Young Investigator Abstracts Resampling-based methods for multiple hypothesis testing in neurophysiological data Asohan Amarasingham(1), Matthew Harrison(2), Gyuri Buzsaki(1) (1) Rutgers University (2) Carnegie Mellon University Many problems in neurophysiological data analysis motivate the use of resampling-based hypothesis tests. This is because some of the uncertainties intrinsic to the experimental setup can cast doubt on a parametric framework, and also because many hypotheses of experimental interest can naturally be translated into resampling schemes. One example is the use of trial shuffling to test hypotheses about differences in spiking activity across experimental conditions, as in analyses of the peri-stimulus time histogram (PSTH). Another example is the use of spike jitter to detect the existence of finely timed spiking relationships in spike trains, over and above the cruder effects of slowly-varying firing rates. Here, we point out that resampling-based techniques can be extended in a simple way to accomodate controls over simultaneous inference. Such extensions have been described previously, but appear to be somewhat overlooked in many applications to neurophysiology. The need for such controls arises frequently because many statistics of interest are multiple-valued and indexed by continuous variables such as time, space, or other stimulus-dependent dimensions. For example, in PSTH analysis, it would be useful to confirm differences across two experimental conditions, but also to identify the subset of times at which the neurons' responses differ. We illustrate these ideas in the context of several typical problems in multi-unit data analysis, such as i) identifying differences in the PSTH across experimental conditions, ii) separating the effects of firing rate from correlations, and iii) detecting monosynaptic interactions in a population of neurons, along with their variation as a function of time and/or space. We draw examples from simulated spike trains that are characterized by nontrivial trial-to-trial variability as well as from experimental multi-unit data recorded from the hippocampus and prefrontal cortex of behaving rats in a delayed alternation task. We also provide some discussion of the mathematical assumptions underlying such controls, and some guidance in their interpretation. Email: asohan@andromeda.rutgers.edu --------------------------------------------------------- --------------------------------------------------------- Spectral analysis of extracellular neuronal signals: A tale of two oscillations Izhar Bar-Gad Bar-Ilan University Spectral analysis of neuronal data is an increasingly common tool in uncovering activity patterns characteristic of both normal behavior and abnormal pathological states. However, analysis of the same oscillatory phenomenon may yield vastly different results based on the type of neurophysiological signal studied. Processing of multi-electrode extracellular recordings yields multiple representations of the original recorded signal: local field potential (low-pass filtered signal), background activity (rectified high-pass filtered signal), multi-unit and single unit activity (spike trains of multiple and single neurons respectively). These signals reflect different properties of the population of neurons surrounding the electrodes. Comparative spectral assessment of these signals allows the formation of a generalized view of the oscillatory processes affecting single neurons and their surrounding networks. These spectral analysis methods are demonstrated via the analysis of oscillatory activity characteristic to Parkinson's disease in human patients undergoing deep brain stimulation (DBS) electrode implantation surgery. This analysis yields new insights regarding the nature of the oscillatory processes and their relation to both anatomy and clinical manifestation of the disease. Email: bargad@gmail.com --------------------------------------------------------- --------------------------------------------------------- The distance between the (initial) eye and arm position is coded in PPC using a gain field Steve W.C. Chang and Lawrence H. Snyder Department of Anatomy & Neurobiology Washington University School of Medicine St. Louis, MO To examine the frame of reference used by the neurons in the posterior parietal cortex during reaching movement planning, we collected neuronal data while monkeys planned reaches to targets at different locations using either the contralateral or ipsilateral arm. Target, and starting eye and arm positions were varied across trials. Then, we fit these neuronal data to a non-linear model, in which cell activity depended on target location with respect to the weighted eye and arm positions. The model also contained linear gain field terms for eye and arm positions to account for activity modulation that is independent from tuning shifts. We asked how data from each cell would fit this general model. We found that cells in the posterior parietal cortex use diverse frames of reference, ranging from pure eye-centered to pure arm-centered, though