Tracking neurons recorded from tetrodes across time

A neural circuit for regulating a behavioral switch in response to prolonged uncontrollability in mice

Persistence in the face of failure helps to overcome challenges. But the ability to adjust behavior or even give up when the task is uncontrollable has advantages. How the mammalian brain switches behavior when facing uncontrollability remains an open question. We generated two mouse models of behavioral transition from action to no-action during exposure to a prolonged experience with an uncontrollable outcome. The transition was not caused by pain desensitization or muscle fatigue and was not a depression-/learned-helplessness-like behavior. Noradrenergic neurons projecting to GABAergic neurons within the orbitofrontal cortex (OFC) are key regulators of this behavior. Fiber photometry, microdialysis, mini-two-photon microscopy, and tetrode/optrode in vivo recording in freely behaving mice revealed that the reduction of norepinephrine and downregulation of alpha 1 receptor in the OFC reduced the number and activity of GABAergic neurons necessary for driving action behavior resulting in behavioral transition. These findings define a circuit governing behavioral switch in response to prolonged uncontrollability.

Working memory and reward increase the accuracy of animal location encoding in the medial prefrontal cortex

The ability to perceive spatial environments and locate oneself during navigation is crucial for the survival of animals. Mounting evidence suggests a role of the medial prefrontal cortex (mPFC) in spatially related behaviors. However, the properties of mPFC spatial encoding and how it is influenced by animal behavior are poorly defined. Here, we train the mice to perform 3 tasks differing in working memory and reward-seeking: a delayed non-match to place (DNMTP) task, a passive alternation (PA) task, and a free-running task. Single-unit recording in the mPFC shows that although individual mPFC neurons exhibit spatially selective firing, they do not reliably represent the animal location. The population activity of mPFC neurons predicts the animal location. Notably, the population coding of animal locations by the mPFC is modulated by animal behavior in that the coding accuracy is higher in tasks involved in working memory and reward-seeking. This study reveals an approach whereby the mPFC encodes spatial positions and the behavioral variables affecting it.

Neuropixels 2.0: A miniaturized high-density probe for stable, long-term brain recordings

Measuring the dynamics of neural processing across time scales requires following the spiking of thousands of individual neurons over milliseconds and months. To address this need, we introduce the Neuropixels 2.0 probe together with newly designed analysis algorithms. The probe has more than 5000 sites and is miniaturized to facilitate chronic implants in small mammals and recording during unrestrained behavior. High-quality recordings over long time scales were reliably obtained in mice and rats in six laboratories. Improved site density and arrangement combined with newly created data processing methods enable automatic post hoc correction for brain movements, allowing recording from the same neurons for more than 2 months. These probes and algorithms enable stable recordings from thousands of sites during free behavior, even in small animals such as mice.

D1 dopamine receptors intrinsic activity and functional selectivity affect working memory in prefrontal cortex

Dopamine D₁ agonists enhance cognition, but the role of different signaling pathways (e.g., cAMP or β-arrestin) is unclear. The current study compared 2-methyldihydrexidine and CY208,243, drugs with different degrees of both D₁ intrinsic activity and functional selectivity. 2-Methyldihydrexidine is a full agonist at adenylate cyclase and a super-agonist at β-arrestin recruitment, whereas CY208,243 has relatively high intrinsic activity at adenylate cyclase, but much lower at β-arrestin recruitment. Both drugs decreased, albeit in dissimilar ways, the firing rate of neurons in prefrontal cortex sensitive to outcome-related aspects of a working memory task. 2-Methyldihydrexidine was superior to CY208,243 in prospectively enhancing similarity and retrospectively distinguishing differences between correct and error outcomes based on firing rates, enhancing the micro-network measured by oscillations of spikes and local field potentials, and improving behavioral performance. This study is the first to examine how ligand signaling bias affects both behavioral and neurophysiological endpoints in the intact animal. The data show that maximal enhancement of cognition via D₁ activation occurred with a pattern of signaling that involved full unbiased intrinsic activity, or agonists with high β-arrestin activity.

Reliability of motor and sensory neural decoding by threshold crossings for intracortical brain-machine interface

OBJECTIVE

For intracortical neurophysiological studies, spike sorting is an important procedure to isolate single units for analyzing specific functions. However, whether spike sorting is necessary or not for neural decoding applications is controversial. Several studies showed that using threshold crossings (TC) instead of spike sorting could also achieve a similar satisfactory performance. However, such studies were limited in similar behavioral tasks, and the neural signal source mainly focused on the motor-related cortical regions. It is not certain if this conclusion is applicable to other situations. Therefore, we compared the performance of TC and spike sorting in neural decoding with more comprehensive paradigms and parameters.

