The softmax function may be interpreted as performing lateral inhibition, leading to sharper inferences.

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• Winner take-all architectures of decision-making are already commonplace in computational neuroscience, and the softmax function provides a smooth approximation.claim0.814
  Neural plausibility argument for softmax policy selection.
• Softmax Functionmethod0.801
  Neuronal dynamics computed from free energy gradients; interpreted as average firing rate of neural populations.
• Softmax Activation Function as Neuronal Modelmethod0.801
  Using softmax to translate membrane potentials into firing rates, implementing lateral inhibition.
• would place-like representations emerge in memory neurons for activation functions other than softmax?question0.765
  Open empirical question left for future work about robustness of place cell emergence.
• The approximate posterior over policies is a softmax function of the negative expected free energy.claim0.760
  Mathematical form of policy selection, eq. (10).
• Superposition hypothesis: neural networks represent more features than dimensions using almost-orthogonal directions.hypothesis0.746
  Explanation for why dictionary learning can recover many more features than dimensions.
• Results may not fully generalize to all models and scenarios because the model organism relies on hints and nudges and Llama Nemotron cannot consistently distinguish evaluation/deployment based on subtle cuesclaim0.742
  Key limitation acknowledged by authors.
• Suppressing reflection is considerably easier than inducing it, because inhibition requires the model to terminate reasoning while enhancement demands additional cognitive effort to re-examine reasoning trajectories.claim0.736
  Key asymmetry finding interpreted mechanistically by the authors.