J.Y.L. and S.B.S. designed the InSynC constructs. J.Y.L. conducted GSK1120212 price and analyzed the hippocampal microisland recordings. J.Y.L. and S.B.S. conducted and analyzed the worm movement and imaging experiments. K.Z. conducted and analyzed the electrophysiological recordings from C. elegans muscle cells. S.N. conducted and analyzed the organotypic slice experiments. R.Y.T., C.D.P., R.M., and Y.J. contributed to the design and analysis of the experiments. All authors contributed to the writing and discussion of the manuscript. “
“In developing mammalian brains, huge numbers of neurons are generated from a relatively small number of neural progenitor cells. For this,

neural progenitors expand by symmetric division before switching to an asymmetric division mode to generate neurons (Götz and Huttner, 2005). In the developing mouse brain, neuroepithelial progenitors (NPs) span from the ventricular to the pial surface of the neural tube before the onset of neurogenesis. At around embryonic day 10.5 (E10.5), neurogenesis begins with the transformation of NPs into radial glial progenitors (RGPs), which express astroglial hallmarks such as brain lipid binding protein (BLBP), the astrocyte-specific glutamate transporter (GLAST), and Tenascin C (TN-C) (Haubensak et al., 2004, Hartfuss selleck products et al., 2001, Kriegstein and Alvarez-Buylla, 2009 and Götz

and Huttner, 2005). RGPs display apical-basal polarity and bear apical and basal processes that maintain their contacts with both the ventricular and pial surfaces. However, the cell bodies of RGPs are confined to the ventricular zone (VZ) that lines the lateral wall of the ventricles. In concert with their cell-cycle state, they undergo interkinetic nuclear migration (INM) (Taverna and Huttner, 2010): RGPs go through mitosis at the apical surface of CYTH4 the VZ. During the G1-S phase of the cell cycle, they migrate basally so that S phase reproducibly occurs on the basal edge of the VZ. RGPs display two modes of cell division. They divide symmetrically and generate two daughter cells that retain RGP properties to expand the number

of neural progenitors. Alternatively, they divide asymmetrically giving rise to distinct daughter cell fates. Asymmetric RGP divisions produce either one RGP and one neuron or generate one RGP and one basal progenitor (BP, also called intermediate progenitor) (Noctor et al., 2004, Calegari et al., 2002 and Miyata et al., 2004). BPs delaminate from the VZ and form the second germinal zone, the subventricular zone (SVZ), where they divide symmetrically to generate two neurons. In some cases, they can also generate two BPs to expand the basal progenitor pool (Noctor et al., 2004 and Attardo et al., 2008). BPs emerge at E10.5 and become abundant from E13.5–E16.5, coinciding with the peak of neurogenesis (Englund et al., 2005). They are thought to be the source of most, if not all, neurons in the cortex (Sessa et al.

We conducted further confirmatory analyses to ensure that the hierarchical regression was robust. Specifically, we subsampled the data in order to reverse the direction of eye movement differences across the conditions. In the original data set, there are more saccades in the Attention-High conditions than the Attention-Low conditions. In order to reverse the direction of this effect on a participant-wise basis, we sorted the trials within each condition according to the number of click here saccades that occurred on that trial. In each Attention-High condition, we took all

scores below the 60th percentile. In each Attention-Low condition, we took all scores above the 40th percentile. As shown in Figure 3A, in the subsampled data, the number of saccades was much larger in the Attention-Low conditions than the Attention-High conditions (F(1,29) = 148.97, p < 0.001). In fact, Baf-A1 the absolute value of the difference between conditions was much larger in the subsampled data than in the original data. As in the original data, the main effect of Memory was significant

(F(1,29) = 4.44, p < 0.05) and the interaction was not significant (F(1,29) = 2.47, p = 0.13). The subsampled data were then subjected to the same analysis as the original data set. If the hierarchical regression is robust, the subsampled data should lead to similar conclusions: the effects of eye movements Histone demethylase have already been satisfactorily modeled, so any further classification of the data on the basis of eye movements should have no effect. Alternatively, if the activation presented in Figure 2 reflects the effects of eye movements, there should be a substantial reversal of these effects when the sub-sampled data are subjected to the same analysis. The same basic pattern of activation

