, 1999). However, the latter identification may be erroneous since subsequent studies showed that RIM-BPs are highly expressed only in brain and not peripherally and tightly bind to RIM ( Wang et al., 2000) and to N-, P/Q-, and L-type Ca2+ channels ( Hibino et al., 2002 and Kaeser et al., 2011). The finding that RIM-BPs biochemically form a complex with RIMs in the active zone (Wang et al., 2000), and the discovery that RIM-BPs bind to Ca2+ channels (Hibino et al., 2002) suggested that they may act to recruit Ca2+ channels to active zones. However, the initial problem with this hypothesis was that RIM-BPs bind nonsynaptic L-type Ca2+ channels

as well as synaptic N- and P/Q-type Ca2+ channels and thus could not account for the specific recruitment of N- and P/Q-type Ca2+ channels to active zones (Hibino et al., 2002). DAPT order This problem was resolved when the RIM PDZ-domains were found to bind to

N- and P/Q-type but not L-type Ca2+ channels (Kaeser et al., 2011), indicating that Ca2+ channels are recruited to active selleck chemicals llc zones by binding simultaneously to both RIM and RIM-BPs (Figure 3). This hypothesis was not only confirmed in rescue experiments with mutant RIM proteins showing that both interactions are essential for recruiting Ca2+ channels to active zones (Kaeser et al., 2011), but also in Drosophila experiments in which mutations in RIM-BP were found to disrupt Ca2+ channel localization ( Liu et al., 2011). The Drosophila experiments additionally revealed that in the absence of RIM-BP, the organization of the active zone was impaired, and the ultrastructural distribution of the ELKS homolog Bruchpilot at active zones was altered, suggesting that RIM-BPs may have additional functions besides assisting RIM in the recruitment of Ca2+ channels. Indeed, the fact that the loss of presynaptic Ca2+ channels in RIM-deficient synapses can be most rescued with a RIM fragment consisting only of its PDZ-domain and RIM-BP binding sequence ( Kaeser et al., 2011) can only be explained by the assumption that RIM-BP

engages in other interactions besides binding to RIMs and Ca2+ channels. Identifying these additional interactions of RIM-BPs will be both challenging and exciting. The C. elegans unc-13 gene was identified as a gene encoding a diacylglycerol-binding protein whose mutation caused an “uncoordinated” phenotype, but nothing was known about the localization or function of this protein ( Maruyama and Brenner, 1991). Characterization of the mammalian homologs of UNC-13—named Munc13s—revealed that Munc13 proteins are active zone proteins essential for synaptic vesicle priming ( Brose et al., 1995 and Augustin et al., 1999). Mammals contain five Munc13 genes (Brose et al., 1995, Song et al., 1998 and Koch et al., 2000). The Munc13-1, -2, and -3 genes encode larger proteins primarily expressed in brain, while the Munc13-4 and BAP3 genes encode smaller proteins primarily expressed outside of brain.

We speculate that these neuroanatomical changes could be the reason why spontaneous activity, which propagates through the same cortical circuits as evoked activity,

becomes more similar to previously presented evoked patterns. We also speculate that the reverbatory activity described here may relate to memory formation in behaving animals. Although the mechanisms underlying memory formation processes are still not well Bortezomib order understood, there is a body of theoretical work going back to Hebb (1949) and Marr (1971) that predicts reverberation (Hebb) and/or reactivation (Marr) as fundamental components of memory consolidation. Such phenomena have since been observed in the hippocampus and cortex of behaving animals (Euston et al., 2007 and Wilson and McNaughton, 1994). These observations, like ours, are consistent with the theory see more but do not demonstrate that memory depends on this replay. However, more recent evidence suggests a direct link between replay and memory. In hippocampus, the reverberation (reactivation) is associated with SPWR events, and studies have now shown that memory is impaired when SPWRs are disrupted immediately following training (Girardeau et al.,

