Geometrical determinant of nonlinear synaptic integration in human cortical pyramidal neurons (2408.05633v1)

Abstract: Neurons integrate synaptic inputs and convert them to action potential output at electrically distant locations. The computational power of a neuron is hence enhanced by subcellular compartmentalization and nonlinear synaptic integration, but the biophysical determinants of these features in human neurons are not completely understood. By examining the synaptic input-output function of human neocortical pyramidal neurons, we found that the nonlinearity threshold at the soma was linearly determined by the shortest path distance from the synapse to the apical trunk, and the slope of this relationship was consistent throughout the dendritic arbor. Analogous rules were found from both supragranular and infragranular layers of the rodent cortex, suggesting that these represent a fundamental property of pyramidal neurons. Additionally, we found that neurons associated with tumor or epilepsy had distinct membrane properties, but the nonlinearity threshold was shifted in amplitude such that the slope of its relationship with synaptic distance remained consistent.