Single Boltzmanns were used here so that x0 corresponded to the half activation. I-X plots at −84 mV and +76 mV for different internal and external Ca2+ conditions were generated as above, but fit to a double Boltzmann equation (Equation 2) as: equation(Equation 2) y=Imax1+eZ1(x0−x)(1+eZ2(x0−x))where Z1 and Z2 are the slope factors and x0 represents

the operating point. Throughout the manuscript, Z2 is presented mTOR inhibitor as the slope. I-X plots were generated for +76 mV potentials by zeroing the MET traces prior to mechanical stimulus onset except when noted. For steps, adaptation time constant fits were obtained at ∼50% peak current using a double exponential decay (Equation 3): equation(Equation 3) y=y0+A1e−(x−x0)/τ1+A2e−(x−x0)/τ2y=y0+A1e−(x−x0)/τ1+A2e−(x−x0)/τ2where τ1 and τ2 are the reported decay constants and A1 and A2 are the amplitudes of respective decay components. Where needed adaptation time constants were fit with a triple exponential decay (Equation 4): equation(Equation 4) y=y0+A1e−(x−x0)/τ1+A2e−(x−x0)/τ2+A3e−(x−x0)/τ3y=y0+A1e−(x−x0)/τ1+A2e−(x−x0)/τ2+A3e−(x−x0)/τ3where KPT-330 clinical trial τ1, τ2, and τ3 are the reported decay constants and A1, A2,

and A3 are the amplitudes of respective decay components. τ3 values were limited to a maximum of 50 ms. Percent adaptation was calculated as (1− Isteady state/Ipeak) ∗ 100. Data were analyzed using jClamp and graphs created using Origin 8.6 and Adobe Illustrator. Statistical analysis used two-tailed Student’s t tests with Excel (Microsoft). All p values presented used paired t tests with comparisons within a cell, and unpaired unequal variance tests across cell conditions. Significance (p values) are ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.005. Data are presented as mean ± SD. This work was supported by NRSA F32 DC109752 and K99 DC013299 to AWP, by DAAD (German academic exchange service) to

T.E., and by RO1 DC003896 from NIDCD to A.J.R. as well as core grant P30-44992. The authors are grateful to Benjamin Chui, who helped with the design and fabrication of silicon devices. Thanks to Gregory Frolenkov for use of his pressure clamp system. “
“Temporal coding within oscillating aminophylline neuronal networks is an important organizational principle (Buzsáki, 2006 and Schnitzler and Gross, 2005). The suprachiasmatic nucleus (SCN) is a neuronal network that controls daily rhythms in mammalian behavior and physiology (Mohawk and Takahashi, 2011). Individual SCN neurons display self-sufficient rhythms in gene expression and electrical activity (Welsh et al., 2010) generated at the molecular level by interacting feedback loops involving the transcription and translation of clock genes (e.g., period2) ( Takahashi et al., 2008). The network-level properties of the SCN sustain robust and coherent oscillations at the population level ( Welsh et al., 2010), and intercellular interactions appear to be absent in most non-SCN tissues ( Stratmann and Schibler, 2006).