APPROACH

Two rhesus macaques implanted with Utah or floating microelectrode arrays (FMAs) in motor or sensory-related cortical regions were trained to perform a motor or a sensory task. Data from each monkey were preprocessed with three different schemes: TC, automatic sorting (AS), and manual sorting (MS). A support vector machine was used as the decoder, and the decoding accuracy was used for evaluating the performance of three preprocessing methods. Different neural signal sources, different decoders, and related parameters and decoding stability were further tested to systematically compare three preprocessing methods.

MAIN RESULTS

TC could achieve a similar (-4.5 RMS threshold) or better (-3.0 RMS threshold) decoding performance compared to the other two sorting methods in the motor or sensory tasks even if the neural signal sources or decoder-related parameters were changed. Moreover, TC was much more stable in neural decoding across sessions and robust to changes of threshold.

SIGNIFICANCE

Our results indicated that spike-firing patterns could be stably extracted through TC from multiple cortices in both motor and sensory neural decoding applications. Considering the stability of TC, it might be more suitable for neural decoding compared to sorting methods.

A Fully Automated Approach to Spike Sorting

Understanding the detailed dynamics of neuronal networks will require the simultaneous measurement of spike trains from hundreds of neurons (or more). Currently, approaches to extracting spike times and labels from raw data are time consuming, lack standardization, and involve manual intervention, making it difficult to maintain data provenance and assess the quality of scientific results. Here, we describe an automated clustering approach and associated software package that addresses these problems and provides novel cluster quality metrics. We show that our approach has accuracy comparable to or exceeding that achieved using manual or semi-manual techniques with desktop central processing unit (CPU) runtimes faster than acquisition time for up to hundreds of electrodes. Moreover, a single choice of parameters in the algorithm is effective for a variety of electrode geometries and across multiple brain regions. This algorithm has the potential to enable reproducible and automated spike sorting of larger scale recordings than is currently possible.

Automated long-term recording and analysis of neural activity in behaving animals

Addressing how neural circuits underlie behavior is routinely done by measuring electrical activity from single neurons in experimental sessions. While such recordings yield snapshots of neural dynamics during specified tasks, they are ill-suited for tracking single-unit activity over longer timescales relevant for most developmental and learning processes, or for capturing neural dynamics across different behavioral states. Here we describe an automated platform for continuous long-term recordings of neural activity and behavior in freely moving rodents. An unsupervised algorithm identifies and tracks the activity of single units over weeks of recording, dramatically simplifying the analysis of large datasets. Months-long recordings from motor cortex and striatum made and analyzed with our system revealed remarkable stability in basic neuronal properties, such as firing rates and inter-spike interval distributions. Interneuronal correlations and the representation of different movements and behaviors were similarly stable. This establishes the feasibility of high-throughput long-term extracellular recordings in behaving animals.

A novel framework for feature extraction in multi-sensor action potential sorting

BACKGROUND

Extracellular recordings of multi-unit neural activity have become indispensable in neuroscience research. The analysis of the recordings begins with the detection of the action potentials (APs), followed by a classification step where each AP is associated with a given neural source. A feature extraction step is required prior to classification in order to reduce the dimensionality of the data and the impact of noise, allowing source clustering algorithms to work more efficiently.

NEW METHOD

In this paper, we propose a novel framework for multi-sensor AP feature extraction based on the so-called Matched Subspace Detector (MSD), which is shown to be a natural generalization of standard single-sensor algorithms.

RESULTS

Clustering using both simulated data and real AP recordings taken in the locust antennal lobe demonstrates that the proposed approach yields features that are discriminatory and lead to promising results.

COMPARISON WITH EXISTING METHOD(S)

Unlike existing methods, the proposed algorithm finds joint spatio-temporal feature vectors that match the dominant subspace observed in the two-dimensional data without needs for a forward propagation model and AP templates.

CONCLUSIONS

The proposed MSD approach provides more discriminatory features for unsupervised AP sorting applications.

