, 2012), and Beta3-integrins (McGeachie et al., 2011). selleckchem The power of model system forward genetics in Drosophila has opened the door to a mechanistic understanding of presynaptic homeostasis. An electrophysiology-based forward genetic screen is ongoing, based on intracellular recordings of neuromuscular transmission, to identify mutations that prevent the homeostatic enhancement of presynaptic neurotransmitter release after pharmacological inhibition of postsynaptic glutamate receptors ( Dickman and Davis, 2009, Müller et al., 2011 and Younger et al., 2013). To date, more than 1,000 mutations and RNAi have been tested ( Dickman and Davis,

2009, Müller et al., 2011 and Younger et al., 2013). Based largely on the results of this forward genetic approach,

a model has emerged to explain how synaptic vesicle release is precisely learn more potentiated at the NMJ. Two presynaptic processes converge to potentiate vesicle fusion during presynaptic homeostasis: (1) potentiation of presynaptic calcium influx and (2) potentiation of the readily releasable pool (RRP) of synaptic vesicles (Figure 4). First, a combination of calcium imaging and genetic data demonstrate that an increase in presynaptic calcium influx through the CaV2.1 calcium channel is necessary to achieve a homeostatic increase in vesicle release (Müller et al., 2011 and Müller et al., 2012). A surprising mechanism Methisazone is employed to modulate presynaptic calcium influx. The involvement of a presynaptic DEG/ENaC sodium leak channel was uncovered in the aforementioned genetic screen. In the emerging model, presynaptic DEG/ENaC channel insertion at or near the nerve terminal causes low-voltage modulation of the presynaptic resting potential due to sodium leak and subsequent potentiation of presynaptic

calcium influx (Figure 5). This model is attractive because it provides an analog mechanism that could fine-tune presynaptic calcium influx according to the demands of the homeostatic signaling system. Low-voltage modulation of neurotransmitter release has been observed in systems ranging from the crayfish NMJ to the rodent hippocampus (Wojtowicz and Atwood, 1983, Awatramani et al., 2005 and Christie et al., 2011), although links to homeostatic plasticity have not been made in these systems. Interestingly, ENaC channels can be considered as homeostatic effector proteins during the systemic control of salt balance (Lifton et al., 2001). Remarkably, the potentiation of presynaptic calcium influx alone is not sufficient to drive a homeostatic change in synaptic vesicle fusion. A parallel increase in the RRP of synaptic vesicles is required (Weyhersmüller et al., 2011 and Müller et al., 2012). An analysis of mutations in RIM (Rab3 Interacting Molecule), which blocks presynaptic homeostasis ( Figure 2C), was particularly informative.

Together, our data shows that differences between MD-astrocytes and IP-astrocytes cannot be explained by serum exposure alone and that serum exposure causes lasting gene expression changes that persist after serum withdrawal. The much closer match of cultured IP-astrocyte gene profiles to those of acutely purified astrocytes indicates that IP-astrocyte cultures are better models of astrocytes than are MD-astrocytes. We therefore assessed whether IP-astrocytes exhibited well-characterized astrocytic functions in culture. MD-astrocytes promote CNS neuron survival learn more in culture (Banker, 1980 and Wagner

et al., 2006). We asked if the cultured IP-astrocytes could similarly promote CNS neuronal survival. We purified P5 retinal ganglion cells (RGCs) by immunopanning as described in Barres et al. (1998) and added conditioned media (CM) from P1 (IP-astrocytes P1 ACM) or P7 astrocytes (IP-astrocytes P7 ACM). RGC growth media (RGC

GM) and MD-astrocytes CM (MD-ACM) were used as positive controls. In the absence of any growth factors or astrocyte-derived media, fewer than 5% of RGCs survive. Both P1 selleckchem ACM and P7 ACM (∗p < 0.05, ∗∗p < 0.01), were as strongly effective at promoting RGC survival for 3 days in culture as was MD-ACM (Figures 5A and 5B). Astrocytes are known to secrete many proteins that have been shown to be important in the CNS for instance apolipoprotein E (APOE), amyloid precursor protein (APP) and thrombospondin 2 (TSP2) (Farber et al., 1995, Mauch et al., 2001 and Christopherson 3-mercaptopyruvate sulfurtransferase et al., 2005). We verified with western blotting that ACM from MD-astrocytes and IP-astros P1 and P7 contained these three proteins. A Coomassie stain was used to verify that equivalent amounts of

