Neural Spike Research Articles

A Synchronous Neural Recording Platform for Multiple High-Resolution CMOS Probes and Passive Electrode Arrays.

Electrophysiological signals in the brain are distributed over broad spatial and temporal scales. Monitoring these signals at multiple scales is fundamental in order to decipher how brain circuits operate and might dysfunction in disease. A possible strategy to enlarge the experimentally accessible spatial and temporal scales consists in combining the use of multiple probes with different resolutions and sensing areas. Here, we propose a neural recording system capable of simultaneous and synchronous acquisitions from a new generation of high-resolution CMOS probes (512 microelectrodes, 25kHz/electrode whole-array sampling frequency) as well as from a custom-designed CMOS-based headstage. While CMOS probes can provide recordings from a large number of closely spaced electrodes on single-shaft devices, the CMOS-based headstage can be used to interface the wide range of available intra- or epi-cortical passive electrode array devices. The current platform was designed to simultaneously manage high-resolution recordings from up to four differently located CMOS probes and from a single 36-channels low-resolution passive electrode array device. The design, implementation, and performances for both ICs and for the FPGA-based interface are presented. Experiments on retina and neuronal culture preparations demonstrate the recording of neural spiking activity for both CMOS devices and the functionality of the system.

Microsaccade-rhythmic modulation of neural synchronization and coding within and across cortical areas V1 and V2.

Primates sample their visual environment actively through saccades and microsaccades (MSs). Saccadic eye movements not only modulate neural spike rates but might also affect temporal correlations (synchrony) among neurons. Neural synchrony plays a role in neural coding and modulates information transfer between cortical areas. The question arises of how eye movements shape neural synchrony within and across cortical areas and how it affects visual processing. Through local field recordings in macaque early visual cortex while monitoring eye position and through neural network simulations, we find 2 distinct synchrony regimes in early visual cortex that are embedded in a 3- to 4-Hz MS-related rhythm during visual fixation. In the period shortly after an MS (“transient period”), synchrony was high within and between cortical areas. In the subsequent period (“sustained period”), overall synchrony dropped and became selective to stimulus properties. Only mutually connected neurons with similar stimulus responses exhibited sustained narrow-band gamma synchrony (25–80 Hz), both within and across cortical areas. Recordings in macaque V1 and V2 matched the model predictions. Furthermore, our modeling provides predictions on how (micro)saccade-modulated gamma synchrony in V1 shapes V2 receptive fields (RFs). We suggest that the rhythmic alternation between synchronization regimes represents a basic repeating sampling strategy of the visual system.

Implications for human odor sensing revealed from the statistics of odorant-receptor interactions.

Binding of odorants to olfactory receptors (ORs) elicits downstream chemical and neural signals, which are further processed to odor perception in the brain. Recently, Mainland and colleagues have measured more than 500 pairs of odorant-OR interaction by a high-throughput screening assay method, opening a new avenue to understanding the principles of human odor coding. Here, using a recently developed minimal model for OR activation kinetics, we characterize the statistics of OR activation by odorants in terms of three empirical parameters: the half-maximum effective concentration EC50, the efficacy, and the basal activity. While the data size of odorants is still limited, the statistics offer meaningful information on the breadth and optimality of the tuning of human ORs to odorants, and allow us to relate the three parameters with the microscopic rate constants and binding affinities that define the OR activation kinetics. Despite the stochastic nature of the response expected at individual OR-odorant level, we assess that the confluence of signals in a neuron released from the multitude of ORs is effectively free of noise and deterministic with respect to changes in odorant concentration. Thus, setting a threshold to the fraction of activated OR copy number for neural spiking binarizes the electrophysiological signal of olfactory sensory neuron, thereby making an information theoretic approach a viable tool in studying the principles of odor perception.