Neurons in cat V1 show significant clustering by degree of tuning

Neighboring neurons in cat primary visual cortex (V1) have similar preferred orientation, direction, and spatial frequency. How diverse is their degree of tuning for these properties? To address this, we used single-tetrode recordings to simultaneously isolate multiple cells at single recording sites and record their responses to flashed and drifting gratings of multiple orientations, spatial frequencies, and, for drifting gratings, directions. Orientation tuning width, spatial frequency tuning width, and direction selectivity index (DSI) all showed significant clustering: pairs of neurons recorded at a single site were significantly more similar in each of these properties than pairs of neurons from different recording sites. The strength of the clustering was generally modest. The percent decrease in the median difference between pairs from the same site, relative to pairs from different sites, was as follows: for different measures of orientation tuning width, 29-35% (drifting gratings) or 15-25% (flashed gratings); for DSI, 24%; and for spatial frequency tuning width measured in octaves, 8% (drifting gratings). The clusterings of all of these measures were much weaker than for preferred orientation (68% decrease) but comparable to that seen for preferred spatial frequency in response to drifting gratings (26%). For the above properties, little difference in clustering was seen between simple and complex cells. In studies of spatial frequency tuning to flashed gratings, strong clustering was seen among simple-cell pairs for tuning width (70% decrease) and preferred frequency (71% decrease), whereas no clustering was seen for simple-complex or complex-complex cell pairs.

Differential entrainment and learning-related dynamics of spike and local field potential activity in the sensorimotor and associative striatum

Parallel cortico-basal ganglia loops are thought to have distinct but interacting functions in motor learning and habit formation. In rats, the striatal projection neuron populations (MSNs) in the dorsolateral and dorsomedial striatum, respectively corresponding to sensorimotor and associative regions of the striatum, exhibit contrasting dynamics as rats acquire T-maze tasks (Thorn et al., 2010). Here, we asked whether these patterns could be related to the activity of local interneuron populations in the striatum and to the local field potential activity recorded simultaneously in the corresponding regions. We found that dorsolateral and dorsomedial striatal fast-spiking interneurons exhibited task-specific and training-related dynamics consistent with those of corresponding MSN populations. Moreover, both MSNs and interneuron populations in both regions became entrained to theta-band (5-12 Hz) frequencies during task acquisition. However, the predominant entrainment frequencies were different for the sensorimotor and associative zones. Dorsolateral striatal neurons became entrained mid-task to oscillations centered ∼ 5 Hz, whereas simultaneously recorded neurons in the dorsomedial region became entrained to higher frequency (∼ 10 Hz) rhythms. These region-specific patterns of entrainment evolved dynamically with the development of region-specific patterns of interneuron and MSN activity, indicating that, with learning, these two striatal regions can develop different frequency-modulated circuit activities in parallel. We suggest that such differential entrainment of sensorimotor and associative neuronal populations, acquired through learning, could be critical for coordinating information flow throughout each trans-striatal network while simultaneously enabling nearby components of the separate networks to operate independently.

Multi-unit recording with iridium oxide modified stereotrodes in Drosophila melanogaster

BACKGROUND

Drosophila is a very favorable animal model for the studies of neuroscience. However, it remains a great challenge to employ electrophysiological approaches in Drosophila to study the neuronal assembly dynamics in vivo, partially due to the small size of the Drosophila brain. Small and sensitive microelectrodes for multi-unit recordings are greatly desired.

NEW METHOD

We fabricated micro-scale stereotrodes for electrical recordings in Drosophila melanogaster. The stereotrodes were modified with iridium oxide (IrO2) under a highly controllable deposition procedure to improve their electrochemical properties. Electrical recordings were carried out using the IrO2 stereotrodes to detect spontaneous action potentials and LFPs in vivo.

RESULTS

The IrO2 electrodes exhibited significantly higher capacitance and lower impedance at 1 kHz. Electrical recording with the IrO2 stereotrodes in vivo demonstrated an average signal-to-noise ratio (SNR) of 7.3 and a significantly improved LFP sensitivity. 5 types of different neurons recorded were clearly separated. Electrophysiological responses to visual and odor stimulation were also detected, respectively.

COMPARISON WITH EXISTING METHOD(S)

The most widely used electrodes for electrical recording in Drosophila are glass microelectrode and sharpened tungsten microelectrode, which are typically used for single-unit recordings. Although tetrode technology has been used to record multi-neuronal activities from Drosophila, the fabricated IrO2 stereotrodes possess smaller geometry size but exhibited comparable recording signal-to noise ration and better sorting quality.

