Remarkably, mitral cell responses under anesthesia on day 7 were indistinguishable from those observed during anesthesia on day 1; thus, the selleck expression of the plasticity induced during wakefulness was blocked when tested under anesthesia (Figure 7B). These results indicate

that the expression of the experience-dependent plasticity of mitral cell responses depends critically on wakefulness. Although previous studies reported that odor-evoked mitral cell activity is enhanced under anesthesia (Adrian, 1950; Rinberg et al., 2006b), how odor coding by mitral cell ensembles differs in the awake and anesthetized state is unclear. In this study, we show that the transition from the awake to anesthetized brain state has a dramatic impact on how olfactory information is represented by ensembles of mitral cells. By imaging large populations of mitral cells in individual mice, we find that odor-evoked ensemble

activity is much sparser and more temporally dynamic in the awake state and that anesthesia increases the density of odor representations by broadening the odor tuning of mitral cells. Importantly, we also show that the sparse and temporally dynamic ensemble activity during wakefullness is more efficient for odor population coding: compared to anesthetized brain states, fewer mitral cell responses in the awake state are required for accurate odor discrimination. BVD-523 cell line The temporal

dynamics of mitral cell ensemble activity have been proposed to contribute to odor coding (Bathellier et al., 2008; Friedrich et al., 2004; Friedrich and Laurent, 2001; Laurent et al., 1996; Mazor and Laurent, 2005). Indeed, here we demonstrate that odor classification in the awake state improves gradually as odor representations develop over time. In contrast, the temporal dynamics of odor representations are reduced in the anesthetized brain state, which contributes to a reduction in the population coding efficiency. We note that the temporal resolution of our imaging approach (∼6.3 Hz) precludes the assessment of finer temporal features of mitral Ribonucleotide reductase cell responses (Bathellier et al., 2008; Cury and Uchida, 2010; Shusterman et al., 2011). Nevertheless, our results reveal a strong temporal component to mitral cell odor representations in awake animals, which may be underestimated in recordings under anesthesia. Variability in respiratory behavior in the awake state could contribute to the temporal dynamics of mitral cell responses (Carey and Wachowiak, 2011; Verhagen et al., 2007). However, as we discuss below, the opposite effects of anesthesia on mitral cells and granule cells make it unlikely that respiration variability can fully account for the changes and we suggest that actions of local inhibitory circuits probably play an important role.

Subsequently, after three more washes, the wells were incubated with serum samples diluted in PBS-TM (1:100) for 1 h at 37 °C, with known seropositive and seronegative samples as reaction controls. The plates were then washed six times and peroxidase-labeled anti-equine IgG, diluted in PBS-TM (1:5000), was added and incubated for 1 h at 37 °C. The reaction was developed after a new washing cycle, by adding the enzyme substrate (0.03% H2O2) and chromogen (0.01 M 2,2-azino-bis-3-ethyl-benzothiazolinesulfonic acid [ABTS; Sigma Chemical Co.]) in 0.07 M citrate-phosphate buffer (pH 4.2). The optical density (OD) was read

at 405 nm after a 40 min of incubation in a plate reader (M2e, Molecular Devices, USA). The cutoff of the reaction was determined selleckchem as the mean OD of the negative control sera plus three standard SKI-606 supplier deviations. Antibody titers were arbitrarily expressed as ELISA index (EI) values, according to the formula IE = OD sample/OD cutoff, as described previously (Silva et al., 2007). Samples with EI values >1.2 were considered positive. Statistical analyses were performed using the GraphPad Prism v. 5.0 (GraphPad

Software, San Diego, USA). Distribution of the serological positivity between the tested parasites (Neospora spp., S. neurona and T. gondii) in samples of mares and foals were analyzed by frequency distribution. Correlation between the antibody levels in mares and pre-colostral foals against the three protozoa was analyzed by the Spearman correlation test. P-values <0.05 those were considered statistically significant. Of the 181 serum samples analyzed, 21.5% (39/181; Fig. 1A) of mares in parturition were positive for Neospora spp., 33.7% (61/181; Fig. 1B) were positive for S. neurona and 27.6% (50/181; Fig. 1C) for T. gondii. Of the Samples collected from pre-colostral foals had a seropositivity frequency of 9.3% (17/181; Fig. 1A), 6.6% (12/181; Fig. 1B) and 6.6% (12/181; Fig. 1C), for Neospora spp., S. neurona and T. gondii, respectively. Assessment of the association between antibody levels against the studied protozoa revealed that 7.1% (13/181) of mares tested in parturition presented specific IgG antibodies to T. gondii and