seen in the main analysis (Figure 2) is also seen in the subsampled data (Figure 3). Although there is an expected slight reduction in the overall magnitude and extent of activation, which results from a reduction in power, the peak activations in parietal cortex are still clearly apparent. Time courses from the subsampled data (Figures 3C and 3D) closely resemble those obtained from the original data set. Similar conclusions were obtained when using the number of saccades between pictures as the measure of interest (Figure S4). There is a hint of residual effects of eye movements in early visual cortex (Figure 3, cool colors). Critically, however, activation of the dorsal attention network persisted despite these modest residual effects. These confirmatory analyses indicate that the hierarchical regression was robust and that the findings reported in Figure 2 cannot be attributed to the effects of eye movements. To identify regions associated with the retrieval of specific perceptual detail, we identified regions showing a significant main effect of Memory.

The combined study based on the computational and experimental techniques helped in identifying novel inhibitors that bind to SAM binding site.21, 22 and 23 The present work is to identify the inhibitor lead molecules for Flavivirus NS5 MTase using computational approach. The

dengue MTase has separate binding sites for RTP and SAM. E-pharmacophore studies were performed using both the sites for studying the substrate and inhibitor binding in the active site of MTase. Finally, these pharmacophores were used as queries for virtual screening using compounds from the Asinex database and induced fit docking (IFD) was carried out for the short-listed compounds. The identification of pharmacophore features

was carried out by aligning all the compounds together in a 3D Cartesian space. The earlier studies focused on the structure-based ZD1839 ic50 virtual screening and ligand-based pharmacophore models, keeping the active site of the protein rigid.18, 19 and 20 BKM120 order The structure-based pharmacophore was used to derive pharmacophore features from the inhibitors or substrates that bind at different sites, separately. The X-ray crystal structures of the dengue MTase complex, MTase–SAM complex (PDB id: 3P97), MTase–SAH complex (PDB id: 1R6A), MTase–RTP complex (PDB id: 1R6A) specific to the Flavivirus were retrieved from Protein Data Bank. 25 During protein preparation, water molecules were removed, hydrogen atoms were added, bond orders were assigned and orientation of hydroxyl groups were optimized. Energy minimization was carried out using OPLS2005 force field to converge RMSD of 0.30Å. The receptor grid was generated around the centroid of the ligand contained by enzyme file and the ligands with cut off size of 10 Å were allowed to dock. The ligands were docked with the active site using the ‘Extra Precision’ Glide algorithm. 26 and 27 Glide includes ligand–protein interaction energies, hydrophobic interactions,

hydrogen bonds, internal energy, π–π stacking interactions and root mean square deviation (RMSD) and desolvation. Finally, best pose of the particular ligand was selected based on the Glide Mannose-binding protein-associated serine protease score. Energy-optimized pharmacophores (e-pharmacophores)28 were evaluated through mapping the energetic terms from the Glide XP scoring function onto atom center. Pharmacophore sites were automatically generated from the protein–ligand docked complex with Phase (Phase, v.3.0, Schrodinger, LLC) using the default set of six chemical features, hydrogen bond acceptor (A), hydrogen bond donor (D), hydrophobic (H), negative ionizable (N), positive ionizable (P), and aromatic ring (R). Glide XP descriptors include terms for hydrophobic enclosure, hydrophobically packed correlated hydrogen bonds, electrostatic rewards, π–π stacking, π cation and other interactions.

This reduction in AP bursts was also apparent in the cumulative probability density function, yielding a significant difference in distribution (K-S test p < 0.01, Figure 8E). Taken together, these experiments show that Na+ currents in first nodes of Ranvier

facilitate the generation of high-frequency APs in the AIS. The present study demonstrates that the first node of Ranvier has a critical role in high-frequency selleck chemicals burst generation in L5 axons. Using direct and indirect approaches, including the analysis of axon length in slices, targeted axotomy, and nodal Na+ channel block at fluorescence-identified branchpoints, it was found that somatic depolarization near threshold recruits a persistent type of Na+ current from the first node of Ranvier.

