Imperial College London
Alternate

ProfessorChristos-SavvasBouganis

Faculty of Engineering, Department of Electrical and Electronic Engineering

Professor of Intelligent Digital Systems
 
 
 
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Contact

 

+44 (0)20 7594 6144christos-savvas.bouganis Website

 
 
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Location

 

904Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Ahmadi:2018:10.1371/journal.pone.0206794,
author = {Ahmadi, N and Constandinou, T and Bouganis, C},
doi = {10.1371/journal.pone.0206794},
journal = {PLoS ONE},
title = {Estimation of neuronal firing rate using Bayesian Adaptive Kernel Smoother (BAKS)},
url = {http://dx.doi.org/10.1371/journal.pone.0206794},
volume = {13},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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-mac
AU  - Ahmadi,N
AU  - Constandinou,T
AU  - Bouganis,C
DO  - 10.1371/journal.pone.0206794
PY  - 2018///
SN  - 1932-6203
TI  - Estimation of neuronal firing rate using Bayesian Adaptive Kernel Smoother (BAKS)
T2  - PLoS ONE
UR  - http://dx.doi.org/10.1371/journal.pone.0206794
UR  - http://hdl.handle.net/10044/1/64615
VL  - 13
ER  -
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