2009 and Ego-Stengel and Wilson, 2010). Furthermore, there are individual differences in reactivation and memory performance, and these are correlated (Gerrard et al., 2008). These data suggest that the replay of task-related activity is involved in memory processes. Note also that our experiments follow the same general design as “classic” Mephenoxalone reactivation experiments (Wilson and McNaughton, 1994). We have a control period before an experience, a repetitive experience, followed by a test period. We show that the activity in the test period resembles the activity in the repetitive experience after controlling for any pre-existing similarity. The only difference is that the

animal is not actually behaving but rather under anesthesia. By the fundamental definition of memory as a recapitulation of neural activity evoked by an experience, this is memory. Thus, we suggest that replay of stimulus-evoked patterns observed in desynchronized brain states in urethane-anesthetized rats could be a useful model for studying mechanisms of memory. We used surgery and recording procedures that have been previously described in detail (Luczak et al., 2007 and Schjetnan and Luczak, 2011). Briefly, for somatosensory experiments, 11 Long Evans rats (400–900 g) were anesthetized with urethane (1.5g/kg intraperitoneally [i.p.]). Rats were then placed in a stereotaxic frame, and a window in the skull was prepared over primary somatosensory cortex (S1) hindlimb area (anteroposterior 1 mm; mediolateral 2 mm; dorsoventral 1.5 mm). For auditory experiments, eight Long Evans rats (250–350 g) were anesthetized with urethane (1.5g/kg i.p.) and placed in a nasal restraint that left the ears free. A window in the skull (2 × 3 mm) was prepared over the primary auditory cortex (Luczak et al.

g., Figures 1A versus 1B), or they could receive different amounts of input (e.g., Figures 1A versus 1C) or have different thresholds (e.g., Figures 1A versus 1D), with each such alternative having important implications for the origin of place and silent cells. With the extracellular recording methods used in nearly all previous place cell studies, one can attempt to infer the input into a place cell based on its spiking output (Mehta et al., 2000); find protocol however, this is problematic for studying silent cells because they rarely spike. More importantly, extracellular methods cannot

measure fundamental intracellular features such as the baseline Vm, AP threshold, or subthreshold Vm dynamics needed to reveal why spikes do or do not occur. But, recently, intracellular recording in freely moving animals has become possible (Lee et al., 2006, Lee et al., 2009 and Long et al., 2010), and hippocampal place cells have been recorded intracellularly in both freely moving (A.K. Lee et al., 2008, click here Soc. Neurosci., abstract [690.22]; Epsztein et al., 2010) and head-fixed (Harvey et al., 2009) rodents, providing an opportunity to directly measure inputs

and intrinsic properties during spatial exploration. Here, we used head-anchored whole-cell recordings in freely moving rats (Lee et al., 2006 and Lee et al., 2009) as they explored a novel maze in order to investigate what underlies the distinction between place and silent cells starting from the very beginning of map formation. We obtained whole-cell current-clamp recordings of dorsal hippocampal CA1 pyramidal neurons as rats moved around

a previously unexplored “O”-shaped arena (for 7.9 ± 2.3 min). Nine rats went around the maze a sufficient number of times in the same direction (clockwise, CW, or counterclockwise, Idoxuridine CCW) to allow determination of whether the recorded neuron was a place (PC, n = 4) or silent (SC, n = 5) cell in that environment based on its spiking (see Experimental Procedures). In three cases, both directions qualified. Since cells in one-dimensional mazes often have different place fields in each direction, including cases with a place field in one but not the other direction, this gave 12 directions (4.9 ± 0.9 laps each) to classify as having place fields (PD, n = 5) or being silent (SD, n = 7). These numbers agree with the extracellularly-determined fraction of place cells in a given environment (Thompson and Best, 1989, Wilson and McNaughton, 1993 and Karlsson and Frank, 2008), suggesting that extracellular methods can accurately sample silent cells. Figure 2 shows an intracellularly recorded place cell that fired in one corner of the maze (Figures 2A and 2B) and had place fields at that location in both directions (Figure 2C).