In the population of burst firing L5 neurons, this nodal Na+ current hyperpolarizes the AP voltage threshold, selleck chemical amplifies the axosomatic AP ADP, and influences the input-output properties in the AIS by facilitating high-frequency spikes. High-frequency bursts are thought to encode specific information about sensory stimuli and greatly increase the reliability of synaptic transmission (de Kock et al., 2007, Kepecs et al., 2002, Lisman, 1997 and Williams and Stuart, 1999). About 40%–60% of the thick-tufted L5 neurons in the neocortex are prone to generate bursts both in vitro (Chagnac-Amitai et al., 1990, Franceschetti et al., 1995, Mason and Larkman, 1990 and Williams and

Stuart, 1999) and in vivo (de Kock and Sakmann, 2008). These neurons fire typically 2–6 APs independent of the type of input stimulus, suggesting specific intrinsic ion channel compositions or neuronal geometries. One cellular mechanism often implicated in burst generation is the slow regenerative feedback between back-propagating axosomatic Na+ APs and the calcium-mediated dendritic depolarization Dipeptidyl peptidase (Mainen and Sejnowski, 1996, Schwindt and Crill, 1999 and Williams and Stuart, 1999). Indeed, L5 neurons lacking large dendritic trees, such as the slender-type L5 neurons or thick-tufted L5 neurons, which are cut at the apical dendrite, switch from IB to RS patterns (Bekkers and Häusser, 2007 and Mason and Larkman, 1990). Whereas each Na+ AP is first initiated in the AIS (Foust et al., 2010, Khaliq and Raman, 2006, Monsivais et al., 2005, Palmer et al., 2010 and Palmer and Stuart, 2006; this study), three lines of evidence show that for the initiation of AP bursts, Na+ current flow from the first node is essential. First, IB firing was absent in L5 axons without a first branchpoint (Figure 2 and Figure 3); second, IB neurons lost the ability for high-frequency discharges after axotomy (Figure 4); and third, nodal Na+ channel block inhibited a significant fraction of the anterograde propagating axonal AP and abolished bursts (Figure 7 and Figure 8).

[K+]o and [Ca2+]o reached a steady-state level during a stable locomotor episode. Screening Library chemical structure The steady state of [K+]o reflects an equilibrium between the neuronal K+ efflux and its clearance from the extracellular space with neuronal Na+/K+ pump (Syková, 1987) and glial cells (Jendelová and Syková, 1991). The decrease in [Ca2+]o mainly involves an uptake into postsynaptic somata and/or dendrites (Heinemann and Pumain, 1981). Lowering [Ca2+]o has been reported to switch the firing mode of various CNS neurons from spiking to bursting (Brocard et al., 2006; Heinemann et al., 1977; Johnson et al., 1994; Su et al., 2001; Tazerart et al.,

2008). In our experiments, the reduction PF-01367338 clinical trial of [Ca2+]o requires a concomitant raise in [K+]o to trigger bursts. This synergistic effect probably results from a joint regulation of INaP and IK, respectively. An increase in INaP appears to be the major link between the reduction in [Ca2+]o and the bursting ability because a decrease of [Ca2+]o shifts the threshold of INaP activation toward more negative values and enhances its amplitude. In agreement with this, our simulation showed that the shift of the threshold of INaP activation toward more negative values plays a major role in the emergence of bursts, and even a subtle shift of activation by −3 mV produces the same effect as increasing INaP conductance by 50%. This is supported by the

sensitivity of pacemaker activity to riluzole and TTX. Changes in pore occupancy of sodium channels by calcium may be responsible for these modifications of INaP ( Armstrong, 1999). Although [K+]o increase does not upregulate INaP, as shown experimentally, our model demonstrates that it facilitates the emergence of INaP-dependent bursts by reducing IK as a result of reduction of EK (see also Rybak et al., 2003). The increased [K+]o also provides an additional depolarization of pacemaker cells via the reduction of the voltage-gated potassium and leak

currents, which also increases the frequency of oscillations. In summary, the regulation of INaP and IK by [Ca2+]o and [K+]o, respectively, may represent a fundamental Mephenoxalone mechanism in generating and regulating the pacemaker activities in other brain areas. Taking into account that changes in [K+]o and [Ca2+]o (1) precede the onset of locomotion, (2) promote INaP-dependent pacemaker properties in putative locomotor CPG cells, and (3) trigger a locomotor episode, a conceptual scheme can be proposed for rhythmogenesis in the mammalian spinal cord. A moderate spiking activity of CPG components causes a reduction in [Ca2+]o and increase in [K+]o. Changes in these concentrations cause the simultaneous regulation of INaP and IK, which together produce at a threshold level a switch from spiking to bursting representing the locomotor oscillations.

