Intriguingly, this representation is linked to input specifically from the vHPC. Numerous reports have demonstrated synchrony between mPFC units and ongoing oscillations in its inputs, particularly the hippocampus (Adhikari et al., 2010a, Jones and Wilson, 2005, Siapas et al., 2005, Sigurdsson et al., 2010 and Taxidis Autophagy Compound Library et al., 2010). Here, we show similar synchrony between mPFC units and ongoing theta-frequency oscillations in the ventral, but not dorsal HPC, consistent with the known roles of these subregions in EPM behavior (Kjelstrup et al., 2002). Moreover, we demonstrate that units that synchronize with the vHPC

have stronger task-related firing patterns. This effect of synchrony on EPM representations suggests that paradigm-related activity in the mPFC is at least facilitated by input from the vHPC. Consistent with this idea, firing in anticipation of a reward in mPFC units is abolished after vHPC lesions (Burton et al., 2009). Here we demonstrate that mPFC representations and open-arm avoidance

are inversely correlated. Animals with mPFC units with strong representations of open versus closed arms are those that fail to avoid the open arms. At the very least, these data argue that the representation present in the mPFC is not used to guide avoidance behavior in avoidant animals; there is no evidence that such a representation exists in these mice. The role of the mPFC representation in the behavior of animals that fail to avoid the open arms is less clear; the time course of unit HIF pathway firing during arm transitions allows for the possibility that such representations help guide choice behavior during exploration.

A causal relationship between the single-unit representation and exploratory behavior is also suggested by the inconsistent effects of mPFC inactivation on EPM behavior in rodents. Some studies report anxiolytic effects (Deacon et al., 2003, Lacroix et al., 2000, Shah et al., 2004, Shah and Treit, 2003, Shah and Treit, 2004 and Stern et al., 2010), while others report anxiogenic or no effects (Klein et al., 2010, Lisboa et al., 2010 and Sullivan and Gratton, 2002). Consistent with our findings, for studies that reported anxiolytic effects of silencing or lesioning the mPFC were those in which the control group showed relatively low levels of anxiety (Figure S3). mPFC inactivation, therefore, appears to reduce open arm exploration only in those animals that would be expected to have robust mPFC representations. Reconciling the current data with our previous findings presents something of a challenge. We have previously shown that increased theta-frequency synchrony between the vHPC and mPFC is associated with increased open arm avoidance (Adhikari et al., 2010b). The current data demonstrate that mPFC neurons that represent safety versus aversiveness are preferentially synchronized to the vHPC.

There is now a need to develop novel integrative approaches that take into account the role of microglial inflammation, astrocytic processes, the BBB sink, and the interaction between every cell of the CNS, in order to develop efficient ways to target such complex pathologies as AD and MS. The Fonds de la Recherche du Québec – Santé (FRQS), Canadian Institutes in Health Research (CIHR), and the Multiple Sclerosis Scientific Research Foundation of Canada support this research. “
“Decision making is an abstract term referring Ku-0059436 molecular weight to the process of selecting a particular

option among a set of alternatives expected to produce different outcomes. Accordingly, it can be used to describe an extremely broad range of behaviors, ranging from various taxes of unicellular organisms to complex political behaviors in human society. Until recently, two different approaches have dominated the studies of decision making. On the one hand, a normative or prescriptive approach addresses the question of what is the best or optimal choice for a given type of decision-making problem. For example, the principle of utility maximization in economics and the concept of equilibrium in the game theory describe how self-interested rational agents should behave individually or in a group, respectively (von Neumann and Morgenstern, 1944). On the other hand, real behaviors of humans and animals

seldom match the predictions of such normative http://www.selleckchem.com/products/BIBW2992.html theories. Thus, empirical studies seek to identify a set of principles that can parsimoniously account for the actual choices