It is interesting that, in the mammalian brain, MAPK activity regulates mRNA translation and memory (Kelleher et al., 2004b). Our data indicate that, in addition to SCOP, calpains control synaptic plasticity and memory, in part via activity-dependent degradation of PAIP2A. Despite the similarity in the mechanisms of activity-dependent regulation of SCOP and PAIP2A levels in the brain, their downstream effectors

PLX4032 mw are different; therefore, memory alterations in Paip2a−/− and SCOP-overexpressing mice are not similar. SCOP-overexpressing mice exhibit impaired novel object recognition LTM ( Shimizu et al., 2007), while PAIP2A deletion results in alterations in contextual fear and spatial LTM. We showed here that, in Paip2a−/− mice, the threshold for induction of the protein synthesis-dependent phase of LTP is lowered, and L-LTP was induced with just 1HFS. Remarkably, the threshold for the induction of CaMKIIα translation was similarly reduced in slices from Paip2a−/− mice. 1HFS in Paip2a−/− slices induced robust CaMKIIα expression, whereas no significant CaMKIIα expression was observed

in WT slices. This indicates that the threshold for induction of L-LTP is determined by the sensitivity of the translational machinery to stimulation, which is negatively controlled by PAIP2A. Long-term memory in Paip2a−/− mice was enhanced after weak training, paralleling the low threshold for induction of L-LTP and translation. In response to more intense stimulation (TBS or strong Ku-0059436 research buy contextual fear conditioning), L-LTP and Linifanib (ABT-869) LTM were impaired, akin to earlier reports using other suppressors of translation, such as 4E-BP2 and GCN2 ( Banko et al., 2005; Costa-Mattioli et al., 2005). Similar to Paip2a−/−, in 4E-BP2 and GCN2−/− hippocampal slices 1HFS elicited L-LTP, while stronger tetanic stimulation (4HFS) led to L-LTP impairment. Moreover, weak

training enhanced LTM in GCN2−/− mice, while more intense training caused LTM deficits. In Tsc2+/− mice, the mTOR pathway, an important regulator of translation, is hyperactivated resulting in impaired memory ( Ehninger et al., 2008). Treatment with the mTOR inhibitor, rapamycin, reversed the learning and memory deficits, indicating that enhanced mTOR activity—and probably translation—are responsible for memory deficits ( Ehninger et al., 2008). A conceivable explanation for the impairment is that strong stimulation in Paip2a−/− mice results in excessive translation leading to impairment of L-LTP and memory. One possibility is that synthesis of proteins detrimental to L-LTP and memory maintenance (negative regulators) after strong stimulation in Paip2a−/− mice leads to L-LTP and memory deficits. Physiologically, this mechanism can serve to protect the brain under conditions of excessive stimulation such as seizure activity.

, 2002 and Dawson et al., 2003; reviewed by Franklin and ffrench-Constant, 2008; Figure 1F). A small proportion of Tyrosine Kinase Inhibitor high throughput screening YFP+ cells were Aquaporin-4+

astrocytes (∼3%), but the great majority of reactive astrocytes were derived from Fgfr3-expressing cells (ependymal cells and/or preexisting astrocytes) ( Young et al., 2010), because they were YFP-labeled in Fgfr3-CreER∗: Rosa26-YFP mice ( Zawadzka et al., 2010). Schwann cells, the myelinating cells of the peripheral nervous system (PNS), are commonly found in remyelinating CNS lesions including some human multiple sclerosis lesions. Often these remyelinating Schwann cells surround blood vessels, which in the past has been taken to suggest that they enter the CNS from the PNS, using the vessels as a migration route. However Zawadzka et al. (2010) found that most remyelinating Schwann cells (Periaxin+) in their