, 2008) and GLR-1::mCherry

in AVA of transgenic worms. We did not observe any BiFC fluorescence in these worms indicating that NMR-2 and SOL-2 do not associate (Figure 3E). This result is consistent with earlier studies showing that neither GLR-1 nor SOL-1 colocalize with NMDARs (Brockie et al., 2001b; Zheng et al., 2004). The BiFC data suggest a model in which SOL-1 and SOL-2 directly interact. To more rigorously test this hypothesis, we asked whether we could detect SOL-1 and GLR-1 after immunoprecipitation of SOL-2. Because of technical limitations (low abundance of auxiliary proteins and receptors in whole worms), we could not reliably detect these proteins in whole worm lysates. Therefore, we coexpressed these proteins in HEK293 cells and used these transfected cells for biochemical studies. We found that SOL-2 was associated with GLR-1 and SOL-1 (Figure S4A) but did not associate with the unrelated transmembrane protein DCC (Keino-Masu et al., 1996). Together, selleck chemicals the biochemical and BiFC data indicate that SOL-1, SOL-2, and GLR-1 physically interact and form a receptor complex. We this website had previously demonstrated that GLR-1 surface expression was not appreciably altered

in sol-1 mutants ( Zheng et al., 2004). Is surface expression of GLR-1 also independent of sol-2? To test this possibility, we double labeled GLR-1 with a HA-epitope (extracellular) and GFP (intracellular) and expressed this functional construct (HA::GLR-1::GFP) in transgenic worms ( Zheng et al., 2004).

We assessed the surface expression of GLR-1 by injecting fluorescently labeled anti-HA antibodies into the pseudocoelomic space of transgenic wild-type worms and sol-2 mutants ( Gottschalk et al., 2005; Zheng et al., 2004). We observed punctate anti-HA antibody fluorescence along the ventral cord in both wild-type worms ( Figure 3F) and sol-2 mutants ( Figure 3G), suggesting that GLR-1 is expressed on the cell surface in sol-2 mutants. We obtained additional evidence to support whatever the claim that GLR-1 surface expression was not appreciably altered in sol-2 mutants by generating a transgenic strain that expressed GLR-1 fused to superecliptic phluorin (SEP, a pH-sensitive variant of GFP) ( Miesenböck et al., 1998). SEP was inserted in the extracellular N-terminal domain four amino acids from the mature N terminus (SEP::GLR-1). In agreement with our antibody studies, we found that the fluorescence intensity of surface-expressed SEP::GLR-1 appeared similar in transgenic sol-2 mutants and wild-type worms ( Figure 3H). Control experiments (acid wash) indicated that the SEP fluorescence signal represented surface receptors ( Figure S4B). Another possible explanation for the reduced glutamate-gated current in sol-2 mutants is reduced surface delivery of SOL-1. However, we also found no appreciable difference in fluorescence intensity when we compared SEP::SOL-1 surface delivery in transgenic wild-type worms and sol-2 mutants ( Figure 3I).

, 2003) and ventral (via thin and pale stripes, DeYoe and Van Essen, 1985, Nakamura et al., 1993 and Nascimento-Silva et al., 2003) pathways, it raises the issue of how these disparity are differentially used in the two pathways. Role of Disparity Selective Responses in V4 in Fine Depth Perception. Although binocular disparity has traditionally been considered a dorsal pathway function (e.g., Livingstone and Hubel, 1988, Sakata et al., 1997 and Gonzalez and Perez, 1998), recent physiological studies are overturning this long-standing belief. Indeed, V4 cells http://www.selleckchem.com/products/hydroxychloroquine-sulfate.html exhibit selectivity for

binocular disparity ( Hinkle and Connor, 2001, Hinkle and Connor, 2005, Watanabe et al., 2002, Tanabe et al., 2004, Tanabe et al., 2005 and Hegdé and Van Essen, 2005a), disparity-defined shape in random-dot stereograms ( Hegdé and Van Essen, 2005b), and 3-D orientation of bars ( Hinkle and Connor, 2002). As shown by studies in both monkeys and humans, these response characteristics are consistent with the use of disparity cues in the

