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In JoVE (1)
Other Publications (17)
- Journal of Neurophysiology
- Journal of Neurophysiology
- Journal of Neuroscience Methods
- Journal of Neurophysiology
- Journal of Neuroscience Methods
- Journal of Neurophysiology
- Journal of Neurophysiology
- The Journal of Neuroscience : the Official Journal of the Society for Neuroscience
- The European Journal of Neuroscience
- Neural Computation
- The Journal of Neuroscience : the Official Journal of the Society for Neuroscience
- Biological Cybernetics
- Journal of Neuroscience Methods
- Proceedings of the National Academy of Sciences of the United States of America
- Cortex; a Journal Devoted to the Study of the Nervous System and Behavior
- The Journal of Neuroscience : the Official Journal of the Society for Neuroscience
- Nature Neuroscience
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Articles by Eran Stark in JoVE
JoVE Neuroscience
Grootschalige Opname van Neuronen door Movable Silicon Probes in Behaving Knaagdieren
1Center for Molecular and Behavioral Neuroscience, University of New Jersey, 2Center for Interdisciplinary Research in Biology, Collège de France, 3Janelia Farm Research Campus, Howards Hughes Medical Institute, 4Deptartment of Psychology, University of Wisconsin at Milwaukee
We beschrijven methoden voor grootschalige opname van meerdere afzonderlijke eenheden en lokale veld potentieel in zich gedragen knaagdieren met siliconen sondes. Drive fabricage, sonde gehechtheid aan het station en de sonde implanteren worden geïllustreerd in voldoende details voor het gemakkelijk reproduceren.
Other articles by Eran Stark on PubMed
Dynamical Organization of Directional Tuning in the Primate Premotor and Primary Motor Cortex
Although previous studies have shown that activity of neurons in the motor cortex is related to various movement parameters, including the direction of movement, the spatial pattern by which these parameters are represented is still unresolved. The current work was designed to study the pattern of representation of the preferred direction (PD) of hand movement over the cortical surface. By studying pairwise PD differences, and by applying a novel implementation of the circular variance during preparation and movement periods in the context of a center-out task, we demonstrate a nonrandom distribution of PDs over the premotor and motor cortical surface of two monkeys. Our analysis shows that, whereas PDs of units recorded by nonadjacent electrodes are not more similar than expected by chance, PDs of units recorded by adjacent electrodes are. PDs of units recorded by a single electrode display the greatest similarity. Comparison of PD distributions during preparation and movement reveals that PDs of nearby units tend to be more similar during the preparation period. However, even for pairs of units recorded by a single electrode, the mean PD difference is typically large (45 degrees and 75 degrees during preparation and movement, respectively), so that a strictly modular representation of hand movement direction over the cortical surface is not supported by our data.
Neuronal Activity in Motor Cortical Areas Reflects the Sequential Context of Movement
Natural actions can be described as chains of simple elements, whereas individual motion elements are readily concatenated to generate countless movement sequences. Sequence-specific neurons have been described extensively, suggesting that the motor system may implement temporally complex motions by using such neurons to recruit lower-level movement neurons modularly. Here, we set out to investigate whether activity of movement-related neurons is independent of the sequential context of the motion. Two monkeys were trained to perform linear arm movements either individually or as components of double-segment motions. However, comparison of neuronal activity between these conditions is delicate because subtle kinematic variations generally occur within different contexts. We therefore used extensive procedures to identify the contribution of variations in motor execution to differences in neuronal activity. Yet, even after application of these procedures we find that neuronal activity in the motor cortex (PMd and M1) associated with a given motion segment differs between the two contexts. These differences appear during preparation and become even more prominent during motion execution. Interestingly, despite context-related differences on the single-neuron level, the population as a whole still allows a reliable readout of movement direction regardless of the sequential context. Thus the direction of a movement and the sequential context in which it is embedded may be simultaneously and reliably encoded by neurons in the motor cortex.
