(2011) study the same cortical region as Hill et al. (2011) (vibrissa motor cortex), but their investigation takes a very different angle and they refer to the recorded region as frontal orienting field (FOF). They show that blocking neural activity in FOF/vMC interferes with a memory guided orienting task. Recordings demonstrate that a large fraction of neurons in FOF/vMC show delay activity that predicts

upcoming orienting movements and this activity occurs without an obvious relation to whisker movements (Figures 2B and 2C). They conclude that such findings corroborate a similarity between the primate frontal eye fields and the rat FOF/vMC. How similar is FOF/vMC to the primate frontal eye field? A major similarity that links LY2157299 both FEF/vMC and the primate FEF to orienting behaviors is that both areas project heavily to deep layers of the superior colliculus, a key subcortical integration site for orienting responses. Lesion data in monkeys showed that combined lesions to the superior colliculus and the FEF result in much more devastating effects on orienting than lesions to

one of the two structures alone (Schiller et al., 1980). Earlier lesion studies in rats had already indicated that FOF/vMC damage can cause neglect-like symptoms and orienting deficits (Crowne et al., 1986). The deficits in memory-guided orienting observed by Erlich et al. (2011) mirror deficits induced by interference with primate frontal eye fields, which causes lasting problems in orienting toward remembered

target locations (Dias and Segraves, 1999). Overall, trans-isomer ic50 frontal cortices seem to have a key function in generating delayed responses, which require working memory. The presence of delay activity (as demonstrated by Erlich et al., 2011; Figure 2B) is a prominent physiological characteristic of neurons in primate frontal cortices and is often regarded as a neural correlate of working memory. In summary, the work of Erlich et al. (2011) lets it appear that—in the midst of all the aforementioned confusion—decades of work on the frontal and rodent Rutecarpine cortices are beginning to converge. Sensor movements of eyes, pinnae, or whiskers are relatively simple movements, yet motor mapping implicates large parts of the frontal cortices in their control. Activity in frontal motor cortices is associated less with the fine detail of orienting movements and more so with the overall control of movements and their preparation. Modulation of neural activity is weak for simple sensor movements. The attentional/orienting deficits imposed by lesions of cortices involved in sensor movements reveal that the function of these cortices goes way beyond pure motor control. That said, a homology of rodent eye, whisker, pinna motor cortex, and primate frontal eye and pinna fields is plausible but remains to be definitively proven.

, 2008 and Sürmeli et al., 2011) ( Figure 6A). In FoxP1 CT99021 ic50 mutant mice, motor neurons establish muscular projections, but retrograde labeling from defined muscles reveals randomly dispersed spinal motor neurons ( Dasen et al., 2008 and Sürmeli et al., 2011) instead of the normally observed clustered and topographically arranged motor neuron pools ( McHanwell and Biscoe, 1981 and Romanes, 1964)

( Figure 6A). Conditional elimination of FoxP1 in motor neurons was used to assess sensory-motor connectivity profiles at postnatal stages by an anatomy-based tracing assay in an otherwise wild-type background ( Sürmeli et al., 2011). These experiments demonstrate that when cell bodies of motor neurons that share a common muscle target are stripped of FoxP1 identity, this website they no longer obey the tight specificity rules observed in wild-type and receive randomized sensory input instead ( Figure 6A). A much more stunning observation was made when sensory-motor

specificity profiles were analyzed according to dorsoventral position of motor neuron cell bodies. In FoxP1 mutant mice, only motor neurons with dorsoventral position similar to the respective wild-type motor pool receive direct sensory input from corresponding sensory afferents, whereas aberrantly positioned motor neurons escape this source of input. These findings suggest that group Ia proprioceptive afferents target dorsoventral spinal positions independent of molecular cues provided by motor neurons and point to motor neuron cell body position in a virtual spatial grid as an important factor for the regulation of specific sensory-motor connections ( Figure 6A). A spatial grid also operates to establish sensory targeting domains in the Drosophila nerve cord, implemented by gradients of signaling molecules but with fundamental differences relative to the mouse ( Tripodi and Arber, 2012). To separately assess respective contributions of molecular identity and cell body position to

the control of sensory-motor aminophylline specificity, mutations in molecular programs exclusively affecting either motor neuron pool identity or cell body position are needed. The ETS transcription factor Pea3 is expressed in two caudal cervical motor neuron pools with ventral cell body position, innervating cutaneous maximus (Cm) and latissimus dorsi (Ld) muscles, but not in a neighboring dorsal pool innervating the triceps (Tri) muscle ( Livet et al., 2002 and Vrieseling and Arber, 2006) ( Figure 6A). Cm and Tri motor neuron pools switch dorsoventral position in Pea3 mutant mice, leading to a configuration shifting the Tri pool to an aberrant ventral position secondary to Pea3 mutation in Cm motor neurons ( Figure 6A). But despite ventral cell body shift, electrophysiological analysis demonstrated that Tri proprioceptors still contact most Tri motor neurons with high accuracy ( Vrieseling and Arber, 2006).