there is a population bias for representing targets in an eye-centered coordinate. This bias was notably stronger on ipsilateral limb compared to contralateral limb trials, suggesting that contralateral limb target representation is more evolved toward the motor domain. Interestingly, for each cell, the gain fields for eye and arm positions were negatively correlated. The strengths of the eye and arm gain fields were roughly matched in individual cells, suggesting that it is the distance between the eye and arm, and not the individual positions of the eye and arm per se, that is important. Indeed, a model using a single gain field term for the distance between eye and arm outperformed the more general model for the vast majority of single neurons. Previous studies have argued that neurons in the parietal reach region and medial intraparietal area code targets with respect to eye position (eye-centered). Cells in dorsal premotor cortex have been described as encoding a complex representation of target and arm position in eye-centered coordinates and target position in arm-centered coordinates. We now describe a simpler representation in parietal cortex, in which ~80% of neurons are best described as encoding target location relative to a point intermediate between the starting eye and arm position, gain-modulated by the distance between the eye and arm. Support Contributed By: NEI Email: steve@eye-hand.wustl.edu --------------------------------------------------------------------------- --------------------------------------------------------------------------- Relating correlation and firing rate in populations of spiking neurons Brent Doiron, University of Pittsburgh Jaime de la Rocha, New York University Eric Shea-Brown, University of Washington Kresimir Josic, University of Houston Alex Reyes, New York University Temporal correlations between spike trains from distinct neurons are widely reported throughout cortical and subcortical areas. However, the impact that correlations have on stimulus coding remains unclear. Using a pair of uncoupled neurons we study the transfer of input current correlations to the correlations in output spike trains. In vitro experiments show that for fixed input correlation coefficient the output correlation coefficient scales with the firing rates of the neurons, and is surprisingly independent of the spike train variance (de la Rocha et al., 2007; Shea-Brown et al., 2008). Analysis of integrate-and-fire model neurons allow for an explicit expression relating the correlation coefficient of the input current and output spike train correlation. Using our theory we show that the correlation-rate relationship obtained from experiments is robust to input heterogeneities, and is a consequence of the threshold nonlinearity inherent to spiking dynamics. The link between correlation and firing rate has interesting consequences for stimulus coding in populations of correlated spiking neurons. An immediate result is that a stimulus tuning of firing rates implies a similar tuning in the spike correlation of a population of neurons. Calculating the Fisher information of a population of correlation-tuned neurons we show how for certain parameter regimes spike correlation can increase the stimulus coding of the population. Extending this result to a two-layer network we show how the correlation-rate relation significantly enhances the amount of propagated information over the network. This is accomplished by mitigating the sometimes deleterious impact of spike correlations on coding with the known benefits of correlation on propagating activity to downstream populations. de la Rocha, Doiron, Shea-Brown, Josic, Reyes, Nature, 448:802-806, 2007 Shea-Brown, Josic, de la Rocha, Doiron, Phys. Rev. Lett., In press, 2008. Email: bdoiron@pitt.edu --------------------------------------------------------------------------- --------------------------------------------------------------------------- Time Series Analyses of the Dependence Structure of Hybrid Data using Mutual Information with an Example of Neurophysiological Data Apratim Guha Department of Statistics and Applied Probability, National University of Singapore, Singapore (joint work with Atanu Biswas, Applied Statistics Unit, Indian Statistical Institute} We propose a model that captures the inter-relationship between spike trains emanating from neurons in the temporal cortex of mice, which is a part of the auditory cortex, and local field potentials in the vicinity. The need is a model which justifiably combines model for a spike train with for the continuous local field potential values. Towards that, we emphasize the requirement of verifying the presence of a dependence structure for the hybrid data set: there are numerous methods available for modelling univariate data, both continuous and discrete, in neuroscience literature which produce excellent results, and may be employed in the event the components of the data set are independent. However, checking for dependence of the components of a hybrid process is not an easy problem, as many of the