Applying Resampling Methods to Neurophysiological Data
Standard statistical techniques do not always provide answers to complex physiological questions because often there are no parametric or non-parametric distributions on which significance can be estimated. Resampling methods provide a battery of tests that can be used in such circumstances. In the past few years these methods have been explored theoretically and are now employed frequently. In this paper we describe a unified framework for the use of such methods in the context of neurophysiological data analysis. We construct specific tests for placing confidence limits on estimates of mutual information and on parameters of circular data, and we present procedures for testing hypotheses on circular and on partitioned data. These tests are explained in detail and illustrated with real data from experiments with behaving monkeys.
Partial Cross-correlation Analysis Resolves Ambiguity in the Encoding of Multiple Movement Features
A classical question in neuroscience is which features of a stimulus or of an action are represented in brain activity. When several features are interdependent either at a given point in time or at distinct points in time, neural activity related to one feature appears to be correlated with other features. Thus techniques that simultaneously consider multiple features cannot account for delayed interdependencies between features. The result is an ambiguity with respect to the encoded features. Here, we resolve this ambiguity by applying a novel statistical method based on partial cross-correlations. The method yields estimates of linear correlations between neural activity and a given feature that are not affected by linear correlations with other features at multiple time delays. The method also provides a graphical output measured on a scale that allows for comparisons between different features, neurons, and experiments. We use real movement data and neural activity simulated according to a wide range of tuning models to illustrate the method. When applied to real neural activity, the procedure yields results that indicate which of the considered features the neural activity is related to and at what time delays.
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.
Encoding of Reach and Grasp by Single Neurons in Premotor Cortex is Independent of Recording Site
Neural activity has been studied during reaching and grasping separately, yet little is known about their combined representation. To study the functional organization of reaching and grasping in the premotor cortex (PM), we trained two monkeys to reach in one of six directions and grasp one of three objects. During prehensile movements, activity of proximal (shoulder and elbow) muscles was mainly modulated by reach direction, whereas distal (finger) muscles were also modulated by grasp type. Using intracortical microstimulation, we identified spatially distinct PM sites from which movements of proximal or distal joints were evoked. In contrast to muscles, modulation of neural activity by reach direction was similar for single units recorded in proximal and distal sites. Similarly, grasp type encoding was the same for units recorded in the different sites. This pattern of encoding reach and grasp irrespective of recoding site was observed throughout the task: before, during, and after prehension movements. Despite the similarities between single units within different sites, we found differences between pairs of units. Pairs of directionally selective units recorded by the same electrode in the same proximal site preferred similar reach directions but not grasp types, whereas pairs of object-selective units recorded in the same distal site tended to prefer the same grasp type but not reach direction. We suggest that the unexpected "mixing neurons" encoding reach and grasp within distal and proximal sites, respectively, provide a neural substrate for coordination between reach and grasp during prehension.
Comparison of Direction and Object Selectivity of Local Field Potentials and Single Units in Macaque Posterior Parietal Cortex During Prehension
Recent studies have shown that the local field potential (LFP) can provide a simple method for obtaining an accurate measure of reaching and saccade behaviors. However, it is not clear whether this signal is equally informative with respect to more complex movements. Here we recorded LFPs and single units (SUs) from different areas in the posterior parietal cortex of macaques during a prehension task and compared LFP selectivity with SU selectivity. We found that parietal LFPs were often selective to target direction or object and that percentages of selective LFPs were similar to percentages of selective SUs. Nevertheless, SUs were more informative than LFPs in several respects. Preferred directions and objects of LFPs usually deviated from a uniform distribution, unlike preferences of SUs. Furthermore, preferences of LFPs did not reflect preferences of SUs even when the two signals were recorded simultaneously via the same electrode. Additionally, selectivity of movement-evoked LFPs appeared only after movement onset, whereas SUs frequently showed premovement selectivity. Spectral analysis revealed a lower signal-to-noise ratio of the LFP signal. Different frequency bands derived from a single LFP site showed inconsistent preferences. Significant relations with target parameters were found for all tested bands of LFP, but effects in the fast (gamma) band exhibited properties that were consistent with contamination of the LFP by residual spiking activity. Taken together, our results suggest that the LFP provides a simple method for extracting ample movement-related information. However, some of its properties make it less adequate for predicting rapidly changing movements.