This raises the question as to how these signaling pathways interact at DMH synapses. If CB1R activation at the presynaptic terminal precludes the effects of NO to enhance GABA release, then the application of a CB1R agonist should block the potentiation of GABA transmission by the NO donor, SNAP. Consistent with this

idea, SNAP failed to increase evoked IPSC amplitude when applied to slices that were continuously perfused with WIN 55,212-2 (104% ± 12.6% of WIN 55,212-2, n = 5, p = 0.646; Figures 5A and 5C). Similarly, it did not affect PPR (baseline: 0.961 ± 0.119; post-drug: 0.883 ± see more 0.178; p = 0.544) or CV (baseline: 0.502 ± 0.071; post-drug: 0.500 ± 0.045; p = 0.962). Conversely, WIN 55,212-2 still effectively depressed IPSCs that were first potentiated by SNAP (36% ± 12.0% of SNAP,

n = 7; Figures 5B and 5D). This change was accompanied by an increase in PPR (baseline: 0.663 ± 0.109; post-drug: 0.950 ± 0.099; p = 0.048) and CV (baseline: 0.332 ± 0.084; post-drug: 0.593 ± 0.117; p = 0.049), consistent with the effect of WIN 55,212-2 in the absence of SNAP. Interestingly, the onset of the WIN 55,212-2–induced LY294002 in vitro depression was accelerated in the presence of SNAP when compared with WIN 55,212-2 alone, as evidenced by a decrease in the decay constant of the depression after drug application by approximately 80% (from 13.0 ± 2.8 min to 2.5 ± 0.7 min; Figure 5D). These data suggest that activation of CB1Rs attenuates the NO-induced increase in GABA release, whereas NO itself enhances the effects of a CB1R ligand. Next, we conducted experiments to determine the consequences of NO production on eCB-mediated LTDGABA. When NO synthesis was inhibited by L-NAME, HFS (100 Hz for 4 s ×

2, 0.05 Hz interval) failed to elicit LTDGABA (111% ± 11.3% of baseline, n = 8, p = 0.350; Figure 5E), the changes in PPR (baseline: 0.860 ± 0.086; post-HFS: 0.826 ± 0.102; p = 0.369), or the changes in CV (baseline: 0.311 ± 0.028; post-HFS: 0.336 ± Sodium butyrate 0.07; p = 0.452). This suggests that NO signaling is required either for eCB production or CB1R signaling. Consistent with the latter idea, direct activation of CB1Rs by WIN 55,212-2 in the presence of L-NAME failed to significantly depress evoked IPSC amplitude (88% ± 10.8% of baseline, n = 6, p = 0.375; Figure 5F), PPR (baseline: 0.903 ± 0.129; post-drug: 0.889 ± 0.092; p = 0.850), or CV (baseline: 0.362 ± 0.067; post-drug: 0.410 ± 0.094; p = 0.168). Overall, these data point to an inherent complexity in the signaling of the retrograde transmitters eCBs and NO in the DMH. Specifically, they argue that eCB signaling prevents NO-mediated potentiation of GABA synapses but that NO signaling is required for eCB-induced depression of GABA signaling. We have demonstrated thus far that CB1R signaling precludes NO-mediated LTPGABA in the DMH. Here, we hypothesized that a physiological state in which CB1R signaling is compromised should favor the induction of LTPGABA.

