Nonetheless, even a cursory inspection of Figure 3 highlights the fact that different animals offer very complementary opportunities: insects are tremendously useful for the study of highly specific social behaviors and their genetic basis; rodents are ideal for optogenetic manipulation; monkeys offer the best glimpse at the neurophysiology underlying complex group behaviors most similar to those of humans; and of course humans are indispensable because they can tell us about

ourselves most directly. We conclude by asking where should we invest our effort, PI3K Inhibitor Library chemical structure thinking ahead to the next 25 years (see Tables 2 and 3). We highlight three especially exciting avenues for the future. Arguably, one of the most exciting methods currently in neurobiology is optogenetics. This approach, especially suitable to the circuit and small-systems level, permits inhibition or excitation

of activity across large populations of cells but with precision at the level of single cells (Deisseroth, 2011 and Zhang et al., 2007). As such, very precise patterns of neural activity can be manipulated in space and time—so precisely, in fact, that in principle they can perfectly emulate the patterns that actually occur in the brain normally. It is thus not just the causal aspect of the method Doxorubicin datasheet that is so impressive but the (future) ability literally to replay the neural

events that would normally constitute a cognitive event. In the near future, these techniques will likely reveal with unprecedented detail the causal relationships between sequences of neural events and social behaviors in many social species including nonhuman primates (Gerits and Vanduffel, 2013). Indeed, although optogenetic approaches are currently too invasive 4-Aminobutyrate aminotransferase for use with humans, it is no longer in the realm of science fiction to consider that tools of this nature may be available for human research in the not-too-distant future as well, a prospect that opens up some very exciting possibilities (Alivisatos et al., 2012). For instance, we could (in principle) reinstantiate the neural state that corresponds to social anxiety; it would not be caused so much as constituted. One could imagine tweaking the neural state slightly, mapping out the boundaries of what people subjectively report as social anxiety, replaying the neural state as modulated by anxiolytic drugs, and so forth. There is little question that these advances will play a large role in helping to biologically constrain theories of social cognition over the next 25 years.

The stimuli were back projected on a screen using a video projector (NEC WT610, 1024 × 768 pixel resolution, 85 Hz) and custom-made software running on an Apple G4 Power PC. The animals viewed the screen at a distance of 57 cm (1 cm = 1° of visual angle). The RDPs were generated by plotting colored dots (white, 13 cd/m2; gray, 1.9 cd/m2; pink, 5.4 cd/m2; green, 0.9 cd/m2; blue, 1.58 cd/m2; red, 0.6 cd/m2; turquoise, 8 cd/m2) on a dark background (black-gray, 0.02 cd/m2) with a density of three dots per degree2 within

a circular stationary virtual aperture. All dots within one RDP moved coherently at a speed of 15°/s and were replotted at the opposite side when Screening Library clinical trial they crossed the border of the aperture. The radius of the aperture was 4°, and its center was 8° away from the fixation spot. The animals started a trial by pressing a button and keeping gaze within a circular window of 2° diameter centered on a small fixation spot (0.06 degree2). After 353 ms, two moving RDPs appeared, one located to the left and the other to the right of the spot. The patterns were composed of white dots on a dark background that moved either up or down relative to the vertical. After a variable interval, from 294

to 646 ms following the RDPs’ onset, the dots in each pattern changed to a different color (e.g., in one pattern to red and in the other to blue). The task for the animals was to select and covertly attend to one RDP (the target) while ignoring the other (the distracter), wait for a brief motion direction change (176 ms duration, 32° intensity click here clockwise from the current direction) in the target, and release the button within 100–650 ms from the change onset. Target direction changes could occur within a time window ranging from 752 to 2940 ms after color-change onset. In order to correctly select the target, the animal had to learn over

several training sessions a color-rank selection rule (gray < pink < green < blue < red < turquoise). Each correctly performed trial was rewarded with a drop of juice. To guarantee that the animal correctly selected the target, on half of the trials, Metalloexopeptidase the distracter pattern located in the opposite visual hemifield changed direction. The monkey had to ignore this distracter change and wait for the target change. Trials in which the monkey responded to the distracter change (false alarms), or did not respond to the target change within the reaction time window (misses), or broke fixation before the target change onset (fixation breaks), were terminated without reward. The different trial types were presented in random sequence. Only correctly performed trials were included in the analysis. Due to limitations in the number of trials that the animals performed during one recording session, we tested only four different colors at a time (instead of six).