CNS lesions were YFP+ in Pdgfra-CreER∗: Rosa26-YFP mice, suggesting that they were derived from NG2-glia ( Figure 1G). In strong support of this, the CNS-resident Schwann cells were also labeled in Olig2-Cre: Rosa26-YFP animals—Olig2 is not thought to be expressed outside of the CNS. Moreover, almost no CNS Schwann cells, but many Schwann cells in peripheral nerves, were labeled in Pzero-CreER∗: Rosa26-YFP mice. (Pzero is expressed in migrating neural crest and differentiated Schwann cells, but not in the oligodendrocyte lineage.) Schwann cells were not a minor side product of NG2-glia because 56% of all YFP+ cells in ethidium bromide-induced lesions were Periaxin+ Schwann cells. (Despite this, most new myelin is oligodendrocyte derived, because Schwann cells each remyelinate only a single internode,

INCB024360 purchase whereas oligodendrocytes remyelinate many.) To our knowledge, this is the clearest example to date of lineage plasticity among NG2-glia in vivo. Since both oligodendrocytes and Schwann cells are myelinating cells, relatively subtle reprogramming might be required to cross between them. In a different demyelinating model—experimental autoimmune encephalomyelitis (EAE), which causes more diffuse and widespread demyelination than gliotoxin injection—Tripathi et al. (2010) found robust production of NG2-glia-derived oligodendrocytes but very few Schwann cells. In EAE, there is a strong inflammatory component to the pathology that is not Rolziracetam present in gliotoxin-induced demyelination, suggesting that the local microenvironment in demyelinated lesions exerts a strong influence on the direction of differentiation of NG2-glia. Only a small fraction of YFP+ cells (1%–2%) were GFAP+ astrocytes in EAE, in keeping with the results from focal demyelination (Zawadzka et al., 2010). A relatively high proportion (∼10%) of YFP+ cells in this EAE study could not be identified, despite much effort with antibodies against microglia, macrophages, B or T cells, neutrophils, vascular endothelial cells, pericytes, neurons, astrocytes, oligodendrocytes, and Schwann cells.

It will therefore be necessary to characterize more subtypes

of early RPCs to ensure that some share identical lineages. In the stochastic model, a given RPC does not have a predefined pattern of mitosis or progeny fate specification. Its lineage is the result of random choices of cell fates made at each cell division by the progeny. It might be difficult to imagine that stochastic lineages from progenitor cells can generate homeostatic tissues with consistent size and cell-type composition. However, studies in other stem cell model systems suggest that this is possible. For example, quantitative analysis showed surprising stochasticity in the progeny of stem cells in self-renewing adult tissues such as the murine epidermis and intestinal epithelium (reviewed in Simons and Clevers, 2011). In these systems, the stem

cells do not follow the classic asymmetrical self-renewing division mode. Instead, they usually divide symmetrically and the resultant selleck kinase inhibitor progeny make their own stochastic choices to stay in the stem cell fate or to move toward a differentiated cell fate. Although this stochasticity results in great variation in the size, cell-type composition, and dynamics of individual stem cell clones, modeling showed that the various cell types can be produced in the correct proportion, while Crenolanib supplier tissue homeostasis can be well maintained at the population level (Simons and Clevers, 2011). Which model better fits the actual vertebrate retinogenesis scenario? Statistical analysis and mathematical modeling of data from in vitro cell culture and time-lapse microscopy had unveiled similar stochasticity in late rat RPCs (Gomes et al., 2011), which choose to divide with three possible outcomes with a specific proportion of each division mode at a given stage of development. These modes give rise to (1) two daughter progenitor cells (PP division), resulting in expansion of the progenitor population; (2) one progenitor daughter cell and one differentiating

daughter cell (self-renewing PD division), which is a stem cell mode that produces neurons with a linear amplification; and (3) two terminally differentiated daughter cells (DD division), a mode that ends the lineage (Figure 1B). The variability in the cell-type birth order and the inability to identify a large Histone demethylase number of identical lineages also showed that the system might rely on stochastic choices of cell fates. However, there were still important questions remaining. Is the stochastic model true in vivo and is it applicable to earlier-stage RPCs? The paper by He et al. (2012) addresses these questions in zebrafish by tracing RPC lineages in vivo in the developing retina. Zebrafish are an excellent model organism for this purpose as their retinas are easily accessible for manipulation and allow live imaging even at early retinogenesis stages. Using photoconvertible fluorescent protein expression in clones induced by heat shock, He et al.