of humans and animals. For example, prospect theory (Kahneman and Tversky, 1979) can predict not only decisions of humans but also those of other animals more accurately than normative theories (Brosnan et al., 2007; Lakshminaryanan et al., 2008; Santos and Hughes, 2009). Similarly, empirical studies have demonstrated that humans often choose their behaviors altruistically and thus deviate from the predictions from the classical game theory (Camerer, 2003). Recently, these two traditional approaches of decision-making research have merged with two additional disciplines. First, it is now increasingly appreciated that learning plays an important role in decision Unoprostone making, although this has been ignored in most economic theories. In particular, reinforcement learning theory, originally rooted in psychological theories of learning in animals (Mackintosh, 1974) and optimal control theory (Bellman, 1957), provides a valuable framework to model how decision-making strategies are tuned by experience (Sutton and Barto, 1998). Second, and more importantly for the purpose of this review, researchers have begun to elucidate a number of important core mechanisms in the brain responsible for various computational steps of decision making and reinforcement learning (Wang, 2008; Kable and Glimcher, 2009; Lee et al., 2012).

, 1994). The important point here is that

Wolfram is a recessive condition. The disease itself (in homozygotes) is characterized by a broad spectrum of psychiatric and neurological disorders, but heterozygote carriers show a purer MD phenotype: in one report, out of 11 individuals carrying a Wolfram mutation, eight Selumetinib clinical trial were hospitalized for major MD, significantly more than the three relatives expected if there were no association between psychiatric hospitalizations and mutations at this locus (Swift and Swift, 2005). The authors argue that “if the population frequency of wolframin mutations that predispose carriers to psychiatric illness is about 1%, with an odds ratio of 7.1, wolframin mutation carriers would be estimated to be about 7% of patients

hospitalized for MD” (Swift and Swift, 2005). Overall, we cannot rule out the possibility that rare large-effect risk alleles exist, but we also cannot extend much hope for their discovery. It is possible that risk alleles with odds ratios between 3 and 4, occurring Neratinib at low frequencies (less than 5%), make a contribution to MD, but their discovery will require either a new generation of genotyping arrays, interrogating rare variants, or the deployment of population-scale sequencing. The second hypothesis to explore is the idea that larger-effect loci might be detected if MD were to be analyzed differently. For example, consider the possibility that MD is not one but two disorders that cannot be differentiated on a clinical basis alone. Suppose that 50 variants contribute to disease through one pathway (leading to one subtype of MD) and 50 to a second pathway (leading to the second subtype). Unbeknownst to investigators, a study contained equal numbers of the two subtypes. Since variation in the first pathway is irrelevant to disease susceptibility in the second subtype, the genetic effect also of loci acting on one pathway is reduced by half, and power is similarly reduced. This point is not merely important

in helping design genetic studies, it is critically important for their interpretation. Without knowledge of the existence of two unrelated mechanisms, it would be difficult, perhaps impossible, to interpret the results of the study. We would be left guessing whether the 100 variants represented one, two, or more mechanistic pathways. Do subforms of genetically homogeneous MD exist? A large literature addresses this issue, not all of it readily summarized; here we tackle two questions that are key to understanding how genetic effects operate in MD: first, how separate is MD from other disorders? Second, is MD one disorder or two, or more? Two disorders that most frequently overlap diagnostically with depressive illness are anxiety and bipolar disorder.

In both mitral and granule cell layers OTR mRNA and OT-immunoreactive fibers have been found (Knobloch et al., 2012; Vaccari et al., 1998; Yoshimura et al., 1993). Neuromodulation by both OT and AVP increased excitability of mitral cells via a V1a receptor (Osako et al., 2000, 2001). Furthermore, the AVP effects could be endogenously triggered

by AVP-producing cells that are locally present in the MOB (Tobin et al., 2010). Specific OTR activation caused a decrease of the inhibitory input from GC on MC LY294002 in vivo neurons through a presynaptic mechanism, an effect that seemed important for the induction of maternal behavior (Yu et al., 1996; Osako et al., 2001). It thus appears that in the MOB and AOB, AVP and OT may reinforce each other’s actions, AVP by increasing excitation, OT by decreasing inhibition. It has been proposed that, through these concerted actions, both AVP and OT applications to the olfactory bulb also lengthen the retention interval for short-term social odor recognition in male rats (Dluzen et al., 1998). Of interest in this context, OT can lower the threshold for LTP induction