Compact standalone platform for neural recording with real-time spike sorting and data logging

Objective. Longitudinal observation of single unit neural activity from large numbers of cortical neurons in awake and mobile animals is often a vital step in studying neural network behaviour and towards the prospect of building effective brain–machine interfaces (BMIs). These recordings generate enormous amounts of data for transmission and storage, and typically require offline processing to tease out the behaviour of individual neurons. Our aim was to create a compact system capable of: (1) reducing the data bandwidth by circa 2 to 3 orders of magnitude (greatly improving battery lifetime and enabling low power wireless transmission in future versions); (2) producing real-time, low-latency, spike sorted data; and (3) long term untethered operation. Approach. We have developed a headstage that operates in two phases. In the short training phase a computer is attached and classic spike sorting is performed to generate templates. In the second phase the system is untethered and performs template matching to create an event driven spike output that is logged to a micro-SD card. To enable validation the system is capable of logging the high bandwidth raw neural signal data as well as the spike sorted data. Main results. The system can successfully record 32 channels of raw neural signal data and/or spike sorted events for well over 24 h at a time and is robust to power dropouts during battery changes as well as SD card replacement. A 24 h initial recording in a non-human primate M1 showed consistent spike shapes with the expected changes in neural activity during awake behaviour and sleep cycles. Significance. The presented platform allows neural activity to be unobtrusively monitored and processed in real-time in freely behaving untethered animals—revealing insights that are not attainable through scheduled recording sessions. This system achieves the lowest power per channel to date and provides a robust, low-latency, low-bandwidth and verifiable output suitable for BMIs, closed loop neuromodulation, wireless transmission and long term data logging.

A Modularized Efficient Framework for Non-Markov Time Series Estimation

We present a compartmentalized approach to finding the maximum a posteriori (MAP) estimate of a latent time series that obeys a dynamic stochastic model and is observed through noisy measurements. We specifically consider modern signal processing problems with non-Markov signal dynamics (e.g., group sparsity) and/or non-Gaussian measurement models (e.g., point process observation models used in neuroscience). Through the use of auxiliary variables in the MAP estimation problem, we show that a consensus formulation of the alternating direction method of multipliers enables iteratively computing separate estimates based on the likelihood and prior and subsequently “averaging” them in an appropriate sense using a Kalman smoother. As such, this can be applied to a broad class of problem settings and only requires modular adjustments when interchanging various aspects of the statistical model. Under broad log-concavity assumptions, we show that the separate estimation problems are convex optimization problems and that the iterative algorithm converges to the MAP estimate. As such, this framework can capture non-Markov latent time series models and non-Gaussian measurement models. We provide example applications involving 1) group-sparsity priors, within the context of electrophysiologic specrotemporal estimation, and 2) non-Gaussian measurement models, within the context of dynamic analyses of learning with neural spiking and behavioral observations.

S184. Sparse representation and classification of neural spikes using supervised dictionary learning

Neural spike sorting is curial for neuroscience applications. For example, spike sorting allows designing of efficient human-computer interfaces. Therefore, many investigations, aiming for accurate computer-aided algorithms for spike detection and classification, have emerged. However, many of the available algorithms require human intervention during the classification step. This study proposes decomposition and classification of spike waveforms using dictionary learning. In sparse decomposition, each spike is represented as a unique sparse combination of the dictionary basis vectors. This can lead to interesting sorting performance. Spiking activity was detected using amplitude thresholding and a 2.5-ms window around the peak value. Under the assumption that each neuron has a consistent waveform, the dictionary atoms were learned using training samples. This learned dictionary was used to get a sparse code representation for the test samples. These codes can be used to reconstruct the original spikes and therefore it provides data compression capability. Classification of the neural spikes was done using the sparse coefficients alongside with the reconstruction error. To ensure high discrimination, the dictionary was learned such that the resultant atoms have correspondence to class labels. In other words, the dictionary is structured of sub-dictionaries each corresponds to a different spiking pattern. Subsequently, an optimization problem was solved to make sure that (i) the sub-dictionaries accurately represent the training data, and (ii) the coding coefficients are sparse and minimize the reconstruction error. A testing sample was classified by sparsely coding it over each sub-dictionary. The reconstruction error for each sub-dictionary is computed. An error function is defined for each class as the summation of the reconstruction error and the difference between the sparse code and that class average. The testing sample was assigned to the class that gave the minimum error. The algorithm was tested on four simulated data sets, each contains spiking activities from three different neurons. Approximately 10% of each class data was used to construct the dictionary. The reconstruction mean-squared error for all the training samples was 3.49. This confirms the reconstruction power and accuracy of the learned dictionary. We measured the accuracy of our algorithm at different noise levels. The classification accuracy of the proposed method is 97% ± 5%. This accuracy outperforms the existing state-of-the-art algorithms. A dictionary learning based spike sorting algorithm has been developed. The results suggest that dictionary-based representation enables us to use the sparse representation of signals without a severe loss of data; besides that, this sparse representation can be also used for classification. This could also enable estimation of missing data points or clipped signals. The reconstruction error can be used to efficiently sort spikes.