protein were loaded (Figure 5C). Both P1 ACM and P7 ACM contained APOE and APP. However, only P7 ACM contained TSP2. This differential protein expression at different astrocyte ages shows that we can use this new culture system to tease apart the roles of astrocytes at different developmental time points based on our ability to purify astrocytes at different ages. Interestingly, MD-ACM contained much higher levels of APP, TSP2, and APOE, molecules known to be critical regulators of synapse formation and function (Figures 5D–5F). These results questioned whether IP-astrocytes were as capable as MD-astrocytes at inducing the formation of structural and functional synapses in culture. To directly address this question, we next tested the ability of IP-astrocytes to induce structural synapses by exposing RGCs to feeder layers of P1, P7 IP-astrocytes, MD-astrocytes or a control with no astrocytes. Neuronal cultures were stained for bassoon, a presynaptic marker and homer, a postsynaptic marker (Figure 5G). The number of colocalized puncta in each condition were quantified and we have plotted the number of colocalized puncta as a fold change over control (Figure 5H). There were significant increases in synapse number over control with MD-astrocytes (fold change = 3.12, p < 0.

Patients completed five to seven blocks during which we collected electrophysiological data continuously (on average, 6.5 blocks for the patients with epilepsy and ASD and 5.6 for epilepsy patients without ASD, resulting in 696 ± 76 trials on average). After each block, the achieved performance was displayed on a screen to participants as an incentive. BCIs

Dolutegravir chemical structure were derived as described previously (Gosselin and Schyns, 2001). Briefly, the BCIs were calculated for each session based on accuracy and RT. Only bubble trials were used. Each pixel C(x,y) of the CI is the correlation of the noise mask at that pixel with whether the trial was correct/incorrect or the RT (Equation 1). Pixels with high positive correlation indicate that revealing this pixel increases task performance.

The raw CI C(x,y) is then rescaled (Z scored) such that it has a Student’s t distribution with N-2 degrees of freedom ( Equation 2). equation(Equation 1) C(x,y)=∑i=1N[Xi(x,y)−X¯(x,y)](Yi−Y¯)∑j=1N[Xj(x,y)−X¯(x,y)]2∑j=1N(Yj−Y¯)2 equation(Equation 2) Z(x,y)=NC(x,y) N AG-014699 molecular weight is the number of trials, Xi(x,y)Xi(x,y) is the smoothened noise mask for trial i, YiYi the response accuracy or the RT for trial i and X¯(x,y) and Y¯ is the mean over all trials. The noise masks Xi(x,y)Xi(x,y) are the result of a convolution of bubble locations (where each center of a bubble is marked with a 1, the rest 0) with a 2D Gaussian kernel with width σ = 10 pixels and a kernel size of 6 σ (exactly as shown to subjects, no further smoothing is applied). Before convolution, images were zero-padded to avoid edge effects. For each session, we calculated two CIs: one based on accuracy and one based on RT. These were then averaged as Z(x,y)=[ZRT(x,y)+Zaccuracy(x,y)]/2

to obtain the BCI for each session. BCIs across patients were averaged using the same equation, resulting not in spatial representations of where on the face image there was a significant association between that part of the face shown and accurate emotion classification (Figures 4A–4C). As a comparison, we also computed the BCIs only considering accuracy (not considering RT) and found very similar BCIs (not shown). Neuronal classification images (NCI) were computed as shown in (Equation 1) and (Equation 2); however, the response YiYi and its average Y¯ was equivalent to spike counts in this case. Otherwise, the calculation is equivalent. Spikes were counted for each correct bubble trial i in a time window of 1.5 s length starting at 100 ms after stimulus onset. Incorrect trials are not used to construct the NCI. An NCI was calculated for every cell with a sufficient number of spikes. The NCI has the same dimension as the image (256 × 256 pixels), but due to the structure of the noise mask used to construct the bubbles trials it is a smooth random Gaussian field in 2D. Nearby pixels are thus correlated and appropriate statistical tests need to take this into account.

We performed WGCNA (Experimental Procedures and Supplemental Experimental Procedures) identifying 24 modules, five of which (correlation > 0.50, p < 0.05) were significantly correlated with GRN knockdown (Table S3). Two of these modules were of particular interest: the green module that contained GRN and the yellow module Sirolimus cost whose module eigengene was most correlated with GRN knockdown.

