Aligning cognitive and neural mechanisms in tiny recurrent neural networks

A central goal of computational neuroscience is to develop models that both accurately predict behaviour and reveal the cognitive and neural mechanisms that generate it. Tiny recurrent neural networks (RNNs) with few hidden units have helped reconcile these demands, combining predictive flexibility with interpretable dynamics. However, although choice logits and their update dynamics are reproducible across fits, this does not imply reproducibility of the internal mechanisms that generate them. Here we show that tiny RNNs fit to subtly different training data (e.g. different cross-validation folds) can yield very different internal update mechanisms and hidden state trajectories, despite near-identical choice logits. This precludes using internal variables from tiny RNNs as candidate mechanisms underlying cognitive and neural computations. Here, we characterise this problem across tiny RNNs trained on behavioural data from mice performing a probabilistic reversal learning task, then demonstrate how a series of biologically inspired constraints can greatly improve mechanistic reproducibility without compromising behavioural predictions. We were then able to interpret these mechanisms to explore possible computations in fronto-striatal circuits. Our results highlight mechanistic reproducibility as a complement to predictive accuracy and interpretability, and provide a framework for using RNN-derived variables to explore hypotheses about behavioural and neural dynamics during flexible decision-making.