most commonly available methods, in particular the correlation techniques, could produce erroneous results (Guha, 2005). In this paper, we study and model the interdependence of the components of hybrid data, of which some are continuous time series and the rest are point processes. The concept of correlation is often not suitable for assessing the dependence structure of hybrid data. We employ mutual information towards assessing the dependence pattern of hybrid data as a sensible alternative. We consider a time series of bivariate hybrid (mixed discrete and continuous) data, and use the concept of mutual information to describe the dependence between the components of a series and also the crossed dependence between the two series. We model and analyze the hybrid time series data from neurons in the temporal cortex of mice. We combine the idea of variable signal plus ongoing activity (VSPOA) models (Chen et al, Chaos, 2006) with an explicit stimuli model of Brown et al (Journal of Neuroscience, 1998) to propose an ARMA-type model for the hybrid process. At its simplest form, both process could be taken as AR(1)-type processes: we assume that for the continuous process the observation at time t depends on the value of the process at time (t-1), and the value of the discrete process at times (t-1) and t; whereas the value of the discrete process is assumed to be dependent on observations of both processes at time (t-1).This cross dependence-structure is validated by mutual information, which is also employed as an eyeball estimate of the goodness of fit along with traditional measures. Variants of the model are discussed and compared. Some simulation results are also given. email:staag@nus.edu.sg --------------------------------------------------------------------------- --------------------------------------------------------------------------- Neural signals correlated with recognition memory in the macaque hippocampus. Michael J. Jutras (1,2,3*). Pascal Fries (4,5), Elizabeth A. Buffalo (1,2,3,6) 1 Yerkes National Primate Research Center, Atlanta, GA. 2 Neuroscience Program, Emory University, Atlanta, GA. 3 Center for Behavioral Neuroscience, Atlanta, GA. 4 F.C. Donders Centre for Cognitive Neuroimaging, Netherlands. 5 Department of Biophysics, Radboud University Nijmegen, Netherlands. 6 Department of Neurology, Emory University School of Medicine, Atlanta, GA. Recognition memory is the ability to perceive recently encountered items as familiar. This ability is impaired following lesions to the hippocampus in both humans and non-human primates. However, despite extensive research, there is currently little evidence for recognition memory signals in the primate hippocampus. We examined hippocampal activity as monkeys performed the Visual Preferential Looking Task (VPLT), a recognition memory task which is highly sensitive to lesions of the hippocampus. This task elicited a range of behavioral performance that allowed us to examine the relationship between trial-to-trial fluctuations in recognition memory performance and neural signals recorded in the hippocampus. In the VPLT, monkeys were allowed to freely view large stimuli (110), which were presented twice each and remained on the screen as long as the monkey continued to look at them, up to a maximum looking time of 5 seconds. Recognition memory was assessed by comparing the amount of time the monkey looked at each stimulus during the first and second presentations. Overall, both monkeys demonstrated significant recognition memory as evidenced by a reduction in looking time during the second presentation relative to the first presentation (p<.001; median reduction in looking time: 67%). However, monkeys displayed a range of behavioral performance, showing good recognition memory for some stimuli (as evidenced by a large reduction in looking time from the first to the second presentation), and poor recognition memory for other stimuli (as evidenced by a small reduction in looking time). We recorded spikes and local field potentials (LFPs) from the hippocampus in two monkeys performing the VPLT. Out of 133 hippocampal multi-units, 94 (71%) responded significantly to stimulus presentation relative to the baseline pre-stimulus period. The firing rates of 28% of these visually-responsive multi-units were significantly modulated depending on whether the stimuli were novel or repeated. Importantly, this modulation in firing rate was significantly correlated with trial-to trial fluctuations in performance such that increased firing rate differences between novel and repeated trials were associated with better memory performance. We also found that visually responsive hippocampal neurons showed increased gamma-band spike-field coherence during encoding that predicted recognition memory performance. Across the population of 221 multi-unit-LFP pairs, a trial-by trial analysis revealed that enhanced gamma-band synchrony during encoding was associated with better ! memory performance. Taken together, these data suggest that memory formation in the hippocampus relies on a combination of