“…Indeed, the predominant explanation for anatomical organization of the brain has focused on minimizing wiring costs while maximizing adaptive topological features (Bullmore & Sporns, 2012; Zhou et al, 2022). As such, we have seen considerations of space implemented in functional feedforward neural network models (Gozel & Doiron, 2022; Huang et al, 2019; Lee et al, 2020). Here we instantiated our core hypothesis mathematically within the seRNN model by providing two challenges to RNNs during supervised learning: (1) long connections should be minimized where possible – reflective of their metabolic cost (Kaiser & Hilgetag, 2006; Sporns, 2011), and (2) connections can only change their weights as a function of their underlying communication – reflective of signal propagation between neuronal units (Betzel et al, 2022; Seguin, Jedynak, et al, 2022; Seguin, Mansour L, et al, 2022; Shimono & Hatano, 2018).…”