Fast and accurate low-dimensional reduction of biophysically accurate neuron models

Realistic modeling of neurons are quite successful in complementing traditional experimental techniques. However, their networks require a computational power beyond the capabilities of current supercomputers, and the present methods used to reduce their complexity do not take into account the key features of the cells nor critical physiological properties. Here we introduce a new, automatic and fast method to map realistic neurons into equivalent reduced models running up to ≈44 times faster while maintaining all the original biophysical mechanisms, a direct link with experimental observables, and a very high accuracy of the membrane potential dynamics during arbitrary synaptic inputs. Using this method an entirely new generation of large-scale simulations of brain regions can be implemented with unprecedented advances to understand higher brain functions.