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J Neurophysiol. Author manuscript; available in PMC 2009 May 1.

Published in final edited form as:

Published online 2008 January 23. doi: 10.1152/jn.00343.2007

PMCID: PMC2430469

NIHMSID: NIHMS52952

Gabriela Czanner,^{1} Uri T. Eden,^{2} Sylvia Wirth,^{5} Marianna Yanike,^{6} Wendy A. Suzuki,^{6} and Emery N. Brown^{1,}^{3,}^{4}

Address for reprint requests and other correspondence: G. Czanner, University of Warwick, WMG, IMC-368, Coventry CV4 7AL, United Kingdom (E-mail: ku.ca.kciwraw@rennazc.g)

The publisher's final edited version of this article is available free at J Neurophysiol

See other articles in PMC that cite the published article.

Recording single-neuron activity from a specific brain region across multiple trials in response to the same stimulus or execution of the same behavioral task is a common neurophysiology protocol. The raster plots of the spike trains often show strong between-trial and within-trial dynamics, yet the standard analysis of these data with the peristimulus time histogram (PSTH) and ANOVA do not consider between-trial dynamics. By itself, the PSTH does not provide a framework for statistical inference. We present a state-space generalized linear model (SS-GLM) to formulate a point process representation of between-trial and within-trial neural spiking dynamics. Our model has the PSTH as a special case. We provide a framework for model estimation, model selection, goodness-of-fit analysis, and inference. In an analysis of hippocampal neural activity recorded from a monkey performing a location-scene association task, we demonstrate how the SS-GLM may be used to answer frequently posed neurophysiological questions including, What is the nature of the between-trial and within-trial task-specific modulation of the neural spiking activity? How can we characterize learning-related neural dynamics? What are the timescales and characteristics of the neuron’s biophysical properties? Our results demonstrate that the SS-GLM is a more informative tool than the PSTH and ANOVA for analysis of multiple trial neural responses and that it provides a quantitative characterization of the between-trial and within-trial neural dynamics readily visible in raster plots, as well as the less apparent fast (1–10 ms), intermediate (11–20 ms), and longer (>20 ms) timescale features of the neuron’s biophysical properties.

Understanding how neurons respond to putative stimuli is a fundamental question in neuroscience. Because neurons are stochastic, recording spiking activity from relevant brain regions during multiple presentations or trials of the same putative stimulus is a commonly used experimental paradigm for characterizing neural responses. Such neural recordings frequently have strong between-trial and within-trial dynamics (Fig. 1). The between-trial dynamics may represent random variations in the neuron’s response to the same stimulus or changes in how the neuron responds to the stimulus. The former may represent noise (Brody 1999; Dayan and Abbott 2001; Lim et al. 2006; Narayan et al. 2006; Ventura et al. 2005), whereas the latter may represent the evolution of a neuron’s receptive field properties (Brown et al. 2001; Gandolfo et al. 2000; Kaas et al. 1983; Mehta et al. 1997; Merzenich et al. 1984) or behavior-related changes such as learning and memory formation (Jog et al. 1999; Wirth et al. 2003; Wise and Murray 1999). The within-trial dynamics may reflect stimulus-specific or task-specific features of the neuron’s responses (Lim et al. 2006; Narayan et al. 2006; Wirth et al. 2003), short timescale biophysical properties, such as absolute and relative refractory periods and bursting, or longer timescale biophysical properties such as oscillatory modulations and other network and local regional characteristics (Dayan and Abbott 2001).

Fifty-five trials of spiking activity recorded from a hippocampal neuron of a macaque monkey in response to the same scene of a multiple location-scene association task presented as the raster plot (*A*), peristimulus time histogram (PSTH) (*B*), and trial-by-trial **...**

Two standard methods for analyzing neuron spiking dynamics are raster plots and the peristimulus time histogram (PSTH). Raster plots (Fig. 1*A*) are informative, two-dimensional graphs of spike times plotted as a function of trial number and time into the trial. However, they offer no means of quantitative analysis. The PSTH (Fig. 1*B*) is computed by totaling the spike counts within bins across trials and dividing by the trial length and number of trials (Abeles 1982; Gerstein 1960; Gerstein and Kiang 1960; Kass et al. 2003; Perkel et al. 1967). Although easy to compute, the PSTH assumes that the spiking activity between trials does not change, does not account for the effects of spike history on current spiking activity, and, by itself, does not provide a framework for statistical inference.

To go beyond use of raster plots, several authors have proposed analyses based on parametric or semiparametric statistical models to analyze between- and within-trial dynamics in neural activity (Brody 1999; Ventura et al. 2005). In these analyses, the components of the between- and within-trial dynamics are estimated sequentially rather than simultaneously, and the effects of history dependence within trial are not considered. Point process likelihood– based generalized linear models (GLMs) (Fahrmeir and Tutz 2001; McCullagh and Nelder 1989) have been used to analyze history dependence in neural spiking activity (Brillinger 1988; Brown et al. 2002, 2003; Kass and Ventura 2001; Paninski et al. 2004; Truccolo et al. 2005). GLM methods are available in nearly every statistical package and have the optimality properties and statistical inference framework common to all likelihood-based techniques. State-space modeling is a highly flexible signal processing paradigm (Durbin and Koopman 2001; Fahrmeir and Tutz 2001; Kitagawa and Gersch 1996; Mendel 1995) that has been applied in studies of neural dynamics including neural receptive field plasticity (Brown et al. 2001; Frank et al. 2002, 2004), neural coding analyses, neural spike train decoding (Barbieri et al. 2004; Brockwell et al. 2004; Brown et al. 1998; Deneve et al. 2007; Eden et al. 2004; Ergun et al. 2007; Paninski et al. 2004; Smith and Brown 2003; Wu et al. 2006), the design of control algorithms for brain–machine interfaces and neural prostheses (Shoham et al. 2005; Srinivasan et al. 2006, 2007a,b; Wu et al. 2006; Yu et al. 2007), and analyses of learning (Smith et al. 2004, 2005, 2007; Wirth et al. 2003). Although combined GLM and state-space methods have been developed in the statistics literature (Dey et al. 2000; Fahrmeir and Tutz 2001), these combined methods have not been used to analyze between-trial and within-trial dynamics of individual neurons.

We develop a state-space generalized linear model (SS-GLM) to formulate a point process representation of between-and within-trial neural spiking dynamics that has the PSTH as a special case. We estimate the model parameters by maximum likelihood using an approximate Expectation Maximization (EM) algorithm, assess model goodness-of-fit using Kolmogorov–Smirnov (K-S) tests based on the time-rescaling theorem, and guide the choice of model order with Akaike’s information criterion. We illustrate our approach using simulated hippocampal spiking activity and actual neural spiking activity of six hippocampal neurons recorded in a macaque monkey executing a learning experiment.

We describe our SS-GLM framework and the experimental protocol from which the data analyzed in this study were collected.

We assume that the spiking activity of a single neuron is a dynamic process that can be analyzed with the state-space framework used in engineering, statistics, and computer science (Durbin and Koopman 2001; Fahrmeir and Tutz 2001; Kitagawa and Gersch 1996; Mendel 1995). The state-space model consists of two equations: a state equation and an observation equation. The state equation defines an unobservable state process that will represent between-trial neural dynamics. Such state models with unobservable processes are often referred to as hidden Markov or latent process models (Durbin and Koopman 2001; Fahrmeir and Tutz 2001; Smith and Brown 2003). The observation equation defines how the observed spiking activity relates to the unobservable state process. The data we observe in the neurophysiological experiments are the series of spike trains. In our analyses the observation model will be a point process that depends on both between- and within-trial dynamics.

We assume that the spike train that is the observation process of our state-space model can be represented as a point process (Brown 2005; Brown et al. 2003; Daley and Vere-Jones 2003; Kass et al. 2005). A point process is a time series of random binary (0, 1) events that occur in continuous time. For neural spike train, the 1’s are the individual spike times and the 0’s are the times of no spike occurrences. Given a trial interval (0, *T*], let *N*(*t*) be the number of spikes counted in interval (0, *t*] for *t* (0, *T*] in some trial. A point process model of a neural spike train can be completely characterized by its conditional intensity function, *λ*(*t*|*H _{t}*), defined as

$$\lambda (t\mid {H}_{t})=\underset{\mathrm{\Delta}\to 0}{lim}\frac{Pr[N(t+\mathrm{\Delta})-N(t)=1\mid {H}_{t}]}{\mathrm{\Delta}}$$

(1)

where *H _{t}* denotes the history of spikes up to time

To facilitate presentation of our model, we present the specific form of the conditional intensity function in discrete time. Assume that a repeated trial experiment is conducted with *K* trials of the same stimulus or task and neural activity is simultaneously recorded with the application of the stimulus or the execution of the task. We index the trials *k* = 1, …, *K*. We define the observation interval as (0, *T*], where *T* is the length of each trial. To obtain the discrete time representation of the conditional intensity function, we choose *L* large and divide the time interval *T* into subintervals of width Δ = *TL*^{−1}. We choose *L* large so that each subinterval contains at most one spike. We index the subintervals = 1, …, *L* and define *n _{k}*

We assume that the conditional intensity function for the spike trains recorded in our experiment at time Δ of trial *k* has the form

$${\lambda}_{k}(\ell \mathrm{\Delta}\mid {\theta}_{k},\gamma ,{H}_{k,\ell})={\lambda}^{S}(\ell \mathrm{\Delta}\mid {\theta}_{k}){\lambda}^{H}(\ell \mathrm{\Delta}\mid \gamma ,{H}_{k,\ell})$$

(2)

where the component *λ ^{S}*(Δ|

To model the effect of the stimulus or task on the neural spiking activity we assume that the first component of the conditional intensity function of Eq. 2 has the form

$$log{\lambda}^{S}(\ell \mathrm{\Delta}\mid {\theta}_{k})=\sum _{r=1}^{R}{\theta}_{k,r}{g}_{r}(\ell \mathrm{\Delta})$$

(3)

where the *g _{r}*(Δ) are a set of

To model the effect of spike history on current neural spiking activity, we assume that the second component on the right side of Eq. 2 can be defined as

$$log{\lambda}^{H}(\ell \mathrm{\Delta}\mid \gamma ,{H}_{k,\ell})=\sum _{j=1}^{J}{\gamma}_{j}{n}_{k,\ell -j}$$

(4)

for *k* = 1, …, *K*, where *γ* = (*γ*_{1}, …, *γ _{j}*, …,

By combining Eqs. 2, 3, and 4, we see that the conditional intensity function, which defines the observation equation of our state space model of neural activity, may be written as

$${\lambda}_{k}(\ell \mathrm{\Delta}\mid {\theta}_{k},\gamma ,{H}_{k,\ell})=exp\left\{\sum _{r=1}^{R}{\theta}_{k,r}{g}_{r}(\ell \mathrm{\Delta})\right\}exp\left\{\sum _{j=1}^{J}{\gamma}_{j}{n}_{k,\ell -j}\right\}$$

(5)

for *k* = 1, …, *K* and = 1, …, *L.* Equation 5 defines a GLM formulation of the conditional intensity function (Brillinger 1988; Kass and Ventura 2001; McCullagh and Nelder 1989; Truccolo et al. 2005). We perform the expansions in Eqs. 3 and 4 in terms of the logs of the stimulus and the spike-history components instead of in terms of the stimulus and spike-history components so that, as Eq. 5 shows, the analysis will always generate a conditional intensity or rate function that is positive.

To complete the definition of our state-space model and to model the relation of spiking activity between trials we define the state equation. We assume stochastic dependence between the *θ _{k}* = {

$${\theta}_{k}={\theta}_{k-1}+{\epsilon}_{k}$$

(6)

for *k* = 1, …, *K*, where *ε _{k}* is an

Equations 5 and 6 define our state-space generalized linear model (SS-GLM). This model can be used to analyze between-trial and within-trial neural spiking dynamics provided we can estimate the unknown parameter *ψ* = (*γ*, *θ*_{0}, Σ). Because the values of *θ _{k}* are unobservable and

To understand what is accomplished in the EM model fitting, we note that the log of the joint probability density of the spike train data and the state process or the complete data log likelihood is (APPENDIX A, Eq. A1)

$$\begin{array}{l}logp(N,\theta \mid \psi )=\sum _{k=1}^{K}\sum _{\ell =1}^{L}[{n}_{k,\ell}log[{\lambda}_{k}(\ell \mathrm{\Delta}\mid {\theta}_{k},\gamma ,{H}_{k,\ell})\mathrm{\Delta}]\\ -{\lambda}_{k}(\ell \mathrm{\Delta}\mid {\theta}_{k},\gamma ,{H}_{k,\ell})]\mathrm{\Delta}]+Klog({(2\pi )}^{-R/2}\mid \mathrm{\sum}{\mid}^{-1/2})\\ -{2}^{-1}\sum _{k=1}^{K}({\theta}_{k}-{\theta}_{k-1}{)}^{\prime}{\mathrm{\sum}}^{-1}({\theta}_{k}-{\theta}_{k-1})\end{array}$$

(7)

Equation 7 is a penalized log-likelihood function and shows, as mentioned earlier, that the random walk model (Eq. 6) for the stimulus coefficients imposes a multivariate stochastic smoothness or regularization constraint on the between-trial component of the conditional intensity or spike rate function (Fahrmeir and Tutz 2001; Kitagawa and Gersch 1996). The factor Σ^{−1} is the smoothing or regularization matrix. In general, the larger the elements of Σ, the rougher the estimates of the between-trial component of the spike rate function, whereas the smaller the elements of Σ, the smoother the estimates of this component. Thus the maximum-likelihood estimate of Σ governs the smoothness of the spike rate function by determining the degree of smoothing that is most consistent with the data.

If we assume that the effect of the stimulus is the same between trials, then *θ _{k}* = {

$${\lambda}_{k}(\ell \mathrm{\Delta}\mid \theta )=exp\left\{\sum _{r=1}^{R}{\theta}_{r}{g}_{r}(\ell \mathrm{\Delta})\right\}$$

(8)

for *k* = 1, …, *K* and = 1, …, *L*. Thus the conditional intensity function is the same for all trials *k* = 1, …, *K*. Next, we choose the basis functions *g _{r}* to be unit pulse functions with equal length

$${g}_{r}(\ell \mathrm{\Delta})=\{\begin{array}{ll}1\hfill & \text{if}\ell =(r-1)L{R}^{-1}+1,\dots ,rL{R}^{-1}\hfill \\ 0\hfill & \text{otherwise}\hfill \end{array}$$

(9)

for = 1, …, *L* (Fig. 2). For the bin in which *g _{r}*(Δ) = 1, the spiking activity obeys a homogeneous Poisson with mean rate exp(

The state-space model in Eqs. 5 and 6 is an extension of the GLM model in Truccolo et al. (2005). In that report, the stimulus effect is the same across trials. That is, the conditional intensity function has the form

$${\lambda}_{k}(\ell \mathrm{\Delta}\mid \gamma ,\theta ,{H}_{k,\ell})=exp\left\{\sum _{r=1}^{R}{\theta}_{r}{g}_{r}(\ell \mathrm{\Delta})\right\}exp\left\{\sum _{j=1}^{J}{\gamma}_{j}{n}_{k,\ell -j}\right\}$$

(10)

Thus the conditional intensity at trial *k* and time Δ is a function of spike history in trial *k* and the time since the onset of the stimulus. This conditional intensity differs between trials because, in any given trial, it depends on the spike history in that trial. The spike history for different trials or, more generally, for different spike events is almost always different. This model can therefore capture implicitly between-trial neural dynamics. We refer to this model as a GLM model. Equations 2–4 make the GLM model with a log-link function (Eq. 10) a special case of our SS-GLM model when the coefficients of the stimulus term are the same for all trials. Truccolo et al. (2005) showed that this formulation of the GLM provided a simple way to perform maximum-likelihood analyses of neural spike trains by using a point process likelihood discretized at a resolution of Δ, readily available GLM fitting procedures such as glmfit in Matlab (The MathWorks, Natick, MA) and point process–based goodness-of-fit procedures based on the time-rescaling theorem as subsequently described.

Before making an inference from a statistical model, it is crucial to assess the extent to which the model describes the data. Measuring quantitatively the agreement between a proposed model and a neural spike train or point process time series is a more challenging problem than measuring agreement between a model and continuous data. Standard distance measures applied in analyses of continuous data, such as average sum of squared errors, are not designed for point process data. One alternative solution to this problem is to apply the time-rescaling theorem (Brown et al. 2002; Ogata 1988; Truccolo et al. 2005) to transform point process measurements such as a neural spike train into a continuous measure appropriate for a goodness-of-fit assessment.

Once a conditional intensity function model has been fit to a neural spike train, we can compute rescaled times from the estimated conditional intensity function as

$${z}_{k,m}=1-exp\left[{\int}_{{u}_{k,m-1}}^{{u}_{k,m}}{\lambda}_{k}(u\mid {\theta}_{k\mid K},\widehat{\gamma},{H}_{k,u})\text{d}u\right]$$

(11)

where *u _{k}*

To evaluate whether the *z _{k}*

Evaluating whether the *z _{k}*

$${z}_{k,m}^{G}={\mathrm{\Phi}}^{-1}({z}_{k,m})$$

(12)

where Φ(·) is the cumulative distribution function of a Gaussian random variable with zero mean and variance 1. If the rescaled times *z _{k}*

We fit several models to the neural spike train data and use Akaike’s Information Criterion (AIC) (Burnham and Anderson 1998; Truccolo et al. 2005) to identify the best approximating model from among a set of possible candidates. We evaluate the AIC as

$$\text{AIC}(p)=-2logP(N\mid \psi )+2p$$

(13)

where *p* is the dimension of *ψ* and log *P*(*N*|*ψ*) is the log likelihood of the observed data computed at the final iteration of the EM algorithm from Eqs. A2, A6, and A7 in APPENDIX A. Minimizing the AIC across a set of candidate models identifies a model from among the candidates that has approximately the smallest expected Kullback–Leibler distance to the “true” model (Burnham and Anderson 1998). Hence, in theory, the optimal model by this criterion is the one with the smallest AIC. In practice a small difference in AIC between two models may be due to random sample fluctuations. Therefore if the AIC difference between two models is <10, we choose the model with the smaller number of parameters. If the AIC difference between two models is >10, we conclude that the model with lower AIC explains substantially more structure in the data (Burnham and Anderson 1998).

The model we consider most appropriate to use to analyze the data is the one that: *1*) is the best approximating model in terms of AIC and *2*) gives the best summary of the data in terms of K-S and ACF plots.

Once the SS-GLM model has been estimated and its goodness-of-fit has been established, the principal objective is to use functions of the model and the model parameters to make inferences about the neural system being studied based on the recorded experimental data. An important property of maximum-likelihood estimation is invariance. That is, if is the maximum-likelihood estimate of a parameter *ψ* in a given model and *h*(*ψ*) is a function of *ψ*, then the maximum-likelihood estimate of *h*(*ψ*) is *h*() (Pawitan 2001). In particular, all of the optimality properties of maximum-likelihood estimation that are true for are also true for *h*() (Pawitan 2001). This seemingly obvious property is not true of all estimation procedures. We use this invariance principle explicitly in all of our analyses by simply computing *h*() as the obvious and optimal estimate of *h*(*ψ*).

The five principal, neurophysiologically relevant functions, *h*(*ψ*), we compute using this important property of maximum-likelihood estimation are: *1*) the stimulus effect on the spiking activity; *2*) the effect of spike history (intrinsic and network dynamics) on current spiking activity; *3*) the spike rate per trial; *4*) between-trial comparisons of the spike rate function; and *5*) within-trial comparisons of the spike rate function. We show how these functions can be used to answer specific neurophysiological questions.

From Eq. 3 it follows that the maximum-likelihood estimate of the stimulus effect, *λ ^{S}*(Δ|

$${\lambda}^{S}(\ell \mathrm{\Delta}\mid {\theta}_{k\mid K})=exp\left\{\sum _{r=1}^{R}{\theta}_{k,r\mid K}{g}_{r}(\ell \mathrm{\Delta})\right\}$$

(14)

where *θ _{k}*

We denote exp{*γ _{j}*},

$$exp\{{\widehat{\gamma}}_{j}\}$$

(15)

for *j* = 1, …, *J*. Because each * _{j}* is the maximum-likelihood estimate of

We define the expected number of spikes on trial *k* in the interval [*t*_{1}, *t*_{2}] as
${\mathrm{\Lambda}}_{k}({t}_{1},{t}_{2})={\int}_{{t}_{1}}^{{t}_{2}}{\lambda}_{k}(u\mid {\theta}_{k},\gamma ,{H}_{k,u})\text{d}u$. Furthermore, we define the spike rate function on interval [*t*_{1}, *t*_{2}] as

$${({t}_{2}-{t}_{1})}^{-1}{\mathrm{\Lambda}}_{k}({t}_{1},{t}_{2})={({t}_{2}-{t}_{1})}^{-1}{\int}_{{t}_{1}}^{{t}_{2}}{\lambda}_{k}(u\mid {\theta}_{k},\gamma ,{H}_{k,u})\text{d}u$$

(16)

The maximum-likelihood estimate of the spike rate function on trial *k* in the interval [*t*_{1}, *t*_{2}] is

$$\begin{array}{l}{({t}_{2}-{t}_{1})}^{-1}{\widehat{\mathrm{\Lambda}}}_{k}({t}_{1},{t}_{2})={({t}_{2}-{t}_{1})}^{-1}{\int}_{{t}_{1}}^{{t}_{2}}{\lambda}_{k}(u\mid {\theta}_{k\mid K},\widehat{\gamma},{H}_{k\mid u})\text{d}u\\ \approx {({t}_{2}-{t}_{1})}^{-1}\sum _{l={l}_{1}}^{{t}_{2}}{\lambda}_{k}(\ell \mathrm{\Delta}\mid {\theta}_{k\mid K},\widehat{\gamma},{H}_{k\mid u})\mathrm{\Delta}\end{array}$$

(17)

where _{1}Δ = *t*_{1} and _{2}Δ = *t*_{2}. We compute confidence intervals for *Λ _{k}*(

An important objective of these analyses is to compare spike rate functions between two trials. These comparisons are central to identifying a learning effect in behavioral learning experiments. We can do this by comparing the spike rates in the two trials in the same time interval. That is, for a given interval [*t*_{1}, *t*_{2}] and trials *m* and *k* we can compute using the maximum-likelihood estimates in Eq. 17

$$Pr[{({t}_{2}-{t}_{1})}^{-1}{\widehat{\mathrm{\Lambda}}}_{m}({t}_{1},{t}_{2})>{({t}_{2}-{t}_{1})}^{-1}{\widehat{\mathrm{\Lambda}}}_{k}({t}_{1},{t}_{2})]$$

(18)

for any *k* = 1, …, *K* − 1 and *m* > *k*. We compute this probability using the Monte Carlo methods described in APPENDIX F.

A second important objective of these analyses is to compare the spike rate function between two disjoint periods within a trial. For example, it is important to compare spiking activity during a baseline period with spiking activity during another period in the trial such as the scene period or the delay period (Fig. 1). Differences in neural activity between a baseline and a specified period are used as one criterion to classify the neurophysiological properties of neurons (Wirth et al. 2003; Wise and Murray 1999). We compare the two spike rates on trial *k* given the two intervals [*t*_{1}, *t*_{2}] and [*t*_{3}, *t*_{4}] defined from Eq. 16 as

$${({t}_{4}-{t}_{3})}^{-1}{\mathrm{\Lambda}}_{k}({t}_{3},{t}_{4})-{({t}_{2}-{t}_{1})}^{-1}{\mathrm{\Lambda}}_{k}({t}_{1},{t}_{2})$$

(19)

and the maximum-likelihood estimate of the difference between these two spike rates is

$${({t}_{4}-{t}_{3})}^{-1}{\widehat{\mathrm{\Lambda}}}_{k}({t}_{3},{t}_{4})-{({t}_{2}-{t}_{1})}^{-1}{\widehat{\mathrm{\Lambda}}}_{k}({t}_{1},{t}_{2})$$

(20)

If the entire confidence interval for the difference between the two spike rate functions is positive (negative), then it is reasonable to infer that the spike rate function in period [*t*_{3}, *t*_{4}] is larger (smaller) than the spike rate in period [ *t*_{1}, *t*_{2}]. If the confidence interval for the difference in the two rates contains zero, then we conclude that the two spike rates do not differ. We compute confidence intervals for the difference (*t*_{4} − *t*_{3})^{− 1} *Λ _{k}* (

To illustrate our framework in the analysis of actual experimental data, we analyze responses of six hippocampal neurons recorded from macaque monkeys performing a location-scene association task. A detailed description of the experiment is in Wirth et al. (2003). The objective of that study was to characterize the response of the neurons to individual scenes and relate it to learning behavior. Here, we analyze in detail the responses of a single hippocampal neuron (see RESULTS) and we summarize the analyses of five other neurons.

