The Spike Clustering and Neuron Tracking Problems
A new multi neuron spike train metric
... will prove better than the ARMV metrics when dealing with real data, the simulated data probably has a different statistical structure than real data. It is noteworthy, however, that both metrics have a similar ...
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SPAN: spike pattern association neuron for learning spatio-temporal sequences
... defined as the temporal difference between a desired and an actual spike. The algorithm is applied to a multi-layer feedforward network in which the output neurons are trained to emit single spikes at desired ...
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A Model-Based Spike Sorting Algorithm for Removing Correlation Artifacts in Multi-Neuron Recordings
... the spike trains of multiple neurons from voltage traces recorded on one or more extracellular ...Traditional spike-sorting methods rely on thresholding or clustering of recorded signals to identify ...
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Learning, self-organisation and homeostasis in spiking neuron networks using spike-timing dependent plasticity
... This suggests that a k-winner-takes-all mechanism which selects k output neurons sparsely in- stead of only one, could be much more efficient regarding pattern storage capacities in the re- current connections of the ...
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K-MEANS CLUSTERING TO IDENTIFY HIGH ACTIVE NEURON ANALYSIS FOR LSD
... [19] , and classification [23]. It determines the optimum solution using suitable algorithms in accordance to the domain and nature of the problems[4]. Computational model and the algorithms are applied in all the ...
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The Clustering Process in Latvia and Existing Problems
... The experience of a number of European countries shows the importance of the availability and implementation of national programs for innovative development. We believe that improving the competitiveness of Latvia is ...
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Automated spike sorting algorithm based on Laplacian eigenmaps and k means clustering
... similar spike amplitude but different widths. The variation in a single neuron amplitude can cause low spike sorting ...of spike sorting using the simple amplitude features increases, thus ...
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Spike Frequency Adaptation Implements Anticipative Tracking in Continuous Attractor Neural Networks
... single neuron level; STD has a time scale of hundreds to thousands of milliseconds and functions at the synapse level; and NFC has a time scale of tens of milliseconds and functions at the network ...the ...
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Automatic online spike sorting with singular value decomposition and fuzzy C-mean clustering
... (Fuzzy SPike Sorting), which is designed to optimize: (i) fast and accurate detection, (ii) offline sorting and (iii) online classification of neuronal spikes with very limited or null human ...of spike ...
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Clustering the Real Time Moving Object Adjacent Tracking
... algorith-mic problems on approximating a given polyline by a simpli ed one with fewer ...trajectory tracking is a min-# prob-lem in R 1+d (d = 2 or 3) in the case of Hausdor dis-tance under the ...
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Collaboratively Tracking Interests for User Clustering in Streams of Short Texts
... TEXT CLUSTERING Organizations today are sitting on vast heaps of data and unfortunately, most of it is unstructured in ...these problems, we will devise an unsupervised text clustering approach that ...
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The chronotron: a neuron that learns to fire temporally-precise spike patterns
... input spike patterns, thus process- ing and memorizing information that is fully temporally coded, both as input and as ...coded spike trains for different inputs belonging to the same ...
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Biological Neural Network Structure and Spike Activity Prediction Based on Multi Neuron Spike Train Data
... possible. Neuron activities are based on the neural network of the ...multi-neuron spike train data can be used as an alternative source to pre- dict the neural network ...the spike ...
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Clustering Neural Spike Trains with Transient Responses
... projective clustering techniques into an ART neural network; the resultant neural network is designated PART [25]–[29] (Figure ...for clustering of neural spike trains that is easily implemented, ...
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Method for training a spiking neuron to associate input-output spike trains
... single spike within [t (f ) d − 3, t (f ) d + 3]ms of the desired spike time t (f ) d ...untrained neuron is very likely to produce incorrect outputs resulting in accuracies close to ...The ...
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Clustering Problems and Clustering Methods for Microarray Data
... Alon, U., Barkai, N., Notterman, D.A., Gish, K., Ybarra, S., Mack, D., Levine, A.J. (1999): Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by ...
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Spike propagation through the dorsal root ganglia in an unmyelinated sensory neuron: a modeling study
... each spike during a train, while at the same time its time course broad- ened, rather than a progressive increase in the AHP with each ...for spike failure since there was no correlation with the slow time ...
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Fisch, Karin (2011): The contribution of spike-frequency adaptation to the variability of spike responses in a sensory neuron. Dissertation, LMU München: Fakultät für Biologie
... synaptic spike trains passing through fast synapses or a white Gaussian input current representing a mixture of intrinsic fluctuations and background synaptic ...the spike generating mechanism, but also by ...
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Optimization of output spike train encoding for a spiking neuron based on its spatiotemporal input pattern
... a neuron is different for different desired output spike trains ...in spike times. Spike times are the most crucial part of information transmission in spiking neural ...desired spike ...
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Model tracking for risk problems
... Model Tracking or Detection problems as we are interested in tracking (or detecting) the most appropriate model for describing the proposed risk model, see, for example, Poor [5] and Snyder ...
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