firing rate changes at the single-cell level and altered patterns of synchronization at the population level. Email: mjutras@emory.edu --------------------------------------------------------------------------- --------------------------------------------------------------------------- Approximate methods for state-space models Shinsuke Koyama Department of Statistics and Center for the Neural Basis of Cognition Carnegie Mellon University A number of important data analysis problems in neuroscience can be solved using state-space models. The optimal estimate of the state is its conditional expectation given the observation histories, but this expectation is computationally demanding when nonlinearities are present. Various authors have therefore used Gaussian approximations to posterior densities that appear in the formulation. In the first part of the talk we investigate this approach, showing that the errors introduced by the approximation are not compounded across time. We then consider second-order expansions, and show that they can provide second-order accuracy in state estimates---but that no additional accuracy is possible by higher-order approximations. We discuss implementation of these methods and illustrate by decoding multielectrode motor cortical data. In the second part of this talk, we develop fast methods for computing the exact MAP (posterior modal) path of state-space models. If the state dynamics are linear and driven by innovations with a log-concave density, and the observation model satisfies certain standard assumptions, then the optimization problem is strictly concave and may be solved rapidly with Newton's method because the Hessian of the loglikelihood is block tridiagonal. We describe applications of this approach to decoding problems. Finally, we have developed real-time software which implements our decoding methods in conjunction with a brain-machine interface. We hope to be able to show results from a monkey using these methods to control 3-dimensional robotic movement. Parts of this work have been done in collaboration with Rob Kass, Lucia Castellanos Perez-Bolde, Cosma Rohilla Shalizi, Liam Paninski, Meel Velliste, Andrew Whitford in the Schwartz lab. Email: koyama@stat.cmu.edu --------------------------------------------------------------------------- --------------------------------------------------------------------------- Representational Similarity Analysis -- a framework for relating computational theory, brain activity, and behavior Nikolaus Kriegeskorte A fundamental challenge for systems neuroscience is to quantitatively relate its three major branches of research: behavioral experimentation, brain-activity experimentation, and computational modeling. We suggest relating the three branches by quantitatively comparing representational dissimilarity matrices. For each pair of experimental conditions (e.g. each pair of stimuli), the representational dissimilarity matrix contains an entry reflecting the dissimilarity of the activity patterns associated with the two conditions. Intuitively, the dissimilarity matrix encapsulates the information carried by a given representation in a brain or computational model. Using the representational dissimilarity matrix as the signature of each representation allows quantitative comparisons between representations without the need for a spatial correspondency mapping (defining, for example, which neuron corresponds to which unit of a computational model). We can relate representations measured in biological brains to representations in computational network models. We can also relate representations between different brain regions, as well as between different individuals and species. Moreover, we can relate different modalities of brain activity measurement (e.g. single-cell recording and fMRI) for a given brain representation. Our approach requires fewer assumptions than formal information-theoretic alternatives and can handle designs with large numbers of conditions (e.g. many stimuli). We demonstrate this method, called ``representational similarity analysis'', by relating object representations between monkey and human IT (measured with single-cell recording and fMRI, respectively) and several computational models. The representational dissimilarity matrices are simultaneously related via second-level application of multidimensional scaling and tested for relatedness and distinctness using randomization and bootstrap techniques. Email: kriegeskorten@mail.nih.gov --------------------------------------------------------------------------- --------------------------------------------------------------------------- Large-scale parameter estimation and dynamic source localization for the magnetoencephalography (MEG) inverse problem Camilo Lamus (1,2) Simona Temereanca (1,3) C.J. Long (1,3) Matti S. Hdmdldinen (3,4) E.N Brown (1,2,4) P.L. Purdon (1,2,3) 1 Dept. of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 2 Department of Anaethesia and Critical Care, Massachusetts General Hospital, Boston, MA. 3 MGH/MIT/HMS Martinos Center for Biomedical