They weren’t just tuning the strength of the incoming signals (the synapses); they were actually training the neuron on *where* those signals should land on its branchy “tree” to get the best results.
Cortical pyramidal neurons possess elaborate dendritic trees with diverse nonlinear membrane conductances and thousands of plastic synapses, suggesting substantial computational capabilities at the single-cell level. Yet, what can a neuron compute remains an open question, largely due to the lack of a systematic framework to quantify its computational capabilities. We introduce TwinProp, a digital-twin-based backpropagation algorithm that enables gradient-based optimization of synaptic strengths and dendritic locations in detailed neuron models via a millisecond-accurate deep neural network (DNN). Using TwinProp, we demonstrate that a detailed model of rat layer 5 pyramidal cell (L5PC) can perform naturalistic image and audio classification tasks at a remarkably high accuracy, significantly surpassing perceptron and leaky integrate-and-fire baselines. The same neuron solves high-dimensional nonlinear problems, including exclusive-or (XOR), 10-bit parity, and random Boolean tasks, demonstrating capabilities typically attributed to multilayer networks. Mechanistically, increasing task complexity recruits distributed dendritic nonlinearities, including NMDA-and voltage-dependent mechanisms; removing these or collapsing dendritic structure markedly impairs performance. These findings identify dendrites as a substrate for high-order feature binding and position single cortical pyramidal neurons as powerful, noise-robust, general-purpose analog computational units. Our results offer testable in vivo predictions and provide a systematic framework linking cellular morpho-electrical properties to computation in both brains and artificial systems.
The authors have declared no competing interest.
ONR, N00014-24–1-2055, N00014-23–1-2051
Further reading.
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High-Level Visual Representations in the Human Brain Are Aligned With Large Language Models.
Artificial Neural Network Language Models Predict Human Brain Responses to Language Even After a Developmentally Realistic Amount of Training.
https://pubmed.ncbi.nlm.nih.gov/38645…
High-Level Visual Representations in the Human Brain Are Aligned With Large Language Models.
https://www.nature.com/articles/s4225…
Theory Is All You Need: AI, Human Cognition, and Causal Reasoning.
https://pubsonline.informs.org/doi/10…
Disentangling the Factors of Convergence between Brains and Computer Vision Models.
Brain–machine interfaces (BMIs) are no longer just science fiction; they are the gateway to a future where thought itself can interact directly with technology. These systems read the brain’s electrical activity and, in turn, stimulate neurons — forming a two-way communication link between biology and machines.
In just a few decades, BMIs have evolved from laboratory curiosities into one of the fastest-growing frontiers in science and engineering. The possibilities are staggering. In the future, neural interfaces could restore vision to the blind, enable paralyzed individuals to move again, facilitate seamless communication between human brains and artificial intelligence, and ultimately power virtual realities that are indistinguishable from the physical world.
This convergence of biology, computing, and neuroscience marks the dawn of a new era — one where the boundaries between human and machine begin to blur.
A research team in Bochum, Germany has unexpectedly found that light can slow down movements in the nanoworld. This is due to quantum friction, a phenomenon that has been poorly understood until now. The findings are published in the journal Nature.
Light is expected to heat particles up or set them in motion. However, the interdisciplinary team at Ruhr University Bochum, Germany, has now proven the opposite. In aqueous solution, fluorescent carbon nanotubes move much slower once they are irradiated with light. During this process, the diffusion constant decreases with light intensity, an effect linked to direct coupling between electrons in the solid and the molecules of the liquid.
“This discovery of light-induced quantum friction fundamentally changes our understanding of interfacial processes,” says researcher Sebastian Kruss, who led the work with Marialore Sulpizi and Martina Havenith.
Research from CCR scientists points toward a strategy for making chimeric antigen receptor (CAR) T-cell therapy, the cell-based immunotherapy that has revolutionized the treatment of some blood cancers, safer and more effective for treating solid tumors.
The study, led by Grégoire Altan-Bonnet, Ph.D., Deputy Chief of the Laboratory of Integrative Cancer Immunology, Naomi Taylor, M.D., Ph.D., Senior Investigator in the Pediatric Oncology Branch, and Paul François, Ph.D., at the University of Montréal, shows how adding certain receptors to CAR T cells can prevent the cells from attacking healthy tissue while simultaneously enhancing their activity against cancer cells. The findings appeared April 10, 2025, in Cell.
CAR T-cell therapy reprograms patients’ immune cells to be effective cancer killers using genetically engineered chimeric antigen receptors (CARs) that are added to their T cells. CARs are designed to recognize molecules on the surface of cancer cells called antigens, which can usually be found on some healthy cells, too. This leads to manageable side effects for patients with blood cancer, but when CAR T cells designed to target solid tumors attack healthy tissue, the effects can be severe.
We think of our accounts of the universe and cosmology as well-founded and value-free. The Big Bang theory is surely one of those. But critics argue this is not the case. It was first put forward by a Catholic priest and physicist, Georges Lemaître, who initially called it the ‘hypothesis of the primeval atom’ — the primeval atom being created by God. As the originator of cosmic inflation theory, Alan Guth, points out the Big Bang says nothing about what banged, why it banged, or what happened before it banged.
A new Yale-led study provides one of the most detailed and comprehensive analyses to date of genetic variation in human populations in Oceania, filling a major gap in representation in genomics research. Despite harboring remarkable diversity, populations in this vast region in the South Pacific historically have been overlooked in global human genetic studies, which have often focused largely on people of European descent, researchers say. The study is published in the journal Science.
“The drastic underrepresentation of Oceanians limits our understanding of human evolution and could exacerbate health inequalities as genomic research is used to develop novel medical treatments,” said lead author Serena Tucci, assistant professor of anthropology in Yale’s Faculty of Arts and Sciences and principal investigator of the Yale Human Evolutionary Genomics Laboratory. “To fill that gap, my research team embarked on a large-scale project to expand what is known about human genetic variation, including genetic variants inherited from extinct hominins.”
The work shows how the genes that ancient humans acquired after mating with extinct hominins continue to shape the biology, health and survival of our species today.
