mlsanityData to 7 Oct 2026

An Overlooked Role of Context-Sensitive Dendrites

arXiv 2408.11019 ↗First posted 20 Aug 2024q-bio.NC cs.AI cs.LGcited by 3

Not on the map. Why?

Abstract (arXiv)

To date, most dendritic studies have predominantly focused on the apical zone of pyramidal two-point neurons (TPNs) receiving only feedback (FB) connections from higher perceptual layers and using them for learning. Recent cellular neurophysiology and computational neuroscience studies suggests that the apical input (context), coming from feedback and lateral connections, is multifaceted and far more diverse, with greater implications for ongoing learning and processing in the brain than previously realized. In addition to the FB, the apical tuft receives signals from neighboring cells of the same network as proximal (P) context, other parts of the brain as distal (D) context, and overall coherent information across the network as universal (U) context. The integrated context (C) amplifies and suppresses the transmission of coherent and conflicting feedforward (FF) signals, respectively. Specifically, we show that complex context-sensitive (CS)-TPNs flexibly integrate C moment-by-moment with the FF somatic current at the soma such that the somatic current is amplified when both feedforward (FF) and C are coherent; otherwise, it is attenuated. This generates the event only when the FF and C currents are coherent, which is then translated into a singlet or a burst based on the FB information. Spiking simulation results show that this flexible integration of somatic and contextual currents enables the propagation of more coherent signals (bursts), making learning faster with fewer neurons. Similar behavior is observed when this functioning is used in conventional artificial networks, where orders of magnitude fewer neurons are required to process vast amounts of heterogeneous real-world audio-visual (AV) data trained using backpropagation (BP). The computational findings presented here demonstrate the universality of CS-TPNs, suggesting a dendritic narrative that was previously overlooked.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 8 of its 29 references that are in the map. First posted on arXiv 20 Aug 2024.

Builds on

8 of 29 references.

References · 8

  1. 2018MINE: Mutual Information Neural Estimationcited by 680
  2. 2018Dendritic cortical microcircuits approximate the backpropagation algorithmcited by 96
  3. 2016Towards deep learning with segregated dendritescited by 71
  4. 2022Single-phase deep learning in cortico-cortical networkscited by 19
  5. 2023Leveraging dendritic properties to advance machine learning and neuro-inspired computingcited by 6
  6. 2022Context-sensitive neocortical neurons transform the effectiveness and efficiency of neural information processingcited by 5
  7. 2022Unlocking the potential of two-point cells for energy-efficient and resilient training of deep netscited by 5
  8. 2023Cooperation Is All You Needcited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.