ventral pathway for object recognition (fine stereopsis involving higher spatial frequencies, retinal disparities < 0.5 deg, stationary or slowly moving objects), and are distinct from this website those in the dorsal pathway for vision related to motion, self-motion, and visually guided behavior (coarse steropsis involving lower spatial frequencies, larger retinal disparities between 0.5–10 deg, and moving targets) ( Neri et al., 2004, Parker, 2007 and Preston et al., 2008). Further confirming V4′s role in fine depth perception, microstimulation in V4 biases behavioral judgment of fine depth ( Shiozaki et al., 2012), whereas microstimulation of MT biases behavioral judgment of coarse but not fine depth ( Uka and DeAngelis, 2006). Consistent with these results, V4 and IT neurons show trial-by-trial response variation correlated with fine depth judgment ( Uka et al., 2005 and Shiozaki et al., 2012), while MT neuron responses correlate with coarse

depth judgment ( Uka and DeAngelis, 2004). Binocular Matching. To calculate binocular disparity, how does from the visual system find the appropriate matching between left and right eye images? A very useful tool for investigating this “binocular correspondence problem” is the random dot stereogram (RDS, Figure 5C, left), a stimulus in which 3D structure is perceived only with appropriate matching of dots in left and right eyes ( Julesz, 1972). The degree of spatial shift of dots between the left and right eyes determines the depth plane perceived. To probe what stage in the visual system binocular correspondence is computed, a control (anticorrelated RDS, aRDS) was designed in which the matching dots were reversed in contrast (e.g.

Aversive chemicals in foods not only stimulate deterrent taste cells but also inhibit taste receptor cells that are activated by attractive compounds. This interaction between bitter and attractive gustatory stimuli has been observed in a wide array of vertebrate and invertebrate animals (Glendinning, 2007). Most studies dealing with the interactions between deterrent and attractive tastants have focused on quinine,

a prototypical bitter compound. see more Electrophysiological recordings in hamsters show that the response to sucrose is inhibited by quinine (Formaker et al., 1997). In the catfish, quinine inhibits the positive gustatory response of several amino acids (Ogawa et al., 1997). Bitter compounds such as quinine are also aversive to flies (Tompkins et al., 1979), and suppress sugar-evoked firings in gustatory receptor neurons (GRNs) (Meunier et al., 2003). Suppression of the stimulatory effect of attractive tastants by deterrent compounds could take place in the taste receptor cells or in higher-processing central pathways. While both sites might contribute to

inhibition of sugar attractiveness selleck chemicals llc by quinine, there is evidence that the afferent taste receptor cells are important for this phenomenon (Formaker et al., 1997 and Talavera et al., 2008). Multiple mechanisms have been proposed to account for inhibition of sweet taste by quinine and other bitter compounds within the peripheral region of the gustatory system. The bitter-sweet interaction could be a consequence of lateral inhibition of sugar-responsive gustatory receptor cells by bitter-activated neurons, similar to the inhibition of olfactory receptor neurons (ORNs) following activation of neighboring ORNs (Vandenbeuch et al., 2004 and Su et al., 2012).

Chemical interactions between the sugars and bitter compounds might also inhibit the attractiveness of the sugars. Competition of sugars and bitter chemicals for the same receptor is also plausible. An important insight into this issue was provided by the demonstration that the effectiveness of the mammalian TRP channel TRPM5, which is indirectly activated by sugars via a G-protein-coupled signaling pathway, is inhibited why by quinine (Talavera et al., 2008). Thus, TRPM5 may provide one molecular mechanism through which quinine inhibits the attractiveness of sugars. In Drosophila, the molecular mechanism underlying the bitter-sweet interaction has been largely unexplored. According to an electrophysiological analysis, the site of this interaction is likely to be in the gustatory bristles (sensilla), which house the GRNs and accessory cells, and involve the taste receptors ( Meunier et al., 2003). In fly GRNs, the largest class of taste receptors is referred to as gustatory receptors (GRs), which are distantly related to olfactory receptors (ORs) ( Clyne et al., 1999, Clyne et al.

