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Results 1 - 10
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515
Príncipe. Spectral clustering of synchronous spike trains
- In Proc. IEEE Int. Joint Conf. on Neural Networks, IJCNN-2007
, 2007
"... Abstract — In this paper a clustering algorithm that learns the groups of synchronized spike trains directly from data is proposed. Clustering of spike trains based on the presence of synchronous neural activity is of high relevance in neurophysiological studies. In this context such activity is tho ..."
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Cited by 4 (4 self)
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Abstract — In this paper a clustering algorithm that learns the groups of synchronized spike trains directly from data is proposed. Clustering of spike trains based on the presence of synchronous neural activity is of high relevance in neurophysiological studies. In this context such activity
A Mixture Model for Spike Train Ensemble Analysis Using Spectral Clustering
- in Proc. of ICASSP
, 2006
"... Identifying clusters of neurons with correlated spiking activity in large-size neuronal ensembles recorded with highdensity multielectrode array is an emerging problem in computational neuroscience. We propose a nonparametric approach that represents multiple neural spike trains by a mixed point pro ..."
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Cited by 1 (1 self)
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Identifying clusters of neurons with correlated spiking activity in large-size neuronal ensembles recorded with highdensity multielectrode array is an emerging problem in computational neuroscience. We propose a nonparametric approach that represents multiple neural spike trains by a mixed point
Out-of-Sample Extensions for LLE, Isomap, MDS, Eigenmaps, and Spectral Clustering
- In Advances in Neural Information Processing Systems
, 2004
"... Several unsupervised learning algorithms based on an eigendecomposition provide either an embedding or a clustering only for given training points, with no straightforward extension for out-of-sample examples short of recomputing eigenvectors. This paper provides a unified framework for extending Lo ..."
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Cited by 139 (3 self)
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Several unsupervised learning algorithms based on an eigendecomposition provide either an embedding or a clustering only for given training points, with no straightforward extension for out-of-sample examples short of recomputing eigenvectors. This paper provides a unified framework for extending
Maximum margin clustering
- Advances in Neural Information Processing Systems 17
, 2005
"... We propose a new method for clustering based on finding maximum margin hyperplanes through data. By reformulating the problem in terms of the implied equivalence relation matrix, we can pose the problem as a convex integer program. Although this still yields a difficult computational problem, the ha ..."
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Cited by 136 (4 self)
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, the hard-clustering constraints can be relaxed to a soft-clustering formulation which can be feasibly solved with a semidefinite program. Since our clustering technique only depends on the data through the kernel matrix, we can easily achieve nonlinear clusterings in the same manner as spectral clustering
Spectral learning
- In IJCAI
, 2003
"... We present a simple, easily implemented spectral learning algorithm which applies equally whether we have no supervisory information, pairwise link constraints, or labeled examples. In the unsupervised case, it performs consistently with other spectral clustering algorithms. In the supervised case, ..."
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Cited by 106 (6 self)
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We present a simple, easily implemented spectral learning algorithm which applies equally whether we have no supervisory information, pairwise link constraints, or labeled examples. In the unsupervised case, it performs consistently with other spectral clustering algorithms. In the supervised case
Statistical Identification of Synchronous Spiking
"... 3 Spike trains and firing rate 5 3.1 Point processes, conditional intensities, and firing rates.............. 7 ..."
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Cited by 4 (3 self)
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3 Spike trains and firing rate 5 3.1 Point processes, conditional intensities, and firing rates.............. 7
Simultaneous Modeling Of Spectrum, Pitch And Duration In HMM-Based Speech Synthesis
, 1999
"... In this paper, we describe an HMM-based speech synthesis system in which spectrum, pitch and state duration are modeled simultaneously in a unified framework of HMM. In the system, pitch and state duration are modeled by multi-space probability distribution HMMs and multi -dimensional Gaussian distr ..."
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Cited by 172 (37 self)
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distributions, respectively. The distributions for spectral parameter, pitch parameter and the state duration are clustered independently by using a decision-tree based context clustering technique. Synthetic speech is generated by using an speech parameter generation algorithm from HMM and a mel-cepstrum based
Spike train clustering using a Lempel-Ziv distance measure
"... Abstract—Multi-electrode array recordings reveal com-plex structures in the firing of spatially distributed neurons. The analysis of this neuronal network activity demands a classification of neurons according to similarities in their firing behavior. If similar spike patterns do not occur syn-chron ..."
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, but have unknown delays within spike trains, this processing step is difficult. To solve this problem, we introduce a Lempel-Ziv complexity-based distance mea-sure. Using our distance measure as the input for a super-paramagnetic clustering algorithm, we achieve an efficient classification of spike trains
Spike train metrics Theoretical background
"... Quantifying similarity and dissimilarity of spike trains is an important requisite for understanding neural codes. Spike metrics constitute a class of approaches to this problem. In contrast to most signal-processing methods, spike metrics operate on time series of all-or-none events, and are, thus ..."
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Quantifying similarity and dissimilarity of spike trains is an important requisite for understanding neural codes. Spike metrics constitute a class of approaches to this problem. In contrast to most signal-processing methods, spike metrics operate on time series of all-or-none events, and are
Temporal structure in neuronal activity during working memory in macaque parietal cortex,”
- Nature Neuroscience,
, 2002
"... The neural basis of working memory is typically studied in non-human primates by recording activity during delayed response tasks 1 . Cue-selective elevations in mean firing rates are found during the delay period in many brain areas using different versions of these tasks Temporally correlated ne ..."
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Cited by 145 (6 self)
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from area LIP in two awake macaques during a memory-saccade task. Using spectral analysis, we found spatially tuned elevated power in the gamma band (25-90 Hz) in LFP and spiking activity during the memory period. Spiking and LFP activity were also coherent in the gamma band but not at lower
Results 1 - 10
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515