The green module contains 902 genes, and this module was then further divided into submodules (Supplemental Experimental Procedures). We focused on the submodule containing GRN (Figures 4A and 4B, left). GO of this submodule, containing 167 genes, revealed that it is primarily comprised of genes related to mitochondrial function (Table selleck kinase inhibitor S5, EASE score p < 0.001), the majority of which decrease with GRN loss. Mitochondria have been implicated as a pivotal organelle in many neurodegenerative diseases, including AD and PD (Morais and De Strooper, 2010 and Swerdlow, 2009). These data indicate that alteration in mitochondrial function is a primary effect of GRN deficiency

in the CNS and support a role for mitochondrial dysfunction in GRN-related FTD as well. The key module that may represent a cellular response to GRN loss is the yellow module, which contains 517 genes and was most correlated with GRN deficiency. We observed that this module contained a submodule with even higher correlation to GRN deficiency, so we chose to analyze this submodule as it represents the group of genes with highest correlation to GRNi (Figures 4A and 4B, right). The yellow submodule contains 241 genes, and more than 95% of these 241 genes increase with GRNi (Table S4). GO analysis of this module (Table S4) demonstrated that it was enriched in the categories of ubiquitin-mediated proteolysis (p < 0.03), Wnt signaling (p < 0.05), and apoptosis (p < 0.02). Notable highly connected (“hub”) genes in this module include Wnt signaling genes, such as FZD2, but also upregulation of proapoptotic genes like CASP9 and MGRN1, the latter a ubiquitin ligase whose depletion has been shown to cause neurodegeneration ( Chakrabarti and Hegde,

2009). Other Wnt signaling genes such as WNT1, CTNNBL1, and VANGL2 are isothipendyl also highly connected within this yellow module. Ubiquitin positive inclusions containing TDP-43 are a hallmark of GRN positive FTD ( Neumann et al., 2006), and upregulated genes within this module that are related to this pathway include the ubiquitin conjugating enzymes, UBE2C and UBE2D3. To test for upregulation of ubiquitination within this model we performed western blotting with an antipolyubiquitin polyclonal antibody, demonstrating a significant increase in polyubiquitinated proteins with GRN knockdown ( Figure S6). The upregulation of these pathways here further confirms an early increase in the ubiquitin protein stress pathways concomitant with GRN loss.

Interestingly, general stresses such as heat shock, viral infection, or translational inhibition also causes Alu to increase

( Li and Schmid, 2001). Future intersecting projects could determine whether AMD-associated events (e.g., complement activation, mtDNA damage, oxidation PF-02341066 order of lipofuscin) lead to DICER1 deficit, Alu RNA accumulation and NLRP3 inflammasome activation. One recent example of such work was the finding that complement C1q, which is present in human AMD drusen, can activate the NLRP3 inflammasome ( Doyle et al., 2012). Do other neurodegenerative disorders also have a pathophysiologic decrease in DICER1? DICER1 is well known for its key role in the biogenesis of miRNAs, which facilitate the degradation or translational inhibition of most mRNAs ( Friedman et al., 2009). Indeed, miRNA deficiency occurs in diseased but not age-matched controls in Alzheimer’s disease, Parkinson’s disease, and Huntington’s disease ( Christensen and Schratt, 2009 and Eacker et al., 2009); whether DICER1 levels are similarly decreased in neurodegenerative diseases other than AMD remains to be seen. Intriguingly, microarray data reveal a reduction of DICER1 in the hippocampus of human Alzheimer’s disease donor tissue ( Blalock et al., 2011). Interestingly, in contrast to the proposed role

of DICER1 deficit in other disorders, the phenotypic outcome of DICER1 deficiency in the experimental model of AMD was independent of miRNA Bioactive Compound high throughput screening perturbation. Instead, the accumulation of Alu RNA was the major driver of RPE toxicity. Based on this finding, it will be interesting to see if Alu RNA plays a role in the expanding compendium of diseases that are defined by DICER1 deficit. Even though perturbation of miRNA maturation appeared to be dispensable for RPE cell health in the DICER1 deficit-induced animal model of AMD, miRNAs might still play a key role in determining the cell viability of RPE. Importantly, in

