Statistical significance of sequential firing patterns in multi-neuronal spike trains

Casey O. Diekman; P. S. Sastry; K. P. Unnikrishnan

doi:10.1016/j.jneumeth.2009.06.018

Statistical significance of sequential firing patterns in multi-neuronal spike trains

Casey O. Diekman, P. S. Sastry, K. P. Unnikrishnan

Research output: Contribution to journal › Article › peer-review

8 Scopus citations

Abstract

Sequential firings with fixed time delays are frequently observed in simultaneous recordings from multiple neurons. Such temporal patterns are potentially indicative of underlying microcircuits and it is important to know when a repeatedly occurring pattern is statistically significant. These sequences are typically identified through correlation counts. In this paper we present a method for assessing the significance of such correlations. We specify the null hypothesis in terms of a bound on the conditional probabilities that characterize the influence of one neuron on another. This method of testing significance is more general than the currently available methods since under our null hypothesis we do not assume that the spiking processes of different neurons are independent. The structure of our null hypothesis also allows us to rank order the detected patterns. We demonstrate our method on simulated spike trains.

Original language	English (US)
Pages (from-to)	279-284
Number of pages	6
Journal	Journal of Neuroscience Methods
Volume	182
Issue number	2
DOIs	https://doi.org/10.1016/j.jneumeth.2009.06.018
State	Published - Sep 15 2009
Externally published	Yes

All Science Journal Classification (ASJC) codes

General Neuroscience

Keywords

Multi-neuron
Precise timing
Spike train
Statistical significance
Temporal firing patterns

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10.1016/j.jneumeth.2009.06.018

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title = "Statistical significance of sequential firing patterns in multi-neuronal spike trains",

abstract = "Sequential firings with fixed time delays are frequently observed in simultaneous recordings from multiple neurons. Such temporal patterns are potentially indicative of underlying microcircuits and it is important to know when a repeatedly occurring pattern is statistically significant. These sequences are typically identified through correlation counts. In this paper we present a method for assessing the significance of such correlations. We specify the null hypothesis in terms of a bound on the conditional probabilities that characterize the influence of one neuron on another. This method of testing significance is more general than the currently available methods since under our null hypothesis we do not assume that the spiking processes of different neurons are independent. The structure of our null hypothesis also allows us to rank order the detected patterns. We demonstrate our method on simulated spike trains.",

keywords = "Multi-neuron, Precise timing, Spike train, Statistical significance, Temporal firing patterns",

author = "Diekman, {Casey O.} and Sastry, {P. S.} and Unnikrishnan, {K. P.}",

note = "Funding Information: We thank Debprakash Patnaik for providing the neuronal simulator and for coding the two-tape algorithm, and Dr. Vijay Nair for helpful discussions. This work was supported in part by a grant from GM R&D Center, Warren, MI. COD was supported by a NSF Graduate Research Fellowship. Most of KPU's work was performed when he was at the GM R&D Center, Warren, MI. ",

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doi = "10.1016/j.jneumeth.2009.06.018",

language = "English (US)",

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T1 - Statistical significance of sequential firing patterns in multi-neuronal spike trains

AU - Diekman, Casey O.

AU - Sastry, P. S.

AU - Unnikrishnan, K. P.

N1 - Funding Information: We thank Debprakash Patnaik for providing the neuronal simulator and for coding the two-tape algorithm, and Dr. Vijay Nair for helpful discussions. This work was supported in part by a grant from GM R&D Center, Warren, MI. COD was supported by a NSF Graduate Research Fellowship. Most of KPU's work was performed when he was at the GM R&D Center, Warren, MI.

PY - 2009/9/15

Y1 - 2009/9/15

N2 - Sequential firings with fixed time delays are frequently observed in simultaneous recordings from multiple neurons. Such temporal patterns are potentially indicative of underlying microcircuits and it is important to know when a repeatedly occurring pattern is statistically significant. These sequences are typically identified through correlation counts. In this paper we present a method for assessing the significance of such correlations. We specify the null hypothesis in terms of a bound on the conditional probabilities that characterize the influence of one neuron on another. This method of testing significance is more general than the currently available methods since under our null hypothesis we do not assume that the spiking processes of different neurons are independent. The structure of our null hypothesis also allows us to rank order the detected patterns. We demonstrate our method on simulated spike trains.

AB - Sequential firings with fixed time delays are frequently observed in simultaneous recordings from multiple neurons. Such temporal patterns are potentially indicative of underlying microcircuits and it is important to know when a repeatedly occurring pattern is statistically significant. These sequences are typically identified through correlation counts. In this paper we present a method for assessing the significance of such correlations. We specify the null hypothesis in terms of a bound on the conditional probabilities that characterize the influence of one neuron on another. This method of testing significance is more general than the currently available methods since under our null hypothesis we do not assume that the spiking processes of different neurons are independent. The structure of our null hypothesis also allows us to rank order the detected patterns. We demonstrate our method on simulated spike trains.

KW - Multi-neuron

KW - Precise timing

KW - Spike train

KW - Statistical significance

KW - Temporal firing patterns

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ER -

Statistical significance of sequential firing patterns in multi-neuronal spike trains

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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