at excitatory synapses between mitral cells and granule cells in the AOB (Fang et al., 2008). The MOB sends projections to the anterior olfactory nucleus, the piriform cortex, some subdivisions of the cortical amygdala, and the medial amygdala. Most projections to the MeA, however, originate from the AOB (Switzer and DeOlmos, 1985; Swanson and Petrovich, 1998). The AOB also projects to the posterior medial subdivision of the cortical amygdala (COApm) and to the bed nucleus of the stria terminalis (BST)

with which http://www.selleckchem.com/products/Dasatinib.html the MeA is reciprocally connected (Alheid and Heimer, 1988). This is the major pathway for processing pheromonal cues and important for social interactions (Brennan and Zufall, 2006; Swanson and Petrovich, 1998), and the MeA is for that reason also called the “vomeronasal amygdala.” In the MeA of male rats, mRNA for V1aR, V1bR, and OTRs is present and binding of specific OTR antagonists has been demonstrated (Arakawa et al., 2010; Veinante and Freund-Mercier, 1997). Male OT knockout mice lack short-term conspecific Metalloexopeptidase social recognition, which can be rescued by local microinjections in MeA of OT prior to the first exposure (Ferguson et al., 2001) and mimicked by antisense oligonucleotides targeting the OTR (Choleris et al., 2007). Interestingly, an OT antagonist injected in the MeA blocked approach behavior to odors of healthy conspecifics, whereas a V1a antagonist blocked avoidance of odor to sick conspecifics, suggesting nonoverlapping, but contrasting, roles for these peptides in this region (Arakawa et al., 2010). In the MeA and the BST, local AVP-producing neurons have been found (Caffé and van Leeuwen, 1983; van Leeuwen and Caffé, 1983) and in the MeA OTergic fibers that originate from the PVN and SON (Knobloch et al., 2012).

The authors were supported by a 5R01EY017921 Grant GDC-0199 manufacturer to R.D., by the European Community’s Seventh Framework Programme (Grant PIRG05-GA-2009-246761),

the General Secretariat for Research and Technology (Grant 9FR27), and the Special Account of Research Funds, University of Crete (Grant 3004) to G.G.G. S.J.G. was supported initially by MH64445 from the National Institutes of Health (USA) and later by the National Institute of Mental Health, Division of Intramural Research. “
“Learning to make choices in a complex world is a difficult problem. The uncertainty attending such decisions requires a trade-off between two contradictory courses of action: (1) to choose from among known options those that are believed to yield the best outcomes, or (2) to explore new, unknown alternatives in hope of an even better result (e.g., when at your favorite restaurant, do you try the chef’s

new special or your “usual” choice?). This well-known exploration-exploitation dilemma (Sutton and Barto, 1998) deeply complicates decision making, with optimal solutions for even simple environments often being unknown or computationally intractable (Cohen et al., 2007). Abundant evidence now supports striatal dopaminergic mechanisms in learning to exploit (see Doll Akt inhibitor and Frank, 2009 and Maia, 2009 for review). By contrast, considerably less is known about the neural mechanisms driving exploration (Aston-Jones and Cohen, 2005, Daw et al., 2006 and Frank et al., 2009). In the reinforcement learning literature, exploration is often modeled using stochastic choice rules. Such rules permit agents to exploit the best known actions for reward while also discovering better actions over time by periodically choosing at random or by increasing stochasticity of choice when options have similar expected values (Sutton and Barto, 1998). A more efficient strategy is to direct exploratory choices

to those actions about which one is most uncertain (Dayan and Sejnowski, 1996 and Gittins and Jones, 1974). Put another way, the drive to explore may vary in proportion to the differential uncertainty about ADP ribosylation factor the outcomes from alternative courses of action. Thus, from this perspective, the brain should track changes in relative uncertainty among options, at least in those individuals who rely on this strategy for exploratory choices. Neurons in prefrontal cortex (PFC) may track relative uncertainty during decision making. Using fMRI, Daw et al., (2006) observed activation in rostrolateral prefrontal cortex (RLPFC; approximately Brodmann area [BA] 10/46) during a “multiarmed bandit task” when participants selected slot machines that did not have the highest expected value. Daw et al. tested whether participants guide exploration toward uncertain options, but did not find evidence for an “uncertainty bonus.