Most of these factors and their impact on the detection of MEG RSNs have been discussed in our recent publications (de Pasquale et al., 2010 and Mantini et al., 2011). Several other features of the present methodology bear discussion. First, it may be argued that, because the MCW procedure identifies epochs of high within-network correlation, NVP-AUY922 datasheet the

presently observed MEG RSNs (Figure 1) are predisposed to replicate the RSN priors derived from fMRI. This concern applies to the diagonal blocks in Figures 2 and 3 representing within-network correlation, but not to the off-diagonal blocks indicating cross-network interactions. In fact cross-network interactions are computed on node pairs that were not used to define the MCWs. Several features of the present methodology mitigate this concern. MCW identification for one seed in one subject is just the first step of the analysis. In the second step, Z-score maps are computed by testing the mean correlation value across subjects

in each voxel against the mean and variance of connectivity across the rest of the brain and over subjects. Importantly, to minimize the influence of fMRI priors, MCWs are identified using only a subset of three nodes from each network, whereas MEG RSN identification 3-MA mw requires that all Z-score maps corresponding to four distinct node subsets ( Table S2) pass a statistical criterion. For example, in the case of the DAN, left FEF was

not input to the MCW algorithm when left PIPS was a seed ( Figure 1C), and similarly, right FEF was not input when right PIPS was a seed. Thus, the maps in Figure 1 include only voxels that are both significant Montelukast Sodium over subjects and survive a logical AND conjunction over node subsets. A related analysis addresses the logical converse. Specifically, providing the MCW procedure with arbitrary combinations of nodes (“fake RNSs”) yielded very few MCWs and no MEG RNSs comparable to the results shown in Figure 1. The “fake RSN” analyses are presented in the Supplemental Information ( Figure S2A) and confirm a similar, previous control analysis (using a different arbitrary node combination; de Pasquale et al., 2010). More broadly, even if the MCW procedure may be predisposed to replicate RSNs corresponding to fMRI priors, this does not account for the frequency specificity of both within- and across-network interactions (e.g., Figure 2). A second question concerns the influence of the external node. Because the MCW procedure contrasts within- versus external-to-network correlations, it is critical to demonstrate that the presently obtained results are independent of the locus of the external node. Accordingly, we repeated several of the present analyses using a total of three different external nodes (RSFG, EXT2, and EXT3), in each case obtaining very similar results (Supplemental Information and Figure S4).

The injection pipette was not removed until 10 min after the end of the infusion to allow diffusion of the virus. Subjects for the behavioral experiment were injected with virus as described above, and dual fiberoptic cannulae (Doric Lenses) were implanted in order to have the tip of the fiberoptic cannulae (200 μm, 0.22 NA) above the left and the right LHb (A-P: −3.6 mm from bregma; M-L: ±0.75 mm;

D-V: −4.0 mm from dura) (see Figure S2) and were secured to the skull with screws and dental cement. Rats were injected subcutaneously with 5 mg/kg carprofen (NSAID) after surgery. Rats (n = 7 in ChR2-YFP group, n = 5 in mCherry control group) used for directed place preference (DPP) underwent surgery at 4–7 weeks old, and behavior experiments were conducted at least 3 weeks after Androgen Receptor signaling Antagonists surgery. DPP was carried out in a shuttle box (50 cm wide × 25 cm deep × 30 cm high; Coulbourne Instrument) equipped with a door separating the two halves and photocell detectors. Walls were modified in order to present different patterns to provide contextual differences. Photocell find more detectors allowed automatic monitoring of rat location in the cage for the duration

of testing. Optical activation of ChR2-YFP-expressing axons was performed by using an optical fiber coupled to a 473 nm solid-state laser diode (OEM Laser Systems) with 20 mW of output from the 200 μm fiber. Directed place preference was designed in order to monitor preference/aversion induced by optical stimulation of the LHb. Throughout the full duration of the test, rats were free to explore both sides of the cage. The first 10 min allowed us to measure preference for either context without manipulation. No preference was found during this first 10 min.

After this 10 min baseline period, optical stimulation (continuous 20 Hz, 5 ms pulse duration) was delivered while the animal was in one context (defined as “context A”). For the next 30 min, optical stimulation of the LHb occurred whenever the rat was located in context A. Optical stimulation was stopped when the animal was in the other side of the cage (context B). Avoidance scores were measured mafosfamide by taking time spent in context B minus time spent in context A divided by total time (120 s). Student’s t test compared avoidance score from period 10–40 min to baseline (0–10 min period). In a different set of DPP testing, pairing of the optical stimulation with context A (20 min) was switched to context B for another 20 min and then paired again with context A for the last 10 min of the 1 hr session (see schematic in Figure 3). One ChR2-YFP-expressing rat lost its cannula before the DPP reversal test, so only six ChR2-YFP-expressing rats were tested for reversal of DPP. Student’s t test compared avoidance score from periods 10–30, 30–50, and 50–60 min to baseline period (0–10 min). Two weeks after surgery, rats were anesthetized with isoflurane before decapitation and brain removal.

