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Mechanisms explaining transitions between tonic and phasic firing in neuronal populations as predicted by a low dimensional firing rate model.

ABSTRACT: Several firing patterns experimentally observed in neural populations have been successfully correlated to animal behavior. Population bursting, hereby regarded as a period of high firing rate followed by a period of quiescence, is typically observed in groups of neurons during behavior. Biophysical membrane-potential models of single cell bursting involve at least three equations. Extending such models to study the collective behavior of neural populations involves thousands of equations and can be very expensive computationally. For this reason, low dimensional population models that capture biophysical aspects of networks are needed. The present paper uses a firing-rate model to study mechanisms that trigger and stop transitions between tonic and phasic population firing. These mechanisms are captured through a two-dimensional system, which can potentially be extended to include interactions between different areas of the nervous system with a small number of equations. The typical behavior of midbrain dopaminergic neurons in the rodent is used as an example to illustrate and interpret our results. The model presented here can be used as a building block to study interactions between networks of neurons. This theoretical approach may help contextualize and understand the factors involved in regulating burst firing in populations and how it may modulate distinct aspects of behavior.

SUBMITTER: Radulescu AR

PROVIDER: S-EPMC2943909 | biostudies-literature | 2010

REPOSITORIES: biostudies-literature

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Mechanisms explaining transitions between tonic and phasic firing in neuronal populations as predicted by a low dimensional firing rate model.

Radulescu Anca R AR

PloS one 20100922 9

Several firing patterns experimentally observed in neural populations have been successfully correlated to animal behavior. Population bursting, hereby regarded as a period of high firing rate followed by a period of quiescence, is typically observed in groups of neurons during behavior. Biophysical membrane-potential models of single cell bursting involve at least three equations. Extending such models to study the collective behavior of neural populations involves thousands of equations and ca ...[more]

PMID: 20877649

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Project description:Neurons use sequences of action potentials (spikes) to convey information across neuronal networks. In neurophysiology experiments, information about external stimuli or behavioral tasks has been frequently characterized in term of neuronal firing rate. The firing rate is conventionally estimated by averaging spiking responses across multiple similar experiments (or trials). However, there exist a number of applications in neuroscience research that require firing rate to be estimated on a single trial basis. Estimating firing rate from a single trial is a challenging problem and current state-of-the-art methods do not perform well. To address this issue, we develop a new method for estimating firing rate based on a kernel smoothing technique that considers the bandwidth as a random variable with prior distribution that is adaptively updated under an empirical Bayesian framework. By carefully selecting the prior distribution together with Gaussian kernel function, an analytical expression can be achieved for the kernel bandwidth. We refer to the proposed method as Bayesian Adaptive Kernel Smoother (BAKS). We evaluate the performance of BAKS using synthetic spike train data generated by biologically plausible models: inhomogeneous Gamma (IG) and inhomogeneous inverse Gaussian (IIG). We also apply BAKS to real spike train data from non-human primate (NHP) motor and visual cortex. We benchmark the proposed method against established and previously reported methods. These include: optimized kernel smoother (OKS), variable kernel smoother (VKS), local polynomial fit (Locfit), and Bayesian adaptive regression splines (BARS). Results using both synthetic and real data demonstrate that the proposed method achieves better performance compared to competing methods. This suggests that the proposed method could be useful for understanding the encoding mechanism of neurons in cognitive-related tasks. The proposed method could also potentially improve the performance of brain-machine interface (BMI) decoder that relies on estimated firing rate as the input.

Project description:IntroductionFibromyalgia (FM) is associated with dysfunctional pain modulation mechanisms, including central sensitization. Experimental pain measurements, such as temporal summation (TS), could serve as markers of central sensitization and have been previously studied in these patients, with conflicting results. Our objective in this study was to explore the relationships between two different protocols of TS (phasic and tonic) and test the associations between these measures and other clinical variables.Materials and methodsIn this cross-sectional analysis of a randomized clinical trial, patients were instructed to determine their pain-60 test temperature, then received one train of 15 repetitive heat stimuli and rated their pain after the 1st and 15th stimuli: TSPS-phasic was calculated as the difference between those. We also administered a tonic heat test stimulus at the same temperature continuously for 30 s and asked them to rate their pain levels after 10 s and 30 s, calculating TSPS-tonic as the difference between them. We also collected baseline demographic data and behavioral questionnaires assessing pain, depression, fatigue, anxiety, sleepiness, and quality of life. We performed univariable analyses of the relationship between TSPS-phasic and TSPS-tonic, and between each of those measures and the demographic and clinical variables collected at baseline. We then built multivariable linear regression models to find predictors for TSPS-phasic and TSPS-tonic, while including potential confounders and avoiding collinearity.ResultsFifty-two FM patients were analyzed. 28.85% developed summation during the TSPS-phasic protocol while 21.15% developed summation during the TSPS-tonic protocol. There were no variables associated TSPS phasic or tonic in the univariable analyses and both measures were not correlated. On the multivariate model for the TSPS-phasic protocol, we found a weak association with pain variables. BPI-pain subscale was associated with more temporal summation in the phasic protocol (ß = 0.38, p = 0.029), while VAS for pain was associated with less summation in the TSPS-tonic protocol (ß = -0.5, p = 0.009).ConclusionOur results suggest that, using heat stimuli with pain-60 temperatures, a TSPS-phasic protocol and a TSPS-tonic protocol are not correlated and could index different neural responses in FM subjects. Further studies with larger sample sizes would be needed to elucidate whether such responses could help differentiating subjects with FM into specific phenotypes.

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Mechanisms explaining transitions between tonic and phasic firing in neuronal populations as predicted by a low dimensional firing rate model.

Publications

Mechanisms explaining transitions between tonic and phasic firing in neuronal populations as predicted by a low dimensional firing rate model.

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