Predicting Movement from Multiunit Activity
Previous studies have shown that intracortical activity can be used to operate prosthetic devices such as an artificial limb. Previously used neuronal signals were either the activity of tens to hundreds of spiking neurons, which are difficult to record for long periods of time, or local field potentials, which are highly correlated with each other. Here, we show that by estimating multiunit activity (MUA), the superimposed activity of many neurons around a microelectrode, and using a small number of electrodes, an accurate prediction of the upcoming movement is obtained. Compared with single-unit spikes, single MUA recordings are obtained more easily and the recordings are more stable over time. Compared with local field potentials, pairs of MUA recordings are considerably less redundant. Compared with any other intracortical signal, single MUA recordings are more informative. MUA is informative even in the absence of spikes. By combining information from multielectrode recordings from the motor cortices of monkeys that performed either discrete prehension or continuous tracing movements, we demonstrate that predictions based on multichannel MUA are superior to those based on either spikes or local field potentials. These results demonstrate that considerable information is retained in the superimposed activity of multiple neurons, and therefore suggest that neurons within the same locality process similar information. They also illustrate that complex movements can be predicted using relatively simple signal processing without the detection of spikes and, thus, hold the potential to greatly expedite the development of motor-cortical prosthetic devices.
Distinct Movement Parameters Are Represented by Different Neurons in the Motor Cortex
Recent studies suggested that a single motor cortical neuron typically encodes multiple movement parameters, but parameters often display strong temporal interdependencies. To address this issue, we recorded single-unit activity while macaque monkeys made continuous movements and employed an analysis that explicitly considered temporal correlations between several kinematic parameters; hand position, velocity, and acceleration. We found that while the activity of almost all motor cortical neurons was modulated during movement, most neurons were related only to a single dominant parameter. The activity of different neurons covaried with different parameters with similar strength, but neurons related to velocity were far more common than neurons related to any other parameter. These results were obtained for neurons recorded in the primary motor (M1) and dorsal premotor (PMd) cortices. Although neural activity tended to precede movement and PMd activity tended to precede M1 activity, time lags were widely dispersed. Shoulder and elbow muscles had the same properties as neurons, but their activity strictly preceded movement. These results demonstrate single neuron specificity and heterogeneity within a population of neurons with respect to movement parameters and time lags. Our results suggest that distinct subsets of motor cortical neurons are involved in computations related to distinct movement parameters.
Dependence of Neuronal Correlations on Filter Characteristics and Marginal Spike Train Statistics
Correlated neural activity has been observed at various signal levels (e.g., spike count, membrane potential, local field potential, EEG, fMRI BOLD). Most of these signals can be considered as superpositions of spike trains filtered by components of the neural system (synapses, membranes) and the measurement process. It is largely unknown how the spike train correlation structure is altered by this filtering and what the consequences for the dynamics of the system and for the interpretation of measured correlations are. In this study, we focus on linearly filtered spike trains and particularly consider correlations caused by overlapping presynaptic neuron populations. We demonstrate that correlation functions and statistical second-order measures like the variance, the covariance, and the correlation coefficient generally exhibit a complex dependence on the filter properties and the statistics of the presynaptic spike trains. We point out that both contributions can play a significant role in modulating the interaction strength between neurons or neuron populations. In many applications, the coherence allows a filter-independent quantification of correlated activity. In different network models, we discuss the estimation of network connectivity from the high-frequency coherence of simultaneous intracellular recordings of pairs of neurons.