Imaging, Charlestown, MA. 4 Harvard-MIT Division of Health Science and Technology, Cambridge, MA Dynamic estimation methods based on linear state-space models have been applied to the inverse problem in magnetoencephalography (MEG), and can improve source localization compared with static methods by incorporating temporal continuity as a constraint. The efficacy of these methods is influenced by how well the state-space model approximates the dynamics of the underlying brain current sources. While some components of the state-space model can be defined from brain anatomy and knowledge of the noise structure, parameters governing the temporal evolution of underlying current sources, i.e. the state noise covariance matrix, are unknown and must be chosen on an ad-hoc basis or estimated from data. We applied the Expectation-Maximization (EM) algorithm to estimate the state noise covariance matrix and dipole sources in an MEG state-space model. This estimation problem is a challenging one due to the size of the state space, typically involving > 5,000 sources to accurately represent the cortical surface. We previously demonstrated in small-scale simulation and ROI-based studies (100's of dipoles) that the resulting source estimates were superior to those provided by static Minimum Norm Estimate (MNE) or dynamic Fixed-Interval Smoother (FIS) employing ad hoc parameter selection. In this work, we extend our previous result to demonstrate that the EM algorithm outperforms the MNE and FIS methods both in a large-scale simulation study and in an analysis of human mu rhythm data. Email: lamus@mit.edu --------------------------------------------------------------------------- --------------------------------------------------------------------------- Markov Models for Neuronal Spike Trains Jeffrey Liebner Lafayette College Sequences of action potentials from neurons, known as spike trains, can be modeled as point processes. One of the simplest ways to model spike times is to assume that they follow a Poisson process. However, this assumption is insufficient when modeling within-trial firing behavior. Generally, the firing behavior of a neuron is dependent upon the past spiking history of the neuron. Neurons have a refractory period during which, after each spike, the probability of firing is greatly reduced. In addition, during repeated trials of an experiment, the firing rate of a neuron may exhibit trial-dependent, slowly-varying changes. This produces excess trial-to-trial variation, beyond what would be expected by random chance. Such trial dependent effects include latency and excitability. Furthermore, the firing patterns of neurons may be correlated with outside sources, such as oscillatory behavior in the brain or the firing of neighboring neurons. These behaviors of neurons lead to significant deviations from the simple Poisson process. In order to incorporate the past spiking behavior of the neuron, a Markov model, namely the inhomogeneous Markov interval process, has previously been proposed. However, in order to accommodate the other effects that are seen, this model has been expanded, allowing it to account for latency and excitability effects. The phase of any recorded oscillatory behavior is also included as a predictor of spiking probability, with the phase being is estimated by smoothing and the Hilbert transform. A Gibbs sampling procedure is utilized to estimate the effect of each aspect. This procedure also applies the mechanism of Bayesian Adaptive Regression Splines (BARS) to obtain smoothed estimates of each effect. The model has been utilized to analyze the variations in the firing behavior of neurons in the locust brain. The work of Mark Stopfer at the National Institutes of Health has revealed variations in the neuronal response of locusts over repeated experimentation, especially with regard to excitability and relation to the oscillations of local field potential. By applying the model to data supplied by the Stopfer lab, these variations were able to be quantified. This presentation will discuss the proposed Markov model and its applications to neuronal data. Email: liebnerj@lafayette.edu --------------------------------------------------------------------------- --------------------------------------------------------------------------- Wide-band local field potential phase predicts time-dependent single unit firing in awake auditory cortex Robert C. Liu and Edgar Galindo-Leon Department of Biology, Emory University Single units (SU) are considered the gold standard for studying neural activity. However, these can be difficult to isolate on electrodes, a problem further exacerbated by modern electrophysiology's move towards multielectrode paradigms. Furthermore, SU's by definition represent only the activity of individual neurons, whereas there is an increasing interest in how populations of neurons behave. These are some of the reasons local field potentials (LFP) have gained prominence as a tool for monitoring local neural networks in vivo. LFP's are believed to reflect slow, non-spiking neural events such as