Results 1  10
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58
Maximum likelihood estimation of a stochastic integrateandfire neural model
 NIPS
, 2003
"... We examine a cascade encoding model for neural response in which a linear filtering stage is followed by a noisy, leaky, integrateandfire spike generation mechanism. This model provides a biophysically more realistic alternative to models based on Poisson (memoryless) spike generation, and can eff ..."
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Cited by 59 (20 self)
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We examine a cascade encoding model for neural response in which a linear filtering stage is followed by a noisy, leaky, integrateandfire spike generation mechanism. This model provides a biophysically more realistic alternative to models based on Poisson (memoryless) spike generation, and can effectively reproduce a variety of spiking behaviors seen in vivo. We describe the maximum likelihood estimator for the model parameters, given only extracellular spike train responses (not intracellular voltage data). Specifically, we prove that the log likelihood function is concave and thus has an essentially unique global maximum that can be found using gradient ascent techniques. We develop an efficient algorithm for computing the maximum likelihood solution, demonstrate the effectiveness of the resulting estimator with numerical simulations, and discuss a method of testing the model’s validity using timerescaling and density evolution techniques. Paninski et al., November 30, 2004 2 1
Estimating a StateSpace Model from Point Process Observations
, 2003
"... A widely used signal processing paradigm is the statespace model. The statespace model is defined by two equations: an observation equation that describes how the hidden state or latent process is observed and a state equation that defines the evolution of the process through time. Inspired by neu ..."
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Cited by 39 (4 self)
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A widely used signal processing paradigm is the statespace model. The statespace model is defined by two equations: an observation equation that describes how the hidden state or latent process is observed and a state equation that defines the evolution of the process through time. Inspired by neurophysiology experiments in which neural spiking activity is induced by an implicit (latent) stimulus, we develop an algorithm to estimate a statespace model observed through point process measurements. We represent the latent process modulating the neural spiking activity as a gaussian autoregressive model driven by an external stimulus. Given the latent process, neural spiking activity is characterized as a general point process defined by its conditional intensity function. We develop an approximate expectationmaximization (EM) algorithm to estimate the unobservable statespace process, its parameters, and the parameters of the point process. The EM algorithm combines a point process recursive nonlinear filter algorithm, the fixed interval smoothing algorithm, and the statespace covariance algorithm to compute the complete data log likelihood efficiently. We use a KolmogorovSmirnov test based on the timerescaling theorem to evaluate agreement between the model and point process data. We illustrate the model with two simulated data examples: an ensemble of Poisson neurons driven by a common stimulus and a single neuron whose conditional intensity function is approximated as a local Bernoulli process.
Dynamic Analyses of Information Encoding in Neural Ensembles
 Neural Computation
, 2004
"... Neural spike train decoding algorithms and techniques to compute Shannon
mutual information are important methods for analyzing how neural
systems represent biological signals.Decoding algorithms are also one of
several strategies being used to design controls for brainmachine interfaces.
Developin ..."
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Cited by 22 (1 self)
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Neural spike train decoding algorithms and techniques to compute Shannon
mutual information are important methods for analyzing how neural
systems represent biological signals.Decoding algorithms are also one of
several strategies being used to design controls for brainmachine interfaces.
Developing optimal strategies to desig n decoding algorithms and
compute mutual information are therefore important problems in computational
neuroscience. We present a general recursive lter decoding
algorithm based on a point process model of individual neuron spiking
activity and a linear stochastic statespace model of the biological signal.
We derive from the algorithm new instantaneous estimates of the entropy,
entropy rate, and the mutual information between the signal and
the ensemble spiking activity. We assess the accuracy of the algorithm
by computing, along with the decoding error, the true coverage probability
of the approximate 0.95 condence regions for the individual signal
estimates. We illustrate the new algorithm by reanalyzing the position
and ensemble neural spiking activity of CA1 hippocampal neurons from
two rats foraging in an open circular environment. We compare the performance
of this algorithm with a linear lter constructed by the widely
used reverse correlation method. The median decoding error for Animal
1 (2) during 10 minutes of open foraging was 5.9 (5.5) cm, the median
entropy was 6.9 (7.0) bits, the median information was 9.4 (9.4) bits, and
the true coverage probability for 0.95 condence regions was 0.67 (0.75)
using 34 (32) neurons. These ndings improve signicantly on our previous
results and suggest an integrated approach to dynamically reading
neural codes, measuring their properties, and quantifying the accuracy
with which encoded information is extracted.
