ReviewCurrent opinion in neurobiology2024
Leveraging dendritic properties to advance machine learning and neuro-inspired computing.
Review in Current opinion in neurobiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed.
- Cellular and subcellular specialization enables biology-constrained deep learning.Cell reports · 2026Article
- Plastic Arbor: A modern simulation framework for synaptic plasticity-From single synapses to networks of morphological neurons.PLoS computational biology · 2026Article
- A Neural Model for V1 That Incorporates Dendritic Nonlinearities and Backpropagating Action Potentials.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Article
- Artificial intelligence meets brain theory (again).Biological cybernetics · 2025Review
- Dendrites endow artificial neural networks with accurate, robust and parameter-efficient learning.Nature communications · 2025Article
- 2D MoSNature communications · 2025Article
- Biophysical and computational insights from modeling human cortical pyramidal neurons.Frontiers in neuroscience · 2025Review
- The calcitron: A simple neuron model that implements many learning rules via the calcium control hypothesis.PLoS computational biology · 2025Article
- Dendrites endow artificial neural networks with accurate, robust and parameter-efficient learning.ArXiv · 2024Article
- Synaptic microarchitecture: the role of spatial interplay between excitatory and inhibitory inputs in shaping dendritic plasticity and neuronal output.Frontiers in cellular neuroscience · 2024Article
- Advancing neural computation: experimental validation and optimization of dendritic learning in feedforward tree networks.American journal of neurodegenerative disease · 2024Article
- Simulations predict differing phase responses to excitation vs. inhibition in theta-resonant pyramidal neurons.Journal of neurophysiology · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The brain is a remarkably capable and efficient system. It can process and store huge amounts of noisy and unstructured information, using minimal energy. In contrast, current artificial intelligence (AI) systems require vast resources for training while still struggling to compete in tasks that are trivial for biological agents. Thus, brain-inspired engineering has emerged as a promising new avenue for designing sustainable, next-generation AI systems. Here, we describe how dendritic mechanisms of biological neurons have inspired innovative solutions for significant AI problems, including credit assignment in multi-layer networks, catastrophic forgetting, and high-power consumption. These findings provide exciting alternatives to existing architectures, showing how dendritic research can pave the way for building more powerful and energy efficient artificial learning systems.
Indexed as
Identifiers
38394956What OpenQuestion holds
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.