Results 1  10
of
19
Campenhout, Linking Nonbinned Spike Train Kernels to Several Existing Spike Train Metrics
 Neurocomputing
"... spike train metrics ..."
(Show Context)
Quantifying Statistical Interdependence by Message Passing on Graphs  PART II: MultiDimensional Point Processes
, 2009
"... Stochastic event synchrony is a technique to quantify the similarity of pairs of signals. First, “events” are extracted from the two given time series. Next, one tries to align events from one time series with events from the other. The better the alignment, the more similar the two time series are ..."
Abstract

Cited by 20 (12 self)
 Add to MetaCart
Stochastic event synchrony is a technique to quantify the similarity of pairs of signals. First, “events” are extracted from the two given time series. Next, one tries to align events from one time series with events from the other. The better the alignment, the more similar the two time series are considered to be. In Part I, onedimensional events are considered, this paper (Paper II) concerns multidimensional events. Although the basic idea is similar, the extension to multidimensional point processes involves a significantly harder combinatorial problem, and therefore, it is nontrivial. Also in the multidimensional, the problem of jointly computing the pairwise alignment and SES parameters is cast as a statistical inference problem. This problem is solved by coordinate descent, more specifically, by alternating the following two steps: (i) one estimates the SES parameters from a given pairwise alignment; (ii) with the resulting estimates, one refines the pairwise alignment. The SES parameters are computed by maximum a posteriori (MAP) estimation (Step 1), in
Explaining Patterns of Neural Activity in the Primary Motor Cortex Using Spinal Cord and Limb Biomechanics Models
, 2006
"... You might find this additional information useful... This article cites 60 articles, 27 of which you can access free at: ..."
Abstract

Cited by 5 (0 self)
 Add to MetaCart
You might find this additional information useful... This article cites 60 articles, 27 of which you can access free at:
Mental state estimation for braincomputer interfaces
 IEEE TRANS BIOMED ENG
, 2009
"... Mental state estimation is potentially useful for the development of asynchronous brain–computer interfaces. In this study, four mental states have been identified and decoded from the electrocorticograms (ECoGs) of six epileptic patients, engaged in a memory reach task. A novel signal analysis tech ..."
Abstract

Cited by 3 (1 self)
 Add to MetaCart
Mental state estimation is potentially useful for the development of asynchronous brain–computer interfaces. In this study, four mental states have been identified and decoded from the electrocorticograms (ECoGs) of six epileptic patients, engaged in a memory reach task. A novel signal analysis technique has been applied to highdimensional, statistically sparse ECoGs recorded by a large number of electrodes. The strength of the proposed technique lies in its ability to jointly extract spatial and temporal patterns, responsible for encoding mental state differences. As such, the technique offers a systematic way of analyzing the spatiotemporal aspects of brain information processing and may be applicable to a wide range of spatiotemporal neurophysiological signals.
Statistical Analysis of the Nonstationarity of Neural Population Codes
, 2006
"... Neural prosthetic technology has moved from the laboratory to clinical settings with human trials. The motor cortical control of devices in such settings raises important questions about the design of computational interfaces that produce stable and reliable control over a wide range of operating ..."
Abstract

Cited by 3 (1 self)
 Add to MetaCart
(Show Context)
Neural prosthetic technology has moved from the laboratory to clinical settings with human trials. The motor cortical control of devices in such settings raises important questions about the design of computational interfaces that produce stable and reliable control over a wide range of operating conditions. In particular, nonstationarity of the neural code across different behavioral conditions or attentional states becomes a potential issue. Nonstationarity has been previously observed in animals where the encoding model representing the mathematical relationship between neural population activity and behavioral variables such as hand motion changes over time. If such an encoding model is formed and learned during a particular training period, decoding performance (neural control) with the model may not be consistent during successive periods even when the same task is repeated. It is critical in both laboratory experiments and in clinical settings to be able to evaluate whether the representation of movement encoded by a neural population has changed or not. Such information can be used as a cue to retrain the system or as feedback to an adaptive decoding algorithm. To that end, we develop a statistical methodology to evaluate changes in the neural code over time using a generative probabilistic decoding model. The changes are evaluated by comparing the likelihoods of firing rates given similar distributions of 2D hand kinematics collected while a primate periodically performs a manual cursor control task. A comparison is performed by measuring a distance between probabilistic encoding models trained at different times. The statistical significance of the distance measurements are justified with a systematic statistical hypothesis test. The experimental results demonstrate that the likelihood changes over different periods with the change being greater when more distant periods are compared.
Quantifying Statistical Interdependence by Message Passing on Graphs PART I: OneDimensional Point Processes
"... We present a novel approach to quantify the statistical interdependence of two time series, referred to as “stochastic event synchrony ” (SES). As a first step, one extracts “events ” from the two given time series. Next, one tries to align events from one time series with events from the other. The ..."
Abstract
 Add to MetaCart
(Show Context)
We present a novel approach to quantify the statistical interdependence of two time series, referred to as “stochastic event synchrony ” (SES). As a first step, one extracts “events ” from the two given time series. Next, one tries to align events from one time series with events from the other. The better the alignment, the more similar the two time series are considered to be. More precisely, the similarity is quantified by the following parameters: time delay, variance of the timing jitter, fraction of “noncoincident ” events, and average similarity of the aligned events. The pairwise alignment and SES parameters are determined by statistical inference. In particular, the SES parameters are computed by maximum a posteriori (MAP) estimation, and the pairwise alignment is obtained by applying the maxPreprint submitted to Neural Computation 10 March 2009product algorithm. This paper (Part I) deals with onedimensional point processes, the extension to multidimensional point processes is considered in a companion paper (Part II). By analyzing surrogate data, it is demonstrated that SES is able quantify both timing precision and event reliability more robustly than classical measures. As an illustration, neuronal spike data generated by the MorrisLecar neuron model is considered. Key words: timing precision, event reliability, stochastic event synchrony, VictorPurpura distance metric, van Rossum distance metric, Schreiber similarity measure, HunterMilton similarity measure, event synchronization measure, coincident event, maximum a posteriori estimation, spike train, MorrisLecar neuron model 1
Bat echolocation modelling using spike kernels with Support Vector Regression.
"... Abstract. From the echoes of their vocalisations bats extract information about the positions of reflectors. To gain an understanding of how target position is translated into neural features, we model the bat’s peripheral auditory system up until the auditory nerve. This model assumes multiple thre ..."
Abstract
 Add to MetaCart
(Show Context)
Abstract. From the echoes of their vocalisations bats extract information about the positions of reflectors. To gain an understanding of how target position is translated into neural features, we model the bat’s peripheral auditory system up until the auditory nerve. This model assumes multiple threshold detecting neurons for each frequency channel where the interspike times are linked to the location of the reflector. To show that this coding process can be reversed we compute the kernel product of the spike trains using a nonbinned spike kernel function. This approach allows doing regression on azimuth and elevation using Support Vector Machines. 1