The spikes of the neuron that we analyze in detail (RESULTS) were recorded during the presentation of the same scene across 55 trials. Each trial lasted 1,700 ms (Fig. 1*A*) and the spikes were recorded with a resolution of 1 ms. For this neuron, each trial time was divided into four epochs (Fig. 1*A*). These were: the fixation period from 0 to 300 ms; the scene presentation period from 300 to 800 ms; the delay period from 800 to 1,500 ms; and the response period from 1,500 to 1,700 ms. Across these 55 trials, this neuron fired 1,911 spikes in response to this scene.

We first illustrate our SS-GLM framework in the analysis of simulated neural spike train data designed to reflect the structure of activity seen in learning experiments (Wirth et al. 2003). To simulate the data we constructed a point process model of the neural spiking activity whose conditional intensity function is a product of a stimulus and a spike history component (Eq. 2).

To construct the stimulus component of the model we chose cardinal splines as the basis functions *g _{r}* (Eq. 3), with

$$\begin{array}{l}log{\lambda}^{S}(t\mid {\theta}_{k})=[u{(t)}^{3}\text{}u{(t)}^{2}\text{}u(t)\text{1}]\\ \times \left[\begin{array}{cccc}-0.5& 1.5& -1.5& 0.5\\ 1& -2.5& 2& -0.5\\ -0.5& 0& 0.5& 0\\ 0& 1& 0& 0\end{array}\right]\times \left[\begin{array}{c}{\theta}_{k,r-1}\\ {\theta}_{k,r}\\ {\theta}_{k,r+1}\\ {\theta}_{k,r+2}\end{array}\right]\end{array}$$

(21)

for *t* (*t _{j}*,

$${\theta}_{k}={F}_{k}{\theta}_{k-1}$$

(22)

where *θ* _{0} = [1, 1.7, 2.2, 3.1, 1.75, 1.75, 1.88, 1.88, 1.75, 1.75, 1] and where *F _{k}* is an 11-dimensional diagonal matrix whose diagonal elements evolve across the trials as

$$\begin{array}{l}\mathit{Trials}\phantom{\rule{0.38889em}{0ex}}1\phantom{\rule{0.38889em}{0ex}}to\phantom{\rule{0.38889em}{0ex}}10:{F}_{1}=\mathit{diag}\phantom{\rule{0.38889em}{0ex}}[\begin{array}{ccccccccccc}1& 1& 0.995& 0.995& 1& 1& 1& 1& 1& 1& 1\end{array}]\\ \mathit{Trials}\phantom{\rule{0.38889em}{0ex}}11\phantom{\rule{0.38889em}{0ex}}to\phantom{\rule{0.38889em}{0ex}}15:{F}_{2}=\mathit{diag}\phantom{\rule{0.38889em}{0ex}}[\begin{array}{ccccccccccc}1& 1& 0.995& 0.995& 1& 1.001& 1.001& 1.001& 1.001& 1& 1\end{array}]\\ \mathit{Trials}\phantom{\rule{0.38889em}{0ex}}16\phantom{\rule{0.38889em}{0ex}}to\phantom{\rule{0.38889em}{0ex}}20:{F}_{3}=\mathit{diag}\phantom{\rule{0.38889em}{0ex}}[\begin{array}{ccccccccccc}1& 1& 0.995& 0.995& 1& 1.001& 1.04& 1.04& 1.001& 1& 1\end{array}]\\ \mathit{Trials}\phantom{\rule{0.38889em}{0ex}}21\phantom{\rule{0.38889em}{0ex}}to\phantom{\rule{0.38889em}{0ex}}30:{F}_{4}=\mathit{diag}\phantom{\rule{0.38889em}{0ex}}[\begin{array}{ccccccccccc}1& 1& 0.995& 0.995& 1& 1.04& 1.04& 1.04& 1.04& 1& 1\end{array}]\\ \mathit{Trials}\phantom{\rule{0.38889em}{0ex}}31\phantom{\rule{0.38889em}{0ex}}to\phantom{\rule{0.38889em}{0ex}}40:{F}_{5}=\mathit{diag}\phantom{\rule{0.38889em}{0ex}}[\begin{array}{ccccccccccc}1& 1& 0.995& 0.995& 1& 1.01& 1.01& 1.01& 1.01& 1& 1\end{array}]\\ \mathit{Trials}\phantom{\rule{0.38889em}{0ex}}41\phantom{\rule{0.38889em}{0ex}}to\phantom{\rule{0.38889em}{0ex}}47:{F}_{6}=\mathit{diag}\phantom{\rule{0.38889em}{0ex}}[\begin{array}{ccccccccccc}1& 1& 0.995& 0.995& 1& 1.002& 1.002& 1.002& 1.002& 1& 1\end{array}]\\ \mathit{Trials}\phantom{\rule{0.38889em}{0ex}}48\phantom{\rule{0.38889em}{0ex}}to\phantom{\rule{0.38889em}{0ex}}50:{F}_{7}=\mathit{diag}\phantom{\rule{0.38889em}{0ex}}[\begin{array}{ccccccccccc}1& 1& 0.995& 0.995& 1& 1.001& 1.001& 1.001& 1.001& 1& 1\end{array}]\end{array}$$

(23)

The system in Eqs. 21–23 yields a stimulus component with two peaks that evolve between and within trials (Fig. 3*A*). The smaller peak between 0 and 1,000 ms decreases across the trials, whereas the larger peak, between 1,000 and 2,000 ms, rapidly increases after trial 25. For trials 1 to 25 the stimulus effect increases from 0 to 500 ms, decreases from 500 to 1,000 ms, and remains constant from 1,000 to 2,000 ms. From trials 26 to 50 the stimulus shows a lower magnitude increase and decrease between 0 to 1,000 ms and then shows a sharp increase and decrease from 1,000 to 2,000 ms.

We assume Δ = 1 ms and we define the history component of the model as (Fig. 3*B*)

$$log{\lambda}^{H}(\ell \mathrm{\Delta}\mid \gamma ,{H}_{k,\ell})=\sum _{j=1}^{20}{\gamma}_{j}{n}_{k,\ell -j}$$

(24)

where

$$\begin{array}{l}{\gamma}_{1}={\gamma}_{2}={\gamma}_{3}={\gamma}_{4}={\gamma}_{5}=-2\\ {\gamma}_{6}={\gamma}_{7}={\gamma}_{8}={\gamma}_{9}={\gamma}_{10}=-1\\ {\gamma}_{11}={\gamma}_{12}={\gamma}_{13}={\gamma}_{14}={\gamma}_{15}=0\\ {\gamma}_{16}={\gamma}_{17}={\gamma}_{18}={\gamma}_{19}={\gamma}_{20}=2\end{array}$$

(25)

We chose the first five parameters of spike-history component (Eq. 25) to be highly negative, to model the absolute and the early part of the relative refractory period of the simulated neuron (Fig. 3*B*). This is the value of exp(−2) in the 1- to 5-ms bins. Thus if the neuron fires once (twice) between 1 to 5 ms prior to time *t*, then the probability of a spike at time *t* decreases because it is multiplied by exp(−1 × 2) = 0.14 [exp(−2 × 2) = 0.02]. We chose the second five coefficients of the spike-history component for the 6- to 10-ms bins (Fig. 3*B*) to be −1 to model the later part of the neuron’s relative refractory period. If the neuron spiked once between 6 and 10 ms prior to time *t*, then the spiking probability is downweighted by multiplying the other components of the conditional intensity function by exp(−1) = 0.37. We chose the third set of five coefficients (11-to 15-ms bins) to be 0 so that a spike occurring in this time interval has no effect on the propensity of the neuron to spike at time *t* because exp(0) = 1. Finally, if the neuron spiked once between 16 and 20 ms prior to time *t*, then the probability of spiking at time *t* is increased by a factor of exp(*γ* _{4}) = exp(0.5) = 1.65. Spiking activity ≥ 20 ms prior to the current time is assumed to have no effect on the spiking probability at the current time.

The conditional intensity function is therefore the product of the stimulus component defined in Eqs. 21–23 (Fig. 3*A*) and the history component defined in Eq. 24 and 25 (Fig. 3*B*). Following Kass and Ventura (2001), we constructed the stimulus component (Fig. 3*A*) with units of spikes per second and the spike-history component (Fig. 3*B*) as a dimensionless time-dependent scaling function. If the value of the spike history component is above (below, equals) one, then multiplying the stimulus component by the history component increases (decreases, does not affect) the firing rate.

We simulated neural spiking activity consisting of *K* = 50 trials each 2 s in length (Fig. 3*C*) by using the conditional intensity function defined in Eqs. 21–25 in a thinning algorithm (Brown et al. 2003, 2005). The simulated data (Fig. 3*C*), in which the PSTH is binned at 10 ms (Fig. 3*D*) and the spike rate is plotted as a function of trial (Fig. 3*E*), show patterns consistent with the between- and within-trial structure of the stimulus (Fig. 3*A*). The spike history component of the model cannot be discerned from these plots.

To analyze the simulated data, we considered two models with a stimulus-only component: a PSTH (Eqs. 8 and 9) and a state-space PSTH [Eqs. 6, 8, and *9; λ ^{H}*(Δ|

The model used to simulate the data differs from the SS-GLM model in two ways. First, we used a deterministic state equation (Eqs. 22 and 23) to ensure the presence of systematic change in the stimulus component and thus in the neural spiking activity both between and within trials. Such an evolution would occur by chance only with the random walk model in Eq. 6. Second, we used cardinal splines rather than unit pulse functions (Eq. 9, Fig. 2) to construct in the stimulus component (Fig. 3*A*) to guarantee smooth changes in the stimulus effect. By using a model to simulate the data that differs from the models used in the analysis we study the more realistic problem of analyzing data for which the “true model” is unknown.

We fit the GLM and PSTH models by maximum-likelihood estimation using GLMfit in Matlab. We fit the state-space models using the EM algorithms in APPENDIX A. To identify the best approximating model we used AIC and K-S goodness-of-fit tests based on the time-rescaling theorem and ACF of the transformed interspike intervals. An analysis of AIC as a function of the number of model parameters (Fig. 4) shows that in order of improving descriptive power (i.e., lowest AIC), the models are the PSTH, the SS-PSTH, the GLM with a 200-ms spike history with coarse binning, and SS-GLM with a 20-ms spike history and coarse binning (Table 1). Therefore to have a candidate from each type of model we consider, we choose these four models and analyze their fits in greater detail.

The histogram of the rescaled interspike intervals (Fig. 5, *A*–*D*) and the K-S plots (Fig. 5*I*) assess the extent to which the distribution of the rescaled interspike intervals are uniform whereas the ACF (Fig. 5, *E*–*H*) assesses the extent to which the transformed times are approximately independent. If the rescaled times are uniformly distributed, the histograms in Figs. 5, *A*–*D* will be uniform and the K-S plot will be close to the 45 ° line. If the transformed times are independent, the ACF will be close to zero and within the 95% confidence intervals.

Of the four models we consider, the PSTH describes the data least well in terms of the K-S and ACF analyses. The SS-PSTH model gives a good fit in terms of ACF but fits less well in terms of the K-S plot. The GLM 200 and the SS-GLM 20 agree closely with the data in terms of both the ACF and K-S plots. That is, for both the GLM 200 and the SS-GLM 20 the rescaled times are consistent with a uniform probability density and the ACF suggests that the transformed times are independent up to lags of 100 consecutive interspike intervals. The AIC, K-S, and ACF analyses suggest that the SS-GLM 20 provides the most parsimonious and accurate summary of the data. Therefore it is the preferred model to use to answer specific neurophysiological questions. The SS-GLM 20 model (Table 1) offers strong evidence that the within-trial dynamics described by the spike history of 20 ms and the between-trial dynamics described by the state-space are important components of the spiking activity.

We next demonstrate how the SS-GLM 20 model can be used to answer specific neurophysiology questions. Because we know the true underlying structure in the data in this simulated example, we also compare the conclusions we draw from SS-GLM 20 with those we draw from the other three candidate models. First, we examine how the stimulus or the task modulates the spiking activity (Eq. 3). The SS-GLM 20 model gives an estimate of the stimulus component (Fig. 6*D*) as a function of trial and time within the trial. It demonstrates that the task-specific activity changes both between and within trials. The SS-GLM 20 (Fig. 6*D*) captures the three-dimensional structure in the true stimulus surface (Fig. 3*A*). This estimate is not smooth because the estimated stimulus component was constructed with the unit pulse functions (Fig. 2) instead of with the spline functions used to simulate the data. The fit of the SS-PSTH (Fig. 6*C*) is not as good as the SS-GLM 20. In addition, the SS-PSTH estimate of the three-dimensional structure in the stimulus (i.e., spike rate as a function of the stimulus as its between- and within-trial components) does not closely agree with the true stimulus component (Fig. 3*A*). By definition, the PSTH (Fig. 6*A*) and GLM 200 (Fig. 6*B*) models cannot provide estimates of the between-trial stimulus components.

