Human cortical pyramidal neurons are larger, exhibit more complex branching patterns, and feature distinctly nonlinear biophysical properties compared to rat cortical pyramidal neurons. Do these unique characteristics make human neurons more functionally complex?.Would that

RT @deangeckt: Excited to share my first first-author paper 🎉 “Strategies of Code-switching in Human-Machine Dialogs”. From my MSc in com….

Dive into the full review here 📷 We would love to hear where you think single-neuron modeling should go next!.

Looking ahead, we reckon the next leap will come from combining high-resolution EM reconstructions of entire human neurons with emerging voltage-imaging tools. These hybrid biophysical/AI models promise to clarify how single-cell properties scale up to network dynamics—and.

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 before even reaching the axon! Thanks to extensive dendritic branching and specialized voltage-gated currents, HL2/3 PNs support roughly 25 independent NMDA-spike compartments—almost twice what rat neurons

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 axon initial segment makes action potentials at the soma remarkably “kinky” – with a steeper rise of voltage, yet

In our new mini-review “Biophysical and computational insights from modeling human cortical pyramidal neurons” ( in @FrontNeurosci, with @SapirShapira2, @danielayoeli, @YLeibner, Huib Mansvelder, Christiaan de Kock, @mikilon, and @Segev_Lab,. We bring.

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

RT @ArtemKRSV: Turns out apical and basal dendrites learn differently!.In the newest video i explore an amazing recent paper by William Wri….

RT @mikilon: Reminder - Graduate students & postdocs: Apply by April 19 2025

Proud to contribute to this large-scale, multi-lab, open-source collaboration led by @LecoqJerome and @AllenInstitute OpenScope to study predictive processing in the brain. Explore our review and planned studies on arXiv:

RT @deangeckt: New neurons in the Neuron SWC Editor. These are abstract neurons that obey Rall’s equivalent electrotonic constraints, each….

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

RT @mikilon: Neuroscience Unleashed in Jerusalem July 20–24 2025.An international graduate school on consciousness, NeuroAI, brain-machine….

RT @geckt65351: 🧠 Updated my open-source SWC Neuron Morphology Editor (runs directly in-browser!)—now with 3D views & custom segment colors….

RT @TMoldwin: Now out in PLOS CB!.

A few wonderful days at #ISFN2025, getting a taste of the current state of neuroscience research in Israel. It was especially exciting to attend a plenary lecture by @brainrhythms that his book "The Brain from Inside Out" really inspired me even before I started my PhD.

RT @geckt65351: 🧠 Built an open-source SWC neuron morphology editor! Visualize and edit neuronal structures in 2D, with easy import/export….

Check out the full story in our preprint. We welcome your feedback and hope this sparks new directions in exploring how single-neuron complexity scales up to network-level functions. (10/10). bioRxiv: