State space methods are a very broad class of methods to analyze temporal data. At its most basic, you assume that your temporal data is generated by a model with underlying parameters. The parameters are time-varying, and usually it’s assumed that the temporal variation is generated through Markovian dynamics. That is, at every time-step, the parameters of the model are determined only by their previous value and a transition probability. Hidden Markov Models (HMMs) and the Kalman filter are canonical examples of state-space models with discrete and continuous states, respectively.

There’s an excellent review by Paninski et al. (2009) on the use of state-space models with continuous hidden variables for neural data analysis. The key point that the authors try to bring across is that you really don’t need the standard computational machinery of Kalman filters in terms of recursive filtering to work with state space models. Rather, you can use standard Generalized Linear Models (GLMs), embed Markovian dynamics in them, and use standard Maximum Likelihood/Maximum A Posterior (ML/MAP) methods to figure out the model parameters.

This is a really neat trick, because GLMs for neural data analysis have been intensely studied, there’s software for them, and a bunch of extensions and generalization have been developed for them.

Let me give you an example application. It’s straightforward to use a GLM to estimate receptive fields. Now add Markovian dynamics to the receptive field parameters. What do you get? A GLM that estimates time-varying receptive fields! You can use this to study learning, remapping, adaptation; the applications are endless.

### The simplest case

Now the math might at first look a little daunting, but to emphasize, if you understand GLMs then you understand state-space GLMs. It’s very instructive to look at the simplest state-space GLM, which contains only one time-varying parameter:

Here, the neuron follows a Poisson process, with a time-varying drive which has Markovian dynamics. Intuitively, if we estimate w(t), we will get a smoothed version of the log of the measured firing rate. So the purpose of this very simply state-space GLM is to obtain a smoothed estimate of a PSTH.

I loaded up some spike data, which looks like this:

Now let’s try and estimate w(t) using standard GLM methodology. Now, we can rewrite y(t) and as:

Where , the identity matrix. This is a plain vanilla GLM. So we’ve got the likelihood term under control, so let’s write out -log p(w), the negative log of the prior. Well:

You will recognize this as a simple penalty based on first-order differences between next-neighbour weights. It’s just a plain vanilla smoothness penalty. Thus:

Where D is a second order difference operator.

### Inference in Matlab

It’s possible to infer the parameters of this model using any software for GLMs with quadratic penalties, including my own Matlab package. I loaded up a 60s PSTH of neural data (y, 5ms bins) from one of our experiments, which looks like this:

Now let’s infer w for different values of the precision :

X = speye(length(y)); P = spdiags(ones(size(y))*[-1,1],[0,1],length(y)-1,length(y)); Q = P'*P; figure(2); for ii = 1:3 glmopts.family = 'poissexp'; results = glmfitqp(y,X,Q*3^ii,glmopts); subplot(3,1,ii); plot(exp(results.w)) end

And here are the results:

It’s really not more complicated than that. Here’s a small portion of the weights w(t) with the corresponding spike train on top:

You can see that each spike gets transformed to a double exponential shape. That makes sense; if you applied a recursive filter of order 1 (say, with filtfilt) this is exactly the kind of behaviour you would expect.

### Extrapolation and interpolation

Now let’s try and extrapolate and interpolate to understand how the thing works. To do this, we can simply set the elements of X corresponding to the time points we want to censor to 0. In that case, the fit will be completely insensitive to the weights corresponding to these time points. Thus the model will rely entirely on the prior to determine the value of the extrapolated or interpolated weights. First, extrapolation:

Xa = X; Xa(end/2+1:end,:) = 0; results = glmfitqp(y,Xa,Q*3,glmopts); plot(results.w(end/2-100:end/2+100));

Extrapolated weights get set to the last value before the cutoff. Makes sense, since with first order dynamics the weights only depend on the last value, and the transition probability is symmetric around this value. As for interpolation:

bounds = [-40,-25,-10]; for ii = 1:3 Xa = X; Xa(end/2+bounds(ii):end/2+25,:) = 0; results = glmfitqp(y,Xa,Q*27,glmopts); subplot(3,1,ii); plot(diff(results.w(end/2-100:end/2+100))); hold on; plot([100,100]+bounds(ii),[-4.5,-2],'k',[125,125],[-4.5,-2],'k'); axis tight; end

The interpolation is purely linear, which is expected if the probability distributions on both sides of the interpolation range are Gaussians with similar variance.

Of course, it’s possible to change the dynamics, and intra/extrapolation will change accordingly. For example, if we set with , this will add a diagonal term to the prior, and weights will extrapolate smoothly to 0.

### Static parameters

It’s also possible to mix dynamic parameters and static parameters. For example, this smoother might not capture well the behaviour of the neuron at short time scales, because of the refractory period. We can add a time lagged version of y to account for the refractory behaviour:

y = histc(ts,0:.001:10); y = y(1:end-1); X = speye(length(y)); P = spdiags(ones(size(y))*[-1,1],[0,1],length(y)-1,length(y)); Q = P'*P; glmopts.family = 'poissexp'; glmopts.algo = 'newton'; yl = y(max(bsxfun(@plus,(1:length(y))',-1:-1:-7),1)); results = glmfitqp(y,[X,yl],blkdiag(Q*27,speye(7)*.01),glmopts); plot(results.w(end-6:end))

And the results:

### Conclusion

Embedding state space dynamics in GLMs is pretty easy. For real applications you’ll probably want to use a less generic GLM implementation than the one I used here to save some computation time. The paper contains many more examples of the utility of state-space GLMs.