Characterization of acid-sensing ion channels in dorsal horn neurons of rat spinal cord

Acid-sensing ion channels (ASICs) are ligand-gated cation channels activated by extracellular protons. In periphery, they contribute to sensory transmission, including that of nociception and pain. Here we characterized ASIC-like currents in dorsal horn neurons of the rat spinal cord and their functional modulation in pathological conditions. Reverse transcriptase-nested PCR and Western blotting showed that three ASIC isoforms, ASIC1a, ASIC2a, and ASIC2b, are expressed at a high level in dorsal horn neurons. Electrophysiological and pharmacological properties of the proton-gated currents suggest that homomeric ASIC1a and/or heteromeric ASIC1a + 2b channels are responsible for the proton-induced currents in the majority of dorsal horn neurons. Acidification-induced action potentials in these neurons were compatible in a pH-dependent manner with the pH dependence of ASIC-like current. Furthermore, peripheral complete Freund's adjuvant-induced inflammation resulted in increased expression of both ASIC1a and ASIC2a in dorsal horn. These results support the idea that the ASICs of dorsal horn neurons participate in central sensory transmission/modulation under physiological conditions and may play important roles in inflammation-related persistent pain.

Nearest neighbour decoding and pilot-aided channel estimation in stationary Gaussian flat-fading channels

We study the information rates of non-coherent, stationary, Gaussian, multiple-input multiple-output (MIMO) flat-fading channels that are achievable with nearest neighbour decoding and pilot-aided channel estimation. In particular, we analyse the behaviour of these achievable rates in the limit as the signal-to-noise ratio (SNR) tends to infinity. We demonstrate that nearest neighbour decoding and pilot-aided channel estimation achieves the capacity pre-logwhich is defined as the limiting ratio of the capacity to the logarithm of SNR as the SNR tends to infinityof non-coherent multiple-input single-output (MISO) flat-fading channels, and it achieves the best so far known lower bound on the capacity pre-log of non-coherent MIMO flat-fading channels. © 2011 IEEE.

Is the assumption of perfect channel-state information in fading channels a good assumption?

Fading channels, which are used as a model for wireless communication, are often analyzed by assuming that the receiver is aware of the realization of the channel. This is commonly justified by saying that the channel varies typically slowly with time, and the receiver is thus able to estimate it. However, this assumption is optimistic, since it is prima facie not clear whether the channel can be estimated perfectly. This paper investigates the quality of this assumption by means of the channel capacity. In particular, results on the channel capacity of fading channels are presented, both when the receiver is aware of the realization of the channel and when it is aware only of its statistics. A comparison of these results demonstrates that information- theoretic analyses of fading channels that are based on the assumption that the receiver is aware of the channel's realization can yield helpful insights, but have to be taken with a pinch of salt. ©2009 IEEE.

Channels that heat up

This paper considers an additive noise channel where the time-κ noise variance is a weighted sum of the squared magnitudes of the previous channel inputs plus a constant. This channel model accounts for the dependence of the intrinsic thermal noise on the data due to the heat dissipation associated with the transmission of data in electronic circuits: the data determine the transmitted signal, which in turn heats up the circuit and thus influences the power of the thermal noise. The capacity of this channel (both with and without feedback) is studied at low transmit powers and at high transmit powers. At low transmit powers, the slope of the capacity-versus-power curve at zero is computed and it is shown that the heating-up effect is beneficial. At high transmit powers, conditions are determined under which the capacity is bounded, i.e., under which the capacity does not grow to infinity as the allowed average power tends to infinity. © 2009 IEEE.