Correlations Between Groups of Premotor Neurons Carry Information About Prehension
How distinct parameters are bound together in brain activity is unknown. Combination coding by interneuronal interactions is one possibility, but, to coordinate parameters, interactions between neuronal pairs must carry information about them. To address this issue, we recorded neural activity from multiple sites in the premotor cortices of monkeys that memorized reach direction and grasp type followed by actual prehension. We found that correlations between individual spiking neurons are generally weak and carry little information about prehension. In contrast, correlations and synchronous interactions between small groups of neurons, quantified by multiunit activity (MUA), are an order of magnitude stronger. A substantial fraction of the information carried by pairwise interactions between MUAs is about combinations of reach and grasp. This contrasts with the information carried by individual neurons and individual MUAs, which is mainly about reach and/or grasp but much less about their combinations. The main contribution of pairwise interactions to the coding of reach-grasp combinations is when animals memorize prehension parameters, consistent with an internal composite representation. The informative interactions between neuronal groups may facilitate the coordination of reach and grasp into coherent prehension.
Parabolic Movement Primitives and Cortical States: Merging Optimality with Geometric Invariance
Previous studies have suggested that several types of rules govern the generation of complex arm movements. One class of rules consists of optimizing an objective function (e.g., maximizing motion smoothness). Another class consists of geometric and kinematic constraints, for instance the coupling between speed and curvature during drawing movements as expressed by the two-thirds power law. It has also been suggested that complex movements are composed of simpler elements or primitives. However, the ability to unify the different rules has remained an open problem. We address this issue by identifying movement paths whose generation according to the two-thirds power law yields maximally smooth trajectories. Using equi-affine differential geometry we derive a mathematical condition which these paths must obey. Among all possible solutions only parabolic paths minimize hand jerk, obey the two-thirds power law and are invariant under equi-affine transformations (which preserve the fit to the two-thirds power law). Affine transformations can be used to generate any parabolic stroke from an arbitrary parabolic template, and a few parabolic strokes may be concatenated to compactly form a complex path. To test the possibility that parabolic elements are used to generate planar movements, we analyze monkeys' scribbling trajectories. Practiced scribbles are well approximated by long parabolic strokes. Of the motor cortical neurons recorded during scribbling more were related to equi-affine than to Euclidean speed. Unsupervised segmentation of simulta- neously recorded multiple neuron activity yields states related to distinct parabolic elements. We thus suggest that the cortical representation of movements is state-dependent and that parabolic elements are building blocks used by the motor system to generate complex movements.
Unbiased Estimation of Precise Temporal Correlations Between Spike Trains
A key issue in systems neuroscience is the contribution of precise temporal inter-neuronal interactions to information processing in the brain, and the main analytical tool used for studying pair-wise interactions is the cross-correlation histogram (CCH). Although simple to generate, a CCH is influenced by multiple factors in addition to precise temporal correlations between two spike trains, thus complicating its interpretation. A Monte-Carlo-based technique, the jittering method, has been suggested to isolate the contribution of precise temporal interactions to neural information processing. Here, we show that jittering spike trains is equivalent to convolving the CCH derived from the original trains with a finite window and using a Poisson distribution to estimate probabilities. Both procedures over-fit the original spike trains and therefore the resulting statistical tests are biased and have low power. We devise an alternative method, based on convolving the CCH with a partially hollowed window, and illustrate its utility using artificial and real spike trains. The modified convolution method is unbiased, has high power, and is computationally fast. We recommend caution in the use of the jittering method and in the interpretation of results based on it, and suggest using the modified convolution method for detecting precise temporal correlations between spike trains.
The Minimum Information Principle and Its Application to Neural Code Analysis
The study of complex information processing systems requires appropriate theoretical tools to help unravel their underlying design principles. Information theory is one such tool, and has been utilized extensively in the study of the neural code. Although much progress has been made in information theoretic methodology, there is still no satisfying answer to the question: "What is the information that a given property of the neural population activity (e.g., the responses of single cells within the population) carries about a set of stimuli?" Here, we answer such questions via the minimum mutual information (MinMI) principle. We quantify the information in any statistical property of the neural response by considering all hypothetical neuronal populations that have the given property and finding the one that contains the minimum information about the stimuli. All systems with higher information values necessarily contain additional information processing mechanisms and, thus, the minimum captures the information related to the given property alone. MinMI may be used to measure information in properties of the neural response, such as that conveyed by responses of small subsets of cells (e.g., singles or pairs) in a large population and cooperative effects between subunits in networks. We show how the framework can be used to study neural coding in large populations and to reveal properties that are not discovered by other information theoretic methods.