CONCLUSIONS

The IrO2 stereotrodes are capable to meet the requirements of multi-unit recording and spike sorting, which will be a useful tool for the electrophysiology-based researches especially in Drosophila and other small animals.

The accuracy and precision of signal source localization with tetrodes

Four-sensor microelectrodes, commonly referred to as tetrodes, have the ability to significantly increase the signal-to-noise ratio of neuronal extracellular recordings. They also provide spatio-temporal information about extracellular action potentials (EAP) which may be used to localize and resolve individual neuronal signal sources. Since the relative position of sensors and neurons whose EAPs are recorded is not known during in vivo experiments, the accuracy and precision of neuronal source localization algorithms remain untested. In this study, electrical signals generated by a stimulator were recorded simultaneously with four recording micropipettes immersed in artificial cerebrospinal fluid. The location of the source was estimated using the multiple signal classification algorithm, with an accuracy and precision of ~4 µm and ~7 µm, respectively. These results suggest that in vivo localization and resolution of individual neuronal sources is feasible.

Multichannel electrophysiological spike sorting via joint dictionary learning and mixture modeling

We propose a methodology for joint feature learning and clustering of multichannel extracellular electrophysiological data, across multiple recording periods for action potential detection and classification (sorting). Our methodology improves over the previous state of the art principally in four ways. First, via sharing information across channels, we can better distinguish between single-unit spikes and artifacts. Second, our proposed "focused mixture model" (FMM) deals with units appearing, disappearing, or reappearing over multiple recording days, an important consideration for any chronic experiment. Third, by jointly learning features and clusters, we improve performance over previous attempts that proceeded via a two-stage learning process. Fourth, by directly modeling spike rate, we improve the detection of sparsely firing neurons. Moreover, our Bayesian methodology seamlessly handles missing data. We present the state-of-the-art performance without requiring manually tuning hyperparameters, considering both a public dataset with partial ground truth and a new experimental dataset.

Selective effects of dopamine depletion and L-DOPA therapy on learning-related firing dynamics of striatal neurons

Despite evidence that dopamine neurotransmission in the striatum is critical for learning as well as for movement control, little is yet known about how the learning-related dynamics of striatal activity are affected by dopamine depletion, a condition faced in Parkinson's disease. We made localized intrastriatal 6-hydroxydopamine lesions in rats and recorded within the dopamine-depleted sensorimotor striatal zone and its contralateral correspondent as the animals learned a conditional maze task. Rather than producing global, nonspecific elevations in firing rate across the task, the dopamine depletion altered striatal projection neuron activity and fast-spiking interneuron activity selectively, with sharply task-specific and cell type-specific effects, and often, with learning-stage selective effects as well. Striatal projection neurons with strong responses during the maze runs had especially elevated responsiveness during the maze runs. Projection neurons that, instead, fired most strongly before maze running showed elevated pre-start firing rates, but not during maze running, as learning progressed. The intrastriatal dopamine depletion severely affected the learning-related patterning of fast-spiking interneuron ensembles, especially during maze running and after extended training. Remarkably, L-DOPA treatment almost entirely reversed the depletion-induced elevations in pre-run firing of the projection neurons, and elevated their responses around start and end of maze runs. By contrast, L-DOPA failed to normalize fast-spiking interneuron activity. Thus the effects of striatal dopamine depletion and restoration on striatal activity are highly dependent not only on cell type, as previously shown, but also on the behavioral activity called for and the state of behavioral learning achieved.

Genetically encoded optical indicators for the analysis of neuronal circuits

In a departure from previous top-down or bottom-up strategies used to understand neuronal circuits, many forward-looking research programs now place the circuit itself at their centre. This has led to an emphasis on the dissection and elucidation of neuronal circuit elements and mechanisms, and on studies that ask how these circuits generate behavioural outputs. This movement towards circuit-centric strategies is progressing rapidly as a result of technological advances that combine genetic manipulation with light-based methods. The core tools of these new approaches are genetically encoded optical indicators and actuators that enable non-destructive interrogation and manipulation of neuronal circuits in behaving animals with cellular-level precision. This Review examines genetically encoded reporters of neuronal function and assesses their value for circuit-oriented neuroscientific investigations.

