Spike Sorting Method Using Exponential Autoregressive Modeling of Action Potentials
نویسنده
چکیده
Neurons in the nervous system communicate with each other by producing electrical signals called spikes. To investigate the physiological function of nervous system it is essential to study the activity of neurons by detecting and sorting spikes in the recorded signal. In this paper a method is proposed for considering the spike sorting problem which is based on the nonlinear modeling of spikes using exponential autoregressive model. The genetic algorithm is utilized for model parameter estimation. In this regard some selected model coefficients are used as features for sorting purposes. For optimal selection of model coefficients, self-organizing feature map is used. The results show that modeling of spikes with nonlinear autoregressive model outperforms its linear counterpart. Also the extracted features based on the coefficients of exponential autoregressive model are better than wavelet based extracted features and get more compact and well-separated clusters. In the case of spikes different in small-scale structures where principal component analysis fails to get separated clouds in the feature space, the proposed method can obtain well-separated cluster which removes the necessity of applying complex classifiers. Keywords—Exponential autoregressive model, Neural data, spike sorting, time series modeling.
منابع مشابه
A Brief Look into Spike Sorting Methods
Spike sorting is a class of techniques used in the analysis of electrophysiological data. Studying the dynamics of neural activity via electrical recording relies on the ability to detect and sort neural spikes recorded from a number of neurons by the same electrode. This article reviews methods for detecting and classifying action potentials, a problem commonly referred to as spike sorting.
متن کاملSpike Sorting by Joint Probabilistic Modeling of Neural Spike Trains and Waveforms
This paper details a novel probabilistic method for automatic neural spike sorting which uses stochastic point process models of neural spike trains and parameterized action potential waveforms. A novel likelihood model for observed firing times as the aggregation of hidden neural spike trains is derived, as well as an iterative procedure for clustering the data and finding the parameters that ...
متن کاملSpectral representation--analyzing single-unit activity in extracellularly recorded neuronal data without spike sorting.
One step in the conventional analysis of extracellularly recorded neuronal data is spike sorting, which separates electrical signal into action potentials from different neurons. Because spike sorting involves human judgment, it can be subjective and time intensive, particularly for large sets of neurons. Here we propose a simple, automated way to construct alternative representations of neuron...
متن کاملInnovative Methodology Generation of Spatiotemporally Correlated Spike Trains and Local Field Potentials Using a Multivariate Autoregressive Process
Gutnisky DA, Josić K. Generation of spatiotemporally correlated spike trains and local field potentials using a multivariate autoregressive process. J Neurophysiol 103: 2912–2930, 2010. First published December 23, 2009; doi:10.1152/jn.00518.2009. Experimental advances allowing for the simultaneous recording of activity at multiple sites have significantly increased our understanding of the spa...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2015