Dendritic action potentials and computation in human layer 2/3 cortical neurons
By A. Gidon, T. Zolnik, Pawel Fidzinski, Felix Bolduan, Athanasia Papoutsi, Panayiota Poirazi, M. Holtkamp, Imre Vida, Matthew E. Larkum. 2020
508 citations recorded by the source corpus.
Human dendrites are special A special developmental program in the human brain drives the disproportionate thickening of cortical layer 2/3. This suggests that the expansion of layer 2/3, along with its numerous neurons and their large dendrites, may contribute to what makes us human. Gidon et al. thus investigated the dendritic physiology of layer 2/3 pyramidal neurons in slices taken from surgically resected brain tissue in epilepsy patients. Dual somatodendritic recordings revealed previously unknown classes of action potentials in the dendrites of these neurons, which make their activity far more complex than has been previously thought. These action potentials allow single neurons to solve two long-standing computational problems in neuroscience that were considered to require multilayer neural networks. Science, this issue p. 83 Dendritic action potentials extend the repertoire of computations available to human neurons. The active electrical properties of dendrites shape neuronal input and output and are fundamental to brain function. However, our knowledge of active dendrites has been almost entirely acquired from studies of rodents. In this work, we investigated the dendrites of layer 2 and 3 (L2/3) pyramidal neurons of the human cerebral cortex ex vivo. In these neurons, we discovered a class of calcium-mediated dendritic action potentials (dCaAPs) whose waveform and effects on neuronal output have not been previously described. In contrast to typical all-or-none action potentials, dCaAPs were graded; their amplitudes were maximal for threshold-level stimuli but dampened for stronger stimuli. These dCaAPs enabled the dendrites of individual human neocortical pyramidal neurons to classify linearly nonseparable inputs—a computation conventionally thought to require multilayered networks.
Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Neuroscience
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Citations in the corpus
- The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence (2020, background): “Already this year we have seen even a single compartment in a dendrite can compute XOR (Gidon et al., 2020), raising the possibility that individual neurons may be much more sophisticated than is often assumed.”
- Kervolutional Neural Networks (2019, background): “It was also demonstrated that higher order non-linear feature maps are able to make subsequent linear classifiers more discriminative [37, 1, 9].”
- Neurosymbolic AI: the 3rd wave (2020, unknown): “course based on the current simple models of neuron. Although this may be about to change through important work on understanding the mind and brain which may produce richer models of neural networks [29], one should note that the recent state-of-the-art results obtained by deep networks using large”