Matched subspace detector based feature extraction for sorting of multi-sensor action potentials

This paper proposes a novel matched subspace detector (MSD) based algorithm for extracting discriminant features from multi-sensor measurements of extracellular action potentials (APs) to facilitate their subsequent separation according to the neuron of origin. The method does not require the construction of AP templates, and is therefore suitable for unsupervised AP sorting applications. In addition, detailed simulations show that the proposed algorithm outperforms existing single-sensor based feature extraction approaches.

Signal source localization with tetrodes: experimental verification

Multi-sensor electrodes for extracellular recording of neuronal action potentials have significantly increased the signal-to-noise ratio (SNR) in neurophysiological experiments, ultimately leading to a more accurate interpretation of scientific data. Apart from improving SNR, we hypothesize that these electrodes can be used to estimate the location of underlying neuronal signal sources, and perhaps other parameters such as the size and shape of neurons whose activities are being recorded. This study introduces the multiple signal classification (MUSIC) algorithm to the problem of neuron localization and presents the first experimental demonstration of signal source localization using commercially available 4-sensor electrodes (tetrodes).

Measuring the quality of neuronal identification in ensemble recordings

Technological advances in electrode construction and digital signal processing now allow recording simultaneous extracellular action potential discharges from many single neurons, with the potential to revolutionize understanding of the neural codes for sensory, motor, and cognitive variables. Such studies have revealed the importance of ensemble neural codes, encoding information in the dynamic relationships among the action potential spike trains of multiple single neurons. Although the success of this research depends on the accurate classification of extracellular action potentials to individual neurons, there are no widely used quantitative methods for assessing the quality of the classifications. Here we describe information theoretic measures of action potential waveform isolation applicable to any dataset that have an intuitive, universal interpretation, that are not dependent on the methods or choice of parameters for single-unit isolation, and that have been validated using a dataset of simultaneous intracellular and extracellular neuronal recordings from Sprague Dawley rats.

Adaptive decoding for brain-machine interfaces through Bayesian parameter updates

Brain-machine interfaces (BMIs) transform the activity of neurons recorded in motor areas of the brain into movements of external actuators. Representation of movements by neuronal populations varies over time, during both voluntary limb movements and movements controlled through BMIs, due to motor learning, neuronal plasticity, and instability in recordings. To ensure accurate BMI performance over long time spans, BMI decoders must adapt to these changes. We propose the Bayesian regression self-training method for updating the parameters of an unscented Kalman filter decoder. This novel paradigm uses the decoder's output to periodically update its neuronal tuning model in a Bayesian linear regression. We use two previously known statistical formulations of Bayesian linear regression: a joint formulation, which allows fast and exact inference, and a factorized formulation, which allows the addition and temporary omission of neurons from updates but requires approximate variational inference. To evaluate these methods, we performed offline reconstructions and closed-loop experiments with rhesus monkeys implanted cortically with microwire electrodes. Offline reconstructions used data recorded in areas M1, S1, PMd, SMA, and PP of three monkeys while they controlled a cursor using a handheld joystick. The Bayesian regression self-training updates significantly improved the accuracy of offline reconstructions compared to the same decoder without updates. We performed 11 sessions of real-time, closed-loop experiments with a monkey implanted in areas M1 and S1. These sessions spanned 29 days. The monkey controlled the cursor using the decoder with and without updates. The updates maintained control accuracy and did not require information about monkey hand movements, assumptions about desired movements, or knowledge of the intended movement goals as training signals. These results indicate that Bayesian regression self-training can maintain BMI control accuracy over long periods, making clinical neuroprosthetics more viable.

Three-dimensional localization of neurons in cortical tetrode recordings

The recording radius and spatial selectivity of an extracellular probe are important for interpreting neurophysiological recordings but are rarely measured. Moreover, an analysis of the recording biophysics of multisite probes (e.g., tetrodes) can provide for source characterization and localization of spiking single units, but this capability has remained largely unexploited. Here we address both issues quantitatively. Advancing a tetrode (≈40-μm contact separation, tetrahedral geometry) in 5- to 10-μm steps, we repeatedly recorded extracellular action potentials (EAPs) of single neurons in the visual cortex. Using measured spatial variation of EAPs, the tetrodes' measured geometry, and a volume conductor model of the cortical tissue, we solved the inverse problem of estimating the location and the size of the equivalent dipole model of the spike generator associated with each neuron. Half of the 61 visual neurons were localized within a radius of ≈100 μm and 95% within ≈130 μm around the tetrode tip (i.e., a large fraction was much further than previously thought). Because of the combined angular sensitivity of the tetrode's leads, location uncertainty was less than one-half the cell's distance. We quantified the spatial dependence of the probability of cell isolation, the isolated fraction, and the dependence of the recording radius on probe size and equivalent dipole size. We also reconstructed the spatial configuration of sets of simultaneously recorded neurons to demonstrate the potential use of 3D dipole localization for functional anatomy. Finally, we found that the dipole moment vector, surprisingly, tended to point toward the probe, leading to the interpretation that the equivalent dipole represents a "local lobe" of the dendritic arbor.