, 2002, Nishimura et al., 2006, Redmond et al., 2000 and Sestan et al., 1999). Much of that work has focused on the regulation of neuronal morphology, and in particular dendritic arborization, by Notch and Numb. For example, consistent with earlier work on embryonic neurogenesis (Berezovska et al., 1999, Franklin et al., 1999, Redmond et al., 2000 and Sestan et al., 1999), one study showed that while disruption of Notch1 in the germinal zone of the postnatal dentate gyrus in vivo led to simpler dendritic trees with fewer branch points, activation of Notch1 led to more elaborate dendritic trees (Breunig et al., 2007). Little signaling pathway is known about how Notch and/or Numb influence neuronal

morphology. Notably, the work of Giniger and colleagues (Giniger, 1998, Le Gall et al., 2008 and Song and Giniger, 2011) has found that the Abl kinase, in particular through interactions with the Rac GTPase (Song and Giniger, 2011), can regulate axonal guidance in Drosophila, and that this process involves the Notch pathway. Furthermore,

work in mammalian cells has suggested that Numb can directly interact with the cdc42 guanine nucleotide exchange factor (GEF) intersectin, SB203580 clinical trial and with EphB2 to influence cytoskeletal dynamics and dendritic spine morphology ( Nishimura et al., 2006). Confirmation and elucidation of these findings would provide exciting new avenues for the study of the mechanistic function of Notch and Numb during neuronal differentiation. In addition to regulating neuronal maturation and morphology, in recent years evidence has accumulated that Notch signaling can modulate the function of mature neurons. Numerous studies have found that Notch is required for synaptic plasticity, learning,

and memory in rodents (Costa et al., 2003, Saura et al., 2004 and Wang et al., 2004), long-term memory formation in Drosophila ( Ge et al., 2004, Matsuno et al., 2009 and Presente et al., 2004), and locomotive behavior in C. elegans ( Chao et al., 2005). For example, spatial learning and memory deficits were observed in mice heterozygous for mutations in Notch1 or CBF1 ( Costa et al., 2003). In addition, reduction of Notch1 expression by 50% (using an anti-sense strategy) resulted in reduced long-term potentiation (LTP) and enhanced long-term depression (LTD) already ( Wang et al., 2004). Furthermore, several studies have provided evidence of the dynamic regulation of Notch following memory consolidation ( Conboy et al., 2007) and neuronal stimulation at the neuromuscular junction ( de Bivort et al., 2009). Consistent with a dynamic role for Notch signaling in neurons, our recent work in mice (Alberi et al., 2011), along with the work of others in fruit flies (Lieber et al., 2011), strongly indicates that Notch signaling is responsive to neuronal activity. In the fly work, Lieber and colleagues have shown that Notch activity occurs in response to odorant receptor activity in olfactory receptor neurons (ORNs) in the antenna.

, 1998). The consistently very shallow slopes indicate that beta oscillations emerge with only small time delays throughout the cortical-BG network. Overall, our results are consistent I-BET151 concentration with ∼20 Hz beta having a selective, distinct role in coordinating information processing within the BG of normal behaving animals. To explore beta timing in more detail, we examined trial-by-trial LFP traces during GO trials (Figure 3A). Epochs of high

beta power appeared to occur stochastically, with some task events either increasing (Cue) or diminishing (Side In) the probability of entering this beta state. Around detected movement onset (Nose Out) the pattern of beta power change was unexpectedly complex, showing a marked dependence on reaction time. For the most rapid responses, the beta ERS began around the time of movement

onset and peaked shortly afterwards (Figures 3A and 3B). On trials with slower responses, the beta ERS began well before movements and was mostly completed by movement onset. To quantify this phenomenon we compared beta power for fast- Cabozantinib clinical trial versus slow-RT trials during the 300 ms epochs immediately preceding and following movement onset ( Figure 3B, top). In both epochs all subjects had a significant difference in beta power (paired t tests before Nose out: for 3 rats p < 10−4, for the other p = 0.024; after Nose out: p < 10−3 for all rats). In addition, we calculated