Neospora spp., 12.7% (23/181) presented double positivity to S. neurona and Neospora spp. and 10.4% (19/181) presented antibodies to S. neurona and T. gondii. With level of IgG antibodies revealed a low positive correlation between anti-T.gondii/anti-Neospora spp. IgG (rs = 0.2386; p = 0.001), anti-S. neurona/anti-T. gondii IgG (rs = 0.5650; p < 0.0001) and anti-S. neurona IgG/anti-Neospora spp. IgG (rs = 0.2953; p < 0.0001). On the other hand, distribution between IgG levels for the three protozoa evaluated in this study from pre colostral foals revealed that double positive samples to T. gondii and Neospora spp. was 6.6% (12/181) with rs = 0.2386 (p = 0.0016), to S. neurona and Neospora spp. was 6% (11/181) with rs = 0.3367 (p < 0.0001) and to S. neurona and T. gondii was 5.

Therefore, the function of UNC79 in mammalian brain may perhaps be to control the stability and trafficking of UNC80, and to determine the localization of the NALCN complex with its various isoforms, thereby indirectly affecting NALCN’s function in various neuronal compartments. In mice and humans, NALCN is expressed in the brain, spinal cord, heart, and pancreas, with the highest mRNA expression levels detected in the brain. In the brain and spinal cord, see more NALCN mRNA is widely expressed, and found in essentially all the neurons (Lu et al., 2007). The expression pattern in the nervous system suggests some fundamental

roles for NALCN, and three basic cellular functions are discussed here. The basal Na+ leak current (IL-Na) is small in most neurons, representing about 10-20 pA of whole cell current at −70 mV

in cultured mouse hippocampal neurons (Lu et al., 2007). Because of its small size, IL-Na is perhaps best measured as the change of holding currents when extracellular Na+ concentration ([Na+]e) is lowered from high (140 mM) to low (14 mM) concentrations under voltage clamping (Raman and Bean, 1997). In the cultured mouse hippocampal neurons, IL-Na can be partially blocked by TTX (∼18%, presumably contributed by the window current through NaV) and by 2 mM Cs (∼10%, likely through HCN channels). The remaining BYL719 in vivo ∼72% current can be almost completely blocked by genetic deletion of Nalcn

or by applying the non-specific NALCN blocker, Gd3+ (10 μM) ( Lu et al., either 2007). The complete elimination of IL-Na by blocking NaVs, HCNs, and NALCN suggests that, in these neurons, these three channels make the major contributions to the resting Na+ leak current, with NALCN having the largest (∼70%) contribution. This is somewhat surprising given that some of the 26 mammalian TRP channels are also found in neurons and, when expressed heterologously, they are open at RMPs ( Ramsey et al., 2006). Many of the TRP channels are used for sensory detection and it’s not clear whether they contribute basal Na+ conductance. The RMP of the Nalcn knockout hippocampal neurons is approximately 10 mV more hyperpolarized than that of wild-type neurons, and is less sensitive to change in [Na+]e. Conversely, overexpression of NALCN leads to a depolarization of ∼20 mV of the RMP ( Lu et al., 2007). In the snail Lymnaea stagnalis, knocking down NALCN in a pacemaker neuron (RPeD1) also leads to an ∼15 mV hyperpolarization of the RMP ( Lu and Feng, 2011). These studies suggest that NALCN is a major player in determining the influence of extracellular Na+ on a neuron’s basal excitability. Like Na+ and K+, extracellular Ca2+ also influences the basal neuronal excitability in many brain regions. The systemic [Ca2+] of the body (∼1.

We also analyzed Tsc1ΔE18/ΔE18 TCA projections as they traversed the striatum and entered the cortex. Similar to Tsc1ΔE12/ΔE12, there was a qualitative excess of RFP+ Tsc1ΔE18/ΔE18 TCA projections within the deep cortical layers. However, a direct comparison of Tsc1ΔE18/ΔE18 and Tsc1ΔE12/ΔE12 vibrissa barrel innervation was precluded because of their different recombination patterns. Regardless, these thalamocortical projection phenotypes in deep layers are consistent see more with disrupted neuronal processes in response to mTOR dysregulation ( Choi et al., 2008). We uncovered multiple electrophysiological

alterations upon early deletion of Tsc1. The increased input capacitance and reduced input resistance are both consistent with increased membrane as a result of cell growth. Notably, action potential dynamics were also altered, yet spike threshold potentials were unaffected. The altered action potentials of Tsc1ΔE12/ΔE12 neurons may partially compensate for the changes in passive properties. As the input resistance of a neuron falls, larger synaptic currents are required to modify membrane voltage. Mutant Tsc1ΔE12/ΔE12 neurons also have larger amplitude, briefer action potentials with normal thresholds, and rates of rise and fall that are considerably faster than normal. The maximum rate-of-rise

