$C(T)$, shown as an intensive quantity, $C(T)/N$. The peak moves towards $T=1$ with increasing $N$, and the growth of $C(T)$ near $T=1$ is faster than linear with $N$. Green arrow points to results for a model of independent neurons whose spike probabilities (firing rates) match the data exactly; the heat capacity is exactly extensive.\[Cv\]](fig4){width="\linewidth"}

Temperature and energy may seem like foreign concepts in the context of real neurons, so the results of Fig \[Cv\] require some interpretation. We have studied one particular network, that is described by some set of parameters. Are the parameters that describe the real network in any sense special? Changing “temperature” allows us to probe along one axis in parameter space, and the fact that we observe a peak in the specific heat means that the real network in poised in parameter space very close to a maximum in the variance of log(probability), which we can think of as the dynamic range of surprise that can be represented by the network.

The temperature is only one axis in parameter space, and along this direction there are variations in both the correlations among neurons and their mean spike rates. As an alternative, we can consider a family 