synaptic input and voltage-gated membrane fluctuations that are synchronized across a nearby population of geometrically-aligned neurons. But how does the activity of this local neural network relate to the spiking of one of its constituent SU's? This question was investigated in the auditory cortex of awake, head-restrained mice listening to species-specific communication calls. Well-isolated SU's were recorded (300-6000 Hz) on a high-impedance (4-6 Mohm) tungsten electrode simultaneously with LFP's that were separately filtered and recorded (2-100 Hz). A recent study of LFP's in anesthetized rat barrel cortex found that the wide-band LFP phase (extracted by Hilbert transform) just prior to whisker stimulation was correlated with the magnitude of the multiunit response, integrated over 50 ms (Haslinger et al, 2006). In our analysis, we also decomposed the LFP into amplitude and phase components, but focused instead on the temporal trajectories of their distributions around a SU spike. We then applied a Bayesian algorithm to predict the post-stimulus time histogram of the SU's response to the communication calls. Our results show that the Hilbert phase is a better predictor of the time course of the SU response than either the Hilbert amplitude or the raw LFP. Moreover, since even the LFP phase distributions derived from spontaneous periods of spiking could predict the stimulus-driven SU response, it appears that the SU's relationship with its local neural network is relatively stable during these different states. Finally, our results indicate that on average, the local network activity seems to follow SU firing, suggesting that the SU's in our recordings tend to drive LFP fluctuations. In summary, our analysis methods provide a new tool for in-depth characterization of the relationship between SU and LFP signals. References Haslinger R. et al, J Neurophysiol. 96(3):1658-63, 2006. Email: robert.liu@emory.edu --------------------------------------------------------------------------- --------------------------------------------------------------------------- Trajectory prediction in rats using an analysis of empirical firing distributions of hippocampal place cells Michael Prerau, Uri Eden Boston University Program in Neuroscience It has been well established that certain hippocampal place cells fire differentially in the same spatial region under different contextual conditions. Previous analyses make several assumptions about place field data that greatly reduce their ability to describe stochastic structure and neural dynamics associated with these cells. Common assumptions about the receptive field characteristics of these neurons hold that the number of counts in any interval has either a normal or inhomogeneous Poisson distribution with means determined by a Gaussian spatial receptive field shape. An analysis of the empirical data shows that in the majority of cases, these assumptions do not hold true. The conventional approach to determining whether a neuron fires differentially under different condition using a low resolution two-way ANOVA, relies extensively on this erroneous assumption. Another widespread assumption that is often not borne out by the data is that the firing properties of t! hese neurons are stationary. We present a Bayesian framework to characterize empirical distributions of neural firing. This method offers greater spatial resolution then previous methods and can capture the complex probability distributions that compose place cell receptive fields. The Bayesian framework allows us to compare the neuronal activity under different contexts as well as to compute the likelihood of newly observed data under the distributions for each context. Our method also provides a quantitative metric of the degree of differential firing shown in a given neuron. One can expand this method to perform population analyses and decode or predict context from population activity. We demonstrate the method by predicting the direction that a rat will turn on a T-maze from firing on the maze stem. Email: prerau@bu.edu --------------------------------------------------------------------------- --------------------------------------------------------------------------- Bayesian Estimation of the time-varing Rate and Irregularity of Neuronal Spiking Takeaki Shimokawa and Shigeru Shinomoto Graduate School of Science, Kyoto University, Sakyo-ku, Kyoto 606-8502, Japan We have recently revealed that there is an aspect of time-local firing irregularity that is invariant with the time and the firing rate for individual cortical neurons in vivo[1,2]. Conversely, Davies et al reported that the time-local firing irregularity varied significantly according to behavioral contexts in some other cortical area[3]. We wish to investigate here the cause of apparent disagreement of these conclusions by thoroughly examining how easily the firing patterns are modified with the firing rate. For this purpose, we develop here the Bayes method that allows us to estimate both the instantaneous rate and irregularity for a given spike sequence: We first