These light-driven oscillations were absent in WT mice (data not shown). By driving MCs at different frequencies (from 25 to 90 Hz), the resulting LFP power exhibited a maximal response at a preferred resonant frequency in the γ range (maximum at ∼66 Hz, Figure 6E), corresponding to the dominant frequency of spontaneous γ oscillations ( Figure 6D). PTX injection (0.5 mM) Depsipeptide price significantly decreased this resonant frequency of oscillations (maximum at ∼50 Hz, Figure 6E). This shift in resonant frequency was also observed after TBOA injection

( Figure S5A). In contrast, an NMDAR antagonist caused a global reduction of light-evoked γ oscillation without changing the resonant frequency ( Figure S5B), consistent with the observed effect on spontaneous γ. Changes in evoked LFP frequency did not result simply from increased

MC firing rate. Indeed, strongly increasing MC firing activity with continuous light stimulation ( Figure 6C) failed to enhance γ power in both baseline and PTX conditions (baseline: +10.6% ± 7.8%, p = 0.455 and PTX: +10.2% ± 6.3%, p = 0.233, with a paired t test; n = 33) and has negligible effects on γ frequency (baseline: +0.77 ± 0.31 Hz and PTX: −0.70 ± 0.26 Hz; n = 33). To investigate the features of dendrodendritic inhibition, we assessed the light-evoked inhibition of MC firing activity triggered by their synchronous activation. A 5 ms light-pulse triggered synchronous spiking followed by a transient inhibition of firing that resumed within ∼10 ms (Figure 6F). This protocol elicited disynaptic inhibition as indicated by the delayed BMN 673 solubility dmso onset of the inhibition (8.8 ± 0.3 ms, n = 13) and confirmed by its partial blockade using MK801 (Figure S5C). Strikingly, reducing inhibitory tone did not modify the amplitude of the light-evoked inhibition (baseline: −60.6% ± 6.5% decrease in the firing rate and PTX: −72.1% ± 4.3%; p = 0.148, with a paired t test,

n = 13) but significantly increased the time to peak (baseline: 2.8 ± 0.4 ms and PTX: 4.0 ± 0.4 ms; p = 0.041) and the decay kinetics of MC firing inhibition (baseline: 6.3 ± 0.6 ms and PTX: 9.1 ± 0.9 ms; Idoxuridine p = 0.023; Figure 6F). Upon PTX application, neither the magnitude of light-evoked firing (baseline: +262.1% ± 24.9% increase in firing and PTX 0.5 mM: +222.6% ± 24.6%, p = 0.222; Figure 6F) nor the mean spontaneous MC firing rate (baseline: 17.4 ± 1.5 Hz and PTX 0.5 mM: 18.5 ± 1.4 Hz; p = 0.33) significantly changed, as already reported in Figure 4C. By recording MCs distant to the stimulation zone, we were also able to record light-evoked lateral inhibition of MC firing (Figure 6G). Here, the lateral inhibition was identified as a light-evoked inhibition of MC firing when light stimuli did not directly increase firing (Figure 6H). In these cells, PTX treatment did not modify the maximum amplitude of light-evoked inhibition (baseline: −69.8% ± 5.8% decrease in firing and PTX: −74.3% ± 6.6%, p = 0.

, 1998). The average peak increase in fluorescence rose smoothly with stimulation frequency in the presence and absence of GABA, but the frequency dependence of vesicle release differed in the two conditions. In comparison to control conditions, the presence of GABA severely attenuated spH signals at low stimulation frequencies but had little effect at high frequencies ( Figures 7A–7C), including when total spike number was kept constant ( Figure S7).

Thus, the presynaptic terminals of ePNs in the LH contain machinery that allows GABA to modulate vesicle release in the manner of a high-pass Romidepsin price filter ( Figure 7C). To examine whether iPNs could supply modulatory GABA to ePN terminals, we expressed a QUAS-spH transgene under GH146-QF control in ePNs and a UAS-dTRPA1 transgene under Mz699-GAL4 control A-1210477 in iPNs. dTRPA1 is a transient receptor potential channel whose Ca2+ conductance gates open at temperatures >25°C ( Hamada et al., 2008), thus stimulating iPN activity. We shifted flies between holding temperatures of 25°C and 32°C while imaging spH fluorescence