The authors noted that, “there is insufficient evidence to conclude that additional physical education time increases academic achievement; however, there is no evidence that it is detrimental.”16 Because studies in adults have suggested that PA may improve executive functions, a type of higher cognitive function,20 Best and Miller14 restricted their review to experimental studies that examined the effect of PA on children’s

executive functions. They found that both acute and chronic exercise may produce improvements in executive functions. Several reviews on PA, cognition and academic achievement www.selleckchem.com/products/MG132.html were published in 2011. Ahn and Fedewa12 reviewed studies on PA and several mental health outcomes, including cognitive impairment and conduct problems, and found a positive association with cognitive functions in randomized studies. Fedewa and Selleck Ribociclib Ahn19 also conducted a thorough meta-analysis of 59 studies that examined the effects of PA or fitness on academic achievement or cognitive functions. The overall effect

size was 0.32, identical to that found earlier by Sibley and Etnier.15 The greatest effects were on math achievement, intelligence quotient (IQ), and reading achievement. In a different type of review, Tomporowski et al.13 described the diverse PA interventions used to assess the effect of PA on children’s mental functions. The review summarized intervention studies on both acute and chronic PA, finding benefits to children’s academic and cognitive performance from both. The authors propose a complex meditational model by which PA may affect academic performance and advocate for studies to

integrate these multiple factors. The importance of this topic led the CDC to conduct a review of PA performed during the school day and academic achievement.6 It found half of the associations between PA and academic achievement until to be positive, with most of the others reporting null associations and only a small percentage finding negative associations. The review concluded that there is either a positive or no relationship between PA and academic performance. As the focus on academics has increased in schools, No Child Left Behind has also taken action to close the achievement gap that exists in academic performance between white and black students. Health disparities accompany the academic achievement gap, including disparities in fitness and obesity between these populations. Efrat18 reviewed seven studies that examined the relationship between PA or fitness and academic-related outcomes in minority children, and found an overall positive relationship. The most recent review17 examined 14 prospective or intervention studies that investigated the effects of PA or fitness on academics and cognition.

“
“The prefrontal cortex (PFC), with its

abundant anatomical interconnections with numerous other cortical and subcortical areas, is thought to play a key role in the integration of information from different brain regions to support various cognitive functions (Fuster, 2001 and Miller and Cohen, 2001). In particular, the PFC is thought to be a pivotal substrate for maintaining information in the absence of changing PS-341 supplier external inputs, and neuronal activity in this brain region is assumed to be critical in working memory (Goldman-Rakic, 1995 and Baddeley, 2003). It has been suggested that the PFC works in synergy with other brain regions, including basal ganglia and the hippocampus, in order to implement memory-related activity (Fuster, 2001 and Miller and Cohen, 2001). It has been shown that recruitment of memory-active neurons in the PFC depends on task-relevant dopamine release from the ventral tegmental area (VTA) neurons (Williams and Goldman-Rakic, 1995, Watanabe et al., 1997, Lewis and O’Donnell, 2000 and Schultz, 2006). Another synergistic mechanism of the PFC that interacts with other brain regions is implied by electroencephalogram (EEG) studies in humans. These experiments have demonstrated that the

power of EEG oscillations in the 3–7 Hz band, recorded from the scalp above the PFC area (called “midline frontal theta”), correlates with working-memory performance (Gevins et al., 1997 and Sauseng et al., 2010). In human studies, it has been tacitly assumed that the midline frontal theta rhythm is generated by the hippocampus (Klimesch et al., 2001, Canolty Torin 1 ic50 et al., 2006 and Fuentemilla et al., 2010). In support of this hypothesis, recent experiments in rodents have shown increased phase coupling between hippocampal theta oscillations (7–9 Hz) and PFC neuronal firing during the working-memory aspects of spatial tasks (Siapas et al., 2005, Jones and Wilson,

2005, Benchenane et al., 2010 and Sigurdsson et al., 2010). However, theta frequency oscillations in the PFC are conspicuously weak or absent (Siapas et al., 2005, Jones and Wilson, 2005 and Sirota et al., 2008), and it is unclear how the mesolimbic dopaminergic system is involved in hippocampal-PFC crotamiton coordination (Benchenane et al., 2010 and Lisman and Grace, 2005). Despite recent progress, the mechanisms underlying the temporal coordination of cell assemblies in the PFC-VTA-hippocampal system have remained ambiguous (Lisman and Grace, 2005). In this study, we performed simultaneous large-scale recordings of neuronal activities and local field potentials in the medial prefrontal cortex (mPFC), the VTA, and the hippocampus of the rat during a working-memory task. We found that a 4 Hz (2–5 Hz band) oscillation is dominant in PFC-VTA circuits and is phase coupled to hippocampal theta oscillations when working memory is in use. Both local gamma oscillations and neuronal firing can become phase locked to the 4 Hz oscillation.

