MINI REVIEW article
Front. Neurosci.
Sec. Neural Technology
Volume 19 - 2025 | doi: 10.3389/fnins.2025.1579715
This article is part of the Research TopicWhat Makes Us Human: From Genes to MachineView all 9 articles
Biophysical and computational insights from modeling human cortical pyramidal neurons
Provisionally accepted- 1Edmond & Lily Safra Center for Brain Sciences, Hebrew University of Jerusalem, Jerusalem, Israel
- 2Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, Faculty of Science, VU Amsterdam, Amsterdam, Netherlands
- 3Department of Neurobiology, The Hebrew University of Jerusalem, Jerusalem, Israel
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The human brain's remarkable computational power enables parallel processing of vast information, integrating sensory inputs, memories, and emotions for rapid learning, adaptability, and creativity -far surpassing present-day artificial systems. These capabilities likely arise, in part, from the distinct properties of human neurons, which have only recently been elucidated through collaborative efforts among neurosurgeons, experimental, and theoretical neuroscientists. This effort has yielded unprecedented morphological and biophysical data on human neurons obtained during epilepsy or tumor surgeries. To integrate and interpret this diverse data, two complementary modeling approaches have emerged: detailed biophysical models, unravelling how morphoelectrical properties shape signal processing in human neurons, and machine learning models, which leverage the biophysical models to uncover hidden structure-function relationships. A major focus has been the disproportionately expanded layers 2/3 of the human cortex, where the large L2/3 pyramidal neurons (HL2/3 PNs) can track high-frequency input modulations, exhibit enhanced dendritic signaling, maintain numerous functional dendritic compartments, and display unique dendritic excitability. More recent efforts extend to modeling human hippocampal, cerebellar, and inhibitory cortical neurons. This review synthesizes key theoretical insights from biophysical and machine-learning models of HL2/3 PNs, and explores their implications for understanding "what makes us human".
Keywords: Human neurons, Pyramidal neurons, dendritic computation, compartmental modeling, biophysical modeling, machine learning models, single neuron computation, structure - function relationship
Received: 19 Feb 2025; Accepted: 02 Jun 2025.
Copyright: © 2025 Shapira, Aizenbud, Yoeli, Leibner, Mansvelder, De Kock, London and Segev. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Daniela Yoeli, Edmond & Lily Safra Center for Brain Sciences, Hebrew University of Jerusalem, Jerusalem, Israel
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