Varying levels of DA D1R stimulation would correspondingly weaken

nonpreferred connections, sharpening tuning under conditions of salient events (e.g., a rewarding stimulus or pressure from a deadline). The sculpting of network inputs may be optimal for performance of a spatial working memory task in which one is trying to maintain the representation of a small location in space but may be harmful when cognitive demands require more flexible network connections (Arnsten et al., 2009). This may explain why D1R stimulation is needed for spatial working memory but actually impairs attentional set-shifting (Robbins and Arnsten, 2009), even though both functions depend on dlPFC. Thus, the optimal neuromodulatory environment depends on the cognitive demands: insightful solutions to problems or creative endeavors that require Selleck AZD2281 wide network connections would be optimal under relaxed, alert conditions with less D1R sculpting (e.g., in the shower),

while more focused work may be best performed under the conditions that increase DA release (e.g., the pressure of working for a reward) (Arnsten et al., 2009). This may also NVP-AUY922 explain why stimulant medications can be helpful for some schoolwork (e.g., math) but harmful to others (e.g., composing a poem or song). The right side of the inverted U in Figure 6A shows the progressive weakening of network connections and progressive decrease in dlPFC firing with increasing stress (Arnsten, 1998, 2009). Evidence of this phenomenon has been seen in human imaging studies, where a found mild uncontrollable stressor (watching a gory movie) impairs working memory and reduces the BOLD signal over the dlPFC, while disinhibiting activity in the amygdala and default mode network (Qin et al., 2009), consistent with loss of dlPFC regulation and strengthening

of more primitive circuits. Data from animals indicate that that the same neurochemical pathways that take PFC off-line (D1R-cAMP and β1-AR-cAMP, α1-AR-Ca+2-PKC) serve to strengthen subcortical and sensory/motor circuits, switching the brain from a reflective to reflexive mode (Arnsten, 2009). The feed-forward nature of these signaling pathways would promote a very rapid switch to primitive circuits, that is, “Going to Hell in a Handbasket” (Arnsten, 2009). Thus, regulatory interactors, such as DISC1-PDE4A, would serve a critical role to reign in feed-forward Ca+2-cAMPsignaling and restore dlPFC top-down regulation of thought and behavior. Loss of this regulation and/or chronic stress exposure leads to architectural changes in PFC pyramidal cells, with loss of spines and retraction of dendrites (Cook and Wellman, 2004; Liston et al., 2006; Radley et al., 2008). The molecular basis for stress-induced atrophy has just begun to be studied.

Nine to ten dishes for each genotype were homogenized in 0.1 M 2-(N-morpholino)ethanesulfonic

acid, 1 mM EGTA, 0.5 mM MgCl2, and protease inhibitors (Roche), pH 6.5. The lysate was then processed as in HA-1077 price (Girard et al., 2005). Sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PGE) and western blotting were carried out by standard procedure. Immunofluorescence of frozen brain sections and cultured neurons (DIV 14–24) was carried out as described (Ferguson et al., 2007 and Ringstad et al., 2001). Fluorescent puncta were quantified as in (Hayashi et al., 2008). Data are presented as number of puncta per 100 μm2 and are normalized to controls. At least ten images from three to six experiments were analyzed for each genotype, and the t test was selleck chemicals used for the statistics. Live mouse fibroblasts

were imaged using a Perkin Elmer Ultraview spinning-disk confocal microscope with 100× CFI PlanApo VC objective. Cortical neurons were plated at a density of 50,000–75,000/cm2 and examined at 20°C–22°C at DIV 10–14. Whole-cell patch-clamp recordings were obtained using a double EPC-10 amplifier (HEKA Elektronik, Germany) and an Olympus BX51 microscope. Series resistance was 3–5 MΩ and was compensated by 50%–70% during recording. The pipette solution contained 137 mM K-Gluconate, 10 mM NaCl, 10 mM HEPES, 5 mM Na2-phosphocreatine, 0.2 mM EGTA, 4 mM Mg2+ATP, and 0.3 mM Na+GTP, pH 7.3. The extracellular solution contained 122 mM NaCl, 2.5 mM KCl, 2 mM CaCl2, 1 mM MgCl2, 10 mM glucose, 20 mM HEPES, 20 μM bicuculin, and 2 μM strychnine, pH 7.3. For mEPSC recordings, 1 μM TTX and 50 μM DAP5 were included in the above solution. EPSCs were elicited by an extracellular stimulation electrode set at ∼200 μm away from the recorded soma, and the output of stimulation was controlled

