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I Gat

Publications and source records attributed to I Gat.

5 recordsLinked to original sources
PubMed2001

Detecting precise firing sequences in experimental data.

A precise firing sequence (PFS) is defined here as a sequence of three spikes with fixed delays (up to some time accuracy Delta), that repeat excessively. This paper provides guidelines for detecting PFSs, verifying their significance through surrogate spike trains, and identifying existing PFSs. The method is based on constructing a three-fold correlation among spikes, estimating the expected shape of the correlation by smoothing, and detecting points for which the correlations significantly protrude above the expected correlation. Validation is achieved by generating surrogate spike trains in which the time of each of the real spikes is randomly jittered within a small time window. The method is extensively tested through application to simulated spike trains, and the results are illustrated with recordings of single units in the frontal cortex of behaving monkeys. Pitfalls which may cause false detection of PFSs, or loss of existing PFSs, include searching for PFSs in which the same neuron participates more than once, and attempting to produce a surrogate with some fixed statistical property.

Action Potentials↗
PubMed2001

Spotting neural spike patterns using an adversary background model.

The detection of a specific stochastic pattern embedded in an unknown background noise is a difficult pattern recognition problem, encountered in many applications such as word spotting in speech. A similar problem emerges when trying to detect a multineural spike pattern in a single electrical recording, embedded in the complex cortical activity of a behaving animal. Solving this problem is crucial for the identification of neuronal code words with specific meaning. The technical difficulty of this detection is due to the lack of a good statistical model for the background activity, which rapidly changes with the recording conditions and activity of the animal. This work introduces the use of an adversary background model. This model assumes that the background "knows" the pattern sought, up to a first-order statistics, and this "knowledge" creates a background composed of all the permutations of our pattern. We show that this background model is tightly connected to the type-based information-theoretic approach. Furthermore, we show that computing the likelihood ratio is actually decomposing the log-likelihood distribution according to types of the empirical counts. We demonstrate the application of this method for detection of the reward patterns in the basal ganglia of behaving monkeys, yielding some unexpected biological results.

Action Potentials↗
PubMed1995

Cortical activity flips among quasi-stationary states.

Parallel recordings of spike trains of several single cortical neurons in behaving monkeys were analyzed as a hidden Markov process. The parallel spike trains were considered as a multivariate Poisson process whose vector firing rates change with time. As a consequence of this approach, the complete recording can be segmented into a sequence of a few statistically discriminated hidden states, whose dynamics are modeled as a first-order Markov chain. The biological validity and benefits of this approach were examined in several independent ways: (i) the statistical consistency of the segmentation and its correspondence to the behavior of the animals; (ii) direct measurement of the collective flips of activity, obtained by the model; and (iii) the relation between the segmentation and the pair-wise short-term cross-correlations between the recorded spike trains. Comparison with surrogate data was also carried out for each of the above examinations to assure their significance. Our results indicated the existence of well-separated states of activity, within which the firing rates were approximately stationary. With our present data we could reliably discriminate six to eight such states. The transitions between states were fast and were associated with concomitant changes of firing rates of several neurons. Different behavioral modes and stimuli were consistently reflected by different states of neural activity. Moreover, the pair-wise correlations between neurons varied considerably between the different states, supporting the hypothesis that these distinct states were brought about by the cooperative action of many neurons.

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