across animals, a T1-map was acquired using a rapid acquisition with refocused echoes (RARE) sequence with a similar image

orientation to the MP-RAGE sequence (TE = 9.6 ms, Multi-TR = 0.5 s, 1 s, 1.9 s, 3.2 s, and 10 s, Rare factor = 2). For the purpose of cross-subject registration, T1-weigted anatomical images were also acquired in the Ergoloid same orientation as that of the 3D EPI and MPRAGE images with the following parameters: TR = 500 ms, TE = 4 ms, flip angle 45°, in-plane resolution 100 μm. Thalamocortical (TC) slices (450 microns) were prepared from adult Sprague-Dawley Rats (6−7 weeks) with some modifications of the method described previously (Agmon and Connors, 1991 and Isaac et al., 1997) Briefly, after rats were anesthetized with isoflurane, the brain was rapidly cooled via transcardiac perfusion with ice-cold sucrose- artificial cerebrospinal fluid (CSF). The brain was removed and placed in ice-cold sucrose-artificial CSF. Paracoronal slices were prepared at an angle of 50° relative to the midline on a ramp at an angle of 10°. Then, slices were incubated in artificial CSF at 35°C for 30 min to recover. Slices were then incubated in artificial CSF at room temperature (23°C −25°C) for 1–4 hr before being placed in the recording chamber for experiments. The standard artificial CSF contained (mM) 119 NaCl, 2.5 KCl, 2.5 CaCl2, 1.3 MgSO4, 1.0 NaH2PO4, 26.

On the input side, we could identify neurons connecting one of the small subunits of the AOTu with the lateral triangle of the LALs (TuLAL1; n = 10; Figure 3F, Table 1). On the output side,

a characteristically shaped neuron was identified that connected large parts of the LAL with regions of the unstructured protocerebrum, located lateral and dorsal to the monarch central body (LAL-PC-neuron; n = 1; Figure 3G; Figure S1G). We next used intracellular recordings Neratinib order to examine the response properties of CC monarch neurons in the context of neural integration of the major skylight cues (polarized and unpolarized light stimuli). There were three considerations for evaluating these recordings. First, which skylight cues are actually processed by the monarch central brain? Second, how do monarchs resolve the directional ambiguity of skylight E-vectors ( Figure 1A)—that

is, do they use spectral gradients for distinguishing the solar and antisolar hemisphere, as suggested for locusts? The third issue was learn more defining how polarized and unpolarized light responses are integrated to ensure that E-vector tuning actually provides an accurate reflection of the solar azimuth over the course of the day ( Figures 1A and 1B). For recordings, a migratory butterfly was mounted in the recording setup. Two types of visual stimuli (linear polarized light and unpolarized light spots) were applied during experimentation (Figure 1C). Prior to recordings, all migrants were housed in 11 hr of light

and 13 hr of darkness, simulating outdoor lighting conditions at capture; physiological experiments were centered around Zeitgeber time (ZT) 5, which was 5 hr after lights-on, so that substantial variation in time-of-day of recording would not confound the results. Nonmigratory monarchs were used for initial recordings and some control experiments. When presented Sitaxentan with zenith-positioned polarized light in the UV range (365 nm), 33 neurons of the monarch brain responded with significant E-vector-dependent modulations of their spike frequency, as revealed by circular statistics (p < 0.05; Figure 4). Polarized UV light was used because the monarch DRA ommatidia only express a UV opsin ( Sauman et al., 2005) and have been shown to be maximally sensitive to wavelengths below 380 nm ( Stalleicken et al., 2006). Of the E-vector-responsive cells, 19 could be identified anatomically postrecording. All of these cells were components of the proposed polarization vision network described above. We identified seven TuLAL1 cells, six TL-type cells, two CL1 cells, an individual TB1 cell, and three CPU1 neurons; hence E-vector-dependent responses were present from the input stage of the polarization vision network (TuLAL1 cells) to the output stage of the CC (CPU1 cells).