ArticleeLife2017
Towards deep learning with segregated dendrites.
Article in eLife, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 136 papers.
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Who cites it
136 citing papers in PubMed.
- Tuft Dendrite Spikes Are Accompanied by Selective Input From Distinct Functional Networks.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
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- Top-down feedback in deep neural networks leads to functional differences during audiovisual integration.eLife · 2026Article
- Hierarchical optimization predicts plasticity in the macaque inferior temporal cortex following object training.Nature communications · 2026Article
- Spike-based alignment learning solves the weight transport problem.Nature communications · 2026Article
- Distinct sensorimotor encoding in tuft dendrites and somata associated with action, correction, and learning.bioRxiv : the preprint server for biology · 2026Article
- Tuft dendrite spikes are accompanied by selective input from distinct functional networks.bioRxiv : the preprint server for biology · 2026Article
- Review
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- Cellular and subcellular specialization enables biology-constrained deep learning.Cell reports · 2026Article
- Backpropagation-free spiking neural networks with the forward-forward algorithm.Scientific reports · 2026Article
- Dendritic heterosynaptic plasticity arises from calcium-based input learning.Communications biology · 2026Article
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- Voltage imaging as a window into neural computation.Neurophotonics · 2026Article
- A dendrite-resolved,bioRxiv : the preprint server for biology · 2026Article
- Three-factor learning in spiking neural networks: An overview of methods and trends from a machine learning perspective.Patterns (New York, N.Y.) · 2025Review
- Behavioral Timescale Synaptic Plasticity: A Burst in the Field of Learning and Memory.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Review
- Plateau potentials are instructive signals for behavioral timescale synaptic plasticity in the neocortex.bioRxiv : the preprint server for biology · 2025Article
- An anatomical substrate of credit assignment in reinforcement learning.bioRxiv : the preprint server for biology · 2025Article
76 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning has led to significant advances in artificial intelligence, in part, by adopting strategies motivated by neurophysiology. However, it is unclear whether deep learning could occur in the real brain. Here, we show that a deep learning algorithm that utilizes multi-compartment neurons might help us to understand how the neocortex optimizes cost functions. Like neocortical pyramidal neurons, neurons in our model receive sensory information and higher-order feedback in electrotonically segregated compartments. Thanks to this segregation, neurons in different layers of the network can coordinate synaptic weight updates. As a result, the network learns to categorize images better than a single layer network. Furthermore, we show that our algorithm takes advantage of multilayer architectures to identify useful higher-order representations-the hallmark of deep learning. This work demonstrates that deep learning can be achieved using segregated dendritic compartments, which may help to explain the morphology of neocortical pyramidal neurons.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.