We then estimated Granger causalities for each direction of influence (OFC-to-amygdala and amygdala-to-OFC)

in the frequency domain (Geweke, 1982) from the AR parameters (Brovelli et al., 2004). We examined the evolution of Granger Galunisertib cell line causality by analyzing brief segments of LFP signal starting 0.5 s before CS onset until US onset (200 ms window, stepped by 50 ms, yielding 43 steps). The short window ensured that the LFPs within it could be considered stationary. For each step, the 200 ms LFP segments from trials of the same type (positive or negative) were concatenated separately and the parameters of the AR model for the resulting time series were estimated using the Nutall-Strand method (Schlögl, 2006). We fixed the AR model order to 50, and assessed model fit by testing for lack of residual correlations (Li and Mcleod, 1981). We determined the statistical significance www.selleckchem.com/products/ch5424802.html for Granger causality at each time-frequency bin using the frequency-domain test described by Breitung and Candelon (2006). To average Granger causality

values, we first normalized these values for each pair to the value estimated for the first time window (−0.5 to −0.3 s relative to CS onset). This was performed separately for each frequency bin between 0 and 100 Hz. For each pair and trial type, we only averaged data from pairs that yielded Granger causality values with four consecutive significant time bins (p < 0.01, spanning 350 ms). To compare the Granger causality in the two different directions of influence (Figure 9A), all trials of the reversal block were combined as described above. At each time bin, the Granger causality values for all frequencies from 5 to 100 Hz were averaged together. We determined the statistical significance of the difference between the two directions (OFC-to-amygdala and amygdala-to-OFC) using a permutation test (10,000 shuffles). To assess the effect of learning on the influence between the amygdala and OFC (Figures 9B and 9C), only the six first trials of each type (12 total) after reversal

and the last six mafosfamide trials of each type in the experiment were used. Granger causality was computed for these two sets of trials, and we compared its relative magnitude in both directions during and after reversal learning. For each set of trials, the Granger causality values were averaged across pairs and trial types as described above. The difference between the mean Granger causality in the two directions was then compared for the during-learning and postlearning sets in the time domain (Figure 9B) by averaging across frequencies from 5 to 100 Hz; Figure 9C does this in the frequency domain by averaging across times from CS onset until the end of the trace interval. The significance of the difference between during-learning and postlearning was assessed by permutation test (10,000 shuffles).

Accordingly, electron microscopy analysis of neonatal DKO brain stem synapses (Figures 2C and 2D) and neuromuscular junctions (Figure S2) revealed the presence of synaptic vesicles, although buy Lumacaftor such vesicles were in general more heterogeneous in size and less numerous than in controls (see below). Clathrin-coated endocytic intermediates were also evident (Figure 2D). Furthermore, cortical neuron primary cultures derived from brains of DKO newborn mice developed and established synapses in vitro with no obvious differences from controls in morphology and synaptic

density (see below), in spite of the extremely low level of total dynamin remaining (accounted for by dynamin 2) relative to control cultures (Figure 2B). The actual contribution of neuronal Selleckchem Ku 0059436 dynamin 2 to the total dynamin pool detected in the cultures is expected to be even lower due to the presence of astrocytes, a cell type where dynamin 2 is more robustly expressed (Ferguson et al., 2007). Levels of a variety of other synaptic proteins tested by western blotting of such cultures, including clathrin coat components, other endocytic proteins, synaptic vesicle

