An Overlooked Role of Context-Sensitive Dendrites

• URL: http://arxiv.org/abs/2408.11019v1
• Date: Tue, 20 Aug 2024 17:18:54 GMT
• Title: An Overlooked Role of Context-Sensitive Dendrites
• Authors: Mohsin Raza, Ahsan Adeel,
• Abstract summary: We show that context-sensitive (CS)-TPNs flexibly integrate C moment-by-moment with the FF somatic current at the soma. This enables the propagation of more coherent signals (bursts), making learning faster with fewer neurons.
• Score: 2.225268436173329
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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