High frequency neural spiking and auditory signaling by ultrafast red-shifted optogenetics

Optogenetics revolutionizes basic research in neuroscience and cell biology and bears potential for medical applications. We develop mutants leading to a unifying concept for the construction of various channelrhodopsins with fast closing kinetics. Due to different absorption maxima these channelrhodopsins allow fast neural photoactivation over the whole range of the visible spectrum. We focus our functional analysis on the fast-switching, red light-activated Chrimson variants, because red light has lower light scattering and marginal phototoxicity in tissues. We show paradigmatically for neurons of the cerebral cortex and the auditory nerve that the fast Chrimson mutants enable neural stimulation with firing frequencies of several hundred Hz. They drive spiking at high rates and temporal fidelity with low thresholds for stimulus intensity and duration. Optical cochlear implants restore auditory nerve activity in deaf mice. This demonstrates that the mutants facilitate neuroscience research and future medical applications such as hearing restoration.

Recovery of early neural spikes from stimulation electrodes using a DC-coupled low gain high resolution data acquisition system

BackgroundNeural responses to electrical stimulation provide valuable information to probe and study the network function. Especially, recording neural responses from the stimulated site provides improved neural interfacing method. However, it is difficult to measure short-delayed responses at the stimulated electrode due to the saturation of the amplifier after stimulation which is called “stimulus artifact”. Despite the advances in handling stimulation artifacts, it is still very challenging to deal with the artifacts if one tries to stimulate and record from the same electrode. New methodIn this paper, we developed a system consisting of 24 bit ADC and low gain DC-amplifier which allows us to record the entire responses including saturation-free stimulus artifact and neural responses with excellent resolution. ResultsOur approach showed saturation-free recording after stimulation, which makes it possible to recover neural spike as early as in 2 ms at the stimulating electrode with digital elimination methods. Comparison with Existing methodsWith our system we could record neural signals after stimulation that was difficult with high gain and high pass filtered recording system due to amplifier saturation. ConclusionsOur new system can enhance interface performance with its higher robustness and with simple system configuration.

Methods for Assessment of Memory Reactivation.

It has been suggested that reactivation of previously acquired experiences or stored information in declarative memories in the hippocampus and neocortex contributes to memory consolidation and learning. Understanding memory consolidation depends crucially on the development of robust statistical methods for assessing memory reactivation. To date, several statistical methods have seen established for assessing memory reactivation based on bursts of ensemble neural spike activity during offline states. Using population-decoding methods, we propose a new statistical metric, the weighted distance correlation, to assess hippocampal memory reactivation (i.e., spatial memory replay) during quiet wakefulness and slow-wave sleep. The new metric can be combined with an unsupervised population decoding analysis, which is invariant to latent state labeling and allows us to detect statistical dependency beyond linearity in memory traces. We validate the new metric using two rat hippocampal recordings in spatial navigation tasks. Our proposed analysis framework may have a broader impact on assessing memory reactivations in other brain regions under different behavioral tasks.

Sub 100 nW Volatile Nano-Metal-Oxide Memristor as Synaptic-Like Encoder of Neuronal Spikes.

Advanced neural interfaces mediate a bioelectronic link between the nervous system and microelectronic devices, bearing great potential as innovative therapy for various diseases. Spikes from a large number of neurons are recorded leading to creation of big data that require online processing under most stringent conditions, such as minimal power dissipation and on-chip space occupancy. Here, we present a new concept where the inherent volatile properties of a nano-scale memristive device are used to detect and compress information on neural spikes as recorded by a multielectrode array. Simultaneously, and similarly to a biological synapse, information on spike amplitude and frequency is transduced in metastable resistive state transitions of the device, which is inherently capable of self-resetting and of continuous encoding of spiking activity. Furthermore, operating the memristor in a very high resistive state range reduces its average in-operando power dissipation to less than 100 nW, demonstrating the potential to build highly scalable, yet energy-efficient on-node processors for advanced neural interfaces.

Stock Price Fluctuation Based Neural Spiking

Stock Price Fluctuation Based Neural Spiking

Distortion Distribution of Neural Spike Train Sequence Matching With Optogenetics.