correlation coefficients between beta power and reaction time at each moment during task performance ( Figure 3B, bottom). A strong positive correlation was found about 750 ms after the Cue event, driven by the ERD that is maximal around movement completion (see Kühn et al., 2004 and Williams et al., 2005 for related observations in humans). In addition, a smaller but reliable correlation occurred ∼30–100 ms before movement initiation. This suggests that the presence of the high-beta state during a critical period delays movement onset, consistent with evidence in humans associating increased beta power Linifanib (ABT-869) with slower movements ( Levy et al., 2002, Brown et al., 2001, Chen et al., 2007 and Pogosyan et al., 2009). The Go/NoGo task variant (Figure 3C) is similar to the Immediate-Go task, except that there are three possible instruction cues: Go left, Go right, or hold in place (NoGo). As before, simply holding before the instruction cue was not associated with elevated beta. However, both Go and NoGo cues were similarly followed after several hundred milliseconds by a beta ERS (Figures 3D and 3E). This observation suggests that planning not to move is also associated with enhanced beta and confirms that the main beta ERS that we analyze here is not rigidly linked to either movement initiation or suppression. At the same time, we observed two interesting differences between GO and NOGO trials.

The voltage dependence of inactivation showed two components, consistent with the presence of at least two

channel types (Figure 8F, green line). The rate of inactivation was high between −120 and −60 mV and again between −35 and −10 mV. Thus, a component of inactivation can selleck chemicals llc be removed by hyperpolarization from Vrest. The collected pharmacology and somatic patch recordings suggest that a Kv1-family KDR channel mediates the suppressive effect of hyperpolarization on subsequent depolarization and firing in retinal ganglion cells and thereby contributes to an intrinsic mechanism for contrast adaptation. The contrast adaptation observed in ganglion cell firing exceeds that present in the subthreshold Vm or excitatory membrane currents (Kim and Rieke, 2001, Zaghloul et al., 2005, Beaudoin et al., 2007 and Beaudoin et al., 2008). This discrepancy implicates intrinsic mechanisms for adaptation within ganglion cells (Gaudry and Reinagel, 2007b). Here, we demonstrate two distinct intrinsic mechanisms for

contrast adaptation in the OFF Alpha ganglion cell: Na channel inactivation and removal of delayed-rectifier K channel (KDR) inactivation. Importantly, both mechanisms act within the physiological range of Vm, and both mechanisms show the appropriate time course to suppress visually-evoked firing during periods of high contrast. Below, we consider the evidence for these two mechanisms, their key properties for evoking adaptation, their interaction with each KU-55933 order Montelukast Sodium other and with synaptic inputs, and their presence in other retinal cell types and neural circuits. One intrinsic mechanism for contrast adaptation, Na channel inactivation, was identified

originally in studies of isolated salamander ganglion cells of unknown type (Kim and Rieke, 2001 and Kim and Rieke, 2003). In these cells, the Na current could be studied directly to characterize activation and inactivation properties. Slow recovery from inactivation (>200 msec) explained low-output gain at high contrast because of the reduced pool of available Na channels, and there was little or no apparent involvement of Ca or K channels (Kim and Rieke, 2001 and Kim and Rieke, 2003). Our results show a similar Na channel mechanism in the intact OFF Alpha ganglion cell. The maximum slope of the action potential, a proxy measure of Na current, suggested reduced channel availability after periods of depolarization and firing (Figure 5). Furthermore, the suppressed firing persisted in the presence of multiple blockers of K and Ca channels, consistent with a Na channel mechanism (Figure 6 and Figure 7). We also identified an intrinsic mechanism for adaptation mediated by KDR channels. In intact cells, brief hyperpolarization within the physiological range (∼10 mV negative to Vrest) reduced subsequent firing to a depolarizing test pulse or contrast stimulus (Figure 1, Figure 2 and Figure 3).

A.K. from the Esther A. & Joseph Klingenstein Foundation, the Edward Mallinckrodt, Selumetinib ic50 Jr. Foundation, the Whitehall Foundation, and the Alzheimer’s Association. We thank Dr. G. Danuzer and Dr. K. Jaqaman for kindly sharing their uTrack particle tracking software. We also thank Dr. S. Mennerick, Dr. V. Cavalli, and Dr. D. Owyoung for their