Motor Cortical Activity Related to Movement Kinematics Exhibits Local Spatial Organization
While it is generally accepted that multiple neurons cooperate to generate movement, the precise mechanisms are largely unknown. One way to generate a robust local control signal is for nearby neurons to share similar properties. To study this possibility, we recorded neural activity from the macaque motor cortex during two drawing tasks: free scribbling, and tracing given paths. We analyzed neural activity in relation to three kinematic parameters - position, velocity, and acceleration - while explicitly considering temporal correlations between them. Single-unit (SU) activity was typically related to one parameter, most often velocity, and tended to precede movement. Different SUs encoded different parameters, but nearby units tended to prefer the same parameter. Moreover, while SUs covered a wide range of positions, velocity directions, and acceleration directions, SUs recorded by the same electrode tended to prefer similar values of the same parameter. Nevertheless, some nearby units exhibited marked differences. Multi-unit activity (MUA), estimating the spiking activity of many neurons around the recording electrode, also tended to be related to one parameter and precede movement. However, overall correlations between MUA and movement were more than twice as strong as SU correlations. Finally, SUs and MUAs recorded by the same electrode tended to share similar properties. These two lines of evidence converge to suggest that activity of motor cortex neurons within approximately 200 micrometers is accumulated in a manner useful for representing a single parameter. However, even within a small region there are also neurons related to other parameters, potentially facilitating coordination between distinct parameters.
Transcranial Electric Stimulation Entrains Cortical Neuronal Populations in Rats
Low intensity electric fields have been suggested to affect the ongoing neuronal activity in vitro and in human studies. However, the physiological mechanism of how weak electrical fields affect and interact with intact brain activity is not well understood. We performed in vivo extracellular and intracellular recordings from the neocortex and hippocampus of anesthetized rats and extracellular recordings in behaving rats. Electric fields were generated by sinusoid patterns at slow frequency (0.8, 1.25 or 1.7 Hz) via electrodes placed on the surface of the skull or the dura. Transcranial electric stimulation (TES) reliably entrained neurons in widespread cortical areas, including the hippocampus. The percentage of TES phase-locked neurons increased with stimulus intensity and depended on the behavioral state of the animal. TES-induced voltage gradient, as low as 1 mV/mm at the recording sites, was sufficient to phase-bias neuronal spiking. Intracellular recordings showed that both spiking and subthreshold activity were under the combined influence of TES forced fields and network activity. We suggest that TES in chronic preparations may be used for experimental and therapeutic control of brain activity.
GABAergic Circuits Mediate the Reinforcement-related Signals of Striatal Cholinergic Interneurons
Neostriatal cholinergic interneurons are believed to be important for reinforcement-mediated learning and response selection by signaling the occurrence and motivational value of behaviorally relevant stimuli through precisely timed multiphasic population responses. An important problem is to understand how these signals regulate the functioning of the neostriatum. Here we describe the synaptic organization of a previously unknown circuit that involves direct nicotinic excitation of several classes of GABAergic interneurons, including neuroptide Y-expressing neurogilaform neurons, and enables cholinergic interneurons to exert rapid inhibitory control of the activity of projection neurons. We also found that, in vivo, the dominant effect of an optogenetically reproduced pause-excitation population response of cholinergic interneurons was powerful and rapid inhibition of the firing of projection neurons that is coincident with synchronous cholinergic activation. These results reveal a previously unknown circuit mechanism that transmits reinforcement-related information of ChAT interneurons in the mouse neostriatal network.
