Is there a way to define a statistic similar to the z-score for the Poisson distribution? Let's say I want to compare samples taken from Poisson distributions that have different values of lambda (rate).  If the samples were from a normal distribution, I could convert each observation into a z-score based on the mean and standard deviation of the distribution that it was taken from.  Then I could compare the z-scores to a common distribution.  
How would I do this for samples taken from a Poisson distribution?  
The specific use case is that I have a whole bunch of samples generated by many processes that have different poisson rates.  The question I am asking is "Is the class of processes Poisson, or are they generated in a significantly more structured way?"  I do not have enough data from any single process to answer this question, but I have more than enough data when I pool across all the processes.
Here is a more in depth explanation:
Place cells are neurons that fire action potentials preferentially when an animal is in a certain location.
I can calculate the rate that a given cell fires in a given location by dividing the number of spikes fired by the amount of time spent in that location.  FR(x)
I have observations of how many spikes were actually fired by a particular cell during a particular time interval at a particular location.
If I had enough data, I could compare my distribution of spike counts at a particular location in a particular time interval to the poisson distribution.
However, this is impossible, because the time interval is different for every observation.  
Thus, I would like a standardized statistic that I could translate each spike count observation into given the spike rate, time interval, and location corresponding to that observation.  I could then pool these standardized statistics and compare them to a single  standard distribution.  
 A: Assuming that the typical number of spikes is large, I would suggest to use a Variance stabilizing transformation. For Poisson distribution, it goes as follows: let $X \sim \mathcal P(\lambda)$, and let $Y \sim 2 \sqrt X$. A first order approximation gives 
$$Y \simeq 2 \sqrt\lambda + {1\over \sqrt\lambda}(X-\lambda),$$
from which we get $\text{var}(Y) \simeq {1\over \lambda} \times \lambda = 1$. Moreover for $\lambda$ large $Y$ is approximately normal $Y \sim \mathcal N(2\sqrt\lambda,1)$. 
You can use $Y - 2\sqrt\lambda = 2(\sqrt X - \sqrt\lambda)$ as $z$-score.
A: Is the reason that you want to test for Poisson distribution, that you think the spikes are generated by a Poisson process (that is, the instantaneous probability of seeing a spike is constant over time)? If so, and you know the length between each spike, consider testing for a Poisson process instead. This amounts to testing that the gaps between spikes follow an exponential distribution, with a fixed rate parameter at each location. This allows you to aggregate all the different events at each location, which hopefully gives you a large enough sample to have a reasonably powerful test. (Of course, few processes are actually Poisson, so it might be more informative to look at how much the distribution deviates from exponential and in what way, but that's a separate question.)
If you don't have between-spike timings, you may have to do something more complicated. You could, for instance, find the maximum-likelihood Poisson rate for a location, and compare your real data to a simulation from the resulting model in various ways to see how different it looks. As far as I know there are no off-the-shelf tests for this case though.
A: This is similar to Ben's question, but are you just trying to test whether a multi-variate Poisson distribution fits this data? Your major problem here is that you're having to estimate the poisson mean for each cell from the data that you're trying to get a p-value/likelihood score/whatever for. Now strictly speaking this may not be a problem, but if you don't have enough data for any of these individually, you're not going to have enough data for them collectively without some additional restriction on your hypothesis (which could come in a variety of forms). 
Here's an example of the model you're vaguely describing. Let $i$ be each cell and $Y_i$ be the count within each cell.
$$
Y_i\sim Poisson(\lambda_i)\\
\lambda_i=\mu+\alpha_i 
$$
This is a general Poisson regression where the number of parameters is equal to the number of observations. Unless the ratio of those two approaches 0, your estimates for the number of parameters are not statistically consistent and your likelihood ratio won't tell you very much. 
Now the model Ben Describes is a little better if you have the time interval data: here let $Y_ij$ by the time between $(j-1)th$ and $j$th observations:
$$
Y_{ij}\sim Exp(\lambda_i)\\
\lambda_i=\mu+\alpha_i 
$$
This is a little bit better, and if the ratio is small enough you may be able to test this via something like a Q-Q Plot on the probabilities of each time (which again, credit to Ben, is roughly what he's describing). If the number of locations is roughly the log of the number of observations, you may be in business there, but that QQ-Plot is still going to have some real bias, especially toward making the big outliers look more normal.
Some real improvements would be if there is any kind of additional knowledge you could impose on the problem. Like for example if you could say that sensors in nearby locations should have similar rates of firing, then you could leverage that knowledge by imposing a linear model on lat/long or maybe a thin-plate spline if you're not sure how the spatial relationship should look. Or maybe if the sensors of a certain "type" and you could say that sensors of the same "type" should have similar rates, and you could again make it linear and do an Anova test, or make a random effects model or something like that. 
Anyway, there are a lot of ways to go with this if you have some kind of additional knowledge, or if your # of locations is $O(\log n)$. HTH