consider the process of generating spikes under a given rate and irregularity, and then invert the conditional probability distribution to infer the rate and the irregularity from the data, with the prior distributions penalyzing the large gradient of the parameters. The penalty hyperparameters are determined by maximizing the (marginal) likelihood, which can be carried out numerically using the expectation-maximization (EM) method and the Kalman filter under the assumption that the distributions of the two parameters are Gaussian. We applied the present Bayesian estimation method to the experimentally recorded spike data taken from Neural Signal Archive[4], and revealed that there is a systematic correlation between firing rate and irregularity, and that the degree of the variability in the firing irregularity greatly depends on the cortical areas. [1] Shinomoto S., Shima K. and Tanji J. (2003) Differences in Spiking Patterns Among Cortical Neurons. Neural Comput 15, 2823-2842. [2] Shinomoto S., Miyazaki Y., Tamura H. and Fujita I. (2005) Regional and Laminar Differences in In Vivo Firing Patterns of Primate Cortical Neurons. J Neurophysiol 94, 567-575. [3] Davies R.M., Gerstein G.L. and Baker S.N. (2006) Measurement of Time-Dependent Changes in the Irregularity of Neural Spiking. J Neurophysiol 96, 906-918. [4] Neural Signal Archive, available online at http://www.neuralsignal.org. Email: shimokawa@ton.scphys.kyoto-u.ac.jp --------------------------------------------------------------------------- --------------------------------------------------------------------------- Uncovering higher-order synchrony within massively parallel spike trains Benjamin Staude [1], Stefan Rotter [2,3], Sonja Gruen [1,4] 1. Unit of Statistical Neuroscience, Theoretical Neuroscience Group, Brain Science Institute, RIKEN, Wako, Japan 2. Theory and Data Analysis, Institute for Frontier Areas of Psychology and Mental Health, Freiburg, Germany 3. Bernstein Center for Computational Neuroscience Freiburg, Germany 4. Bernstein Center for Computational Neuroscience Berlin, Germany The cell assembly hypothesis [1] postulates dynamically interacting groups of neurons as building blocks of cortical information processing. Synchronized spiking across large neuronal groups was later suggested as a potential signature for active assemblies [2], and analysis methods for assembly detection focussed on the estimation of higher-order correlations among simultaneously recorded neurons [3,4]. However, the number of parameters in presently available techniques grows exponentially with the number of recorded neurons, which requires vast sample sizes. As a consequence, most attempts to detect active cell assemblies resort to pairwise interactions. These, however, do not allow to infer on large synchronized neuronal pools, and are insensitive for sparse synchronous events [5]. As massively parallel extracellular recordings are becoming available, the limited experimental evidence in favor of the cell assembly hypothesis has been assigned, among other things, to a lack of suitable analysis tools [6]. Here we present a novel procedure to detect synchronized spiking in large neuronal pools that circumvents the need for extensive sample sizes. Based on estimates of only a few low-order cumulants of the superimposed and discretely sampled activity of all recorded neurons (population spike counts) we devise a statistical test for the presence of higher-order synchrony among the spike trains. The test exploits the simple fact that absence of higher-order synchrony imposes constraints on correlations of lower order, which can be estimated via the respective cumulants of the population spike counts. The inference of higher-order synchrony from measured cumulants of lower order circumvents the need to estimate large numbers of higher-order parameters and therefore is less susceptible to the limited sample sizes from in vivo recordings than previous approaches. The method is tested for correlated Poisson processes, where correlations of various orders are induced by 'inserting' appropriate patterns of near-synchronous spikes [7]. When applied to simulated data, the test is surprisingly sensitive for higher-order synchrony present in the data, even if their effects on pairwise correlation coefficients c are very small (in the range of c ~ 0.01, compare cf. [5]). The applicability of the method is illustrated by estimates for the required sample sizes and the robustness against deviations from the Poisson assumption. Acknowledgments. Funded by NaFoG Berlin, the German Ministry for Education and Research (BMBF grants 01GQ01413 and 01GQ0420), and the Stifterverband fur die Deutsche Wissenschaft References. 