Differential dynamics of activity changes in dorsolateral and dorsomedial striatal loops during learning

The basal ganglia are implicated in a remarkable range of functions influencing emotion and cognition as well as motor behavior. Current models of basal ganglia function hypothesize that parallel limbic, associative, and motor cortico-basal ganglia loops contribute to this diverse set of functions, but little is yet known about how these loops operate and how their activities evolve during learning. To address these issues, we recorded simultaneously in sensorimotor and associative regions of the striatum as rats learned different versions of a conditional T-maze task. We found highly contrasting patterns of activity in these regions during task performance and found that these different patterns of structured activity developed concurrently, but with sharply different dynamics. Based on the region-specific dynamics of these patterns across learning, we suggest a working model whereby dorsomedial associative loops can modulate the access of dorsolateral sensorimotor loops to the control of action.

A Bayesian clustering method for tracking neural signals over successive intervals

This paper introduces a new, unsupervised method for sorting and tracking the action potentials of individual neurons in multiunit extracellular recordings. Presuming the data are divided into short, sequential recording intervals, the core of our strategy relies upon an extension of a traditional mixture model approach that incorporates clustering results from the preceding interval in a Bayesian manner, while still allowing for signal nonstationarity and changing numbers of recorded neurons. As a natural byproduct of the sorting method, current and prior signal clusters can be matched over time in order to track persisting neurons. We also develop techniques to use prior data to appropriately seed the clustering algorithm and select the model class. We present results in a principal components space; however, the algorithm may be applied in any feature space where the distribution of a neuron's spikes may be modeled as Gaussian. Applications of this signal classification method to recordings from macaque parietal cortex show that it provides significantly more consistent clustering and tracking results than traditional methods based on expectation-maximization optimization of mixture models. This consistent tracking ability is crucial for intended applications of the method.

Stable encoding of task structure coexists with flexible coding of task events in sensorimotor striatum

The sensorimotor striatum, as part of the brain's habit circuitry, has been suggested to store fixed action values as a result of stimulus-response learning and has been contrasted with a more flexible system that conditionally assigns values to behaviors. The stability of neural activity in the sensorimotor striatum is thought to underlie not only normal habits but also addiction and clinical syndromes characterized by behavioral fixity. By recording in the sensorimotor striatum of mice, we asked whether neuronal activity acquired during procedural learning would be stable even if the sensory stimuli triggering the habitual behavior were altered. Contrary to expectation, both fixed and flexible activity patterns appeared. One, representing the global structure of the acquired behavior, was stable across changes in task cuing. The second, a fine-grain representation of task events, adjusted rapidly. Such dual forms of representation may be critical to allow motor and cognitive flexibility despite habitual performance.

A Robotic Neural Interface for Autonomous Positioning of Extracellular Recording Electrodes

In this paper we describe a set of algorithms and a novel miniature device that together can autonomously position electrodes in neural tissue to obtain high-quality extracellular recordings. This robotic system moves each electrode to detect the signals of individual neurons, optimize the signal quality of a target neuron, and then maintain this signal over time. Such neuronal signals provide the key inputs for emerging neuroprosthetic medical devices and serve as the foundation of basic neuroscientific and medical research. Experimental results from extensive use of the robotic electrodes in macaque parietal cortex are presented to validate the method and to quantify its effectiveness.