To obtain another view of the between trial comparisons in spiking rates, we follow Smith et al. (2005) and compute the following probability matrix (Eq. 18) using the SS-GLM 20 model: The entry in row *k* and column *m* is the probability that the spike rate on trial *m* (*x*-axis in Fig. 6*E*) is greater than the spike rate on trial *k* (*y*-axis in Fig. 6*E*). The gray scale displays the comparison (Fig. 6*E*) with the light (dark) shades corresponding to a low (high) probability. For contrast, the red areas (Fig. 6*E*) show the trial pairs for which this probability is ≤ 0.95. This is not a significance test, but rather use of the approximate joint distribution of the maximum-likelihood estimates of the coefficients of the stimulus effect (APPENDICES B and F) to compute empirical Bayes’ estimates of these probabilities (Smith et al. 2005). As a consequence, there is no multiple hypothesis testing problem with these comparisons.

The first 23 rows show that from approximately trial 24 to trial 50, there is at least a 0.95 probability that spiking activity on these trials is greater than the spiking activity at trials 1 to 23. Rows between 23 and 35 show that spiking activity is steadily increasing between trials, although this rate is getting smaller. Finally at about trial 35 this increase in spiking activity between trials stops. The spiking activity between trial 36 and trial 50 does not differ because all the corresponding probabilities are >0.05 and <0.95, which is illustrated by the gray areas. Furthermore, the spiking activity between trial 1 and trial 20 does not differ. These differences are also suggested by the raster plot (Fig. 3*A*).

To provide an example of a more detailed analysis of the within-trial dynamics, we show for the true stimulus component at trial 50 (Fig. 6*F*, black curve), 95% simultaneous confidence intervals (Fig. 6*F*, red vertical lines with bars). The state-space model analysis gives an estimate of the joint distribution of the stimulus components of the model. Thus we can construct confidence intervals for any function of the stimulus that is of interest (METHODS, APPENDICES D, E, and F). The confidence intervals cover the true stimulus component for this trial. The PSTH (Fig. 6*A*) and the GLM 200 (Fig. 6*B*) provide an average rather than a trial-specific estimate of the stimulus effect. As a consequence, their confidence intervals fail to cover the true stimulus component at trial 50. We conclude that this neuron has a stimulus-specific response that has both strong between- and within-trial components. Thus Fig. 6*E* and *F* provide quantitative analyses of the structure seen in the raster plot (Fig. 3*A*) and confirm that there is a very high probability of both between- and within-trail task-modulated changes in this neuron’s spiking activity.

Characterizing between-trial dynamics in neural activity is important in behavioral experiments because a frequent objective of these studies is to relate the between-trial changes in neural activity to between-trial changes in behavioral variables such as a learning curve or reaction time measurements (Eichenbaum et al. 1986; Siegel and Castellan 1988; Smith et al. 2004, 2005, 2007; Wirth et al. 2003). The strong between-trial dynamics of this neuron were already evident in the estimated stimulus surface (Fig. 6*D*). To examine another representation of these between-trial dynamics that is closer to the between-trial spike rate estimate computed in learning experiments, we estimate the spike rate within 0.3 and 2 s (Eq. 17) for our four models: the PSTH, the SS-PSTH, the GLM 200, and the SS-GLM 20 (Fig. 7). The PSTH estimate of the between-trial spike rate dynamics (Fig. 7*A*) and its 95% confidence intervals are flat and do not capture the between-trial changes in rate. The GLM 200 model overestimates the between-trial changes in rate between trial 1 and trial 28 and underestimates it from trial 31 to trial 50 (Fig. 7*B*). The SS-PSTH and SS-GLM 20 models capture well the dynamics of the between-trial spiking dynamics because their 95% confidence intervals (APPENDIX E) cover the true change in spike rate between trials (Fig. 7, *C* and *D*).

We examine the spike-history component in detail by plotting the estimate obtained from the GLM 200 model (Fig. 8, *A* and *B*) and the SS-GLM 20 model (Fig. 8*C*). The estimate (Fig. 8*C*, solid line) is in good agreement with the true spike history effect because the 95% confidence intervals for this model cover the true history component (Fig. 8*C*, white squares). In contrast, the 95% confidence intervals for the spike-history function computed from the GLM 200 model do not cover the true spike-history function (Fig. 8*A*). The GLM 200 model overestimates the true history component at all time bins. The PSTH and the SS-PSTH models do not provide an estimate of the spike-history dependence.

From this analysis we conclude that the spiking activity of this neuron is strongly modulated by a spike-history component for which the short-term components 1 to 10 ms most likely represent the intrinsic dynamics of the neuron such as the absolute and relative refractory period, whereas the components from 15 to 20 ms could represent late intrinsic dynamics and perhaps the effects of network dynamics (Truccolo et al. 2005).

In conclusion, the state-space GLM analysis provides a highly informative summary of the between- and within-trial dynamics of this neuron, which can be interpreted as possible learning-related dynamics (stimulus- or task-specific modulation), and intrinsic and network dynamics (spike-history component).

To illustrate our state-space GLM framework to characterize actual multiple trial neural spiking activity, we analyze the spike trains of the hippocampal neuron (Fig. 1) recorded from the monkey executing the location-scene association task (see METHODS). We demonstrate how to select the most appropriate model from among a set of candidate models to describe the data and show how that model may be used to characterize the properties of the neuron’s spiking activity and to answer specific neurophysiological questions.

We considered two models with a stimulus-only component: a PSTH (Eqs. 8 and 9) and a state-space PSTH [Eqs. 6, 8, and *9; λ ^{H}*(Δ|

We fit the GLM and PSTH models by maximum likelihood using GLMfit in Matlab and the state-space models using the EM algorithms in APPENDIX A. Model selection and goodness-of-fit were carried out using AIC and K-S goodness-of-fit tests based on the time-rescaling theorem and ACF of the Gaussian transformed interspike intervals. An analysis of AIC as a function of the number of model parameters (Fig. 9) shows that in order of improving descriptive power (i.e., lowest AIC), the models are the PSTH, the SS-PSTH, the GLM with a 100-ms spiking history, and the SS-GLM with a 100-ms spiking history (Table 2). Therefore to have a candidate from each type of model we consider, we choose these four models and analyze their goodness-of-fit in greater detail.

Of the four models we consider, the PSTH describes the data least well in terms of the plots of the transformed times, ACF and K-S analyses (Fig. 10, *A*, *E*, and *I*, green curve). The SS-PSTH model gives a good fit in terms of ACF (Fig. 10*G*) but fits less well in terms of the transformed times and K-S plot (Fig. 10, *C* and *I*, black curve). The transformed times of the GLM 100 appear to be slightly more uniform (Fig. 10*B*) than those of the SS-GLM 100 (Fig. 10*D*). All of the ACF components of the SS-GLM 100 (Fig. 10*H*) appear to be independent up to a lag of 100 consecutive interspike intervals, whereas several of the ACF components of the GLM 100 (Fig. 10*F*) are outside the 95% confidence bounds, suggesting that these latter transformed times are not independent. The K-S plots of the GLM 100 (Fig. 10*I*, blue curve) and the SS-GLM 100 (Fig. 10*I*, red curve) are almost indistinguishable, as suggested by their respective plots of the transformed times (Fig. 10, *B* and *D*).

The AIC, K-S, and ACF analyses suggest that the SS-GLM 100 provides the most parsimonious and accurate summary of the data of the four models. Therefore it is the preferred model to use to answer specific neurophysiological questions. The SS-GLM 100 model (Table 2) offers strong evidence that the within-trial dynamics described by the spike history of 100 ms and the between-trial dynamics described by the state-space model are important components of the spiking activity. We next demonstrate how the SS-GLM 100 model can be used to answer specific neurophysiological questions and we compare the conclusions drawn from it with those drawn from the other three candidate models.

We first examine how the stimulus, which in this case is a location-scene association task, modulates the spiking activity (Eq. 3). The SS-GLM 100 model gives an estimate of the stimulus component (Fig. 11*D*) as a function of trial and time within the trial. As suggested by the raster plot of the spiking activity (Fig. 1*A*), the three-dimensional surface (Fig. 11*D*) shows that there are strong task-specific changes both between and within trials. This estimate is not smooth because the estimated stimulus component was constructed with the unit pulse functions (Eq. 9). The fit of the SS-PSTH is not as good as that of the SS-GLM 100. Furthermore, the SS-PSTH estimate of the stimulus (Fig. 11*C*) is about 1.4-fold higher than the stimulus component estimated from the SS-GLM 100 model (Fig. 11*D*). By definition, the PSTH (Fig. 11*A*) and GLM 100 (Fig. 11*B*) models cannot provide estimates of the between-trial stimulus components. The SS-GLM 100 model demonstrates that the location-scene association task strongly modulates the spiking activity of this neuron.

However, to make this statement firmly, it is important to conduct a formal statistical analysis of these changes. Our SS-GLM analysis estimates the joint probability distribution of the model’s stimulus components. Therefore we can construct confidence intervals or make probability statements about any given aspect of the stimulus using the Monte Carlo algorithms in APPENDICES D, E, and F. Using the SS-GLM 100 model, we compute, as we did for the simulated data example (Fig. 6*E*), the empirical Bayes’ probability matrix (Eq. 18) (Smith et al. 2005) in which the entry in row *k* and column *m* is the probability that the spike rate on trial *m* (*x*-axis in Fig. 11*E*) is greater than the spike rate on trial *k* (*y*-axis in Fig. 11*E*). The gray scale displays the comparison (Fig. 11*E*) with the light (dark) shades corresponding to a low (high) probability. For contrast, the red areas (Fig. 11*E*) show the trials for which this probability is ≥0.95.

The first five rows show that from trial 17 to trial 55 there is at least a 0.95 probability that spiking activity on these latter trials is greater than the spiking activity on trials 1 to 5 (Fig. 11*E*, red areas). Row 6 shows that from trial 19 to trial 55 there is at least a 0.95 probability that spiking activity on these trials is greater than the spiking activity on trial 6, whereas rows 7 to 19 show that from trial 20 to trial 55, there is a >0.95 probability that spiking activity on these trials is greater than the spiking activity on trials 19 or less. Finally, row 24 shows that spiking activity from trial 25 to trial 55 is greater than spiking activity on trials 1 to 24. In contrast, the spiking activity between trial 1 and trial 14 and between 25 and 55 do not differ because the corresponding probabilities are almost all (except of 6) >0.05 and <0.95 (Fig. 11*E*, dark gray areas).

Therefore we conclude that there is a very high probability of a difference in spike rate between trials beginning as early as trial 17 compared with the first six trials and definitively by trial 25 and all subsequent trials compared with all trials prior to trial 25. This latter difference is clearly evident in Fig. 1*A*. Thus Fig. 11, *D* and *E* provides quantitative analyses of the structure seen in the raster plot (Fig. 1*A*) and confirms that there is a very high probability that this neuron has strong between-trial, task-related modulation of its spiking activity.

The three-dimensional structure of the response surface (Fig. 11*D*) shows that our analysis provides for each trial a within-trial estimate of the task modulation of the neural spiking activity. To illustrate, we show the estimated stimulus component and its simultaneous 95% confidence intervals at trials 1 (Fig. 11*F*, green curve) and 25 (Fig. 11*F*, red curve). Because the 95% confidence intervals for the two curves do not overlap between 1.1 to 1.7 s, we conclude that the task-specific activity at trial 25 is greater than that at trial 1 within this time interval. Figure 11*F* shows that we can compute for any trial the within-trial estimate of the task-modulated dynamics and that we can compare these dynamics to those in any other trial. These trial-specific within-trial estimates of the task-modulated dynamics are more informative than the PSTH (Fig. 1*B*), which provides only an average estimate of the within-trial, task-modulated dynamics.

Characterizing between-trial dynamics in neural activity is important in behavioral experiments because a frequent objective of these studies is to relate the between trial changes in neural activity to between trial changes in behavioral variables such as learning curves or reaction time measurements (Eichenbaum et al. 1986; Siegel and Castellan 1988; Smith et al. 2004, 2005, 2007; Wirth et al. 2003). In this case, these between-trial dynamics may represent behavior-related changes.