by an isolated pulse stimulator (Model 2100, AM Systems) and synchronized by Pulse software (HEKA). The holding potential was −70 mV for all the experiments without correction of liquid-junction potential. Data were analyzed with Igor Pro 5.04. Imaging of neurons expressing synaptopHluorin or vGLUT1-pHluorin (Voglmaier et al., 2006) under the chicken-β-actin promoter was performed 13–20 days after plating, essentially as described (Mani et al., Thiamine-diphosphate kinase 2007 and Sankaranarayanan and Ryan, 2000). Neurons were subjected to electrical field stimulation at 10 Hz using a Chamlide stimulation chamber (Live Cell Instrument, Seoul, Korea) and imaged at room temperature in Tyrode’s solution containing 119 mM NaCl, 2.5 mM KCl, 2 mM CaCl2, 2 mM MgCl2, 25 mM HEPES (pH 7.4), 30 mM glucose, 10 μM CNQX, and 50 μM APV using a Nikon Eclipse Ti-E microscope with a 60× Apo (1.49 numerical aperture) objective and a EMCCD iXon 897 (Andor Technologies) camera. The average fluorescence of at least 48 fluorescent synaptic boutons was monitored over time and used to generate traces of the fluorescence signal by a custom-written macro using Igor Pro 5.04.

, 2008). Different phases of adult neurogenesis are subject to regulation by pharmacological manipulations, mostly through various neurotransmitter systems (reviewed by Jang et al., 2008). Both the dentate gyrus and olfactory bulb are enriched with inputs from many brain regions that release different neurotransmitters and neuropeptides. Among classic neurotransmitters, glutamate, GABA, and probably acetylcholine directly regulate migration, maturation, integration, and survival of newborn

neurons. In most of other cases, it is not always clear whether pharmacological manipulations act by directly affecting neural precursors and newborn DAPT mouse neurons or through indirect modulation of the niche. Interestingly, antidepressants used in clinics, through changes in serotonin and nonrepinephrine levels, increase neural progenitor proliferation, accelerate dendritic development, and enhance survival of newborn neurons in the adult hippocampus (reviewed by Sahay and Hen, 2007 and Warner-Schmidt and Duman, 2006). Our understanding of extracellular cues that regulate targeted neuronal migration, axon/dendritic development, and synapse formation during adult neurogenesis is limited.

A number of adhesion molecules (e.g., β1-integrin, PSA-NCAM, Tenascin-R) and extracellular cues (e.g., GABA, NRGs and Slits) are known to regulate the stability, motility, or directionality of neuronal migration during adult SVZ neurogenesis (reviewed Selleckchem Kinase Inhibitor Library by Lledo et al., 2006 and Ming and Song, 2005). In the dentate gyrus, reelin signaling prevents new neurons from migrating into the hilus region; loss of reelin expression from local interneurons after pilocarpine-induced seizures may explain the ectopic hilar localization of new granule cells (Gong et al., 2007). Cell-cycle regulators, transcription factors, and epigenetic

factors are major intracellular regulators of adult neurogenesis (Zhao et al., 2008). Cell-cycle inhibitors, including p16, p21, and p53, play major roles in maintaining the quiescence of adult neural precursors; deletion of these factors leads to transient activation and subsequent depletion of the precursor pool. Sequential DNA ligase activation of different transcription factors ensures proper development of adult neural precursors. Sox2 is a major mediator of Notch signaling in maintaining the precursor pool in the adult SGZ (Ehm et al., 2010). Shh appears to be a direct target of Sox2 in neural precursors and deletion of Sox2 in adult mice results in a loss of hippocampal neurogenesis (Favaro et al., 2009). Orphan nuclear receptor TLX is also required for self-renewal and maintenance of neural precursors in the adult brain, probably through a canonical Wnt/β-catenin pathway (Qu et al., 2010).