To test this, we examined biological functions represented in the dark red, BMN 673 cost turquoise, and pink modules, the three most preserved in VSP (Figures 4G and 4H, Table S3). The turquoise module was the largest in the network (4,616 probes representing 2,743 known genes; Table S2). It was the only module enriched for many functional

terms related to hormone binding, morphogenesis, neurogenesis, and development, implicating it in steroid sensitivity and the ongoing neurogenesis known to occur throughout the adult songbird striatum (Table S4; Nottebohm, 2004 and Kim et al., 2004). The turquoise, dark red, and pink modules were enriched for neuron and oligodendrocyte gene markers (turquoise: genes > 10-fold enriched in oligodendrocytes, p = 0.05, dark red: genes > 20-fold enriched in neurons, p = 0.03, Fisher’s exact test; Table S2; Cahoy et al., 2008) and markers of striatal and pallidal neurons (pink: p < 0.02; Table S2), consistent with the mixed striatal and pallidal nature of what was formerly known as the avian “striatum” (Farries and Perkel, 2002 and Reiner et al., 2004). These findings are congruent with the idea that PF-02341066 ic50 the preserved modules represent functions common across

the striato-pallidum. Given the large number of genes in the song modules, we sought to identify the potentially most important genes for further study. We used two basic approaches (Figure 7); both began by restricting further analysis to the singing-related modules. In one approach, we then focused on song module genes with high GS.motifs.X and MM, i.e., genes highly interconnected within their module (hub genes) and strongly coupled to singing, and screened them for enriched functions and biological features. The other approach is exemplified above in the Biological Significance of Singing-Related Modules section where we functionally

annotated the singing-related Amisulpride modules, then prioritized enriched functional terms based on TS scores (Supplemental Experimental Procedures; Table S4), highlighting sets of tightly interconnected singing-related genes that were both important in the module and shared an enriched common feature. We used these approaches to select pathways in which to test for the presence of constituent proteins in area X. The importance of studying molecules in the context of biological pathways, rather than simply validating mRNA expression, is underscored by our finding that gene coexpression relationships, rather than expression levels per se, determine molecular microcircuitry underlying vocal-motor-specific behavior.

0005, one-way ANOVA compared to the −20 ms data set, n = 5–8 per pairing interval), similar to the ∼2-fold increase in PSP size when GABAR antagonists were applied under baseline conditions. This indicates that the timing dependence for the suppression of inhibition is closely tuned to the optimal −20 ms pairing interval that elicits ITDP. The specificity with which ITDP reduces the SC-evoked IPSP versus the PP-evoked IPSP suggests that ITDP does not depress inhibition globally. Given that inhibition is highly compartmentalized with nonoverlapping

Trametinib price populations of INs targeting the CA1 PN soma and dendrites (Klausberger and Somogyi, 2008), we next asked whether soma- or dendrite-targeting INs were regulated learn more by ITDP. Whole-cell current-clamp recordings obtained separately from CA1 PN soma and apical dendrites (∼250 μm from the soma in SR) showed that induction of ITDP caused a much smaller increase in the SC-evoked dendritic

SC PSP (1.44-fold ± 0.04-fold change; p < 0.001, n = 5) than in the somatic SC PSP (2.61-fold ± 0.22-fold change; p < 0.001, n = 7; p < 0.005, dendrite versus soma, t test; Figures 3B and 3C). The PP-evoked dendritic PSP was unaltered during ITDP (p = 0.5083), similar to the somatic PP PSP. The small size of dendritic ITDP is surprising, as the induction of ITDP requires summation of PP and SC PSPs, which should be greatest in the PN dendrite. Might the difference between somatic and dendritic ITDP

arise from a differential suppression of inhibition at the two compartments? In support of this idea, we found that dendritic ITDP was not altered when GABARs were blocked continuously throughout the experiment (p = 0.812, dendritic SC ITDP, control versus +SR, CGP; Figures 3B2 and 3B3). This contrasts with the large decrease in somatic ITDP during GABAR blockade (Figures 3C2 and 3C3). These results suggest that dendritic ITDP results almost exclusively from SC eLTP, which is similar in size to the SC eLTP at the soma. Although it may seem surprising that the increased somatic SC PSP during ITDP does not passively propagate to cause TCL a larger increase in the dendritic SC PSP (Figures 3B3–3C3), our computational model confirms that a selective loss of somatic inhibition does not significantly boost the local dendritic PSP evoked by SC inputs (Figure S2). Next, we used optogenetics to identify the specific class of perisomatic-targeting interneurons involved in ITDP. We focused on the two major IN classes known to target the CA1 PN soma and perisomatic dendrites: the PV and CCK basket cells (Freund and Katona, 2007). We used a recombinant adeno-associated virus (rAAV) to express channelrhodopsin-2 fused to EYFP (ChR2-EYFP) (Boyden et al., 2005) selectively in cells that expressed Cre recombinase. Injection of this virus (rAAV-DIO-EF1α-ChR2-EYFP; Zhang et al.