The strong between-trial dynamics of this neuron were already evident in the raster plot (Fig. 1*A*), the empirical spike rate function (Fig. 1*C*), the estimated response surface (Fig. 11*D*), the probability matrix (Fig. 11*E*), and the within-trial estimates of the task-specific activity (Fig. 11*F*). To provide another display of these between-trial dynamics that is closer to the empirical spike rate estimate (Fig. 1*C*), computed typically in multiple trial experiments as the number of spikes per trial divided by the trial length, we estimate the spike rate per trial within 0.2 and 1.7 s (Eq. 17), respectively, for the GLM 100 model (Fig. 12*A*) and the SS-GLM 100 model (Fig. 12*B*). The spike rate estimated from the GLM 100 model (Fig. 12*B*, solid black curve) exceeds the empirical spike rate (Fig. 12*A*, solid gray curve) between trial 1 and trial 24, whereas it underestimates the spike rate between trial 26 and trial 55. Moreover, because the GLM 100 model does not consider explicitly between-trial dynamics, its 95% confidence intervals (Fig. 12*A*, dashed black curve) estimate only a small amount of uncertainty in its spike rate. The SS-GLM 100 model estimate (Fig. 12*B*, solid black curves) and its wider 95% confidence intervals (Fig. 12*A*, dashed black curve) capture well the between-trial spiking dynamics. The empirical spike rate estimate (Fig. 12*B*, gray curves) is closer to the SS-GLM 100 estimate. Because we established in the goodness-of-fit analyses that the SS-GLM 100 model provides the best description of the data, its spike rate estimate is more credible and informative than either the GLM 100 or the empirical spike rate estimates (Figs. 1*C* and and12,12, gray curves). Therefore the SS-GLM model can be used to characterize the between-trial changes in this neuron’s spiking activity.

An important question to answer at the start of the analysis of a neurophysiological experiment consisting of multiple trials is whether activity in a specified period of the task is different from activity in the baseline period. This analysis is performed at the start to determine whether a neuron has any evidence of task-specific activity. If the null hypothesis of no task-specific activity is rejected (not rejected) the neuron is included in (excluded from) further analysis. A standard convention is to use an ANOVA to answer this question (Wirth et al. 2003; Wise and Murray 1999). This approach is usually less than optimal because, except in the case of extremely high spike rates, neural spiking activity is rarely well described by the Gaussian assumptions that are required to correctly apply ANOVA. Although we have already shown with the analyses in Figs. 11 and and1212 that this neuron has strong task-specific modulation of it is spiking activity, we demonstrate that the appropriate analog of this ANOVA can be carried out very easily using our SS-GLM framework.

The question we ask is, “Is there a difference in task-specific activity between the delay period (Fig. 1, 800 –1,500 ms) and the fixation period (Fig. 1, 0 –300 ms)?” We consider the fixation period as a baseline period and the delay period as a period of possible task-specific activity. We used one-way ANOVA on the spike rates estimated from the PSTH and found significant task-specific activity with *P* < 0.05. The ANOVA does not give details about the differences in response across the task without a further post hoc testing analysis. Using the SS-GLM 100 we estimated the difference in the spike rates for each trial using Eq. 20 with *t*_{1} = 200, *t*_{2} = 300, *t*_{3} = 800, *t*_{4} = 1,500 ms. The estimate of this difference by trial (black curve) with 95% confidence intervals (black dashed curves) is shown in Fig. 13. The estimate of this difference is positive from trial 15 to trial 55 and the lower bound of the 95% interval for the difference is positive from trial 20 to trial 55. Therefore our analysis confirms the ANOVA finding of task-specific activity and shows that the difference between the activity in the delay and fixation periods is significant beginning at trial 20. The empirical estimate of the observed rate difference (Fig. 13, gray curve) computed on each trial as the number of spikes in the delay period divided by the length of the delay period minus the number of spikes in the baseline period divided by the length of the baseline period suggests a similar conclusion, but is noisier and does not provide confidence intervals.

We examine the spike-history component in detail by plotting the estimate obtained from the GLM 100 (Fig. 14*A*) and the SS-GLM 100 models (Fig. 14*B*). The spike-history components of the two models suggest that the neuron has strong absolute refractory (1–5 ms) and relative refractory (6 –10 ms) periods. The spiking propensity of this neuron is not modulated by spiking activity 11 to 20 ms prior to the current time. It also shows a strong positive modulation by spiking activity from 21 to 100 ms prior to the current time that may be due to late intrinsic dynamics and perhaps the effect of network dynamics (Truccolo et al. 2005). The positive modulation of the GLM 100 model is slightly greater than that of the SS-GLM 100 model. These two models demonstrate that this neuron’s spiking activity is strongly modulated by intrinsic and possibly network dynamics. These spike-history modulations of the neural spiking activity cannot be discerned from the standard raster, PSTH, and spike rate plots (Fig. 1).

In conclusion, we see that the SS-GLM analysis provides a highly informative summary of this neuron’s spiking activity that estimates using one model the between-trial and within-trial spiking dynamics that were estimated separately from the SS-PSTH and the GLM 100, respectively. The between-trial dynamics and part of the within-trial dynamics may be interpreted as strong behavioral or learning effects (stimulus- or task-specific modulation), whereas the other component of the within-trial dynamics represents strong intrinsic and possibly network dynamics (spike-history component) of the neuron.

We use the SS-GLM model to analyze the spiking activity of five other hippocampal neurons recorded from monkeys executing the location-scene association task. The neural responses varied dramatically from very sparse spiking activity (Fig. 15*C*, *column 1*) to very intense spiking (Fig. 15*E*, *column 1*). The differences in the raster plots are reflected in the differences in the estimated stimulus components for each neuron (Fig. 15, *column 2*). For each neuron the SS-GLM analysis shows significant spike-history modulation (Fig. 15, *column 3*). These analyses illustrate that despite the fact that these are the same type of neurons recorded during the same task, the nature of their intrinsic and possibly network dynamics show wide differences. In particular, the timescales and characteristics of the extent to which each of the five neurons depends on its spike history differ appreciably. Neurons B and C show significant decreases in their between-trial or learning components in their experiments, whereas none of the other three neurons shows significant between-trial changes in their activity (Fig. 15, *column 4*).

Summary analysis of SS-GLM model fits to spiking activity from five additional hippocampal neurons (*rows A*–*E*) recorded from monkeys executing the location-scene association task. The analysis includes the raster plots (*column 1*), the between-trial **...**

Further details of these analyses may be found at our website (https://neurostat.mgh.harvard.edu/SSGLM).

In developing our state-space generalized-linear model, we believe, as suggested by Dayan and Abbott (2001), that analyses of neural responses should simultaneously characterize the neuron’s stimulus response and its biophysical properties. Spike-triggered average (Schwartz et al. 2006), reverse correlation (Dayan and Abbott 2001), Wiener kernel (Rieke et al. 1997), PSTH (Gerstein 1960), and GLM methods (Brillinger 1988; Brown et al. 2002, 2003, 2004; Harris et al. 2003; Kass and Ventura 2001; Kass et al. 2005; Paninski et al. 2004; Truccolo et al. 2005) have been used to relate neural responses to applied stimuli. Because, in addition, the GLM has been used to relate spiking activity simultaneously to a stimulus, biophysical properties (spike history), and other covariates (Truccolo et al. 2005), we chose it from among these several methods to be the basis for constructing our analysis methods for multiple-trial neurophysiology experiments.

A GLM alone could be successful in predicting single-neuron activity in multiple trial experiments if there were no between-trial changes in either the task-specific or the spike-history effects (Kass et al. 2005; Truccolo et al. 2005). However, in many behavioral experiments, a key objective is to determine whether there is an evolution of the task-specific response between trials because such dynamics provide evidence for important behavioral changes such as learning. Thus to account for between-trial dynamics in the task-specific activity, we developed a state-space extension of the GLM (Eqs. 5 and 6). The state-space formulation captures the evolution of the task-specific activity by allowing the coefficients of the model’s stimulus component (Eqs. 3 and 6) to differ between trials. In this way, the stimulus effect at each trial can be different. We guard against overfitting by imposing the continuity constraint that the coefficients at trial *k* should be stochastically close to the coefficients at trial *k* − 1 (Eq. 6). The stimulus component, described at each trial by the linear combinations of unit pulse functions (Eq. 9 and Fig. 2), is not constant, and thus has a within-trial component as well. The other component of the within-trial dynamics is captured by the model’s spike-history component (Eq. 4). It is designed to capture the biophysical properties of the neuron such as the effects on the neuron’s spiking propensity of its absolute and relative refractory periods and possible network dynamics.

The random walk model (Eqs. 3 and 6) provides a simple way to let the coefficients of the stimulus functions be different on different trials subject to a plausible constraint that prevents the parameter estimation problem from becoming intractable. For example, in the actual data example we consider, without the random walk constraint, the number of parameters to be estimated for the stimulus component with the number of basis functions being *R* = 11 in an experiment with 55 trials would be 11 coefficients/trial × 55 trials = 605 coefficients. Because the coefficients of the between-trial components of the stimulus represented by the random walk model are an unobservable process, we estimated them, along with the model parameters using an EM algorithm (Smith and Brown 2003; Smith et al. 2004, 2005, 2007). We demonstrated how AIC, K-S plots, and ACF analyses can be used to identify the best form of the SS-GLM to approximate the deterministic state-space point process model used to simulate the data. The SS-GLM captured the important features of the stimulus and spike-history components. Our results further showed how the between-trial and within-trial neural spiking dynamics can be identified with the stimulus components of these models and how the spike-history component can be identified with the biophysical properties of the neuron.

In the analysis of the neural spiking activity from the monkey performing the location-scene association task, the SS-PSTH clearly improved on the PSTH by including a between-trial component. In fact, the SS-PSTH gave a quantitative formulation (Fig. 11*C*) of the structure seen in the raster plots (Fig. 1*A*). That is, it provides an estimate of the task-specific effect as a function of trial and time within the trial. The GLM 100 with a PSTH stimulus component gave an appreciably more accurate description of the data that, despite not having an explicit component for between-trial dynamics, improved appreciably on the SS-PSTH in terms of the AIC model selection analysis (Fig. 9) and the K-S plot and ACF goodness-of-fit analyses (Fig. 10). The ability of the GLM 100 to describe the data successfully underscores the importance of the spike history (the neuron’s biophysical properties) in providing an accurate prediction of the neuron’s spiking activity. As a consequence, it was not a surprise that by including a stimulus component that had between-trial and within-trial dynamics and a spike-history component, the SS-GLM improved on both the SS-PSTH and the GLM in terms of the model selection (Fig. 9) and goodness-of-fit (Fig. 10) criteria.

The AIC showed that the SS-GLM 100 provided a better approximating model than the GLM 100 (Fig. 9 and Table 2). However, the fit of the GLM 100 in terms of K-S plots (Fig. 10*I*, blue curve) appeared to be as good as the fit of SS-GLM 100, even though the GLM 100 modeled the stimulus as a PSTH instead with a between-trial component. The GLM 100 gave a good fit because, although the same coefficients were used to estimate the spike-history component on each trial, a different set of initial spikes was used to start the estimation on each trial (Eq. 4). This is analogous to starting a differential equation from different sets of initial conditions. This feature of the GLM 100 allowed it to estimate implicitly different effects between trials in the absence of an explicit between-trial model component.

Despite this apparently good fit of the GLM 100, several other properties of the GLM 100, in addition to AIC and the ACF, demonstrate why it is not the best approximating model. First, because it had no between-trial model component, the GLM 100 does not provide an explicit estimate of a between-trial or behavior-related effect. Second, simulation of the SS-GLM 100 generated data like those seen in the experiment, whereas simulation of the GLM 100 did not (results not shown). Third, the results from the SS-GLM 100 analysis are robust in the sense that a model estimated on just the odd trials predicted very well the spiking activity on the even trials and vice versa in terms of K-S plots and ACF analyses (results, not shown). Also, the estimates of the stimulus and spike-history model components are nearly identical to those estimated from the full data set. This was not true for the GLM 100. Finally, it was not possible with either the SS-GLM 100 or the GLM 100 to predict the spiking activity at trials 28 to 55 from that in trials 1 to 27 or vice versa, confirming the importance of between-trial dynamics across the entire experiment. These results demonstrate the importance of using multiple measures of goodness-of-fit and of checking the descriptive and predictive properties of models that have apparently good fits to assess the validity of the approximations.