, 2007 and Toki et al., 2001). However, functions, regulation, and effectors of neuronal RasGRPs are unknown. We have elucidated regulatory properties, a physiological function and effectors of RGEF-1b, a RasGRP homolog, in C. elegans sensory neurons. RGEF-1b has conserved catalytic, EF-hand, and C1 domains JAK inhibitor that are hallmark features of RasGRPs. PMA and DAG recruit RGEF-1b

from cytoplasm to membranes and stimulate its catalytic activity. Both LET-60 and RAP-1 are RGEF-1b substrates. Thus, RGEF-1b is a new, but prototypical RasGRP. The rgef-1 gene promoter is active in neurons. Transcription was initiated just prior to hatching of L1 larvae. Promoter activity was sustained in all larval stages and adulthood. After hatching, C. elegans relies on extrinsic stimuli (food, ions, etc.) to guide its behavior. The neuron-specific and temporal

patterns of rgef-1 gene expression suggest RGEF-1b could mediate behavioral responses to environmental stimuli throughout postembryonic life. Chemotaxis to volatile odorants was impaired in RGEF-1b-deficient animals. Panneuronal or AWC-selective expression of RGEF-1b-GFP restored chemotaxis in rgef-1−/− animals. Conversely, GDC-0199 in vivo panneuronal or AWC-selective expression of dominant-negative RGEF-1bR290A-GFP disrupted odorant-induced chemotaxis in WT animals. Thus, RGEF-1b is indispensable for a fundamentally important C. elegans behavior. Odorant-induced chemotaxis enables acquisition of nutrients that optimize health and reproduction. The discovery that RGEF-1b mediates chemotaxis

establishes a neuronal function for a click here RasGRP. The insight that expression of RGEF-1b in AWC neurons restores chemotaxis to attractive odorants illuminates the cellular basis for the rgef-1−/− phenotype. Signals disseminated by a DAG-activated RasGRP in AWC sensory neurons are essential for complex behavior of an intact animal. RGEF-1b loads GTP onto LET-60 and RAP-1. Expression of constitutively active LET-60G12V in AWC neurons restored chemotaxis to odorants in RGEF-1b-deficient animals. Accumulation of dominant-negative LET-60S17N in AWC neurons (WT background) inhibited chemotaxis. Neither constitutively active nor dominant-negative RAP-1 affected chemotaxis. Thus, RGEF-1b couples odorant stimuli to chemotaxis via LET-60-GTP. The AGE-1-AKT-1 and LIN-45-MEK-2-MPK-1 signaling modules are effectors of LET-60-GTP (Han et al., 1993 and Nanji et al., 2005). Elimination of AGE-1 activity from a temperature sensitive mutant and characterization of nematodes carrying a hypomorphic allele of age-1 revealed that PI3K deficiency enhanced chemotaxis to BZ and BU. Thus, the LET-60-AGE-1-AKT-1 pathway is not required for attraction to odorants. Expression of constitutively active MEK-2-GFP(gf) restored AWC-dependent chemotaxis in the rgef-1−/− background.

