Noise Characterization, Modeling, and Reduction for In Vivo Neural Recording

Studying signal and noise properties of recorded neural data is critical in developing more efficient algorithms to recover the encoded information. Important issues exist in this research including the variant spectrum spans of neural spikes that make it difficult to choose a globally optimal bandpass filter. Also, multiple sources produce aggregated noise that deviates from the conventional white Gaussian noise. In this work, the spectrum variability of spikes is addressed, based on which the concept of adaptive bandpass filter that fits the spectrum of individual spikes is proposed. Multiple noise sources have been studied through analytical models as well as empirical measurements. The dominant noise source is identified as neuron noise followed by interface noise of the electrode. This suggests that major efforts to reduce noise from electronics are not well spent. The measured noise from in vivo experiments shows a family of 1/f^x spectrum that can be reduced using noise shaping techniques. In summary, the methods of adaptive bandpass filtering and noise shaping together result in several dB signal-to-noise ratio (SNR) enhancement

Active C4 electrodes for local field potential recording applications

Extracellular neural recording, with multi-electrode arrays (MEAs), is a powerful method used to study neural function at the network level. However, in a high density array, it can be costly and time consuming to integrate the active circuit with the expensive electrodes. In this paper, we present a 4 mm × 4 mm neural recording integrated circuit (IC) chip, utilizing IBM C4 bumps as recording electrodes, which enable a seamless active chip and electrode integration. The IC chip was designed and fabricated in a 0.13 μm BiCMOS process for both in vitro and in vivo applications. It has an input-referred noise of 4.6 μV rms for the bandwidth of 10 Hz to 10 kHz and a power dissipation of 11.25 mW at 2.5 V, or 43.9 μW per input channel. This prototype is scalable for implementing larger number and higher density electrode arrays. To validate the functionality of the chip, electrical testing results and acute in vivo recordings from a rat barrel cortex are presented.R01 NS072385 - NINDS NIH HHS; 1R01 NS072385 - NINDS NIH HH

A neural probe with up to 966 electrodes and up to 384 configurable channels in 0.13 μm SOI CMOS

In vivo recording of neural action-potential and local-field-potential signals requires the use of high-resolution penetrating probes. Several international initiatives to better understand the brain are driving technology efforts towards maximizing the number of recording sites while minimizing the neural probe dimensions. We designed and fabricated (0.13-μm SOI Al CMOS) a 384-channel configurable neural probe for large-scale in vivo recording of neural signals. Up to 966 selectable active electrodes were integrated along an implantable shank (70 μm wide, 10 mm long, 20 μm thick), achieving a crosstalk of −64.4 dB. The probe base (5 × 9 mm2) implements dual-band recording and a 1

A statistical model for in vivo neuronal dynamics

Single neuron models have a long tradition in computational neuroscience. Detailed biophysical models such as the Hodgkin-Huxley model as well as simplified neuron models such as the class of integrate-and-fire models relate the input current to the membrane potential of the neuron. Those types of models have been extensively fitted to in vitro data where the input current is controlled. Those models are however of little use when it comes to characterize intracellular in vivo recordings since the input to the neuron is not known. Here we propose a novel single neuron model that characterizes the statistical properties of in vivo recordings. More specifically, we propose a stochastic process where the subthreshold membrane potential follows a Gaussian process and the spike emission intensity depends nonlinearly on the membrane potential as well as the spiking history. We first show that the model has a rich dynamical repertoire since it can capture arbitrary subthreshold autocovariance functions, firing-rate adaptations as well as arbitrary shapes of the action potential. We then show that this model can be efficiently fitted to data without overfitting. Finally, we show that this model can be used to characterize and therefore precisely compare various intracellular in vivo recordings from different animals and experimental conditions.Comment: 31 pages, 10 figure

Bioresorbable silicon electronics for transient spatiotemporal mapping of electrical activity from the cerebral cortex.