during electrical stimulation of ePN axons. Like the direct application of GABA ( Figure 7C), the thermal activation of iPNs had a frequency-dependent effect on ePN synaptic output ( Figure 7D): transmission at 130 Hz was unaffected by iPN activity, whereas transmission at 40 Hz was roughly cut in half ( Figure 7D). Thus, iPN projections to the LH regulate the transmission characteristics of ePN terminals. To simulate the impact of the inhibitory high-pass filter on odor discrimination, we passed the ePN activity vectors of 110 odors (Hallem and Carlson, 2006 and Hallem et al., 2004) through a filter with the empirically derived transmission characteristics (Figure 7C). Because iPN activity scales with the overall drive to the olfactory system (Figure 5G), the strength of the filter was adjusted linearly with the number of glomerular channels an odor activates. We assumed that the maximal blocking effect, corresponding to the transmission curve

in 50 μM GABA (Figure 7C), is achieved when ePN spike rates in 22 of the 24 characterized glomeruli exceed 30 Hz (Figure 5G). Comparisons of all 5,995 possible pairwise distances between the filtered TCL vectors with their 5,995 unfiltered counterparts showed that inhibition shifts the distributions of both Euclidean and cosine distances toward larger values (Figures 8A–8D). Replotting the data from Figure 2 against these increased ePN distances preserved the shape of the distance-discrimination function, only displacing it to the right (Figure S8). Knowledge of the transmission characteristics of the inhibitory high-pass filter should enable a prediction of WT performance from the measured behavior of flies lacking the distance-enhancing effect of the filter.

As a control, differences in firing rate between rewarded and unrewarded trials in the same block PD173074 purchase were compared using the same procedure in the interval from −1.5 to 0.5 s in the absence of odor (the prestimulus interval) to assess the effectiveness of the correction for multiple comparisons. Odors did not elicit divergent responses in this control time range

(data not shown). At test was also used to classify units as “responsive.” The rate of firing in the RA (0.5 to 2.5 s) was compared with the firing rate during the reference interval (−1.5 to 0.5 s). The FDR was used to correct for multiple comparisons, and a unit was classified as responsive only if p values fell below FDR in at least two or more blocks. We would like to thank Drs. Gidon Felsen, Nathan Schoppa, and Dan Tollin for discussions; Dr. Ed Hsu; Osama Abdulla; and the University BMS-777607 of Utah Small Animal MRI Facility. This work was funded by NIH grants DC00566 (D.R.), DC04657 (D.R.), DC008855 (D.R.), DC008066 (W.D.), and DC002994 (M.L.). “
“The neocortex is the largest part of the mammalian brain, yet its function is still poorly understood. Anatomical and physiological studies have emphasized the vertical (or “columnar”) nature of its connectivity (Hubel and Wiesel, 1977, Lorente de Nó, 1949 and Mountcastle,

1982), giving rise to the proposal that the neocortex is composed of repetitions of a basic modular unit, performing essentially the same computation on different inputs (Douglas et al., 2004, Hubel and Wiesel, 1974, Lorente de Nó, 1949 and Mountcastle, 1982). Consistent with this hypothesis, in different species and cortical

areas, the cortex develops in a stereotypical fashion (Katz and Shatz, 1996) with similar interlaminar connections (Burkhalter, 1989, Douglas et al., 2004 and Gilbert and Wiesel, 1979). At the same time, there are structural differences among cortical areas and species (DeFelipe, 1993), so each cortical region could still have a specific, dedicated circuit. Crucial to this debate is the knowledge of how different subtypes of cortical neurons connect to each other, an issue for which there is only scant available data. Although some studies find TCL great specificity in cortical connections (Callaway, 1998, Hubel, 1988 and Thomson and Lamy, 2007), others have proposed that cortical neurons connect without any specificity (Braitenberg and Schüzt, 1991 and Peters and Jones, 1984), forming perhaps a neural network, or a “tabula rasa,” on which activity-dependent developmental rules could sculpt mature circuits (Kalisman et al., 2005, Rolls and Treves, 1998 and Stepanyants et al., 2002). To measure the specificity in cortical connections, one would need techniques that reveal synaptically connected neurons. In the last decade, electrophysiological recordings from connected cortical neurons in brain slices (Thomson et al.