Measuring the signal-to-noise ratio of a neuron

Czanner, Gabriela; Sarma, Sridevi V.; Ba, Demba; Eden, Uri T.; Wu, Wei; Eskandar, Emad N.; Lim, Hubert H.; Temereanca, Simona; Suzuki, Wendy; Brown, Emery N.

doi:10.1073/pnas.1505545112

Cited by 43 publications

(25 citation statements)

References 37 publications

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“…Indeed, true prediction accuracy (TPA) can be attained only if the true genetic values for the target set are available. The signal-to-noise ratio (SNR) (Czanner et al, 2015 ) for each method, with respect to each target set, was calculated as the sample variance of the predicted genetic values over the sample variance of the estimated residuals associated to the target phenotypes. Note that the SNR is related to genomic based heritabilities (De los Campos et al, 2013b ; Janson et al, 2015 ).…”

Section: Methodsmentioning

confidence: 99%

A Unified and Comprehensible View of Parametric and Kernel Methods for Genomic Prediction with Application to Rice

2016

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One objective of this study was to provide readers with a clear and unified understanding of parametric statistical and kernel methods, used for genomic prediction, and to compare some of these in the context of rice breeding for quantitative traits. Furthermore, another objective was to provide a simple and user-friendly R package, named , which allows users to perform RKHS regression with several kernels. After introducing the concept of regularized empirical risk minimization, the connections between well-known parametric and kernel methods such as Ridge regression [i.e., genomic best linear unbiased predictor (GBLUP)] and reproducing kernel Hilbert space (RKHS) regression were reviewed. Ridge regression was then reformulated so as to show and emphasize the advantage of the kernel “trick” concept, exploited by kernel methods in the context of epistatic genetic architectures, over parametric frameworks used by conventional methods. Some parametric and kernel methods; least absolute shrinkage and selection operator (LASSO), GBLUP, support vector machine regression (SVR) and RKHS regression were thereupon compared for their genomic predictive ability in the context of rice breeding using three real data sets. Among the compared methods, RKHS regression and SVR were often the most accurate methods for prediction followed by GBLUP and LASSO. An R function which allows users to perform RR-BLUP of marker effects, GBLUP and RKHS regression, with a Gaussian, Laplacian, polynomial or ANOVA kernel, in a reasonable computation time has been developed. Moreover, a modified version of this function, which allows users to tune kernels for RKHS regression, has also been developed and parallelized for HPC Linux clusters. The corresponding package and all scripts have been made publicly available.

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Section: Methodsmentioning

confidence: 99%

A Unified and Comprehensible View of Parametric and Kernel Methods for Genomic Prediction with Application to Rice

2016

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“…For example, each set of parameter values comes with its own probability that informs us about how likely this set represents the observed data. In the past, the combination of Bayesian inference and point processes has been successfully applied to action potential spike trains in neurons [ 35 , 36 ], but to our knowledge, this is the first time that Ca 2+ spike sequences have been analysed in this way. While we can draw on these previous results, the substantial differences between action potential spike trains and Ca 2+ spike sequences (e.g.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian approach to modelling heterogeneous calcium responses in cell populations

et al. 2017

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Calcium responses have been observed as spikes of the whole-cell calcium concentration in numerous cell types and are essential for translating extracellular stimuli into cellular responses. While there are several suggestions for how this encoding is achieved, we still lack a comprehensive theory. To achieve this goal it is necessary to reliably predict the temporal evolution of calcium spike sequences for a given stimulus. Here, we propose a modelling framework that allows us to quantitatively describe the timing of calcium spikes. Using a Bayesian approach, we show that Gaussian processes model calcium spike rates with high fidelity and perform better than standard tools such as peri-stimulus time histograms and kernel smoothing. We employ our modelling concept to analyse calcium spike sequences from dynamically-stimulated HEK293T cells. Under these conditions, different cells often experience diverse stimulus time courses, which is a situation likely to occur in vivo. This single cell variability and the concomitant small number of calcium spikes per cell pose a significant modelling challenge, but we demonstrate that Gaussian processes can successfully describe calcium spike rates in these circumstances. Our results therefore pave the way towards a statistical description of heterogeneous calcium oscillations in a dynamic environment.

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“…One limitation of the STPM is that the history effects are restricted to the last spike only. To evaluate effects evoked beyond the last spike, we considered the GLM ( Truccolo et al, 2005 ; Czanner et al, 2015 ) with conditional intensity

of the form

where s ( t ) is the driving force and h ( τ ) is the spike history kernel.…”

Section: Methodsmentioning

confidence: 99%

“…Here, we propose a mechanism that explains the precise patterns of single-neuron responses as an interplay between synaptic inputs and intrinsic refractory properties of the neuron ( Berry and Meister, 1998 ; Czanner et al, 2015 ). To test this hypothesis, we develop simple models capturing the two processes, and we are able to fit the parameters of the models to extracellular recordings of single-unit activity in the somatosensory cortex.…”

Section: Introductionmentioning

confidence: 99%

Refractoriness Accounts for Variable Spike Burst Responses in Somatosensory Cortex

Teleńczuk

Kempter

Curio

et al. 2017

eNeuro

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Neurons in the primary somatosensory cortex (S1) respond to peripheral stimulation with synchronized bursts of spikes, which lock to the macroscopic 600-Hz EEG waves. The mechanism of burst generation and synchronization in S1 is not yet understood. Using models of single-neuron responses fitted to unit recordings from macaque monkeys, we show that these synchronized bursts are the consequence of correlated synaptic inputs combined with a refractory mechanism. In the presence of noise these models reproduce also the observed trial-to-trial response variability, where individual bursts represent one of many stereotypical temporal spike patterns. When additional slower and global excitability fluctuations are introduced the single-neuron spike patterns are correlated with the population activity, as demonstrated in experimental data. The underlying biophysical mechanism of S1 responses involves thalamic inputs arriving through depressing synapses to cortical neurons in a high-conductance state. Our findings show that a simple feedforward processing of peripheral inputs could give rise to neuronal responses with nontrivial temporal and population statistics. We conclude that neural systems could use refractoriness to encode variable cortical states into stereotypical short-term spike patterns amenable to processing at neuronal time scales (tens of milliseconds).

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Measuring the signal-to-noise ratio of a neuron

Cited by 43 publications

References 37 publications

A Unified and Comprehensible View of Parametric and Kernel Methods for Genomic Prediction with Application to Rice

A Unified and Comprehensible View of Parametric and Kernel Methods for Genomic Prediction with Application to Rice

A Bayesian approach to modelling heterogeneous calcium responses in cell populations

Refractoriness Accounts for Variable Spike Burst Responses in Somatosensory Cortex

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