proteins, and cytoskeletal proteins, were not changed in a significant way relative to controls (Figure 2E). However, a significant decrease was observed in the levels of Rab3, syndapin/pacsin 1, sorting nexin 9 (SNX9), as well as of parvalbumin and the vesicular GABA transporter (VGAT), two makers of GABAergic interneurons (Figure 2E). Levels of glutamic acid decarboxylase 65 (GAD65), another specific component of GABAergic neurons, were also decreased, although this decrease was just above the limit of significance (Student’s t test, p = 0.056). Loss of Rab3 may reflect excess degradation of of this protein in the absence of synaptic vesicles, whereas loss of parvalbumin, VGAT, and GAD65 may indicate selective vulnerability of GABAergic interneurons due to their high level of tonic activity. Decreased levels of syndapin and SNX9 may arise from the property of these proteins to form complexes with dynamin and, thus, their destabilization in the absence of dynamin 1 and 3, although other dynamin-interacting

proteins such as amphiphysin 1, amphiphysin 2, and endophilin 1 maintained their normal levels. Syndapin is a major dynamin-binding partner in neurons, and the partner whose interaction with dynamin 1 is regulated by Cdk5-dependent phosphorylation and calcineurin-dependent dephosphorylation of dynamin 1 (Anggono et al., 2006). Phosphorylation on conserved sites within dynamin 3 suggests that similar regulatory mechanisms may control dynamin 3 functions and interactions (Larsen et al., 2004). The properties of synaptic transmission in DKO neurons were assessed in primary neuronal cultures obtained from newborn pups because this experimental system allows neurons and synapses to undergo a maturation that is not achievable in the intact mice due to their perinatal lethality.

1 ± 9.6 spikes/s; n = 79; Figure 1D).

Consistent with earlier reports, however, these responses were barely modulated by stimulus orientation (Sohya et al., 2007, Niell and Stryker, 2008, Kerlin et al., 2010, Zariwala et al., 2011, Ma et al., 2010, Bock et al., 2011 and Hofer et al., 2011). To estimate the overall selectivity for stimulus orientation http://www.selleckchem.com/products/a-1210477.html we computed the orientation selectivity index (OSI), the ratio of the modulation in response caused by changing orientation to the average response across orientations. OSI was extremely low for PV cells (0.1 ± 0.1; n = 63), significantly lower than in Pyr cells (0.4 ± 0.2; n = 60; p < 1 × 10−11 Wilcoxon rank-sum test; Figure 1). Only 3% of PV cells, as compared to 65% of Pyr cells, had an OSI > 0.25 (Figure 1D). Furthermore PV cells were more broadly tuned than Pyr cells. To estimate the tuning sharpness we calculated the half width at half height (HWHH) of a double Gaussian fit to the tuning curve of each cell; PV cells: 52 ± 24 degrees; n = 63; Pyr cells: 42 ± 23 degrees; n = 60; p < 0.05). Finally, the contrast response function of PV cells differed in two clear ways from that of Pyr cells (Figure 1D). First, the maximal

firing rate was two times higher for PV cells than for Pyr cells (9.1 ± 5.6 spikes/s; n = 43; versus 4.5 ± 3.0 spikes/s; n = 30). Second, the increase in firing rate of PV cells with increasing contrast, captured by the exponent of the curve fitted to contrast responses, for PV cells was significantly shallower science than for Pyr cells click here (2 ± 2; n = 43; versus 3.0 ±

2.5; n = 30; p < 0.005). Thus, in contrast to a previous report (Runyan et al., 2010) the response properties to visual stimuli of PV cells differ markedly from those of Pyr cells. Next, we assessed the impact of optogenetic manipulation on the visual responses of PV cells. We recorded from Arch- or ChR2-expressing layer 2/3 PV cells at least two weeks after viral injection and illuminated the exposed cortex with a fiber-coupled LED (470 nm, Figure 2). Since strong suppression of inhibition can result in runaway activity (Prince, 1978) and strong activation of PV cells can completely silence cortical activity (not shown), we perturbed PV cell firing over a moderate range chosen to fall within the reported firing rates of these neurons in active awake mice (Niell and Stryker, 2010). Control measurements in uninjected animals established that illumination by itself did not affect visual responses (Figure S5). Photo stimulation of Arch significantly reduced the firing rate of targeted PV cells, both spontaneous (from 3.0 ± 3.5 to 1.9 ± 3.4 spikes/s; n = 31; p < 0.02 paired Wilcoxon sign-rank test) and visually evoked (from 9.2 ± 7.3 to 6.6 ± 7.0 spikes/s; n = 31; p < 0.0001; Figure S2A). PV cell firing rate decreased at all contrasts tested (Figure 2D) and was well described by a linear fit (0.6 × control rate − 0.4 spikes/s).