constructive comments on the manuscript. “
“Over the course of development, numerous molecules are repurposed to function in distinct cellular contexts (Charron and Tessier-Lavigne, 2007). During the earliest phases of neural development the Hedgehog signaling pathway plays an important role establishing patterning of the central nervous system. (Ericson et al., 1995, Roelink et al., 1995, Xu et al., 2005 and Xu et al., 2010). The secreted protein Sonic Hedgehog (Shh) is expressed in the notochord and floor plate of the neural tube and, cells adopt

specific fates based upon their level of exposure to the established Shh gradient. At later stages, during development of the telencephalon, Sonic Hedgehog adopts a similar function where it is expressed in the ventral telencephalon and functions to maintain ventral identity through its regulation of expression of the transcription factor Nkx2.1 (Xu et al., 2010). Shh is also expressed in adult neural stem cell niches where it helps maintain adult neural stem cell identity (Machold et al., 2003 and Palma et al., 2005). Cell fate specification by Shh is regulated through the canonical selleck chemical Shh signaling pathway whereby binding the Shh receptor Patched (Ptc) relieves inhibition of the transmembrane protein Smoothened (Smo) (Rohatgi et al., 2007). Smoothened signaling leads to the activation of else the Gli family of transcription factors, which mediates the cell fate specification functions of Shh (Ahn and Joyner, 2005 and Palma et al., 2005). Later in development, after the tissues have been specified, Shh expressed from the floor plate functions to guide spinal cord commissural axons

across the ventral midline (Charron et al., 2003), and Shh expressed at the chiasm functions as a regulator of retinal ganglion cell growth cone extension (Trousse et al., 2001). The Shh-dependent guidance of commissural axons in the spinal cord appears to require the Shh coreceptor Boc (Okada et al., 2006), but does not require Gli transcriptional activation (Yam et al., 2009). Shh expression has also been observed in both the juvenile and adult cerebral cortex (Charytoniuk et al., 2002) outside of known progenitor zones. Recently Shh expression has also been identified in cortical pyramidal neurons (Garcia et al., 2010). However, the function Shh in cortical neurons and the type of neurons expressing Shh remained unknown.

One solution to minimizing such events is to keep head-restraint periods short (<1 s). A second solution, which we used for head-restraint periods of up to 8 s, was to deliver intermittent water reward (0.5–1 Hz) during head restraint. Gamma-secretase inhibitor A third solution, which

we used for 6 s long head-restraint periods without intermittent water reward, was to provide a rat-activated release switch. We observed that rats pushed on the floor of the cage when they attempted to withdraw their head from the headport. In this approach, the floor of the cage was mounted on a low-friction linear slide with a 2.5 mm travel. Movement of the floor toward the kinematic clamp, caused by the animal pushing with its hind legs, would depress a 1.67 N force snap action switch, which was used to trigger release of the clamp. The release switch appeared to be successful in preventing aversion to the clamp and allowed successful training for long head-restraint

periods: in sessions with 6 s long head-restraint periods and without any water reward during head restraint, an average check details of less than one trial per session was aborted by early release. To determine whether the voluntary head-restraint system could be used with newly developed methods for high-throughput behavioral training, we incorporated a second generation, fully automated, head-restraining system into a semiautomated rat training facility (Erlich et al., 2011 and Brunton et al., 2013). In this facility, rats are placed into operant chambers for a 1.5–2 hr behavioral training session by husbandry isothipendyl staff blind to the experiment being performed. During the behavioral training session, fully automated custom software controls the progression of rats across the stages of training. At the end of the session, the rat is removed from the chamber and is

replaced by the next rat to be trained. In this way, six to nine rats per box can be trained daily while husbandry staff monitor the rats’ health and weights and provide food and supplementary water. Human intervention is required only for animal transport and husbandry, allowing the facility to be readily scaled to many automated boxes running in parallel. To automate training stage 1, we mounted the center nose poke on a linear translation stage driven by a stepper motor driver and robotically controlled by signals from a computer running behavioral training software. After each successful trial, the nose poke was moved 200 μm away from the inside of the chamber. To automate training stage 2, we provided piston pressure by a voltage-controlled pneumatic regulator, which was in turn controlled by the behavioral training software. Computer control over piston pressure enabled the gradual ramping increase of piston pressure at the beginning of each head-restraint trial. This prevented loud noises or jerking movements during piston deployment, which facilitated rapid acclimation of rats to the kinematic clamp.