during the Kaneko et al. study, the mice were not exposed to the various stressors implicated in AMD—perhaps miRNA perturbation in AMD serves a key role only when coupled with some other RPE insult. Notably, miRNA expression regulates AMD-associated events, including inflammation (O’Neill et al., 2011) and angiogenesis (Sen et al., 2009) (Figure 4). Finally, DICER1 regulation of gene expression might also be achieved by miRNA-independent mechanisms, such as Dicer-dependent chromatin modifications (Woolcock et al., 2011); also, the Alu RNAs that accumulate in DICER1 deficiency may modulate translation ( Häsler and Strub, 2006) or repress gene and miRNA transcription ( Yakovchuk et al., 2009). In conclusion, there is great potential for DICER1 to mediate the intersection of multiple AMD-associated mechanisms of disease ( Figure 4). To be sure, there is no shortage of future research directions that revolve around the broad-reaching functions of DICER1.

, 1968 and Peters et al., 1968). While the molecular composition of this granular layer is not fully understood, by analogy with nodes of Ranvier it is thought to contain a high density of voltage-gated channels together with specialized anchoring proteins important for action potential generation. In addition, the AIS of some neuronal cell types, such as cortical pyramidal neurons, receives

synaptic input (Figure 1) (Somogyi et al., 1998). Experiments in the 1950s proposed that action potentials (APs) are initiated in the proximal axon, at either the axon hillock (Fuortes et al., 1957) or the initial segment (Araki and Otani, 1955 and Coombs et al., 1957). Aided by advances in electrical and optical recording techniques, recent data have provided direct evidence in support of these early observations,

showing that APs are initiated at the distal end of the AIS Bioactive Compound Library in a large range of neuronal cell types. These studies have in addition revealed that the AIS is not just a trigger zone for AP generation, but also plays a key role in regulating the integration Pexidartinib order of synaptic input, as well as intrinsic excitability and transmitter release. In this review we focus on the detailed electrical properties of the AIS and describe how these unique properties influence synaptic integration and shape neuronal output. We refer the reader to excellent recent reviews on the physiology of the axon proper and the molecular structure of the AIS (Debanne et al., 2011 and Rasband, 2010). While it why has long been thought that APs are initiated in the AIS of neurons in the mammalian CNS, this is not the case in all species. For instance, multipipette recording and voltage-sensitive dye imaging indicate that AP initiation in invertebrate neurons can occur at multiple locations, which can act independently (Calabrese

and Kennedy, 1974, Maratou and Theophilidis, 2000, Meyrand et al., 1992, Tauc, 1962 and Zecević, 1996). These studies indicate that invertebrate neurons lack the functional polarization found in neurons of the mammalian CNS (Rasband, 2010). It is therefore relevant to ask why and when in evolution did neurons develop an AIS, thereby defining a single locus for AP generation? Insights into the evolution of the AIS have been obtained by studying the gene sequences of Na+ and K+ channels, which are localized to the AIS via an interaction with the cytoskeletal scaffolding protein Ankyrin G. Ankyrin G, widely used as a marker for the AIS, is restricted in expression to the AIS and nodes of Ranvier and required for targeting of voltage-gated Na+ channels to the AIS (Kordeli et al., 1995 and Zhou et al., 1998). This occurs via an interaction between Ankyrin G and a conserved nine amino acid sequence in the II-III domain of Na+ channels (Garrido et al., 2003).

Nonetheless, performance may have improved for the focal cue because working memory was needed

only to hold the relevant one item instead of all four items with the distributed cue. If this account holds, it raises the question of what process acts to exclude irrelevant information from working memory in the focal cue condition. One possibility is that efficient selection in a matter akin to what we have formulated here acts as a gatekeeper that excludes irrelevant information from working memory. Indeed, exclusion of irrelevant items in working memory is a key factor improving performance in working memory tasks (Vogel et al., 2005), thus suggesting that attentional enhancement in the form of efficient selection may be a key process in determining the efficacy of working C646 concentration memory. Whether attention improves performance through sensory

enhancement or efficient selection may critically depend on the types of tasks used to probe attentional effects. Sensory enhancement and efficient selection are not mutually exclusive, rather they are both likely to contribute to the computational processes by which attention improves performance (Eckstein et al., 2000, Lu and Dosher, 1998 and Palmer et al., 2000). On the one hand, many experiments have limited the number NLG919 of behaviorally relevant stimuli; for example by presenting one or two stimuli on a blank background, thus limiting demand on the neural processes that isothipendyl govern the efficiency of selection (Carrasco et al., 2000, Lu and Dosher, 1998, Morrone et al., 2002 and Pestilli et al., 2009). For these types of tasks, the bottleneck in performance may therefore be in the fidelity of the stimulus representation. Correspondingly, single-unit studies using such tasks have reported signal enhancement in the form of gain changes (Martinez-Trujillo and Treue, 2002, McAdams and Maunsell, 1999, Reynolds