Moreover, following exposure to MAQ, alternating illumination between 380 nm and 500 nm produced no change in the basal current in TREK1ΔC-S121C-transfected cells (Figure 2B). Because the cytoplasmic N-terminal domain and the first transmembrane segment (M1) of TREK1 are sufficient to dimerize with the full-length check details TREK1 channel (Veale et al., 2010), we hypothesized that TREK1ΔC would dimerize with the wild-type TREK1 channel (WT) and produce a functional channel (Figure 2A). In contrast with the lack of photomodulation of current

in MAQ-labeled cells expressing TREK1ΔC(S121C) alone (Figure 2B), coexpression of TREK1ΔC(S121C) with WT in HEK293 cells yielded a TREK1 current that was strongly photomodulated (Figure 2C). This indicates that TREK1ΔC(S121C) assembles with the WT subunits and that the heteromeric channel goes to the cell surface, where the TREK1ΔC(S121C) subunit is labeled by

the charged, membrane-impermeant MAQ endowing the channel with regulation by light via photoisomerization of MAQ. From here on, we refer to the TREK1ΔC(S121C) subunit that contains the cysteine photoswitch attachment site and that is retained internally unless coassembled with a WT native subunit as the TREK1 photoswitchable conditional subunit (TREK1-PCS). For the approach to work as intended, the heteromeric TREK1-PCS/WT BKM120 channel would need to retain normal functions of the TREK1 channel. We tested the TREK1-PCS/WT heteromeric channel to determine whether it was regulated by external and L-NAME HCl internal stimuli in the same way as WT. To do this, we examined the sensitivity to stimuli of total WT current in cells expressing WT alone and compared this to the photoblocked current component from cells coexpressing the TREK1-PCS along with WT, where the light-sensitive current is attributed solely to the heteromeric TREK1-PCS/WT

channel labeled with MAQ on the TREK1-PCS. TREK1 channels are inhibited by external acidification, due, it has been proposed, to titration of a histidine residue in P1 (Cohen et al., 2008 and Sandoz et al., 2009), an effect that has been attributed to C-type inactivation (Bagriantsev et al., 2011, Cohen et al., 2008 and Sandoz et al., 2009). We found that the light-gated current obtained from MAQ-labeled HEK293T cells coexpressing the TREK1-PCS and WT subunit is also inhibited by external acidification (Figure 3A). This inhibition of the photogated current in the TREK1-PCS/WT heterodimer was 53.6% ± 8% (n = 8), similar to the 60.6% ± 5% (n = inhibition of total current in WT alone (p > 0.7, t test). We next investigated the regulation of the TREK1-PCS/WT heterodimer channel by internal modification induced by GPCR activation. Gi-coupled receptors have been shown to enhance TREK1 current (Cain et al., 2008).

3 ± 0.9 mV, n = 7, p > 0.05 versus wild-type). These results suggest that GIRK channels, which contain GIRK1 subunits,

are constitutively active at rest in POMC neurons and contribute to the resting membrane potential of POMC neurons. In support of this, POMC neurons from GIRK1 knockout mice had a significantly higher input resistance as determined by hyperpolarizing current steps (1,514 ± 118 MΩ in GIRK1 knockout versus 1,142 ± 76 MΩ in wild-type mice) (Figure S2B). POMC neurons from GIRK2 knockout mice had a slightly higher input resistance (1,382 ± 112 MΩ), but the difference was not significant. We next examined the requirement of GIRK1 or GIRK2 subunits in the baclofen-induced hyperpolarization of the membrane potential of POMC neurons. Baclofen hyperpolarized 11 of 14 (78.6%) POMC-hrGFP neurons from wild-type mice by −15.1 ± 2.1 mV (from −54.3 ± Selleckchem IBET151 1.7 mV in control to −69.4 ± 2.4 mV in baclofen, n = 11; Figure S2C). The hyperpolarization was accompanied by a 40.8% ± 6.2% decreased input resistance with a reversal potential of −91.2 ± 1.6 mV, supportive of K+ as the major cation responsible for the membrane hyperpolarization (Figures S2D and S2E). In GIRK1 knockout mice, baclofen hyperpolarized 2 of 16 (12.5%) POMC-hrGFP neurons (hyperpolarized by −8 mV and −9 mV), while