On the importance of static nonlinearity in estimating spatiotemporal neural filters with natural stimuli

Understanding neural responses with natural stimuli has increasingly become an essential part of characterizing neural coding. Neural responses are commonly characterized by a linear-nonlinear (LN) model, in which the output of a linear filter applied to the stimulus is transformed by a static nonlinearity to determine neural response. To estimate the linear filter in the LN model, studies of responses to natural stimuli commonly use methods that are unbiased only for a linear model (in which there is no static nonlinearity): spike-triggered averages with correction for stimulus power spectrum, with or without regularization. Although these methods work well for artificial stimuli, such as Gaussian white noise, we show here that they estimate neural filters of LN models from responses to natural stimuli much more poorly. We studied simple cells in cat primary visual cortex. We demonstrate that the filters computed by directly taking the nonlinearity into account have better predictive power and depend less on the stimulus than those computed under the linear model. With noise stimuli, filters computed using the linear and LN models were similar, as predicted theoretically. With natural stimuli, filters of the two models can differ profoundly. Noise and natural stimulus filters differed significantly in spatial properties, but these differences were exaggerated when filters were computed using the linear rather than the LN model. Although regularization of filters computed under the linear model improved their predictive power, it also led to systematic distortions of their spatial frequency profiles, especially at low spatial and temporal frequencies.

Tracking Spike-Amplitude Changes to Improve the Quality of Multineuronal Data Analysis

During extracellular electrophysiological recording experiments, the waveform of neuronal spikes recorded from a single neuron often changes. These spike-waveform changes make single-neuron identification difficult, particularly when the activities of multiple neurons are simultaneously recorded with a multichannel microelectrode, such as a tetrode or a heptode. We have developed a tracking method of individual neurons despite their changing spike amplitudes. The method is based on a bottom-up hierarchical clustering algorithm that tracks each neuron's spike cluster during temporally overlapping clustering periods. We evaluated this method by comparing spike sorting with and without cluster tracking of an identical series of multineuronal spikes recorded from monkey area-TE neurons responding to a set of visual stimuli. According to Shannon's information theory, errors in spike-amplitude tracking reduce the expected value of the amount of information about a stimulus set that is transferred by the spike train of a cluster. In this study, cluster tracking significantly increased the expected value of the amount of information transferred by a spike train (p < 0.01). Additionally, the stability of the stimulus preference and that of the cross-correlation between clusters improved significantly (p < 0.000001). We conclude that cluster tracking improves the quality of multineuronal data analysis.

In vivo two-photon imaging reveals a role of arc in enhancing orientation specificity in visual cortex

Cortical representations of visual information are modified by an animal's visual experience. To investigate the mechanisms in mice, we replaced the coding part of the neural activity-regulated immediate early gene Arc with a GFP gene and repeatedly monitored visual experience-induced GFP expression in adult primary visual cortex by in vivo two-photon microscopy. In Arc-positive GFP heterozygous mice, the pattern of GFP-positive cells exhibited orientation specificity. Daily presentations of the same stimulus led to the reactivation of a progressively smaller population with greater reactivation reliability. This adaptation process was not affected by the lack of Arc in GFP homozygous mice. However, the number of GFP-positive cells with low orientation specificity was greater, and the average spike tuning curve was broader in the adult homozygous compared to heterozygous or wild-type mice. These results suggest a physiological function of Arc in enhancing the overall orientation specificity of visual cortical neurons during the post-eye-opening life of an animal.

Spike sorting: Bayesian clustering of non-stationary data

Spike sorting involves clustering spikes recorded by a micro-electrode according to the source neurons. It is a complicated task, which requires much human labor, in part due to the non-stationary nature of the data. We propose to automate the clustering process in a Bayesian framework, with the source neurons modeled as a non-stationary mixture-of-Gaussians. At a first search stage, the data are divided into short time frames, and candidate descriptions of the data as mixtures-of-Gaussians are computed for each frame separately. At a second stage, transition probabilities between candidate mixtures are computed, and a globally optimal clustering solution is found as the maximum-a-posteriori solution of the resulting probabilistic model. The transition probabilities are computed using local stationarity assumptions, and are based on a Gaussian version of the Jensen-Shannon divergence. We employ synthetically generated spike data to illustrate the method and show that it outperforms other spike sorting methods in a non-stationary scenario. We then use real spike data and find high agreement of the method with expert human sorters in two modes of operation: a fully unsupervised and a semi-supervised mode. Thus, this method differs from other methods in two aspects: its ability to account for non-stationary data, and its close to human performance.

