Scaling Laws and Tradeoffs in Recurrent Networks of Expressive Neurons

ArXi:2605.12049v1 Announce Type: cross Cortical neurons are complex, multi-timescale processors wired into recurrent circuits, shaped by long evolutionary pressure under stringent biological constraints. Mainstream machine learning, by contrast, predominantly builds models from extremely simple units, a default inherited from early neural-network theory. We treat this as a normative architectural question.