As illustrated

in the list in Table 2, this is an extremely broad question, and obviously I cannot discuss it in detail in a brief commentary such as this. However, I will note that epigenetic mechanisms may be particularly relevant to multifactorial diseases with low genetic penetrance, such as schizophrenia and depression (Petronis, 2010). Thus, epigenomically based mechanisms for these disorders may help fill a void where, historically, genomic analyses have not led to clearly identifiable causes. In addition, disorders that are triggered by just one or only a few experiences, but that are henceforth enduring, also seem likely candidates to be epigenetically mediated. In this line of thinking, disorders such as drug addiction, posttraumatic stress disorder (PTSD), epilepsy, and schizophrenia might selectively GSI-IX involve the cooptation of epigenetic mechanisms used for development and learned behavior to subserve behaviorally disadvantageous, but obdurate, behavioral change. This is the corollary to the preceding question—if epigenetic mechanisms are broadly involved in CNS disorders,

might epigenetic targets as a category be broadly applicable to drug development? This is a very active area of investigation at present, with drug discovery efforts www.selleckchem.com/products/pci-32765.html ongoing in the areas of cognitive enhancers for learning disabilities, Alzheimer’s disease, neurodegenerative disorders, schizophrenia, depression, addiction, generalized stress disorders, and PTSD (Kazantsev and Thompson, 2008, Fischer et al., 2007, Anier et al., 2010, Kilgore et al., 2010, Peleg et al., 2010, Renthal and Nestler, 2008, Szyf, 2009, Monsey et al., 2011 and Oliveira et al., 2012). Some aspects of this question are among the most contentious areas in the epigenetics field at present. Broadly speaking, epigenetic transgenerational effects come in two flavors. The first type is not transgenerational in the heritable sense, but rather is experience dependent. For example, Michael Meaney’s group, his collaborators, and scientific

descendants have demonstrated that maternal nurturing behavior regarding newborn pups triggers DNA methylation changes in CNS glucocorticoid receptors of offspring others that persist into the adult and effect behavioral change (Champagne and Curley, 2009, Weaver et al., 2004 and Weaver et al., 2005). Discovery of these experience-dependent changes in the epigenome is the prototype for the first category of transgenerational effects, and such experience-driven epigenomic changes in the CNS have been documented to occur with a number of both positive and negative environmental effects in offspring. Thus, several examples of the persisting CNS epigenomic effects on offspring of parental behavior and environmental insult have survived the rigors and skepticism of peer review (Champagne and Curley, 2009 and Roth et al., 2009).

Such findings, and the attention-for-learning account overall, are consistent with the EVC model of dACC, insofar as the signals driving top-down attention and/or increases in learning rates may be considered as control signals. Thus buy Screening Library far,

we have treated the specification of identity and intensity separately. In reality, however, the identity and intensity of the control signal must be jointly specified (see Figure 2). For example, to perform color naming in the Stroop task, the control system must specify both the identity of the control signal (the color naming task), as well as the intensity needed to overcome any conflict from the word. Such circumstances are representative of a broader class of conditions often described as default override. In general, this refers to situations in which the task to be performed is less automatic than the default behavior in that circumstance; that is, the behavior that Tariquidar in vivo would normally occur in absence of control and is the source of conflict. Under such circumstances, adequate control is needed

to override the default response, and execute the specified task (see, e.g., Shah and Oppenheimer, 2008). Some of the earliest neuroimaging studies of cognitive control established a role for dACC in overriding default behavior (e.g., Paus et al., 1993). The dACC’s involvement in overriding defaults has been seen Ergoloid not only when the participant is explicitly instructed to perform the nondefault behavior, but also when they voluntarily make a choice

that runs counter to current task- or context-defined defaults, including choices to go against gain versus loss frames (De Martino et al., 2006), against the status quo (Fleming et al., 2010), against Pavlovian response-outcome associations (Cavanagh et al., 2013), or against a group decision (Tomlin et al., 2013). Three additional circumstances that involve default override, and that have begun to attract considerable attention, are exploration, foraging, and intertemporal choice. Exploration. This refers to behavior that favors information gathering with the prospect that this will yield greater future reward, over the pursuit of behavior with known and usually more immediate reward (i.e., exploitation). People generally exhibit a strong bias toward the pursuit of more immediate reward, so exploitation can be considered the default. Choosing to explore therefore requires overriding this default, and thus allocating control. Accordingly, the EVC model predicts that exploration should engage dACC (for related accounts, see Aston-Jones and Cohen, 2005 and Khamassi et al., 2010). This prediction was borne out in a study carried out by Daw et al. (2006). Participants chose among four options providing probabilistic reward that varied gradually over time.

