Analysing Rescaling, Discretisation, and Linearisation in RNNs for Neural System Modelling
For computational neuroscientists, this provides a formal guarantee that common preprocessing steps do not alter fundamental network properties, enabling more reliable modelling of neural circuits.
The paper proves that temporal rescaling, discretisation, and linearisation commute pairwise in RNNs, preserving network controllability. This ensures structural consistency when modelling neural dynamics for decision-making and motor control.
Recurrent Neural Networks (RNNs) are widely used to model neural activity in Computational Neuroscience. Here, we explore the mathematical foundations of three fundamental procedures that can be implemented: temporal rescaling, discretisation, and linearisation. These techniques provide crucial tools for characterising the behaviour of RNNs, offering insights into their temporal dynamics, facilitating practical computational implementation, and allowing for linear approximations for analysis. We discuss the flexible order in which these procedures can be applied, emphasising their importance in modelling and analysing RNNs for neuroscience and formally prove that these three operations commute pairwise. We also explicitly describe the conditions under which these procedures can be considered interchangeable. Our findings directly inform the design of biologically plausible $\mathtt{RNN}$ models for simulating neural dynamics observed in decision-making circuits and motor control, where temporal scaling and stability are critical for matching experimental recordings. {Furthermore, we show that this exact commutativity guarantees the structural preservation of the network's controllability, preventing the emergence of inaccessible state-spaces under numerical discretisation or temporal rescaling.