This paper uses a simple optogenetic model to compare the timing distortion between a randomly-generated target spike sequence and an externally-stimulated neuron spike sequence. Optogenetics is an emerging field of neuroscience where neurons are genetically modified to express light-sensitive receptors that enable external control when the neurons fire. Two different measures are studied to determine the timing distortion. The first measure is the delay in externally-stimulated spikes. The second measure is the root-mean-square-error between the filtered outputs of the target and stimulated spike sequences. The mean and the distribution of the distortion are derived in closed form when the target sequence generation rate is sufficiently low. The derived results are verified with simulations. The proposed model and distortion measures can be used to measure the deviation between neuron spike sequences that are prescribed and what can be achieved via external stimulation. Given the prominence of neuronal signaling within the brain and throughout the body, optogenetics has significant potential to improve the understanding of the nervous system and to develop treatments for neurological diseases. This work is a step towards an analytical model to predict whether different spike trains were observed from the same external stimulus, and the broader goal of understanding the quantity and reliability of information that can be carried by neurons.

Hue opponency: chromatic valence functions, individual differences, cortical winner-take-all opponent modeling, and the relationship between spikes and sensitivity.

Neural spike rate data are more restricted in range than related psychophysical data. For example, several studies suggest a compressive (roughly cube root) nonlinear relationship between wavelength-opponent spike rates in primate midbrain and color appearance in humans, two rather widely separated domains. This presents an opportunity to partially bridge a chasm between these two domains and to probe the putative nonlinearity with other psychophysical data. Here neural wavelength-opponent data are used to create cortical competition models for hue opponency. This effort led to creation of useful models of spiking neuron winner-take-all (WTA) competition and MAX selection. When fed with actual primate data, the spiking WTA models generate reasonable wavelength-opponent spike rate behaviors. An average psychophysical observer for red-green and blue-yellow opponency is curated from eight applicable studies in the refereed and dissertation literatures, with cancellation data roughly every 10nm in 18 subjects for yellow-blue opponency and 15 subjects for red-green opponency. A direct mapping between spiking neurons with broadband wavelength sensitivity and human psychophysical luminance yields a power law exponent of 0.27, similar to the cube root nonlinearity. Similarly, direct mapping between the WTA model opponent spike rates and psychophysical opponent data suggests power law relationships with exponents between 0.24 and 0.41.

A novel adaptive real-time detection algorithm for an area-efficient CMOS spike detector circuit

For implantable neural detection and recording systems, computationally simple methods are preferred over complex algorithms for power efficiency. Amplitude threshold is the simplest and the most widely employed method for neural spike detection. However, this conventional method for setting the threshold is sensitive to the spike firing rate and spike amplitude. In this work, a novel approach is described for a real-time calculation of the detection threshold in a way that is robust to the change in spike firing rate as well as spike amplitude. An algorithm is proposed that adaptively sets the threshold from background activity by approximating the nonlinear energy operator (NEO). The detection method can distinguish neural spikes accurately with firing rate greater than 100 Hz. The spike detector circuit dissipates 5.1 μW of power per channel and occupies 0.018 mm2 of layout area when implemented in 0.18 μm CMOS process.

A Copula-Based Granger Causality Measure for the Analysis of Neural Spike Train Data.

In systems neuroscience, it is becoming increasingly common to record the activity of hundreds of neurons simultaneously via electrode arrays. The ability to accurately measure the causal interactions among multiple neurons in the brain is crucial to understanding how neurons work in concert to generate specific brain functions. The development of new statistical methods for assessing causal influence between spike trains is still an active field of neuroscience research. Here, we suggest a copula-based Granger causality measure for the analysis of neural spike train data. This method is built upon our recent work on copula Granger causality for the analysis of continuous-valued time series by extending it to point-process neural spike train data. The proposed method is therefore able to reveal nonlinear and high-order causality in the spike trains while retaining all the computational advantages such as model-free, efficient estimation, and variability assessment of Granger causality. The performance of our algorithm can be further boosted with time-reversed data. Our method performed well on extensive simulations, and was then demonstrated on neural activity simultaneously recorded from primary visual cortex of a monkey performing a contour detection task.

