  The recent discovery of a connection between Transformers and Modern Hopfield
Networks (MHNs) has reignited the study of neural networks from a physical
energy-based perspective. This paper focuses on the pivotal effect of the
inverse temperature hyperparameter $\beta$ on the distribution of energy minima
of the MHN. To achieve this, the distribution of energy minima is tracked in a
simplified MHN in which equidistant normalised patterns are stored. This
network demonstrates a phase transition at a critical temperature
$\beta_{\text{c}}$, from a single global attractor towards highly pattern
specific minima as $\beta$ is increased. Importantly, the dynamics are not
solely governed by the hyperparameter $\beta$ but are instead determined by an
effective inverse temperature $\beta_{\text{eff}}$ which also depends on the
distribution and size of the stored patterns. Recognizing the role of
hyperparameters in the MHN could, in the future, aid researchers in the domain
of Transformers to optimise their initial choices, potentially reducing the
necessity for time and energy expensive hyperparameter fine-tuning.