of an action potential is proportional to peak inward sodium current in many neurons ( Cohen et al., 1981). Therefore, these changes in spike kinetics strongly suggest that voltage-gated sodium and potassium channels are altered in the mutant cells. The spike shapes are consistent selleck compound with either higher membrane channel densities or altered single-channel properties, such as subunit composition or phosphorylation, that affect conductance and gating dynamics. In support of these possibilities, the mTOR pathway has been reported to control expression levels and subunit composition Fossariinae of some voltage-gated ion channels ( Raab-Graham et al., 2006). Multiple ion channel involvement is further suggested by changes in both the tonic and burst firing modes of mutant cells. The reduced slope of the tonic frequency/current

relationship in mutant cells is most easily explained as a consequence of their lower input resistance, while more rapid intraburst spiking is likely due to changes in ion channels. In addition to altered spike-related sodium and potassium channels, it is possible that the rapid intraburst spiking in Tsc1ΔE12/ΔE12 cells is caused by altered density or kinetics of low-threshold calcium channels. Additionally, the ectopic production of PV, a protein that acts as a slow Ca2+ buffer, in Tsc1ΔE12/ΔE12 thalamic relay neurons may disrupt internal Ca2+ dynamics, which can affect gene transcription, synaptic function, and membrane potential and could contribute to some of the physiological changes we describe ( Schwaller, 2010).

, 2007; Hasselmo and Giocomo, 2006). In addition, nAChRs expressed in deep layer pyramidal neurons may contribute to direct Lenvatinib ic50 excitation of these cells (Bailey et al., 2010; Kassam et al., 2008; Poorthuis

et al., 2012). ACh also modulates synaptic transmission in cortical circuits (Figure 3). Activation of α4β2 nAChRs on thalamocortical terminals enhances glutamate release in both sensory and association cortex (Gil et al., 1997; Lambe et al., 2003; Oldford and Castro-Alamancos, 2003), whereas activation of mAChRs on terminals of parvalbumin-expressing interneurons decreases the probability of GABA release onto the perisynaptic compartment of pyramidal neurons and therefore reduces postsynaptic inhibition of pyramidal neurons (Kruglikov and Rudy, 2008). These interneurons normally decrease the response of cortical neurons to feed-forward excitation

(Gabernet et al., 2005; Higley and Contreras, 2006), and the reduction of GABA release from these interneurons by ACh therefore enhances the ability of thalamocortical inputs to stimulate pyramidal neuron firing (Kruglikov and Rudy, 2008). In contrast, mAChRs located on pyramidal cell axon terminals suppress corticocortical CH5424802 clinical trial transmission (Gil et al., 1997; Hsieh et al., 2000; Kimura and Baughman, 1997; Oldford and Castro-Alamancos, 2003). Moreover, the ACh-mediated increased excitability of dendrite-targeting interneurons described above likely contributes to reduced efficacy of intracortical communication. The simultaneous enhancement of feed-forward inputs from the thalamus through cholinergic actions on parvalbumin-positive interneurons and suppression of intracortical feedback inputs through effects on dendrite-targeting interneurons may increase the “signal-to-noise” ratio in cortical networks, making neurons more sensitive to external stimuli. In keeping with this view, mAChR activation strongly suppresses the spread of intracortical activity, leaving responses

to thalamic inputs relatively intact (Kimura et al., 1999). Intriguingly, in the prefrontal cortex, the expression of nAChRs in deep pyramidal cells others may produce layer-specific cholinergic modulation, selectively enhancing activity of output neurons (Poorthuis et al., 2012). Although the cellular and synaptic effects of ACh described above provide a potential mechanism for the ability of ACh to increase signal detection and modulate sensory attention, a number of observations suggest that this simple model is incomplete. ACh, acting via M4 mAChRs, directly inhibits spiny stellate cells in somatosensory cortex receiving thalamic input (Eggermann and Feldmeyer, 2009). Furthermore, activation of M1 mAChRs hyperpolarizes pyramidal neurons via a mechanism dependent on fully loaded internal calcium stores that occurs more quickly than the closure of M-type potassium channels (Gulledge et al., 2007; Gulledge and Stuart, 2005).

We found that the DIMD shows a nearly identical activity profile to the DCMD ( Figures 7C and 7D). There was no significant difference in the amplitude of the peak firing rate between the two neurons ( Figure S5A) except at l/|v| = 10 ms. The DCMD peak firing rate, however, occurred slightly earlier than the DIMD for small l/|v| values ( Figure S5B).