Transfection of CNIH-2 alone did not rescue synaptic AMPA receptors whereas transfection with γ-8 produced mEPSCs that decayed with a τ of ∼2.5 ms (Figure 7D). Importantly, coexpression of CNIH-2 with γ-8 slowed mEPSCs (τ∼4 ms) and did not have significant effects on amplitude relative to wild-type or γ-8-transfected stargazer granule cells (Figure 7D). Taken together, these results show that CNIH-2 can modulate decay kinetics of synaptic AMPA receptors through synergic actions with γ-8-containing receptors. We next evaluated

for CNIH-2 modulation BKM120 mouse of cyclothiazide (CTZ) actions on kainate-evoked currents (IKA) from AMPA receptors, for which the hippocampal neuronal phenotype has yet to be recapitulated with coexpression of GluA and TARP subunits. Previous studies CX-5461 mouse found that CTZ potentiates kainate-evoked currents ∼2-fold in hippocampal neurons (Patneau et al., 1993), whereas in oocytes injected with GluA1 + γ-8, CTZ augments kainate-evoked currents by only ∼40% (Tomita et al., 2007a). In the present studies, CTZ minimally potentiated kainate-evoked currents from GluA1o/2 + γ-8 (Figures 8A5 and 8B). By contrast, CTZ potentiation of kainate-evoked currents for GluA1o/2 alone was ∼12-fold (Figures 8A1 and 8B), which was not significantly different from

CTZ-potentiated kainate-evoked currents from GluA1o/2 + CNIH-2 (∼7-fold). Importantly, coexpression of CNIH-2 with γ-8 modulated GluA1o/2 receptors to yield CTZ potentiation of kainate currents of ∼2-fold, which was quantitatively similar to that observed in acutely isolated hippocampal neurons (Figures 8A3, 8A6, and 8B). The effect of CNIH-2 on CTZ-mediated potentiation of kainate-evoked currents was sensitive to a 50% reduction in the amount of CNIH-2

transfected, which minimized the potentiation of kainate currents to near γ-8 alone levels (Figure 8A4). These data suggest that CNIH-2 stoichiometry in AMPA receptors may modulate CTZ pharmacology (Figure 8B). Furthermore, this requirement for both γ-8 and CNIH-2 to produce hippocampal AMPA receptor-like kainate/CTZ pharmacology was also observed for transfections with GluA1i/GluA2 heteromeric receptors (Figure S7). Cultured hippocampal neurons transfected with CNIH-2 all shRNA exhibited reduced CTZ potentiation of IKA (Figure 8B). CNIH-2 knockdown also produced resensitization in only one out of nine hippocampal neurons (data not shown), supporting the hypothesis that complete elimination of CNIH-2 expression is necessary to reveal γ-8-mediated resensitization, whereas a graded stoichiometric mechanism likely explains the effect of CNIH-2 on kainate/CTZ pharmacology. Collectively, these results indicate that γ-8 and CNIH-2 are required to recapitulate native hippocampal AMPA receptor complexes.

, 2005); thus, in general, the two modes of Eph and ephrin interaction outlined in our model do not appear to require axon-axon interaction. However, it is still worth considering whether

repulsive forward signaling from, for example, ephrinAs on medial LMC axons to EphAs on lateral axons might contribute to their segregation prior to entry into the limb mesenchyme ( Lance-Jones and Landmesser, 1981a). Our data suggest that Ephs and ephrins reside in three types of microdomains or patches in LMC neuron growth cones: (1) Eph only or (2) ephrin only microdomains in growth cones expressing low levels Tyrosine Kinase Inhibitor Library of ephrins and (3) microdomains containing both Ephs and ephrins in growth cones expressing high levels of ephrins. Our observation that a knockdown of ephrin leads to a redistribution of Ephs and ephrins to Eph- or ephrin-exclusive