Bioresorbable silicon electronics technology offers unprecedented opportunities to deploy advanced implantable monitoring systems that eliminate risks, cost and discomfort associated with surgical extraction. Applications include postoperative monitoring and transient physiologic recording after percutaneous or minimally invasive placement of vascular, cardiac, orthopaedic, neural or other devices. We present an embodiment of these materials in both passive and actively addressed arrays of bioresorbable silicon electrodes with multiplexing capabilities, which record in vivo electrophysiological signals from the cortical surface and the subgaleal space. The devices detect normal physiologic and epileptiform activity, both in acute and chronic recordings. Comparative studies show sensor performance comparable to standard clinical systems and reduced tissue reactivity relative to conventional clinical electrocorticography (ECoG) electrodes. This technology offers general applicability in neural interfaces, with additional potential utility in treatment of disorders where transient monitoring and modulation of physiologic function, implant integrity and tissue recovery or regeneration are required

Data-driven modeling of the olfactory neural codes and their dynamics in the insect antennal lobe

Recordings from neurons in the insects' olfactory primary processing center, the antennal lobe (AL), reveal that the AL is able to process the input from chemical receptors into distinct neural activity patterns, called olfactory neural codes. These exciting results show the importance of neural codes and their relation to perception. The next challenge is to \emph{model the dynamics} of neural codes. In our study, we perform multichannel recordings from the projection neurons in the AL driven by different odorants. We then derive a neural network from the electrophysiological data. The network consists of lateral-inhibitory neurons and excitatory neurons, and is capable of producing unique olfactory neural codes for the tested odorants. Specifically, we (i) design a projection, an odor space, for the neural recording from the AL, which discriminates between distinct odorants trajectories (ii) characterize scent recognition, i.e., decision-making based on olfactory signals and (iii) infer the wiring of the neural circuit, the connectome of the AL. We show that the constructed model is consistent with biological observations, such as contrast enhancement and robustness to noise. The study answers a key biological question in identifying how lateral inhibitory neurons can be wired to excitatory neurons to permit robust activity patterns

A Novel Long-term, Multi-Channel and Non-invasive Electrophysiology Platform for Zebrafish.

Zebrafish are a popular vertebrate model for human neurological disorders and drug discovery. Although fecundity, breeding convenience, genetic homology and optical transparency have been key advantages, laborious and invasive procedures are required for electrophysiological studies. Using an electrode-integrated microfluidic system, here we demonstrate a novel multichannel electrophysiology unit to record multiple zebrafish. This platform allows spontaneous alignment of zebrafish and maintains, over days, close contact between head and multiple surface electrodes, enabling non-invasive long-term electroencephalographic recording. First, we demonstrate that electrographic seizure events, induced by pentylenetetrazole, can be reliably distinguished from eye or tail movement artifacts, and quantifiably identified with our unique algorithm. Second, we show long-term monitoring during epileptogenic progression in a scn1lab mutant recapitulating human Dravet syndrome. Third, we provide an example of cross-over pharmacology antiepileptic drug testing. Such promising features of this integrated microfluidic platform will greatly facilitate high-throughput drug screening and electrophysiological characterization of epileptic zebrafish

Computation of Interaural Time Difference in the Owl's Coincidence Detector Neurons

Both the mammalian and avian auditory systems localize sound sources by computing the interaural time difference (ITD) with submillisecond accuracy. The neural circuits for this computation in birds consist of axonal delay lines and coincidence detector neurons. Here, we report the first in vivo intracellular recordings from coincidence detectors in the nucleus laminaris of barn owls. Binaural tonal stimuli induced sustained depolarizations (DC) and oscillating potentials whose waveforms reflected the stimulus. The amplitude of this sound analog potential (SAP) varied with ITD, whereas DC potentials did not. The amplitude of the SAP was correlated with firing rate in a linear fashion. Spike shape, synaptic noise, the amplitude of SAP, and responsiveness to current pulses differed between cells at different frequencies, suggesting an optimization strategy for sensing sound signals in neurons tuned to different frequencies