the usual way; for details see Appendix \[app:DoS1\]. Furthermore, we can change the effective temperature by considering a one parameter family of models, $$P(\{\sigma_{\rm i}\}; T) = {1\over {Z(T)}} \exp\left[ -{1\over T}E (\{\sigma_{\rm i}\})\right] ,$$ with $E (\{\sigma_{\rm i}\})$ as before in Eq (\[HKpair\]). The goal is to see whether there is anything special about the value $T=1$ that describes the real system.

Results for the heat capacity of our model vs $T$ are shown in Fig \[Cv\]. There is a dramatic peak in this plot, and as we look at larger and larger groups of neurons, the peak grows and moves closer to $T=1$, which is the model of the actual network. Importantly, the heat capacity grows even when we normalize by $N$, so that the specific heat, or heat capacity per neuron, is growing with $N$, as expected at a critical point.

![[**Heat capacity in maximum entropy models of neurons responding to naturalistic stimuli.**]{} [**(A)**]{} Heat capacity, $C(T)$, computed for subnetworks of $N=20,\, 40,\, 80,\, 120$, neurons. Points are averages, and error bars are standard deviations, across 30 choices of subnetwork at each $N$. For details of the computations see Appendix \[app:DoS1\]. [**(B)**]{} Zoom–in of the peak of 