patches suggests that ephrin protein expression levels control the relocalization of Ephs and ephrins, which in turn shifts the balance between cis-attenuation and parallel trans-signaling. Although the detailed mechanism of how ephrin levels mediate Eph/ephrin redistribution remains to be clarified, when compared with the compacted and highly ordered Eph-ephrin complex assembled to generate a trans-signaling center, Ephs are loosely packed in the absence of trans-interaction ( Brückner et al., 1999), and thus are possibly more susceptible to cis-binding by ephrins. Regardless of which Eph protein domains are bound by ephrins in cis, the attenuation of ephrin:Eph forward signaling might be caused by intercalation selleck chemical of ephrins

into Eph domains, leading to diminished degree of Eph receptor clustering, an event essential for downstream signaling ( Egea et al., 2005). Similarly, our observation that some neurons express high levels of ephrins, possibly in excess and therefore unbound to Ephs in cis, also raises the question of whether such free ephrins might elicit attractive reverse signaling in response to EphAs provided in trans. In medial LMC neuron growth cones expressing high levels of ephrin-As, we fail to find any obvious ephrin-A-only isothipendyl microdomains or attractive ephrin-A reverse signaling. This suggests that in these neurons, free ephrin-As might be dispersed throughout the cell surface without compact clustering that is prerequisite for reverse signaling in response to EphAs in trans ( Palmer et al., 2002). In order to terminate signaling or to allow subsequent signaling events, the Eph-ephrin complexes can be removed from the cell surface by endocytosis (Marston et al., 2003 and Zimmer et al., 2003) implicating ephrin cleavage (Hattori et al., 2000 and Janes et al., 2005). Our observation indicating Eph and ephrin cis-interactions raise the question of whether microdomains containing both Ephs and ephrins reside on the cell surface or whether they are present intracellularly.

When the MC was in the low firing rate regime, a clear increase in firing could be observed during light stimulation, followed by a decrease (Figure 6C). When the same MC was firing at a higher rate, excitation was less prominent (Figure 6D). We analyzed the significance of the excitatory effect by comparing our data to 100,000 randomly aligned

histograms (see Experimental Procedures for details). We found three of six cells to have a significant excitatory response (p < 0.01). Population analysis of these experiments, with the firing rate of each cell normalized to the prestimulus period, is shown in Figures 6E and 6F. With 240 pA current injection, AON input had a dual effect consisting VE-821 ic50 of a brief increase in firing probability followed by a more prolonged decrease. On average firing probability was increased

to a peak of 9.5 ± 11.3 times the baseline with a latency of 7 ± 1.7 ms (n = 6; Figure 6E). The average firing in the 10 ms periods of light stimulation was 5 ± 7.8 times the rate during the 10 ms right before stimulation (n = 6, p < 0.01, rank-sum test). In the 15 ms following light stimulation, firing was reduced to 0.4 ± 0.5 of baseline values (p < 0.05, rank-sum test) (Figure 6E). With 300 pA, Selleck Epigenetic inhibitor AON input had a smaller effect on firing probability during light stimulation, increasing it to a peak of 2.0 ± 0.5 times the baseline, and an average increase of 1.8 ± 0.7 times baseline values in the 10 ms period of light stimulation (n = 6; p < 0.01, rank-sum test). The inhibitory effect with 300 pA was manifested as a decrease of the average firing rate to 0.5 ± 0.5 of baseline Metalloexopeptidase values (p < 0.05, rank-sum test; Figure 6F).

This inhibition was followed by a rebound increase in firing rate presumably due to the intrinsic biophysical properties of MCs (Balu and Strowbridge, 2007). These results indicate that AON inputs can have multiple effects on MCs, depending on their ongoing activity, in part due to the newly discovered direct excitatory inputs. We next tested the functional significance of the AON inputs to MCs in vivo. We used tungsten electrodes to record the activity of single MCs from the dorsal OB in anesthetized rats 2–4 weeks postinjection of the virus. Breathing was continuously monitored with a piezoelectric belt that was wrapped around the rat’s torso and a light stimulus consisting of a pair of 40 ms stimuli, separated by 50 ms, was delivered every 15 s. Putative MCs/TCs were identified based on their depth and their strong breathing related firing pattern (Macrides and Chorover, 1972). Previous studies have noted that GCs are not visible to extracellular electrodes (Kay and Laurent, 1999; Rinberg et al., 2006; Doucette et al., 2011). Figure 7A shows an example of such an experiment. Single units were identified by stereotyped spike waveforms identified using cluster analysis (Figure 7A1). Figure 7A2 shows five traces aligned by the light stimulus (blue square).