An isolated rabbit cardiac sinoatrial node (SAN) tissue preparation was used experimentally to map activation times and conduction velocities of extracellular cardiac action potential (AP) propagation. Extracellular recordings were carried out using a two-dimensional array of unipolar Ag-AgCl microelectrodes connected to a 128-channel data acquisition system. A 20(th) order, low-pass Butterworth filter, with a cut-off frequency of 50 Hz, was used in conjunction with a Matlab algorithm to map activation times and conduction velocities. Results show an initial slow-down of the activation wavefront emanating from the SAN, followed by acceleration in some regions, particularly near the Superior Vena Cava, as it travels towards the SAN periphery.
The noise-reduction algorithm as presented in the Methods section "sparse sampling and data recovery" 1 seeks to estimate and reduce the spectral contribution of aliased thermal noise, to improve the signal-to-noise ratio (SNR) for highly multiplexed neural recording systems, where implementing adequate antialiasing filters is a challenge.Unfortunately, because of errors described in our corrections 3 to a companion paper describing the details of these signal processing algorithms 2 , the techniques employed do not improve the SNR of high-density acquisition systems limited by noise aliasing beyond what is achievable with conventional band-pass filters.Here we summarize the incorrect statements regarding the effects of thermal noise aliasing, incorrect assertions in the paper on our ability to remove aliased noise, the consequence of these errors on the general applicability of the noise-reduction algorithm, and our revised recommendations on processing under-sampled spike (action potential) recordings.The discussion of the trade-off between area and noise for multiplexed acquisition systems, our reports on the recording array hardware and software, and the biological demonstration of the system's recording and electrical stimulation performance remain valid.We will also show here that while the algorithm does improve SNR for action potential recordings from highly multiplexed acquisition systems with incomplete antialiasing, as a result of these errors, the signal processing efforts employed deliver little utility over a linear band-pass filter in most cases.
Abstract In traditional electrophysiology, spatially inefficient electronics and the need for tissue-to-electrode proximity defy non-invasive interfaces at scales of more than a thousand low noise, simultaneously recording channels. Using compressed sensing concepts and silicon complementary metal-oxide-semiconductors (CMOS), we demonstrate a platform with 65,536 simultaneously recording and stimulating electrodes in which the per-electrode electronics consume an area of 25.5 μm by 25.5 μm. Application of this platform to mouse retinal studies is achieved with a high-performance processing pipeline with a 1 GB/s data rate. The platform records from 65,536 electrodes concurrently with a ~10 µV r.m.s. noise; senses spikes from more than 34,000 electrodes when recording across the entire retina; automatically sorts and classifies greater than 1700 neurons following visual stimulation; and stimulates individual neurons using any number of the 65,536 electrodes while observing spikes over the entire retina. The approaches developed here are applicable to other electrophysiological systems and electrode configurations.
Generic ionic models optimized to replicate experimentally recorded cardiac action potentials (APs) from the central and peripheral sinoatrial node (SAN), the natural pacemaker of the heart, as well as atrial intact-myocytes are implemented in a realistic 2D model of rabbit SAN geometry. The model was used to investigate two frequently-proposed modes of SAN architecture: the gradient and mosaic hypotheses. In a simplified gradient arrangement, the peripheral SAN region acts as a transition zone between the central SAN and atrium and is required for spontaneous rhythmic initiation of APs from central SAN into the atria. Furthermore, the application of optimized single cell parameters to the realistic 2D rabbit geometry did not accurately replicate experimentally recorded APs. On the other hand, in an adapted mosaic geometry, peripheral SAN cells were not required to produce spontaneous regular excitation.
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