Human neurons are more functionally complex: their richer morphology and stronger synaptic nonlinearities give them extra computational power. Fitting deep neural nets to match human input-output dynamics consistently required greater network depth than for rodent models.

Single human neurons are wired to perform nontrivial logical computations! Due to extensive dendritic branching and specialized vol-gated currents, HL2/3 PNs support ~25 independent NMDA-spike compartments—almost twice than rat neurons—and can implement XOR-like operations via dendritic Ca²⁺ spikes.

Dendritic load imposed on human dendrites accelerates EPSP propagation down the dendrites, while dendritic high‐density of h-channels enables faithful transfer of theta-band signals (that are associated with various learning and memory processes) from dendrites to soma.

Load is all you need. The extensive membrane surface area of the dendrites “loads” the soma with additional capacitance and conductance. This load imposed on the AIS makes action potentials at the soma remarkably “kinky” – with a steeper rise of voltage, yet sensitive to rapid input fluctuations.

What makes human pyramidal neurons uniquely suited for complex information processing? How can human neurons’ distinct properties contribute to our advanced cognitive abilities?

Just got back from the GRC Dendrites meeting in Ventura, California! I presented my research on how single neurons can implement complex nonlinear functions — amazing discussions and brilliant minds all around. #Neuroscience #GRCdendrites #ComputationalNeuroscience

It’s not just size and shape. Synaptic properties, especially those governing NMDA receptor dynamics - play a critical role. Human neurons exhibit steeper NMDA nonlinearities, elevating their computational complexity. (7/11)

Structural features matter. larger dendritic trees, more branches, and complex morphologies correlate strongly with higher complexity. Human neurons aren’t just big - they’re wired to integrate inputs in more sophisticated ways. (6/11)

Applying the FCI, we find that human cortical pyramidal neurons consistently rank higher in I/O complexity than rat neurons. This suggests that human neurons can perform more intricate computations at the single-cell level. (5/11)

Our new metric, the FCI (Functional Complexity Index), measures a neuron’s I/O complexity based on how well (or poorly) a fixed deep network can replicate its output.
A good fit = simpler I/O, while a poor fit = more complex I/O. (4/11)

In our new preprint, we examined 24 reconstructed human and rat neurons from all cortical layers, with experimentally matched electrophysiology. After simulating responses to diverse inputs, we fit a DNN to each neuron’s I/O - quantifying how “hard” it is to predict their output. (3/11)

Human cortical pyramidal neurons are larger, with more elaborate branching, and distinct nonlinear biophysical properties compared to rat cortical pyramidal neurons.

Are they more functionally complex? Could that boost the human brain’s computational power? and is that what makes us human? (1/11)