the remaining neurons were unchanged in response to baclofen (Figure S2F). In GIRK2 knockout mice, baclofen hyperpolarized 4 of 7 (57.1%) POMC-hrGFP neurons by −4.0 ± 0.7 mV (from −51.8 ± learn more 1.1 mV in control to −55.8 ± 1.8 mV in baclofen, n = 4) (Figure S2G). These results support a key role of GIRK1 subunits, but not GIRK2, in both constitutively active and GABAB-activated GIRK currents in POMC neurons (Figures S2H and S2I). We next determined the requirement of GIRK1 subunits in the mCPP induced depolarization of POMC-hrGFP neurons in GIRK1 knockout mice (Figure 3E). Perfusion of mCPP depolarized the membrane potential of 6 of 18 (33.3%) POMC-hrGFP neurons from GIRK1 knockout

mice by 5.2 ± 0.3 mV (n = 6), Linifanib (ABT-869) which was similar to the effect of mCPP observed in POMC neurons from wild-type mice. Together, these data suggest that inhibition of constitutively active GIRK channels (Chen and Johnston, 2005) is not responsible for the mCPP-induced excitation of POMC neurons. In order to further determine the conductance involved in the mCPP-induced depolarization, POMC neurons from wild-type mice were monitored for changes in input resistance and neuronal excitability. In current clamp configuration, continuous recordings of membrane potential were interrupted by hyperpolarizing rectangular current steps (500 ms; −10 to −50 pA; arrows in Figures 1F and 1G). In control ACSF, the whole-cell input resistance of POMC neurons was 1,323 ± 60 MΩ (n = 59), similar to previous reports (Hill et al., 2008 and Hill et al., 2010). The mCPP-induced depolarization of POMC neurons was accompanied by a reversible 17.1% ± 1.

First, the signals related to the difference between the temporally discounted values for the two alternative targets,

which reliably predicts the animal’s choice, were more robust and found more frequently in the dorsal striatum. Second, the signals related to the direction of the animal’s eye movement during intertemporal choice were found only in the dorsal striatum. Therefore, the dorsal Sunitinib in vivo striatum is likely to play a more important role in choosing a particular action based on temporally discounted values than the ventral striatum. Previous single-neuron recording studies in the primate striatum have also shown that signals related to specific movements are largely confined to the dorsal striatum, including the caudate nucleus and putamen, whereas reward-related signals tend to be distributed evenly across different subdivisions of the striatum (Apicella et al., 1991, Schultz et al., 1992, Williams et al., 1993, Bowman et al., 1996, Hassani et al., 2001, Cromwell and Schultz, 2003, Kawagoe et al., 1998, Ding and Hikosaka, 2006 and Kobayashi et al., 2007). In some of these studies, the position of the target associated with a large reward was fixed for a block of trials during an instructed delay task, while the direction of the required movement was selected

randomly (Kawagoe et al., 1998, Ding and Hikosaka, 2006 and Kobayashi et al., 2007). These studies have found that some neurons in the caudate nucleus change their activity according to the position of the target associated DAPT with a large reward. In reinforcement learning theory, the value of reward expected from a particular action is referred to as action values (Sutton and Barto, 1998), and could be used to select an action to maximize reward intake. Indeed, it has been shown

that during a free-choice task, some neurons in the dorsal striatum change their activity according to the action values of specific movements (Samejima et al., 2005, Lau and Glimcher, Cytidine deaminase 2008 and Kim et al., 2009b). These results suggest that the dorsal striatum might play an important role in selecting an action with the most desirable outcomes, when the likelihood of reward from each action needs to be estimated from experience (O’Doherty et al., 2004, Tricomi et al., 2004 and Kimchi and Laubach, 2009). The results from the present study show that the dorsal striatum might also contribute to intertemporal choice by encoding the difference in the temporally discounted values for alternative outcomes. In addition, neurons in both CD and VS encoded the sum of the temporally discounted values with a time course similar to their difference, suggesting that the signals related to the temporally discounted values of the two targets were combined heterogeneously across different striatal neurons, similar to the activity related to action values in the posterior parietal cortex (Seo et al., 2009).