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An Introductory to Statistical Models of Neural Data SCS-IPM به نام خالق ناشناخته ها.

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Presentation on theme: "An Introductory to Statistical Models of Neural Data SCS-IPM به نام خالق ناشناخته ها."— Presentation transcript:

1 An Introductory to Statistical Models of Neural Data SCS-IPM به نام خالق ناشناخته ها

2  Spike trains carry information in their temporal patterning, yet they are often highly irregular across time and across experimental replications.  all-or- nothing nature of a sequence of neuronal action potentials together with their stochastic structure suggests that a neuronal spike train can be viewed as a point process.

3  A point process is a stochastic process composed of a sequence of binary events that occur in continuous time.  Three primary ways to characterize a point process: probability model of Spike time, interspike intervals (ISI) and counting processes. Statistical model for spike trains: A probabilistic description of the sequence of spikes.

4 Modeling of analyses of neural system Deterministic models:  Hodgkin and Huxley  Integrate-and-fire(IF)  Neural network - For actual neurons, the deterministic representation is never completely true as many factors which these models assume are rarely known with certainty, even in controlled experiments. -In general, the deterministic models cannot suggest strategies or methods to analyze the non-deterministic properties of neural spike trains. Stochastic models: ???

5 Stochastic Integrate -and -fire models (IF) - Non-leaky integrator with excitatory Poisson inputs Poisson process with constant rate parameter Magnitude of each excitatory input Neuron discharges an action potential when - Non-leaky integrator with excitatory and inhibitory Poisson inputs

6 Stochastic integrate-and-fire (IF) While the stochastic IF model has a long history (1960’s), it has provided much insight into the behavior of single neurons and neural populations. 3 approach of the IF model: -As a diffusion process -As a state-space model -Via simpler point-process models Leaky stochastic IF model Brownia n motion After each threshold crossing,is reset to Membrane conductance Input current

7 “Spike-response” model (Gerstner and Kistler, 2002; Paninski et al., 2004) -By changing the shape and magnitude of h(.), we can model a variety of interspike interval behavior, refractory period, firing rate saturation or spike-rate adaptation, burst effects in the spike train. -It is also natural to consider similar models for the conductance g(t) following a spike time (Stevens and Zador, 1998; Jolivet et al., 2004). -We would like to model the effects of an external stimulus on the observed spike train. : Stimulus Fitting models to data:

8 The IF model as a state-space model (hidden Markov” models) This model consists of two processes: an unobserved (“hidden”)Markovian process, and an observed process which is related to the hidden process in a simple instantaneous manner (Brown et al., 1998; Smith and Brown, 2003; Czanner et al., 2008;, Salimpour et al., 2011; Shimazaki et al., 2012) V (t) is a hidden Markovian process which we observe only indirectly, through the spike times, which may be considered a simple function of V (t): the observed spike variable at time t is zero if V (t) is below threshold, and one if

9 Conditional Intensity Function Since most neural systems have a history-dependent structure that makes Poisson models inappropriate, it is necessary to define probability models that account for history dependence. Any point process can be completely characterized by its conditional intensity function (Daley and Vere-Jones, 2003) history of the spiking activity up to time t Above equation states that the conditional intensity function multiplied by gives the probability of a spike event in a small time interval

10 By using this Bernoulli approximation and joint probability density of observing a spike train N(T):total number of spikes observed in the interval characterizes the distribution of firing at exactly the observed spike times the probability of not firing any other spikes in the observation interval The functional relation between a neuron’s spiking activity and these biological and behavioral signals is often called the neuron’s receptive field. Log conditional intensity = stimulus + stimulus history + spiking history + trial + LFP

11 Ex: Conditional intensity model for a hippocampal place cell The covariates for this model are x(t) and y(t), the animal’s x- and y position.

12 Generalized Linear Models  Generalized linear models (GLMs) provide a simple, flexible approach to modeling relationships between spiking data and a set of covariates to which they are associated (an extension of the Linear regression model)  Generalized Linear Models Are a Flexible Class of Spiking Models That Are Easy to Fit by Maximum Likelihood is a collection of covariates that are related to the spiking activity is a collection of functions of those covariates. is a parameter set

13 The goal of a single neuron GLM is to predict the current number of spikes using the recent spiking history and the preceding stimulus. represent the vector of preceding stimuli up to but not including time t. be a vector of preceding spike counts up to but not including time t.

14 distributed according to a Poisson distribution whose conditional intensity Stimulus filter of the neuron (i.e. receptive field), post-spike filter to account for spike history dynamics (e.g. refractoriness, bursting, etc.) Link function Parameter

15 How can we efficiently fit the model to spike train data?

16 GLM Network Models

17 Autoregressive Model (AR) Criterion Autoregressive Transfer (Minimum Description Length) p:Model order, N: length of the signal, variance of the error sequence Evaluating a statistical Models: AIC KS Q-Q Plot

18 Determining the uncertainty of the parameter estimates Confidence intervals around the parameter estimates based on the Fisher information Examine the confidence intervals computed foreach parameter of model based on Fisher information Cramér–Rao bound:

19 Goodness- of-fit of neural spiking models Measuring quantitatively the agreement between a proposed model for spike train data Time-rescaling theorem: To transform point processes data into continuous measures and then assess goodness-of-fit. Given a point process with conditional intensity function and occurrence times Then these are independent, exponential random variables with rate parameter 1. -If is constant and equal to 1 everywhere, then this is a simple Poisson process with independent, exponential ISIs, and time does not need to be rescaled. Define

20 Because the transformation is oneto- one, any statistical assessment that measures the agreement between the values and an exponential distribution directly evaluates how well the original model agrees with the spike train data. A Kolmogorov– Smirnov (KS) plot is a plot of the empirical cumulative distribution function (CDF) of the rescaled zj’s against an exponential CDF

21 Another approach to measuring agreement between the model and data is to construct a quantile–quantile (Q-Q) plot, which plots the quantiles of the rescaled ISIs against those of exponential distribution (Q-Q) plot


23 Thanks

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