Modelbased decoding, information estimation, and changepoint detection in multineuron spike trains
 UNDER REVIEW, NEURAL COMPUTATION
, 2007
"... Understanding how stimulus information is encoded in spike trains is a central problem in computational neuroscience. Decoding methods provide an important tool for addressing this problem, by allowing us to explicitly read out the information contained in spike responses. Here we introduce several ..."
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Cited by 19 (12 self)
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Understanding how stimulus information is encoded in spike trains is a central problem in computational neuroscience. Decoding methods provide an important tool for addressing this problem, by allowing us to explicitly read out the information contained in spike responses. Here we introduce several decoding methods based on pointprocess neural encoding models (i.e. “forward ” models that predict spike responses to novel stimuli). These models have concave loglikelihood functions, allowing for efficient fitting via maximum likelihood. Moreover, we may use the likelihood of the observed spike trains under the model to perform optimal decoding. We present: (1) a tractable algorithm for computing the maximum a posteriori (MAP) estimate of the stimulus — the most probable stimulus to have generated the observed single or multiplespike train response, given some prior distribution over the stimulus; (2) a Gaussian approximation to the posterior distribution, which allows us to quantify the fidelity with which various stimulus features are encoded; (3) an efficient method for estimating the mutual information between the stimulus and the response; and (4) a framework for the detection of changepoint times (e.g. the time at which the stimulus undergoes a change in mean or variance), by marginalizing over the posterior distribution of stimuli. We show several examples illustrating the performance of these estimators with simulated data.
Multiflow Attacks Against Network Flow Watermarking Schemes
"... We analyze several recent schemes for watermarking network flows based on splitting the flow into intervals. We show that this approach creates time dependent correlations that enable an attack that combines multiple watermarked flows. Such an attack can easily be mounted in nearly all applications ..."
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Cited by 15 (4 self)
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We analyze several recent schemes for watermarking network flows based on splitting the flow into intervals. We show that this approach creates time dependent correlations that enable an attack that combines multiple watermarked flows. Such an attack can easily be mounted in nearly all applications of network flow watermarking, both in anonymous communication and stepping stone detection. The attack can be used to detect the presence of a watermark, recover the secret parameters, and remove the watermark from a flow. The attack can be effective even if different the watermarks in different flows carry different messages. We analyze the efficacy of our attack using a probabilistic model and a Markovmodulated Poisson process (MMPP) model of interactive traffic. We also implement our attack and test it using both synthetic and realworld traces, showing that our attack is effective with as few as 10 watermarked flows. Finally, we propose a countermeasure that defeats the attack by using multiple watermark positions. 1
Splinebased nonparametric regression for periodic functions and its application to directional tuning of neurons. Stat Med
 Statistics in Medicine
, 2005
"... The activity of neurons in the brain often varies systematically with some quantitative feature of a stimulus or action. A wellknown example is the tendency of the firing rates of neurons in the primary motor cortex to vary with the direction of a subject’s arm or wrist movement. When this movement ..."
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Cited by 11 (3 self)
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The activity of neurons in the brain often varies systematically with some quantitative feature of a stimulus or action. A wellknown example is the tendency of the firing rates of neurons in the primary motor cortex to vary with the direction of a subject’s arm or wrist movement. When this movement is constrained to vary in only two dimensions, the direction of movement may be characterized by an angle, and the neuronal firing rate can be written as a function of this angle. The firing rate function has traditionally been fit with a cosine, but recent evidence suggests that departures from cosine tuning occur frequently. We report here a new nonparametric regression method for fitting periodic functions and demonstrate its application to the fitting of neuronal data. The method is an extension of Bayesian Adaptive Regression Splines (BARS) and applies both to normal and nonnormal data, including Poisson data, which commonly arise in neuronal applications. We compare the new method to a periodic version of smoothing splines and some parametric alternatives and find the new method to be especially valuable when the smoothness of the periodic function varies unevenly across its domain.
The Bicycle Model
 in the Northeast Asian Economic Cooperation”, An Unpublished Paper, Korea Development Institute
, 2000
"... In this paper, we investigate a timesensitive image retrieval problem, in which given a query keyword, a query time point, and optionally user information, we retrieve the most relevant and temporally suitable images from the database. Inspired by recently emerging interests on query dynamics in in ..."