Standard questions asked in analyses of multiple-trial neurophysiological experiments include: Does a neuron have task-specific activity? What is the nature of the stimulus or task-specific activity between trial and within trial? In learning experiments, is there evidence of learning-related neural dynamics? These three questions are all related yet, in current analyses, the first is answered with ANOVA; the second with raster plots, ANOVA, and the PSTH; and the third by plotting spike rates across trials. The most compelling feature of our SS-GLM analysis is its ability to give detailed, quantitative answers to all of these neurophysiology questions from the estimated stimulus component of our model. This is because our model captures the rich between-trial and within-trial structure readily apparent in the raster plots. Our inference framework allows us to ask specific questions about this structure and other features of the data in a way that is more appropriate than the Gaussian-based framework associated with ANOVA or the Poisson-based analyses commonly used with the PSTH. The SS-GLM analysis obviates the problem of multiple hypothesis tests or the use of techniques to correct for multiple hypothesis tests by estimating the joint distribution of the stimulus components. By so doing, we actually conduct an empirical Bayes’ analysis of the neuron’s response to the stimulus rather than conducting formal hypothesis tests (Carlin and Louis 2001). Our results (Figs. 11–15) demonstrate that our approach leads to far more informative analyses than current methods.

As reported in previous GLM analyses (Truccolo et al. 2005), the spike-history component of our model readily identifies short-, intermediate-, and long-timescale biophysical properties of the neuron (Fig. 14). These important properties are not considered in the current analyses of multiple-trial experiments. Moreover, they cannot be deduced from any of the standard plots or analyses of these data (Fig. 1). We showed that these biophysical properties can be quite different even for neurons recorded in the same experiment (Fig. 15). Studies of the differences in these biophysical properties of the neurons may provide a new dimension along which to characterize the properties of the neural response. The model selection (Fig. 9) and the goodness-of-fit (Fig. 10) analyses for the GLM 100 also showed that these biophysical properties can be of equal or greater importance than the stimulus component in predicting the spiking activity of a neuron. Therefore their simultaneous estimation along with the stimulus- or task-specific component of the model is crucial for correctly identifying the appropriate magnitude of the neuron’s stimulus response. Finally, the strength of the spike-history component in the data from all six of the neurons analyzed here demonstrates that the spiking activity of these neurons is highly non-Poisson because, in a Poisson model, spiking activity is independent of history. Furthermore, the spike-history component of our model makes explicit that the spike rate determines spiking activity and that previous spiking activity affects the current spike rate. Therefore future instantiations of the SS-GLM may offer new insights into the rate versus temporal code debate in neuroscience (Shadlen and Newsome 1994; Victor 1999).

To establish what new neurophysiological insights can be gained from our new methods, a detailed study of all of the neurons recorded in the location-scene association task (Wirth et al. 2003) will be the topic of a future report. In addition, we are investigating two extensions of the current work: extensions of the model and extensions of the estimation procedures. First, we chose unit pulse functions (Fig. 2) to model the stimulus so that the PSTH would be a special case of our SS-GLM. We can replace these functions with any other functions deemed appropriate to model the structure in the stimulus. Second, in addition to behavioral experiments, many other types of neuroscience experiments record the responses of single neurons to repeated applications of the same stimulus. For example, these include light for retinal neurons, odors for olfactory neurons, and sounds for auditory neurons. We can extend the stimulus component of the model to include analyses of stimuli in these experiments. Wiener kernel methods are often used to model the stimulus–response relation for these stimuli (Rieke et al. 1997). Third, the stimulus component of the model can also be easily modified to include other covariates that are simultaneously recorded with the application of a stimulus such as spiking activity of other neurons (Truccolo et al. 2005) and local field potentials. Fourth, our current state-space model uses the most rudimentary form possible to describe the between-trial dynamics: a random walk model. Thus a fifth important extension of the current model would be to develop more complex state-space representations guided both by more detailed hypotheses about the between-trial neural dynamics and by use of the model selection and goodness-of-fit techniques to evaluate formally the significance of the new formulations. Similarly, if deemed appropriate, the between-trial dynamics can be extended in a straightforward manner to include the spike-history component of the model. This could allow characterization of another aspect of the neuron’s plasticity on perhaps a faster timescale (Frank et al. 2004). Finally, an alternative form of the state-space model could also be a way to incorporate other types of dynamics such as cortical up– down states (Haslinger et al. 2006; Ji and Wilson 2007).

The use of the EM algorithm to perform the model fitting by maximum likelihood can be extended to Bayesian estimation by formulating prior distributions for the model parameters. Successful extensions of the EM algorithm methods for fitting state-space models to behavioral data to Bayesian approaches using the Bayesian Analysis Using Gibbs Sampling (BUGS) program have been recently reported (Smith et al. 2007). A key advantage of this extension is that each change of the model does not require writing a new EM fitting algorithm as is presently the case with any changes in the SS-GLM. Finally, the view of the model fitting as an *L*^{2} regularization problem (Eq. 7) suggests that for other neural systems other types of regularization, such as *L*^{1} regularization (Hastie et al. 2003), may be more appropriate and could be used as alternatives. These extensions will be the topics of future reports.

The Matlab software used to implement the methods presented here is available at our website (https://neurostat.mgh. harvard.edu/SSGLM/MatlabCode).

We thank A. Smith for helpful discussions and J. Scott for technical help with the manuscript. The final editorial changes in the manuscript were done while G. Czanner was at the University of Warwick, UK supported by a Wellcome Trust fellowship.

GRANTS

This work was supported by National Institutes of Health Grants DA-015644 to E. N. Brown and W. A. Suzuki; MH-59733 to E. N. Brown; MH-58847 to W. A. Suzuki; McKnight Foundation grant to W. A. Suzuki; National Science Foundation Grant IIS-0643995 to U. T. Eden; and Fondation pour la Recherche Médicale, France grant to S. Wirth.

We use an EM algorithm to find the maximum-likelihood estimates of *ψ* = (*γ*, *θ*_{0}, Σ). This requires us to maximize the expectation of the complete data log-likelihood (Dempster et al. 1977). The complete data likelihood is the joint probability density of *N* and *θ*, which for our model is

$$\begin{array}{l}p(\theta ,N\mid \psi )=p(N\mid \theta ,\psi )\times p(\theta \mid \psi )\\ =exp\left\{\sum _{k=1}^{K}\sum _{\ell =1}^{L}{n}_{k\ell}log[{\lambda}_{k}(\ell \mathrm{\Delta}\mid {\theta}_{k},\gamma ,{H}_{k,\ell})\mathrm{\Delta}]-{\lambda}_{k}(\ell \mathrm{\Delta}\mid {\theta}_{k},\gamma ,{H}_{k,\ell})\mathrm{\Delta}\right\}\\ \times {[{(2\pi )}^{-R/2}\mid \mathrm{\sum}{\mid}^{-1/2}]}^{K}\\ \times exp\left\{-{2}^{-1}\sum _{k=1}^{K}({\theta}_{k}-{\theta}_{k-1}{)}^{\prime}{\mathrm{\sum}}^{-1}({\theta}_{k}-{\theta}_{k-1})\right\}\end{array}$$

(A1)

where the first term on the right-hand side of the Eq. A1 is the joint probability mass function of a point process characterized by a conditional density function *λ _{k}*(Δ|

$$\begin{array}{l}Q(\psi \mid {\psi}^{(i)})=E[\sum _{k=1}^{K}\sum _{\ell =1}^{L}{n}_{k,\ell}log[{\lambda}_{k}(\ell \mathrm{\Delta}\mid {\theta}_{k},\gamma ,{H}_{k,\ell})\mathrm{\Delta}]\\ -{\lambda}_{k}(\ell \mathrm{\Delta}\mid {\theta}_{k},\gamma ,{H}_{k,\ell})\mathrm{\Delta}\Vert N,{\psi}^{(i)}]-{2}^{-1}\mathit{KR}log(2\pi )-{2}^{-1}Klog\mid \mathrm{\sum}\mid \\ -{2}^{-1}E\left[\sum _{k=1}^{K}({\theta}_{k}-{\theta}_{k-1}{)}^{\prime}{\mathrm{\sum}}^{-1}({\theta}_{k}-{\theta}_{k-1})\Vert N,{\psi}^{(i)}\right]\end{array}$$

(A2)

where *E*[· || *N*, *ψ*^{(i)}] denotes the expectation of the indicated quantity taken with respect to the probability density of *θ* given *N* and *ψ*^{(i)}. On expanding the terms in Eq. A2, we see that computing the expected value of the complete data log-likelihood requires us to evaluate the expected value of the unobserved process *θ _{k}*|

A recursive linear filter algorithm for computing *θ _{k}*

$${\theta}_{k\mid k-1}={\theta}_{k-1\mid k-1}$$

$${W}_{k\mid k-1}={W}_{k-1\mid k-1}+\mathrm{\sum}$$

$$\begin{array}{l}{\theta}_{k\mid k}={\theta}_{k\mid k-1}+{W}_{k\mid k}\sum _{\ell =1}^{L}{\lambda}_{k}{(\ell \mathrm{\Delta}\mid {\theta}_{k},\gamma ,{H}_{k,\ell})}^{-1}\\ \times \frac{\partial {\lambda}_{k}(\ell \mathrm{\Delta}\mid {\theta}_{k},\gamma ,{H}_{k,\ell})}{\partial \theta}[{n}_{k,\ell}-{\lambda}_{k}(\ell \mathrm{\Delta}\mid {\theta}_{k},\gamma ,{H}_{k,\ell})\mathrm{\Delta}]{\mid}_{\theta ={\theta}_{k\mid k-1}}\end{array}$$

$$\begin{array}{l}{W}_{k\mid k}=-[-{W}_{k\mid k-1}^{-1}+\sum _{\ell =1}^{L}\{{\nabla}^{2}log{\lambda}_{k}(\ell \mathrm{\Delta}\mid {\theta}_{k},\gamma ,{H}_{k,\ell})\\ \times [{n}_{k,\ell}-{\lambda}_{k}(\ell \mathrm{\Delta}\mid {\theta}_{k},\gamma ,{H}_{k,\ell})\mathrm{\Delta}]\\ -\nabla log{\lambda}_{k}(\ell \mathrm{\Delta}\mid {\theta}_{k},\gamma ,{H}_{k,\ell})\nabla {\lambda}_{k}(\ell \mathrm{\Delta}\mid {\theta}_{k},\gamma ,{H}_{k,\ell})\mathrm{\Delta}\}{\mid}_{\theta ={\theta}_{k\mid k-1}}{]}^{-1}\end{array}$$

for *k* = 1, …, *K*. We evaluate the quantities in the following order: *θ _{k}*

Given the estimates *θ _{k}*

$${\theta}_{k\mid K}={\theta}_{k\mid k}+{A}_{k}({\theta}_{k+1\mid K}-{\theta}_{k+1\mid k})$$

$${A}_{k}={W}_{k\mid k}{W}_{k+1\mid k}^{-1}$$

$${W}_{k\mid K}={W}_{k\mid k}+{A}_{k}({W}_{k+1\mid K}-{W}_{k+1/k}){A}_{k}^{\prime}$$

for *k* = *K* − 1, …, 1 and the initial conditions *θ _{K}*

The covariance estimate, *Cov* [*θ _{k}*,

$${W}_{k,u\mid K}=\mathit{Cov}[{\theta}_{k},{\theta}_{u}\parallel N,{\psi}^{(i)}]={A}_{k}\mathit{Cov}[{\theta}_{k+1},{\theta}_{u}\parallel N,{\psi}^{(i)}]$$

for 1 ≤ *k* ≤ *u* ≤ *K*. We have that *W _{k,k}*

Finally, we need to evaluate the term

$$E[{\lambda}^{S}(\ell \mathrm{\Delta}\mid {\theta}_{k})\parallel N,{\psi}^{(i)}]=E\left[exp\left\{\sum _{r=1}^{R}{\theta}_{k,r}{g}_{r}(\ell \mathrm{\Delta})\right\}\Vert N,{\psi}^{(i)}\right]$$

This can be accomplished by expanding exp(*θ _{k}*

$$E[exp({\theta}_{k,r})\parallel N,{\psi}^{(i)}]\doteq exp({\theta}_{k\mid K,r})+{2}^{-1}exp({\theta}_{k\mid K,r}){W}_{k\mid K,r,r}$$

where *θ _{k}*

In the M-step, we maximize the expected value of the complete data log likelihood (Eq. A2) with respect to the parameter vector *ψ* = (*γ*, *θ*_{0}, Σ). In so doing, we obtain new iterates
${\psi}^{(i+1)}=[{\gamma}^{(i+1)},{\theta}_{O}^{(i+1)},{\mathrm{\sum}}^{(i+1)}]$. There is no closed form solution for
${\gamma}^{(i+1)}={\gamma}_{1}^{(i+1)},\dots ,{\gamma}_{J}^{(i+1)}$; thus we use a Newton–Raphson algorithm (Fahrmeir and Tutz 2001) to find *γ*^{(}^{i}^{+ 1)} as the solution to

$$\begin{array}{l}{\gamma}_{m+1}^{(i)}={\gamma}_{m}^{(i)}-{[{\nabla}^{2}Q({\gamma}_{m}^{(i)},{\theta}_{0}^{(i)},{\mathrm{\sum}}^{(i)}\mid {\psi}^{(i)})]}^{-1}\\ \times \nabla Q[{\gamma}_{m}^{(i)},{\theta}_{0}^{(i)},{\mathrm{\sum}}^{(i)}\mid {\psi}^{(i)}]\end{array}$$