The simplest explanation for these results is that the DCMD and the DIMD—given its close resemblance to the DCMD—can interchangeably and equally well mediate jump escape behaviors. According to this hypothesis, because EPSPs elicited in the FETi by these neurons summate, the reduction in jump probability and the increase in variability following nerve cord sectioning would be at least partially Selleck Temsirolimus explained by the absence of one of them, resulting in delayed cocontraction and a smaller number of subsequent extensor spikes. We conclude that the DCMD is not necessary for jump escape behaviors, provided that the

second nerve cord remains intact, check details since the DIMD can presumably take over its role. Next, we selectively ablated the DCMD in one nerve cord by filling it intracellularly with 6-carboxy-fluorescein, a phototoxic dye, and shining laser light onto it (Experimental Procedures). In addition, we sectioned the other nerve cord. This allowed us to determine whether the DCMD is necessary among descending contralateral neurons for the generation of looming-evoked escape behaviors. Since other axons, including the DIMD TCL receiving input from the ipsilateral eye, should remain intact in the spared nerve cord, we used stimulation of the ipsilateral

eye as a control ( Figure 8, inset). We could successfully carry out the ablation procedure in 9 locusts (out of 40 locusts in which the procedure was attempted), as evidenced by the selective disappearance of the DCMD spikes from extracellular recordings in response to looming stimuli (Figures S6A and S6B and Laser Ablation Optical Setup). We could subsequently elicit jumps in four of these locusts. An additional five animals prepared for but did not carry out a jump in response to looming stimuli to either eye. Since these experiments were carried out without a telemetry backpack, we analyzed the jump preparation sequence in these nine locusts based on simultaneously acquired video recordings. The timing of the IJM (see Figure 1 and Figure 3), which is a proxy for the activation onset of flexor motor neurons in intact animals (Fotowat and Gabbiani, 2007), did not differ when stimulating the eye ipsi- or contralateral to the remaining nerve cord. However, it showed higher variability in response to stimulation of the contralateral eye and a lower correlation with l/|v| (Figure 8; ρcontra = 0.48, p = 0.009; ρipsi = 0.69, p < 10−9).

The responses to the sequences with deviant probability of 5% are presented in the left column of Figure 4. In the LFP recordings (Figure 4B, left), the responses to standard tones in the Random condition were mostly Fulvestrant mouse larger than in the Periodic condition (99/124 frequencies and recording locations, 80%). Furthermore, the average response to standards in the Random condition was larger than the response to standards in the Periodic condition (one-tailed

paired t test, t = 6.88, df = 123, p = 1.94∗10−10). While only a minority of the individual cases showed significant difference between the responses to standards in the two conditions, in most (34/40) of these cases the response to the standard in the Random condition was larger than in the Periodic condition. Although the tests Hydroxychloroquine purchase were not corrected for multiple comparisons, note that at a significance level of 5%, about 6/124 cases are expected to be detected by chance, much less than the 40 recording locations that were actually found. Similar results were found for the MUA (Figure 4A,

left): a majority of the cases (60/85, 71%) had larger responses in the Random than in the Periodic condition. The average response was significantly larger in the Random condition as well (one-tailed paired t test, t = 5.33, df = 98, p = 6.18∗10−7). Moreover, most of the individual (21/23) data points that had a significant difference (p < next 0.05) between the responses in the two conditions showed larger responses in the Random condition. There were again a substantially larger number of recording locations with significant differences than expected by chance for a test with a significance level of 5% (about 4/85). In contrast, the responses to the deviants did not show a consistent effect of sequence

type (Figures 4C and 4D, left). About half of the recordings showed responses that were larger in the Random than in the Periodic condition (LFP: 66/138, MUA: 36/81). In addition, the average responses were not different from each other (LFP: paired t test, t = 0.82, df = 153, p = 0.41; MUA: paired t test, t = −0.21, df = 94, p = 0.83). Finally, individual points with significant differences between the Random and Periodic responses were about equally divided above and below the diagonal (LFP: 13/21 Random > Periodic; MUA: 6/14 Random > Periodic). In conclusion, MUA and LFP responses to the standard tones showed the same tendencies as the intracellular responses when the deviant probability was 5%: the responses to standards were larger in the Random than in the Periodic condition. On the other hand, the responses to the deviants, while being possibly affected to a small extent by the type of the sequence, did not show a consistent effect. The tendencies we observed depended on the probability of the deviants. These effects can be seen in Figure 4 and are quantified in Tables 1 and 2.