Single cells in the motor and somatosensory cortex of rats were stimulated in vivo with broadband fluctuating currents applied juxtacellularly. Unlike the DC current steps used previously, fluctuating stimulation currents reliably evoked spike trains with precise timing of individual spikes. Fluctuating currents resulted in strong cellular responses at stimulation frequencies beyond the inverse membrane time constant and the mean firing rate of the neuron. Neuronal firing was associated with high rates of information transmission, even for the high-frequency components of the stimulus. Such response characteristics were also revealed in additional experiments with sinusoidal juxtacellular stimulation. For selected cells, we could reproduce these statistics with compartmental models of varying complexity. We also developed a method to generate Gaussian stimuli that evoke spike trains with prescribed spike times (under the constraint of a certain rate and coefficient of variation) and exemplify its ability to achieve precise and reliable spiking in cortical neurons in vivo. Our results demonstrate a novel method for precise control of spike timing by juxtacellular stimulation, confirm and extend earlier conclusions from ex vivo work about the capacity of cortical neurons to generate precise discharges, and contribute to the understanding of the biophysics of information transfer of single neurons in vivo at high frequencies.
In this paper, we investigate the fundamental linkage between underwater electric potential (UEP) signatures and their related electric fields above the waterline, which are introduced as above water electric potential (AEP) signatures. As a first step, the field distribution for an underwater point source excitation (fundamental solution) is derived analytically, using an adjusted method of images. Subsequently a numerical approach is introduced, whereby the calculation of the stationary current density distribution and electrostatic fields are coupled within an FEM simulation. Simulation results are presented for the aforementioned point source, as well as for a submarine model, where the latter includes considering non-linear polarization curves to model the electrochemical behavior at the metal–seawater interface. Finally, the relevance of AEP signatures in the context of anti-submarine warfare (ASW) is discussed. Our results show that AEP signatures inevitably occur along with UEP signatures, and could therefore in principal be used to detect submerged submarines via airborne sensors. However, an estimation of the expectable signal-to-noise-ratio (SNR) suggests that AEP signatures are difficult to exploit and therefore entail a much lower risk compared to other signatures.
The underwater electric potential (UEP) signature is an electric signal, which can be exploited by naval mines to be utilized as a possible trigger indicator and may cause severe damage to the vessel and the onboard crew. Hence, knowing the UEP signature as exactly as possible can help to evaluate a possible risk of the vessel being detected by naval mines or if the UEP signature is within a noncritical region. As the UEP signature differs for changes of the corrosion protection system, the UEP signature is usually unknown for new conditions. In this work, we present a simple mathematical formulation to predict the UEP signature based on the mere use of a single reference UEP signature, and the corresponding currents, which are excited by the impressed current cathodic protection (ICCP) system. With this methodology, deviations below 10% between the maximum of the simulated UEP signature and the predicted UEP signature can be achieved, even in the presence of the nonlinear corrosion process. Furthermore, a corrosion protective coating of the propellers can significantly reduce the influence of the nonlinear corrosion process on the total UEP signature to improve the prediction accuracy of the superposition formulation as presented in this work.
Single cell stimulation in vivo is a powerful tool to investigate the properties of single neurons and their functionality in neural networks. We present a method to determine a cell-specific stimulus that reliably evokes a prescribed spike train with high temporal precision of action potentials. We test the performance of this stimulus in simulations for two different stochastic neuron models. For a broad range of parameters and a neuron firing with intermediate firing rates (20-40 Hz) the reliability in evoking the prescribed spike train is close to its theoretical maximum that is mainly determined by the level of intrinsic noise.
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