(A3)

where
${\gamma}_{m}^{(i)}$ is the *m*th iterate of Newton–Raphson algorithm at the *i*th iteration of the EM algorithm and (^{2}) is the first (second) partial derivative with respect to vector *γ*. The initial value
${\gamma}_{O}^{(i)}$ is set equal to *γ* ^{(}^{i}^{)}, the estimate from the previous EM iteration. When the absolute value of the difference of the consecutive iterates,
$\mid {\gamma}_{m+1}^{(i)}-{\gamma}_{m}^{(i)}\mid $ changes <10^{−2} in all *J* elements, we stop the Newton–Raphson iterations and take
${\gamma}^{(\text{i}+1)}={\gamma}_{m+1}^{(i)}$. The other parameter updated in the M-step is
$\mathrm{\sum}=\mathit{diag}\{{\sigma}_{1}^{2},\dots ,{\sigma}_{R}^{2}\}$. The closed form solution that maximizes the expected value of the complete data log-likelihood with respect to Σ is

$${\mathrm{\sum}}^{(i+1)}=\frac{1}{K}\sum _{k=1}^{K}E[({\theta}_{k}-{\theta}_{k-1})({\theta}_{k}-{\theta}_{k-1}{)}^{\prime}\parallel N,{\psi}^{(i)}]$$

(A4)

for *r* = 1, …, *R* and *k* = 1, …, *K*. Choosing as the basis functions for the model in Eq. 5 the unit pulse functions (Eq. 9) makes all of the off-diagonal elements of Σ^{(}^{i}^{+ 1)} zero. The last parameter updated in M-step is the initial value of the unobserved state process, *θ*_{0} = (*θ*_{0,1}, …, *θ*_{0,}* _{R}*). Its closed form solution is

$${\theta}_{0}^{i+1}=E[{\theta}_{1}\parallel N,{\gamma}^{(i)},{\theta}_{0}^{(i)},{\mathrm{\sum}}^{(i)}]$$

(A5)

For the AIC comparisons (see METHODS) we need to evaluate the observed data log-likelihood, log *P*(*N*|). To do so we note that because *P*[*N*|*ψ*^{(}^{i}^{+ 1)}] = *P*[*θ*, *N*|*ψ*^{(}^{i}^{+ 1)}]/*P*[*θ* |*N*, *θ*^{(}^{i}^{+ 1)}], it follows that log *P*(*N*|*ψ*) = log *P*(*θ*, *N*|*ψ*) − log *P*(*θ*|*N*, *ψ*) and that (Hastie et al. 2001)

$$\begin{array}{l}logp[N\parallel {\psi}^{(i+1)}]=E[logP(\theta ,N\parallel {\psi}^{(i+1)})\mid N,{\psi}^{(i)}]\\ -E[logP(\theta \parallel N,{\psi}^{(i+1)})\parallel N,{\psi}^{(i)}]\\ =Q[{\psi}^{(i+1)}\mid {\psi}^{(i)}]-R[{\psi}^{(i+1)}\mid {\psi}^{(i)}]\end{array}$$

(A6)

Because the random variable *θ*|*N*, *ψ*^{(}^{i}^{)} is approximately Gaussian with mean *θ*^{(}^{i}^{)} = *E*[*θ*|*N*, *ψ*^{(}^{i}^{)}] and covariance *W*^{(}^{i}^{)} = *Cov* [*θ*|*N*, *ψ*^{(}^{i}^{)}] computed in the E-step of *i*th EM iteration, it follows that

$$\begin{array}{l}R[{\psi}^{(i+1)}\mid {\psi}^{(i)}]\\ =-{2}^{-1}\mathit{KR}log(2\pi )-{2}^{-1}log\parallel {W}^{(i)}\mid \\ =-{2}^{-1}tr[{W}^{(i)}{({W}^{(i+1)})}^{-1}]-{2}^{-1}[{\theta}^{(i+1)}-{\theta}^{(i)}{]}^{\prime}{[{W}^{(i)}]}^{-1}[{\theta}^{(i+1)}-{\theta}^{(i)}]\end{array}$$

At the last EM iteration *ψ*^{(}^{i}^{+ 1)} is the maximum-likelihood estimate of *ψ* and we have that *R*[*ψ*^{(}^{i}^{+ 1)}|*ψ*^{(}^{i}^{)}] is approximately

$$R[{\psi}^{(i+1)}\mid {\psi}^{(i)}]\approx -{2}^{-1}\mathit{KR}log(2\pi )-{2}^{-1}log\mid {W}^{(i)}\mid -{2}^{-1}\mathit{KR}$$

(A7)

The details on how to accelerate this EM algorithm are given on our website (https://neurostat.mgh.harvard.edu/SSGLM/MatlabCode).

We approximate the probability density *p*(*θ*_{1}, …, *θ _{K}*|

$$W=\left[\begin{array}{cccc}{W}_{1\mid K}& {W}_{1,2\mid K}& \cdots & {W}_{1,K\mid K}\\ {W}_{2,1\mid K}& {W}_{2\mid K}& \cdots & {W}_{2,K\mid K}\\ \cdots & \cdots & \cdots & \cdots \\ {W}_{K,1\mid K}& {W}_{K,2\mid K}& \cdots & {W}_{K\mid K}\end{array}\right]$$

(B1)

where the *θ*_{1|}* _{K}*, …,

The asymptotic covariance matrix for the maximum likelihood = (, *θ _{0}^* Σ

$$\begin{array}{l}I(\widehat{\psi};N)={I}_{c}(\widehat{\psi};N)-{I}_{m}(\widehat{\psi};N)={E}_{\widehat{\psi}}\left[-\frac{{\partial}^{2}logp(\theta ,N\mid \psi )}{\partial \psi \mid {\psi}^{\prime}}\mid N\right]\\ -{E}_{\widehat{\psi}}\left[\left(\frac{\partial logp(\theta ,N\mid \psi )}{\partial \psi}\right){\left(\frac{\partial logp(\theta ,N\mid \psi )}{\partial \psi}\right)}^{\prime}\mid N\right]\end{array}$$

(C1)

where *p*(*θ*, *N*|*ψ*) is the complete data likelihood (Eq. A1). The first term on the right side of Eq. C1 contains terms of the form *E*[*θ _{k}θ_{k}*

- For
*r*= 1, …,*R*draw a random sample of size*M*from a Gaussian distribution with mean vector (_{c}*θ*_{1,r|}, …,_{K}*θ*_{K}_{,r|}) and covariance matrix_{K}*W*(APPENDIX B). Denote the draws as $({\theta}_{1,r}^{c},\dots ,{\theta}_{K,r}^{c})$, for*c*= 1, …,*M*. Further, let_{c}$${\theta}^{c}-\left[\begin{array}{ccc}{\theta}_{1,1}^{c}& \cdots & {\theta}_{1,R}^{c}\\ \vdots & & \vdots \\ {\theta}_{K,1}^{c}& \cdots & {\theta}_{K,R}^{c}\end{array}\right]$$ - For every
*c*compute$${S}_{c}=\frac{\partial logp({\theta}^{c},N\mid \widehat{\psi})}{\partial \psi}$$ - Take ${M}_{c}^{-1}{\sum}_{c=1}^{{M}_{c}}{S}_{c}{S}_{c}^{\prime}$ as an estimate of
*I*(|_{m}*N*).

In our analyses we use *M _{c}* = 100.

A Monte Carlo algorithm for computing *K* × *L* simultaneous confidence intervals for the stimulus effect, *λ ^{S}*(Δ|

- For
*r*= 1, …,*R*draw a random sample of size*M*from a Gaussian distribution with mean vector (_{c}*θ*_{1,r|}, …,_{K}*θ*_{K}_{,}_{r}_{|}) and covariance matrix_{K}*W*(APPENDIX B). Denote the draws as $({\theta}_{1,r}^{c},\dots ,{\theta}_{K,r}^{c})$, for*c*= 1, …,*M*._{c} - For every sample
*c*, trial*k*, and time bin , compute ${\lambda}^{c}=exp\{{\sum}_{r=1}^{R}{\theta}_{k,r}^{c}{g}_{r}(\ell \mathrm{\Delta})\}$, where*c*= 1, …,*M*;_{c}*k*= 1, …,*K*; and = 1, …,*L*. - For every
*k*and order the values*λ*from the smallest to the largest and denote the ordered values as^{c}*λ*.^{c} - The 100(1 −
*α*)% confidence interval for*λ*(Δ|^{S}*θ*) is [_{k}*λ*^{(}^{c}^{′)},*λ*^{(}^{c}^{″)}], for*k*= 1, …,*K*, where*λ*^{(}^{c}^{′)}and*λ*^{(}^{c}^{″)}are defined such that*α*/2 =*c*′/*M*and 1 − (_{c}*α*/2) =*c*″/*M*for_{c}*α*(0, 1).

Choosing *α* = 0.05 yields 95% confidence intervals. In our analyses we take *M _{c}* = 3,000.

We compute simultaneous confidence intervals for the true spike rate function Eq. 16 and the true difference in spike rate functions defined in Eq. 19. We refer to the two functions as *h*(*ψ; θ _{k}*). The

- For
*r*= 1, …,*R*draw a random sample of size*M*from a Gaussian distribution with mean vector (_{c}*θ*_{1,}_{r}_{|}, …,_{K}*θ*_{K}_{,}_{r}_{|}) and covariance matrix_{K}*W*(APPENDIX B). Denote the draws as $({\theta}_{1,r}^{c},\dots ,{\theta}_{K,r}^{c})$, for*c*= 1, …,*M*._{c} - For every sample
*c*and trial*k*compute ${h}^{c}=h(\widehat{\psi};{\theta}_{k,1}^{c},\dots ,{\theta}_{k,R}^{c})$, where*c*= 1, …,*M*,_{c}*k*= 1, …,*K*. - For every
*k*order the values*h*from the smallest to the largest and denote the ordered values as^{c}*h*^{(c)}. - The 100(1 −
*α*)% confidence band for*h*(*ψ; θ*), is [_{k}*h*^{(c′)},*h*^{(c″)}] for*k*= 1, …,*K*, where*h*^{(c′)}and*h*^{(c″)}are such that*α*/2 =*c*′/*M*and 1 − (_{c}*α*/2) =*c*″/*M*for_{c}*α*(0, 1).

Choosing *α* = 0.05 yields 95% confidence intervals. In our analyses we take *M _{c}* = 300.

We show how to evaluate Pr [(*t*_{2} −*t*_{1})^{−1} * _{m}*(

- For
*r*= 1, …,*R*draw a random sample of size*M*from a Gaussian distribution with mean vector (_{c}*θ*_{1,}_{r}_{|}, …,_{K}*θ*_{K}_{,}_{r}_{|}) and covariance matrix_{K}*W*(APPENDIX B). Denote the draws as $({\theta}_{1,r}^{c},\dots ,{\theta}_{K,r}^{c})$, for*c*= 1, …,*M*._{c} - For every sample
*c*and trial*k*compute ${h}_{k}^{c}={({t}_{2}-{t}_{1})}^{-1}{\sum}_{\ell ={\ell}_{1}}^{{\ell}_{2}}{\lambda}_{k}(\ell \mathrm{\Delta}\mid {\theta}_{k,[\ell /R]}^{c},\dots ,{\theta}_{K,[\ell /R]}^{c},\widehat{\gamma},{H}_{k\mid \text{u}})\mathrm{\Delta}$, where*c*= 1, …,*M*,_{c}*k*= 1, …,*K*. - For every pair of trials
*m*,*k*,*m*>*k*,*m*= 1, …,*K*,*k*= 1, …,*K*let S_{Mc}be the number of cases for which ${h}_{m}^{c}>{h}_{k}^{c}$. - For each
*m*,*k*pair such that*m*>*k*, we estimate Pr [(*t*_{2}−*t*_{1})^{−1}(_{m}*t*_{1},*t*_{2}) > (*t*_{2}−*t*_{1})^{−1}(_{k}*t*_{1},*t*_{2})] as ${M}_{c}^{-1}{S}_{{M}_{c}}$.

In our analyses we use *M _{c}* = 300.

