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