Adaptive filtering enhances information transmission in visual cortex

Sensory neuroscience seeks to understand how the brain encodes natural environments. However, neural coding has largely been studied using simplified stimuli. In order to assess whether the brain's coding strategy depends on the stimulus ensemble, we apply a new information-theoretic method that allows unbiased calculation of neural filters (receptive fields) from responses to natural scenes or other complex signals with strong multipoint correlations. In the cat primary visual cortex we compare responses to natural inputs with those to noise inputs matched for luminance and contrast. We find that neural filters adaptively change with the input ensemble so as to increase the information carried by the neural response about the filtered stimulus. Adaptation affects the spatial frequency composition of the filter, enhancing sensitivity to under-represented frequencies in agreement with optimal encoding arguments. Adaptation occurs over 40 s to many minutes, longer than most previously reported forms of adaptation.

Computing spike directivity with tetrodes

The ability of neurons to generate electrical signals is strongly dependent on the evolution of ion-specific pumps and channels that allow the transfer of charges under the influence of electric fields and concentration gradients. This paper presents a novel method by which flow of these charge fluxes may be computed to provide directivity of charge movement. Simulations of charge flow as well as actual electrophysiological data recorded by tetrodes are used to demonstrate the method. The propagation of charge fluxes in space in data from simulation and actual recordings during action potential can be analyzed using signals recorded by tetrodes. Variation in spike directivity can be estimated by computing singular value decomposition of the estimated 3D trajectory data. The analysis of the spike model can be accomplished by performing simulations of presumed equivalent moving charges recorded by the tetrode tips. For in vivo spike recordings, the variation of spike directivity could be obtained using several spikes of selected neurons considering the charge movement model (CMM). The relationship between computer simulation results and tetrode data recordings is examined. The paper concludes by showing that the method for calculating directivity in actual spike recordings is robust. The method allows for improved filtering of data and more importantly may shed light on furthering the study of spatio-temporal encoding in neurons.

Real-time and automatic sorting of multi-neuronal activity for sub-millisecond interactions in vivo

Recent in vitro electrophysiological studies have revealed that neighboring interneurons interact with each other in a sub-millisecond time range via gap junctions and that individual dendritic compartments generate local excitation spikes and back-propagated spikes within a single-neuron. However, most in vivo electrophysiological studies using behaving animals only focus on activity rates of single-neurons and/or large neuronal populations without considering the potential role of such sub-millisecond interactions among neurons. This neglect is due to the limitation of ordinary in vivo multi-neuronal recording and spike sorting techniques applied to behaving animals. Though independent component analysis (ICA) is a powerful method to overcome certain limitations, ICA has a serious problem in that the number of single-electrodes (microwires) must be more than the number of single-neurons to be recorded. Our recently-developed method has solved this limitation of ICA, but a few problems have remained: the computational load is heavy, the method can be used only for off-line, not real-time, processing, and the electrode-neuron drift problem remains unsolved. In this paper, solving all these problems, we introduce a novel system consisting of automatic and real-time spike sorting with ICA in combination with a newly developed multi-electrode, dodecatrode. The system has the potential to answer some important neurobiological questions that have not been explored in in vivo electrophysiological experiments: how sub-millisecond interactions between closely neighboring single-neurons act in freely behaving animals. The system promises to be a bridge connecting electrophysiological studies in vitro and in vivo.

Probing feature selectivity of neurons in primary visual cortex with natural stimuli

One way to characterize neural feature selectivity is to model the response probability as a nonlinear function of the output of a set of linear filters applied to incoming signals. Traditionally these linear filters are measured by probing neurons with correlated Gaussian noise ensembles and calculating correlation functions between incoming signals and neural responses. It is also important to derive these filters in response to natural stimuli, which have been shown to have strongly non-Gaussian spatiotemporal correlations. An information-theoretic method has been proposed recently for reconstructing neural filters using natural stimuli in which one looks for filters whose convolution with the stimulus ensemble accounts for the maximal possible part of the overall information carried the sequence of neural responses. Here we give a first-time demonstration of this method on real neural data, and compare responses of neurons in cat primary visual cortex driven with natural stimuli, noise ensembles, and moving gratings. We show that the information-theoretic method achieves the same quality of filter reconstruction for natural stimuli as that of well-established white-noise methods. Major parameters of neural filters derived from noise ensembles and natural stimuli, as well as from moving gratings are consistent with one another. We find that application of the reverse correlation method to natural stimuli ensembles leads to significant distortions in filters for a majority of studied cells with non-zero reverse-correlation filter.