Identification of synaptic interactions

Brillinger, David R.; Bryant, H L; Segundo, J. P.

doi:10.1007/bf00365087

Cited by 157 publications

(104 citation statements)

References 25 publications

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“…The cross-correlation histograms functionalities we have included in STAR are very close to what is described under this name in Brillinger et al [1976]. The main difference is that we don't use the square root transformation to stabilize the variance since there is no variance to stabilize under the null hypothesis of no correlation.…”

Section: Cross Correlation Histogramsmentioning

confidence: 79%

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Automatic spike train analysis and report generation. An implementation with R, R2HTML and STAR

Pouzat

Chaffiol

2009

Journal of Neuroscience Methods

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Section: Cross Correlation Histogramsmentioning

confidence: 79%

“…If several neurons are recorded simultaneously then cross-correlation histograms [Perkel et al, 1967b, Brillinger et al, 1976] are likely to be generated.…”

Section: Introductionmentioning

confidence: 99%

Automatic spike train analysis and report generation. An implementation with R, R2HTML and STAR

Pouzat

Chaffiol

2009

Journal of Neuroscience Methods

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“…The data here is from the identified nerve cell L10 of Aplysia californica. The experiments in which these data were collected are described in detail in Bryant, Ruiz Marcos and Segundo (1973), Bryant and Segundo (1976) and Brillinger et al (1976).…”

Section: Temporal Point Process Neurophysiologymentioning

confidence: 99%

Synthetic plots: some history and examples

Brillinger

2014

São Paulo J. Math. Sci.

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Jerzy Neyman and Elizabeth Scott developed the idea of synthetic plots. These plots are a display of the data values of an experiment side by side with a display of simulated data values, with the simulation-based on a considered stochastic model. The Neyman and Scott work concerned the distribution of galaxies on the celestial sphere. A review of their wo is presented here followed by personal examples from hydrology, neuroscience, and animal motion.

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“…The estimation procedures used in the frequency domain approach are set out in Rosenberg et al (1989). Partial spectra were used to identify a network of neurons as in Brillinger et al (1976). In summary, the spectra are estimated by dividing each 60-s record into 58 contiguous intervals of 1,024 ms and the estimates of the spectra are taken to be the average of the periodograms computed for each interval.…”

Section: Isolated Mammalian Muscle Spindle Datamentioning

confidence: 99%

Combining frequency and time domain approaches to systems with multiple spike train input and output

2009

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A frequency domain approach and a time domain approach have been combined in an investigation of the behaviour of the primary and secondary endings of an isolated muscle spindle in response to the activity of two static fusimotor axons when the parent muscle is held at a fixed length and when it is subjected to random length changes. The frequency domain analysis has an associated error process which provides a measure of how well the input processes can be used to predict the output processes and is also used to specify how the interactions between the recorded processes contribute to this error. Without assuming stationarity of the input, the time domain approach uses a sequence of probability models of increasing complexity in which the number of input processes to the model is progressively increased. This feature of the time domain approach was used to identify a preferred direction of interaction between the processes underlying the generation of the activity of the primary and secondary endings. In the presence of fusimotor activity and dynamic length changes imposed on the muscle, it was shown that the activity of the primary and secondary endings carried different information about the effects of the inputs imposed

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Identification of synaptic interactions

Cited by 157 publications

References 25 publications

Automatic spike train analysis and report generation. An implementation with R, R2HTML and STAR

Automatic spike train analysis and report generation. An implementation with R, R2HTML and STAR

Synthetic plots: some history and examples

Combining frequency and time domain approaches to systems with multiple spike train input and output

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