et al., 2000 and Williford and Maunsell, 2006), and reductions of correlated noise (Cohen and Maunsell, 2009 and Mitchell et al., 2009). On the other hand, tasks in which the relevant signals must be selected out of many possible alternatives place higher demands on selection efficiency (Eckstein et al., 2000, Palmer et al., 2000 and Pelli, 1985). For these tasks, the bottleneck in performance may not be the fidelity of the stimulus representation, but the efficiency of selection. Moreover, tasks in which relevant and irrelevant stimuli are placed in near proximity to each other may result in selection of relevant signals and suppression of irrelevant signals at stages of the visual system in which both stimuli are within the same receptive field (cf. “biased-competition”; Desimone and Duncan, 1995).

Typical colonies were subjected to biochemical tests for confirmation according to the Compendium of Methods for the Microbiological Examination of Foods from the American

Public Health Association ( Labbé, 2001). After homogenization and dilution in peptone water 0.1% w/v, the slurries diluted mortadella samples were subjected to heat treatment at 75 °C for 20 min to inactivate the viable cells and activate the dormant spores. Subsequently, 1 ml aliquots of appropriate dilutions were inoculated in a series of three tubes containing culture medium Reinforced Clostridial Medium (RCM, Oxoid Ltd., England, UK) and covered with a thioglycollate agar seal (2% agar and 0.1% sodium thioglycollate) for generation of anaerobic atmosphere. The tube’s series were incubated at 37 °C for 7 days with periodic evaluations every 24 h. Tubes with characteristic growth (turbidity and gas production) were considered positive and interpreted in the appropriate MPN tables selleck chemical (Most Probable Number). The results are expressed in MPN of spores per gram of sample (MPN/g) (Scott et al., 2001). C. perfringens counts were taken in mortadella (control samples) produced without inoculum of the target organism to verify the contamination of samples, which may result in interference of the observed results. Total plate count (Plate Count Agar PCA, HiMedia, India) 37 °C for 24 to 48 h, was estimated. Treatments were arranged in split plot factorial

designs with different Bleomycin EO concentrations (0.0%, 0.78%, 1.56% and 3.125%) and nitrite levels (0 ppm, 100 ppm and 200 ppm) for the plots and times

of storage (1, 10, 20 and 30 days) for the subplot. The data were obtained from three independent experiments and the means were from triplicate results. The data obtained were subjected to analysis of variance (ANOVA), much and the comparison between means was determined by Scott–Knott test adopting a 5% significance level. The statistical analyses of data were carried out using statistical R software (2010). The EO of winter savory (S. montana L.) was subjected to a detailed GC–MS analysis to determine its chemical composition. As shown in Table 1, 26 compounds were identified representing 99.48% of the total EO. The average extraction yield of the S. montana EO was 0.47% (4.7 ml/kg of spice dried aerial parts) in a MFB. The major groups of the compounds were monoterpene hydrocarbons and phenolic compounds. Thymol (28.99%), p-cymene (12.00%), linalool (11.00%) and carvacrol (10.71%) were found to be the major chemical constituents of the investigated EO. The observed values for the diameter of inhibition zones in determining the MIC of EO on C. perfringens are shown in Fig. 1. The evaluated variable (concentration) was significant (p = 7.25e− 06), with larger inhibition zones at higher concentrations of the savory EO. We observed formation of inhibition zones at concentrations higher than 1.

The small changes in transmission that we observe are likely to be secondary to changes in NMJ morphology. To this point, the phenotypes caused by loss of hts/adducin strongly resemble the effects observed following loss of presynaptic α-/β-Spectrin ( Pielage et al., 2005) or presynaptic Ankyrin2L ( Pielage et al., 2008). This is consistent with prior demonstration that Adducin is a component of the submembranous

spectrin-Ankyrin selleck compound lattice ( Bennett and Baines, 2001). We now describe a phenotype of NMJ expansion that is completely unique to the loss of hts/adducin. The loss of Hts causes two striking phenotypes of enhanced synaptic growth. First, the number of type Ib synaptic boutons is increased by approximately 50% in hts mutant animals

