Many recent studies have employed task-based modeling with recurrent neural networks (RNNs) to infer the computational function of different brain regions. These models are often assessed by quantitatively comparing the low-dimensional neural dynamics of the model and the brain, for example using canonical correlation analysis (CCA). However, the nature of the detailed neurobiological inferences one can draw from such efforts remains elusive. For example, to what extent does training neural networks to solve simple tasks, prevalent in neuroscientific studies, uniquely determine the low-dimensional dynamics independent of neural architectures? Or alternatively, are the learned dynamics highly sensitive to different neural architectures?

UPDATE after rebuttal: authors have addressed some of my concerns, so I'm updating my score to 8. To summarize, this paper aims to shed light on the connections between artificial recurrent neural networks and biological networks, in order to gain insight into neural circuit functionality through studying RNNs. More specifically, the paper comments on the ability for RNNs to mimic the behavior SNNs and neural recordings despite a vast difference in their inherent architectures. Such a phenomenon may suggest neural invariants, which act universally (in the context of a task) across either all RNN and SNN architectures, or broader groups containing various architectures in each. The paper does not look at neural recordings or SNNs, but instead trains 96 RNNs of various combinations of architectures, activations, network sizes, and L2 regularizations on three separate tasks (discrete memory, pattern formation, and analog memory) common to computational neuroscience. For each task singular value canonical correlation analysis (SVCCA) and MDS are used to determine the representational geometry of the RNNs and a numerical approach to dynamical systems analysis (and again with MDS) is used to gain insight into the topological stability structure.

Many recent studies have employed task-based modeling with recurrent neural networks (RNNs) to infer the computational function of different brain regions. These models are often assessed by quantitatively comparing the low-dimensional neural dynamics of the model and the brain, for example using canonical correlation analysis (CCA). However, the nature of the detailed neurobiological inferences one can draw from such efforts remains elusive. For example, to what extent does training neural networks to solve simple tasks, prevalent in neuroscientific studies, uniquely determine the low-dimensional dynamics independent of neural architectures? Or alternatively, are the learned dynamics highly sensitive to different neural architectures?

Many recent studies have employed task-based modeling with recurrent neural networks (RNNs) to infer the computational function of different brain regions. These models are often assessed by quantitatively comparing the low-dimensional neural dynamics of the model and the brain, for example using canonical correlation analysis (CCA). However, the nature of the detailed neurobiological inferences one can draw from such efforts remains elusive. For example, to what extent does training neural networks to solve simple tasks, prevalent in neuroscientific studies, uniquely determine the low-dimensional dynamics independent of neural architectures? Or alternatively, are the learned dynamics highly sensitive to different neural architectures?

This basic question in the theory of knowledge seems to be beyond the scope of experimental investigation. An accessible version of this question is whether different observers of the same sense data have the same neural representation of these data: how much of the neural code is universal, and how much is individual? Differences in the neural codes of different individuals may arise from various sources: First, different individuals may use different'vocabularies' of coding symbols. Second, they may use the same symbols to encode different stimulus features.