- Abeles M. Quantification, smoothing, and confidence limits for single-units’ histograms. J Neurosci Methods. 1982;5:317–325. [PubMed]
- Barbieri R, Frank LM, Nquyen DP, Quirk MC, Solo V, Wilson MA, Brown EN. Dynamic analyses of information encoding by neural ensembles. Neural Comput. 2004;16:227–308.
- Box GEP, Jenkins GM, Reinsel GC. Time Series Analysis, Forecasting and Control. 3. Englewood Cliffs, NJ: Prentice Hall; 1994.
- Brillinger DR. Maximum likelihood analysis of spike trains of interacting nerve cells. Biol Cybern. 1988;59:189–200. [PubMed]
- Brockwell AE, Rojas AL, Kass RE. Recursive Bayesian decoding of motor cortical signals by particle filtering. J Neurophysiol. 2004;91:1899–1907. [PubMed]
- Brody CD. Disambiguating different covariation types. Neural Comput. 1999;11:1527–1535. [PubMed]
- Brown EN. Theory of point processes for neural systems. In: Chow CC, Gutkin B, Hansel D, Meunier C, Dalibard J, editors. Methods and Models in Neurophysics. Paris: Elsevier; 2005. pp. 691–726.
- Brown EN, Barbieri R, Eden UT, Frank LM. Likelihood methods for neural data analysis. In: Feng J, editor. Computational Neuroscience: A Comprehensive Approach. London; CRC Press: 2003. pp. 253–286.
- Brown EN, Barbieri R, Ventura V, Kass RE, Frank LM. The time-rescaling theorem and its application to neural spike train data analysis. Neural Comput. 2002;14:325–346. [PubMed]
- Brown EN, Frank LM, Tang D, Quirk MC, Wilson MA. A statistical paradigm for neural spike train decoding applied to position prediction from ensemble firing patterns of rat hippocampal place cells. J Neurosci. 1998;18:7411–7425. [PubMed]
- Brown EN, Kass RE, Mitra PP. Multiple neural spike train data analysis: state-of-the-art and future challenges. Nat Neurosci. 2004;7:456– 461. [PubMed]
- Brown EN, Nguyen DP, Frank LM, Wilson MA, Solo V. An analysis of neural receptive field plasticity by point process adaptive filtering. Proc Natl Acad Sci USA. 2001;98:12261–12266. [PubMed]
- Burnham KP, Anderson DR. Model Selection and Inference. A Practical Information-Theoretic Approach. New York: Springer-Verlag; 1998.
- Carlin BP, Louis TA. Bayes and Empirical Bayes Methods for Data Analysis. 2. Boca Raton, FL: Chapman & Hall/CRC; 2000.
- Daley D, Vere-Jones D. An Introduction to the Theory of Point Process. 2. New York: Springer-Verlag; 2003.
- Dayan P, Abbott L. Theoretical Neuroscience. Oxford, UK: Oxford Univ. Press; 2001.
- deJong P, MacKinnon MJ. Covariances for smoothed estimates in state space models. Biometrika. 1988;75:601– 602.
- Dempster AP, Laird NM, Rubin DB. Maximum likelihood from incomplete data via the EM algorithm (with discussion) J R Stat Soc B. 1977;39:1–38.
- Deneve S, Duhamel JR, Pouget A. Optimal sensorimotor integration in recurrent cortical networks: a neural implementation of Kalman filters. J Neurosci. 2007;27:5744–5756. [PubMed]
- Dey DK, Ghosh SK, Mallick BK, editors. Generalized Linear Models: A Bayesian Perspective. New York: Marcel Dekker; 2000.
- Durbin J, Koopman SJ. Time Series Analysis by State Space Methods. Oxford, UK: Oxford Univ. Press; 2001.
- Eden UT, Frank LM, Barbieri R, Solo V, Brown EN. Dynamic analysis of neural encoding by point process adaptive filtering. Neural Comput. 2004;16:971–998. [PubMed]
- Eichenbaum H, Fagan A, Cohen NJ. Normal olfactory discrimination learning set and facilitation of reversal learning after medial temporal damage to rats: implications for an account of preserved learning abilities in amnesia. J Neurosci. 1986;6:1876–1884. [PubMed]
- Ergun A, Barbieri R, Eden UT, Wilson MA, Brown EN. Construction of point process adaptive filter algorithms for neural systems using sequential Monte Carlo methods. IEEE Trans Biomed Eng. 2007;54:419– 428. [PubMed]
- Fahrmeir L, Tutz G. Multivariate Statistical Modeling Based on Generalized Linear Models. 2. New York: Springer-Verlag; 2001.
- Frank LM, Eden UT, Solo V, Wilson MA, Brown EN. Contrasting patterns of receptive field plasticity in the hippocampus and the entorhinal cortex: an adaptive filtering approach. J Neurosci. 2002;22:3817–3830. [PubMed]
- Frank LM, Stanley GB, Brown EN. Hippocampal plasticity across multiple days of exposure to novel environments. J Neurosci. 2004;24:7681–7689. [PubMed]
- Gandolfo F, Li C, Benda BJ, Schioppa CP, Bizzi E. Cortical correlates of learning in monkeys adapting to a new dynamical environment. Proc Natl Acad Sci USA. 2000;97:2259–2263. [PubMed]
- Gentle JE. Numerical Linear Algebra for Applications in Statistics. Berlin: Springer-Verlag; 1998. pp. 93–95.
- Gerstein GL. Analysis of firing patterns in single neurons. Science. 1960;131:1811–1812. [PubMed]
- Gerstein GL, Kiang NY-S. An approach to the quantitative analysis of electrophysiological data from single neurons. Biophys J. 1960;1:15–28. [PubMed]
- Harris KD, Csicsvari J, Hirase H, Dragoi G, Buzsáki G. Organization of cell assemblies in the hippocampus. Nature. 2003;424:552–556. [PubMed]
- Haslinger R, Ulbert I, Moore CI, Brown EN, Devor A. Analysis of LFP phase predicts sensory response of barrel cortex. J Neurophysiol. 2006;96:1658–1663. [PubMed]
- Hastie T, Tibshirani R, Friedman JH. The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Springer Series in Statistics) New York: Springer Science; 2003.
- Hearn D, Baker MP. Computer Graphics, C version. Upper Saddle River, NJ: Prentice Hall; 1996.
- Ji D, Wilson MA. Coordinated memory replay in the visual cortex and hippocampus during sleep. Nat Neurosci. 2007;10:100–107. [PubMed]
- Jog MS, Kubota Y, Connolly CI, Hillegaart V, Graybiel AM. Building neural representations of habits. Science. 1999;286:1745–1749. [PubMed]
- Kaas JH, Merzenich MM, Killackey HP. The reorganization of somatosensory cortex following peripheral nerve damage in adult and developing mammals. Annu Rev Neurosci. 1983;6:325–356. [PubMed]
- Kass RE, Ventura V. A spike train probability model. Neural Comput. 2001;13:1713–1720. [PubMed]
- Kass RE, Ventura V, Brown EN. Statistical issues in the analysis of neuronal data. J Neurophysiol. 2005;94:8–25. [PubMed]
- Kass RE, Ventura V, Cai C. Statistical smoothing of neuronal data. Network Comput Neural Syst. 2003;14:5–15. [PubMed]
- Kitagawa G, Gersch W. Smoothness Priors Analysis of Time Series (Lecture Notes in Statistics, vol. 116) New York: Springer-Verlag; 1996.
- Lim HH, Anderson DJ. Auditory cortical responses to electrical stimulation of the inferior colliculus: implications for an auditory midbrain implant. J Neurophysiol. 2006;96:975–988. [PubMed]
- McCullagh P, Nelder JA. Generalized Linear Models. 2. Boca Raton, FL: Chapman & Hall/CRC; 1989.
- McLachlan GJ, Krishnan T. The EM Algorithm and Extensions. New York: Wiley; 1997.
- Mehta MR, Barnes CA, McNaughton BL. Experience-dependent, asymmetric expansion of hippocampal place fields. Proc Natl Acad Sci USA. 1997;94:8918–8921. [PubMed]
- Mendel JM. Lessons in Estimation Theory for Signal Processing, Communication, and Control. Upper Saddle River, NJ: Prentice Hall; 1995.
- Merzenich MM, Nelson RJ, Stryker MP, Cynader MS, Schoppmann A, Zook JM. Somatosensory cortical map changes following digit amputation in adult monkeys. J Comp Neurol. 1984;224:591– 605. [PubMed]
- Narayan R, Graña G, Sen K. Distinct time scales in cortical discrimination of natural sounds in songbirds. J Neurophysiol. 2006;96:252–258. [PubMed]
- Ogata Y. Statistical models for earthquake occurrences and residual analysis for point processes. J Am Stat Assoc. 1988;83:9–27.
- Paninski L. Maximum likelihood estimation of cascade point process neural encoding models. Network. 2004;4:243–262. [PubMed]
- Pawitan Y. All Likelihood: Statistical Modeling and Inference Using Likelihood. New York: Oxford Univ. Press; 2001.
- Perkel DH, Gerstein GL, Moore GP. Neuronal spike trains and stochastic point processes. I. The single spike trains. Biophys J. 1967;7:391– 418. [PubMed]
- Rieke F, Warland D, de Ruyter van Steveninck R, Bialek W. Spikes: Exploring the Neural Code. Cambridge, MA: MIT Press; 1997.
- Schwartz O, Pillow JW, Rust NC, Simoncelli EP. Spike-triggered neural characterization. J Vis. 2006;6:484–507. [PubMed]
- Shadlen MN, Newsome WT. Noise, neural codes and cortical organization. Curr Opin Neurobiol. 1994;4:569–579. [PubMed]
- Shoham S, Paninski LM, Fellows MR, Hatsopoulos NG, Donoghue JP, Normann RA. Statistical encoding model for a primary motor cortical brain-machine interface. IEEE Trans Biomed Eng. 2005;52:1312–1322. [PubMed]
- Siegel S, Castellan NJ. Nonparametric Statistics for the Behavioral Sciences. New York: McGraw-Hill; 1988. p. 64.
- Smith AC, Brown EN. Estimating a state-space model from point process observations. Neural Comput. 2003;15:965–991. [PubMed]
- Smith AC, Frank LM, Wirth S, Yanike M, Hu D, Kubota Y, Graybiel AM, Suzuki W, Brown EN. Dynamic analysis of learning in behavioral experiments. J Neurosci. 2004;24:447– 461. [PubMed]
- Smith AC, Stefani MR, Moghaddam B, Brown EN. Analysis and design of behavioral experiments to characterize population learning. J Neurophysiol. 2005;93:1776–1792. [PubMed]
- Smith AC, Wirth S, Wendy AS, Brown EN. Bayesian analysis of interleaved learning and response bias in behavioral experiments. J Neurophysiol. 2007;97:2516–2524. [PubMed]
- Srinivasan L, Brown EN. A state-space framework for movement control to dynamic goals through brain-driven interfaces. IEEE Trans Biomed Eng. 2007a;54:526–535. [PubMed]
- Srinivasan L, Eden UT, Mitter SK, Brown EN. General purpose filter design for neural prosthetic devices. J Neurophysiol. 2007b;98:2456–2475. [PubMed]
- Srinivasan L, Eden UT, Willsky AS, Brown EN. A spate-space analysis for reconstruction of goal-directed movement using neural signals. Neural Comput. 2006;18:2465–2494. [PubMed]
- Truccolo W, Eden UT, Fellow MR, Donoghue JP, Brown EN. A point process framework for relating neural spiking activity for spiking history, neural ensemble and extrinsic covariate effects. J Neurophysiol. 2005;93:1074–1089. [PubMed]
- Ventura V, Cai C, Kass RE. Trial-to-trial variability and its effect on time-varying dependency between two neurons. J Neurophysiol. 2005;94:2982–2939. [PubMed]
- Victor JD. Temporal aspects of neural coding in the retina and lateral geniculate. Comput Neural Syst. 1999;10:R1–R66. [PubMed]
- Wirth S, Yanike M, Frank LM, Smith AC, Brown EN, Suzuki WA. Single neurons in the monkey hippocampus and learning of new associations. Science. 2003;300:1578–1584. [PubMed]
- Wise SP, Murray EA. Role of the hippocampal system in conditional motor learning: mapping antecedents to action. Hippocampus. 1999;9:101–117. [PubMed]
- Wu W, Gao Y, Bienenstock E, Donoghue JP, Black MJ. Bayesian population decoding of motor cortical activity using a Kalman filter. Neural Comput. 2006;18:80–118. [PubMed]
- Yu BM, Kemere C, Santhanam G, Afshar A, Ryu SI, Meng TH, Sahani M, Shenoy KV. Mixture of trajectory models for neural decoding of goal-directed movements. J Neurophysiol. 2007;97:3763–3780. [PubMed]

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