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Conductive elastomers present desirable qualities for sensing pressure in-vivo, such as high piezoresistance in tiny volumes, conformability and, biocompatibility. Many PDMS-based electrically conductive nanocomposites however, are susceptible to electrical drift following repeated stress cycles and chemical aging. Innovative approaches are needed to stabilize their percolation network against deformation to improve reproducibility and facilitate sensor calibration. One approach we propose here is to decouple the tunnelling-percolation network of HOPG nanoparticles from the incomplete viscoelastic recovery of the PDMS matrix by inserting minute amounts of insulating SiO$_2$ nanospheres. SiO$_2$ nanospheres effectively reduce the number of nearest neighbours at each percolation node switching off the parallel electrical pathways that might become activated by incomplete viscoelastic relaxation. We varied the size of SiO$_2$ nanospheres and their filling fraction to demonstrate nearly complete piezoresistance recovery when SiO$_2$ and HOPG nanoparticles have equal diameters ($\approx$400nm) and SiO$_2$ and HOPG volume fractions are 1% and 29.5% respectively. We demonstrate an in-vivo blood pressure sensor based on this bi-filler composite.

We construct neuron models from data by transferring information from an observed time series to the state variables and parameters of Hodgkin-Huxley models. When the learning period completes, the model will predict additional observations and its parameters uniquely characterise the complement of ion channels. However, the assimilation of biological data, as opposed to model data, is complicated by the lack of knowledge of the true neuron equations. Reliance on guessed conductance models is plagued with multi-valued parameter solutions. Here, we report on the distributions of parameters and currents predicted with intentionally erroneous models, over-specified models, and an approximate model fitting hippocampal neuron data. We introduce a recursive piecewise data assimilation (RPDA) algorithm that converges with near-perfect reliability when the model is known. When the model is unknown, we show model error introduces correlations between certain parameters. The ionic currents reconstructed from these parameters are excellent predictors of true currents and carry a higher degree of confidence, >95.5%, than underlying parameters, >53%. Unexpressed ionic currents are correctly filtered out even in the presence of mild model error. When the model is unknown, the covariance eigenvalues of parameter estimates are found to be a good gauge of model error. Our results suggest that biological information may be retrieved from data by focussing on current estimates rather than parameters.

Many neurological diseases originate in the dysfunction of cellular ion channels. Their diagnosis presents a challenge especially when alterations in the complement of ion channels are a priori unknown. Current approaches based on voltage clamps lack the throughput necessary to identify the mutations causing changes in electrical activity. Here, we introduce a single-shot method for diagnosing changes in the complement of ion channels from changes in the electrical activity of a cell. We developed data assimilation (DA) to estimate the parameters of individual ion channels and from these parameters reconstruct the ionic currents of hippocampal CA1 neurons to within 11% of their actual value. DA correctly predicts which ionic current is altered and by how much after we blocked the BK, SK, A and HCN channels with selective antagonists of known potency. We anticipate our assay will transform the treatment of neurological disease through comprehensive diagnosis and drug screening., Comment: 42 pages, 6 figures, 3 tables

Neural coupled oscillators are a useful building block in numerous models and applications. They were analyzed extensively in theoretical studies and more recently, in biologically realistic simulations of spiking neural networks. The advent of mixed-signal analog/digital neuromorphic electronic circuits provides new means for implementing neural coupled oscillators on compact low-power spiking neural network hardware platforms. However, their implementation on this noisy, low-precision and inhomogeneous computing substrate raises new challenges with regards to stability and controllability. In this work, we present a robust, spiking neural network model of neural coupled oscillators and validate it with an implementation on a mixed-signal neuromorphic processor. We demonstrate its robustness showing how to reliably control and modulate the oscillator's frequency and phase shift, despite the variability of the silicon synapse and neuron properties. We show how this ultra-low power neural processing system can be used to build an adaptive cardiac pacemaker modulating the heart rate with respect to the respiration phases and compare it with surface ECG and respiratory signal recordings of dogs at rest. The implementation of our model in neuromorphic electronic hardware shows its robustness on a highly variable substrate and extends the toolbox for applications requiring rhythmic outputs such as pacemakers., Comment: 14 pages, 4 figures

We theoretically investigate electrical transport in a quantum Hall system hosting bulk and edge current carrying states. Spatially varying magnetic and electric confinement creates pairs of current carrying lines that drift in the same or opposite directions depending on whether confinement is applied by a magnetic split gate or a magnetic strip gate. We study the electronic structure through calculations of the local density of states and conductivity of the channel as a function of the chirality and wave-function overlap of these states. We demonstrate a shift of the conductivity peaks to high or low magnetic field depending on chirality of pairs of edge states and the effect of chirality on backscattering amplitude associated with collisional processes., Comment: Latex files + figures. Supplementary information is available in a .pdf file

We report on the transition from magnetic edge to electric edge transport in a split magnetic gate device which applies a notch magnetic field to a two-dimensional electron gas. The gate bias allows tuning the overlap of magnetic and electric edge wavefunctions on the scale of the magnetic length. Conduction at magnetic edges - in the 2D-bulk - is found to compete with conduction at electric edges until magnetic edges become depleted. Current lines then move to the electrostatic edges as in the conventional quantum Hall picture. The conductivity was modelled using the quantum Boltzmann equation in the exact hybrid potential. The theory predicts the features of the bulk-edge cross-over in good agreement with experiment., Comment: latex file, four figures( Experiment and Theory) , Rapid Communication ( supplementary information available at https://journals.aps.org/prb/supplemental/10.1103/PhysRevB.96.081302 or upon request)