compared to wild-type controls. This increase in bouton number is observed in all of our mutations and is even stronger (192% compared to control) following RNAi-mediated presynaptic knockdown of Hts ( Figures 5B–5H). Furthermore, this phenotype is completely rescued by presynaptic expression of Hts-M in hts mutant animals (“pre rescue” in Figure 5G). The increase in total bouton number is particularly remarkable given that many of the NMJs that we quantified are also undergoing significant synapse retraction (see above). This aspect is reflected in the large variance of bouton number that we observe in both hts mutant and htsRNAi animals (see histogram, Figure 5H). Thus, the quantification of bouton number most screening assay likely underestimates the growth-promoting effect caused by loss of presynaptic Hts/Adducin. Based on these data, we conclude that Hts/Adducin also has a potent Linifanib (ABT-869) activity that restricts the expansion and elaboration

of the presynaptic nerve terminal. A second remarkable feature of hts mutant NMJs is the appearance of abundant, small-caliber membrane protrusions from the NMJ. These membrane protrusions retain presynaptic proteins like Synapsin and Brp and postsynaptic glutamate receptors, indicating that they may contain functional active zones ( Figures 5B, 5C, 5D, and 5F). In many cases, we observe small glutamate receptor clusters at the distal ends of these protrusions that are not yet opposed by presynaptic Brp. This suggests that these are newly forming synapses as live imaging studies previously demonstrated the appearance of postsynaptic glutamate receptors prior to the appearance of the presynaptic active zone marker Brp ( Rasse et al., 2005). Different motoneurons elaborate terminals of different caliber at the Drosophila NMJ. The type Ib boutons are large-diameter boutons. The type Is boutons often coinnervate muscles with type Ib. The type II and type III boutons are much smaller caliber boutons and express peptide neurotransmitters. The small-caliber protrusions that we observed originate from existing type Ib boutons, demonstrating that these protrusions represent altered growth of type Ib processes.

, 1989, 1990, 1991): visual responses, strong delay activity, and postsaccadic activity (Figure S2C). In the

context of visual-saccadic tasks, the neurons seemed typical. It could be that metacognitive processing in PFC (and/or FEF) occurs in selleck kinase inhibitor specific, yet rare, neurons. FEF and PFC activity also may be more dependent on spatial parameters of the task than SEF. FEF neurons can have quite spatially restricted visual receptive and movement fields (Bruce and Goldberg, 1985), but even when we analyzed target locations confined to those fields, we found no metacognition-related effects. Our results complement a recent report that LIP activity correlated with monkeys’ tendency to opt-out of making a decision (Kiani and Shadlen, 2009), suggesting that the activity signals confidence. Both the fundamental task design and the visual stimuli used in the LIP study differed from those used here. Moving-dots stimuli (Kiani and Shadlen, 2009) require evidence accumulation over time, but the decision stage of our task requires detection of a single brief stimulus. A possible advantage HIF inhibitor of our task is that its brief stimulus presentation demands a more immediate monitoring of the decision to guide the eventual metacognitive judgment. Given the short latency at which the metacognitive signals separated and the long duration of the separation,

SEF neuronal activity seems to transcend general confidence and correspond more to monitoring of the monkeys’ percept. Another possible advantage of our task is that we were able to establish that the metacognition-related signals in SEF represented processes beyond reward anticipation, which was less clear in LIP using the opt-out task (Kiani and Shadlen, 2009) or in OFC using a delayed reward task (Kepecs et al., 2008). Studies of metacognition naturally lead to questions about broader implications. One interpretation is that metacognition is associated with conscious awareness (Nelson, 1996), but we favor a more conservative view that self-monitoring does not presuppose self-awareness

(Reder and Schunn, 1996). As we argued previously (Middlebrooks and Sommer, 2011), metacognition may Ergoloid be to cognition as corollary discharge is to action; both describe the ability of the brain to internally monitor its operations. Just as it appears that all animals that move have internal circuits for monitoring their movements (Crapse and Sommer, 2008), all animals with even rudimentary cognitive abilities may monitor those abilities. This monitoring ability, however, does not necessarily imply states of self-awareness anywhere near the levels experienced by humans. Two male rhesus monkeys (labeled N, 6.6 kg, and S, 6.0 kg) were surgically prepared for neuronal recordings and eye position measurements. Using aseptic procedures, ceramic screws and an acrylic implant were affixed to the skull.