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Cited by 11 (2 self)
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In this paper, we investigate a timesensitive image retrieval problem, in which given a query keyword, a query time point, and optionally user information, we retrieve the most relevant and temporally suitable images from the database. Inspired by recently emerging interests on query dynamics in information retrieval research, our timesensitive image retrieval algorithm can infer users ’ implicit search intent better and provide more engaging and diverse search results according to temporal trends of Web user photos. We model observed image streams as instances of multivariate point processes represented by several different descriptors, and develop a regularized multitask regression framework that automatically selects and learns stochastic parametric models to solve the relations between image occurrence probabilities and various temporal factors that influence them. Using Flickr datasets of more than seven million images of 30 topics, our experimental results show that the proposed algorithm is more successful in timesensitive image retrieval than other candidate methods, including ranking SVM, a PageRankbased image ranking, and a generative temporal topic model.
Testing for and Estimating Latency Effects for Poisson and NonPoisson Spike Trains
, 2004
"... Determining the variations in response latency of one or several neurons to a stimulus is of interest in different contexts. Two common problems concern correlating latency with a particular behavior, for example, the reaction time to a stimulus, and adjusting tools for detecting synchronization bet ..."
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Cited by 10 (6 self)
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Determining the variations in response latency of one or several neurons to a stimulus is of interest in different contexts. Two common problems concern correlating latency with a particular behavior, for example, the reaction time to a stimulus, and adjusting tools for detecting synchronization between two neurons. We use two such problems to illustrate the latency testing and estimation methods developed in this article. Our test for latencies is a formal statistical test that produces a pvalue. It is applicable for Poisson and nonPoisson spike trains via use of the bootstrap. Our estimation method is model free, it is fast and easy to implement, and its performance compares favorably to other methods currently available.
Efficient spikesorting of multistate neurons using interspike nonparametric method for automatic neural spikes clustering 23 hal00639412, version 1  9 Nov 2011 intervals information. Journal of neuroscience methods
, 2006
"... We demonstrate the efficacy of a new spikesorting method based on a Markov Chain Monte Carlo (MCMC) algorithm by applying it to real data recorded from Purkinje cells (PCs) in young rat cerebellar slices. This algorithm is unique in its capability to estimate and make use of the firing statistics a ..."
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Cited by 9 (0 self)
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We demonstrate the efficacy of a new spikesorting method based on a Markov Chain Monte Carlo (MCMC) algorithm by applying it to real data recorded from Purkinje cells (PCs) in young rat cerebellar slices. This algorithm is unique in its capability to estimate and make use of the firing statistics as well as the spike amplitude dynamics of the recorded neurons. PCs exhibit multiple discharge states, giving rise to multimodal interspike interval (ISI) histograms and to correlations between successive ISIs. The amplitude of the spikes generated by a PC in an “active ” state decreases, a feature typical of many neurons from both vertebrates and invertebrates. These two features constitute a major and recurrent problem for all the presently available spikesorting methods. We first show that a Hidden Markov Model with 3 logNormal states provides a flexible and satisfying description of the complex firing of single PCs. We then incorporate this model into our previous MCMC based spikesorting algorithm [31, Pouzat et al, 2004, J. Neurophys. 91, 29102928] and test this new algorithm on multiunit recordings of bursting PCs. We show that our method successfully classifies the bursty spike trains fired by PCs by using an independent single unit recording from a patchclamp pipette. 1 1
Statistical encoding model for a primary motor cortical brainmachine interface
 IEEE Trans Biomed Eng
"... Abstract—A number of studies of the motor system suggest that the majority of primary motor cortical neurons represent simple movementrelated kinematic and dynamic quantities in their timevarying activity patterns. An example of such an encoding relationship is the cosine tuning of firing rate with ..."
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Cited by 8 (3 self)
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Abstract—A number of studies of the motor system suggest that the majority of primary motor cortical neurons represent simple movementrelated kinematic and dynamic quantities in their timevarying activity patterns. An example of such an encoding relationship is the cosine tuning of firing rate with respect to the direction of hand motion. We present a systematic development of statistical encoding models for movementrelated motor neurons using multielectrode array recordings during a twodimensional (2D) continuous pursuittracking task. Our approach avoids massive averaging of responses by utilizing 2D normalized occupancy plots, cascaded linearnonlinear (LN) system models and a method for describing variability in discrete random systems. We found that the expected firing rate of most movementrelated motor neurons is related to the kinematic values by a linear transformation, with a significant nonlinear distortion in about 1 3 of the neurons. The measured variability of the neural responses is markedly nonPoisson in many neurons and is well captured by a “normalizedGaussian ” statistical model that is defined and introduced here. The statistical model is seamlessly integrated into a nearlyoptimal recursive method for decoding movement from neural responses based on a Sequential Monte Carlo filter. Index Terms—Discrete distribution, LN model, neural decoding, neuroprosthetics, sequential MonteCarlo. I.