Testing a neural coding hypothesis using surrogate data.
نویسندگان
چکیده
Determining how a particular neuron, or population of neurons, encodes information in their spike trains is not a trivial problem, because multiple coding schemes exist and are not necessarily mutually exclusive. Coding schemes generally fall into one of two broad categories, which we refer to as rate and temporal coding. In rate coding schemes, information is encoded in the variations of the average firing rate of the spike train. In contrast, in temporal coding schemes, information is encoded in the specific timing of the individual spikes that comprise the train. Here, we describe a method for testing the presence of temporal encoding of information. Suppose that a set of original spike trains is given. First, surrogate spike trains are generated by randomizing each of the original spike trains subject to the following constraints: the local average firing rate is approximately preserved, while the overall average firing rate and the distribution of primary interspike intervals are perfectly preserved. These constraints ensure that any rate coding of information present in the original spike trains is preserved in the members of the surrogate population. The null-hypothesis is rejected when additional information is found to be present in the original spike trains, implying that temporal coding is present. The method is validated using artificial data, and then demonstrated using real neuronal data.
منابع مشابه
Testing for Stochastic Non- Linearity in the Rational Expectations Permanent Income Hypothesis
The Rational Expectations Permanent Income Hypothesis implies that consumption follows a martingale. However, most empirical tests have rejected the hypothesis. Those empirical tests are based on linear models. If the data generating process is non-linear, conventional tests may not assess some of the randomness properly. As a result, inference based on conventional tests of linear models can b...
متن کاملPower optimization of a piezoelectric-based energy harvesting cantilever beam using surrogate model
Energy harvesting is a conventional method to collect the dissipated energy of a system. In this paper, we investigate the optimal location of a piezoelectric element to harvest maximum power concerning different excitation frequencies of a vibrating cantilever beam. The cantilever beam oscillates by a concentrated sinusoidal tip force, and a piezoelectric patch is integrated on the beam to gen...
متن کاملPersian Handwritten Digit Recognition Using Particle Swarm Probabilistic Neural Network
Handwritten digit recognition can be categorized as a classification problem. Probabilistic Neural Network (PNN) is one of the most effective and useful classifiers, which works based on Bayesian rule. In this paper, in order to recognize Persian (Farsi) handwritten digit recognition, a combination of intelligent clustering method and PNN has been utilized. Hoda database, which includes 80000 P...
متن کاملImproved preservation of autocorrelative structure in surrogate data using an initial wavelet step
Surrogate data generation algorithms are useful for hypothesis testing or for generating realisations of a process for data extension or modelling purposes. This paper tests a well known surrogate data generation method against a stochastic and also a hybrid wavelet-Fourier transform variant of the original algorithm. The data used for testing vary in their persistence and intermittency, and in...
متن کاملExact hypothesis testing and confidence interval for mean of the exponential distribution under Type-I progressive hybrid censoring
Censored samples are discussed in experiments of life-testing; i.e. whenever the experimenter does not observe the failure times of all units placed on a life test. In recent years, inference based on censored sampling is considered, so that about the parameters of various distributions such as normal, exponential, gamma, Rayleigh, Weibull, log normal, inverse Gaussian, ...
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:
- Journal of neuroscience methods
دوره 172 2 شماره
صفحات -
تاریخ انتشار 2008