A Multivariate Hawkes Process With Gaps in Observations

Given a collection of entities (or nodes) in a network and our intermittent observations of activities from each entity, an important problem is to learn the hidden edges depicting directional relationships among these entities. Here, we study causal relationships (excitations) that are realized by a multivariate Hawkes process (MHP). The MHP and its variations (spatio-temporal point processes) have been used to study contagion in earthquakes, crimes, neural spiking activities, the stock and foreign exchange markets, and so on. In this paper, we consider the MHP with gaps (MHPG) in observations. We propose a variational problem for detecting sparsely hidden relationships with an MHP that takes into account the gaps from each entity. We bypass the problem of dealing with a large amount of missing events by introducing a small number of unknown boundary conditions. In the case where our observations are sparse (e.g., from 10% to 30%), we show through numerical simulations that robust recovery with MHPG is still possible even if the lengths of the observed intervals are small but they are chosen accordingly. The numerical results also show that the knowledge of gaps and imposing the right boundary conditions are very crucial in discovering the underlying patterns and hidden relationships.

ERAASR: an algorithm for removing electrical stimulation artifacts from multielectrode array recordings

Objective. Electrical stimulation is a widely used and effective tool in systems neuroscience, neural prosthetics, and clinical neurostimulation. However, electrical artifacts evoked by stimulation prevent the detection of spiking activity on nearby recording electrodes, which obscures the neural population response evoked by stimulation. We sought to develop a method to clean artifact-corrupted electrode signals recorded on multielectrode arrays in order to recover the underlying neural spiking activity. Approach. We created an algorithm, which performs estimation and removal of array artifacts via sequential principal components regression (ERAASR). This approach leverages the similar structure of artifact transients, but not spiking activity, across simultaneously recorded channels on the array, across pulses within a train, and across trials. The ERAASR algorithm requires no special hardware, imposes no requirements on the shape of the artifact or the multielectrode array geometry, and comprises sequential application of straightforward linear methods with intuitive parameters. The approach should be readily applicable to most datasets where stimulation does not saturate the recording amplifier. Main results. The effectiveness of the algorithm is demonstrated in macaque dorsal premotor cortex using acute linear multielectrode array recordings and single electrode stimulation. Large electrical artifacts appeared on all channels during stimulation. After application of ERAASR, the cleaned signals were quiescent on channels with no spontaneous spiking activity, whereas spontaneously active channels exhibited evoked spikes which closely resembled spontaneously occurring spiking waveforms. Significance. We hope that enabling simultaneous electrical stimulation and multielectrode array recording will help elucidate the causal links between neural activity and cognition and facilitate naturalistic sensory protheses.

Targeting CD14 on blood derived cells improves intracortical microelectrode performance

Intracortical microelectrodes afford researchers an effective tool to precisely monitor neural spiking activity. Additionally, intracortical microelectrodes have the ability to return function to individuals with paralysis as part of a brain computer interface. Unfortunately, the neural signals recorded by these electrodes degrade over time. Many strategies which target the biological and/or materials mediating failure modes of this decline of function are currently under investigation. The goal of this study is to identify a precise cellular target for future intervention to sustain chronic intracortical microelectrode performance. Previous work from our lab has indicated that the Cluster of Differentiation 14/Toll-like receptor pathway (CD14/TLR) is a viable target to improve chronic laminar, silicon intracortical microelectrode recordings. Here, we use a mouse bone marrow chimera model to selectively knockout CD14, an innate immune receptor, from either brain resident microglia or blood-derived macrophages, in order to understand the most effective targets for future therapeutic options. Using single-unit recordings we demonstrate that inhibiting CD14 from the blood-derived macrophages improves recording quality over the 16 week long study. We conclude that targeting CD14 in blood-derived cells should be part of the strategy to improve the performance of intracortical microelectrodes, and that the daunting task of delivering therapeutics across the blood-brain barrier may not be needed to increase intracortical microelectrode performance.

Inverse stochastic resonance induced by non-Gaussian colored noise

Abstract This paper mainly studies the phenomenon of inverse stochastic resonance induced by non-Gaussian noise in a representative Hodgkin–Huxley model. Firstly, spiking manners of neurons under different input currents are investigated and different current thresholds and bifurcation diagram are given. Besides, the limit cycle with different input current are plotted. Then, taking the average firing rate as a measurement, the result shows that a minimum exists in the curve corresponding to the intensity, correlation time and deviation coefficient of non-Gaussian noise, which is named inverse stochastic resonance(ISR). More important, it is found that the mean firing rate will remain unchanged with noise intensity and deviation coefficient when correlation time is sufficiently large and finally trends toward a constant value which is completely determined by input current. Further, the present results will be benefit to investigate the functional roles of randomness in neural spiking.

Memristors fire away

Neuromorphic computing based on fully memristive neural networks could offer a scalable and lower-cost alternative to existing neural spiking chips based solely on CMOS technology.