1. Hebb. Organization of behavior. Wiley, 1949 2. Abeles. Local cortical circuits. Springer, 1982 3. Martignon et al. Biol Cyber 1995, 73:69-81 4. Nakahara & Amari. Neural Comput 14:2296-2316, 2002 5. Schneidman et al. Nature 440:1007-1012, 2006 6. Brown et al. Nat Neurosci 7:456-461, 2004 7. Ehm et al. EJS 1:473-495, 2007 Email: staude@brain.riken.jp --------------------------------------------------------------------------- --------------------------------------------------------------------------- Model-based optimal inference of spike times and calcium dynamics given noisy and intermittent calcium- fluorescence imaging Joshua Vogelstein, Department of Neuroscience, Johns Hopkins School of Medicine. Liam Paninski, Department of Statistics, Columbia University. As recent advances in calcium sensing technologies enable us to simultaneously image many neurons, complementary analytical tools must also be developed to maximize the utility of this experimental paradigm. While the observations are fluorescence movies, the signals of interest are the spike trains and/or time- varying intracellular calcium concentrations, [Ca2+]. Inferring the value of these "hidden states" is often problematic for a number of reasons: (i) observations are inherently both noisy and intermittent, (ii) the relationship between fluorescence, [Ca2+], and spike trains is nonlinear, and (iii) the parameters governing the dynamics of these states are typically unknown. We develop a number of algorithms designed to optimally infer precise spike times and [Ca2+] dynamics, given the above features. In particular, we develop 3 iterative algorithms: (i) a log-barrier method for non-negative deconvolution, (ii) a "greedy" projection pursuit regression for further constraining the inferred spike train, and (iii) a particle filtering approach for computing the marginal distribution of [Ca2+] and spikes at each time step, conditioned on all the fluorescence observations. Each algorithm recursively infers the underlying spike train, and then estimates the model parameters. All of these approaches facilitate incorporating stimulus information, potentially improving the inference fidelity over previously described methods. The methods also differ somewhat from one another. The first two assume a relatively simple observation and spiking model, and can therefore be made extremely efficient, costing O(T) time per iteration. We use these fast methods to initialize the parameters for the more sophisticated, but more computationally expensive particle filtering, which confers several advantages over the fast methods. First, nonlinear dye saturation is incorporated into the model. Second, spike inference can consider refractoriness - or even coupling from other neurons - if that information is available. Third, because we compute the marginal distribution of spikes at each time, conditioned on all the observations, noise is better accounted for, and errorbars on the estimates may be provided. Fourth, the absolute [Ca2+] may be determined without additional experimentation or ratiometric sensors. Finally, while this method is optimal for a set of model assumptions, many of these assumptions may be relaxed for greater accuracy if desired. To test the degree to which these algorithms successfully infer spike times from fluorescence movies, analysis of experimental data is currently underway. Email: joshuav@jhu.edu --------------------------------------------------------------------------- --------------------------------------------------------------------------- Reconstructing stereotyped movement by coupling trajectory decoding and landmark-time decoding in the motor cortex Wei Wu Department of Statistics Florida State University A number of decoding methods, varying from common linear Gaussian models to more complicated point process frameworks, have been developed to infer hand movement from neuronal firing activity in the motor cortex. Most of these methods focus on estimating subject's hand trajectory in a continuous movement. We recently proposed a template-based time identification decoding approach with linear convolution and dynamic programming. We showed that if a stereotyped movement is well represented by a sequence of targets (or landmarks), then the main structure of the movement will be better addressed by detecting the reaching times at those targets. Both trajectory decoding and landmark-time decoding have advantages respectively, whereas a coupling of these two different strategies has not been examined. Here we propose the synergy that comes from combining these two approaches for a stereotyped movement under a state-space framework, where the recordings were made in the arm area of primary motor cortex in an awake behaving monkey using a chronically implanted multi-electrode array. We at first identify the target times using an improved version of the template-based method. The identification is highly accurate with median error around 60 ms. Then we include the detected targets as a linear control input in the kinematic model of the state-space formulation. Such an inclusion is justified by the empirical linear relationship between the kinematics and target position in the recorded data. Experimental results show that the coupling model includes the benefits from both approaches and significantly improves the d! ecoding accuracy. This work was done in collaboration with Zhiyi Chi and Nicholas G. Hatsopoulos. Email: wwu@stat.fsu.edu