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bioRxiv · 10.64898/2026.08.20.746078

Learning with interacting dendrites improves neuronal familiarity detection

Cai, F.·Benna, M. K.
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Abstract

Biological neurons can perform nonlinear computations within their dendrites and support branch-localized plasticity. This raises the possibility that single cells can store memories more efficiently and with less interference by confining synaptic modifications to specific dendrites. We study a parallel-dendrite model performing online familiarity detection and compare three dendrite-update rules during learning: (i) independent thresholding, (ii) an interacting rule that adapts the target local dendritic activation per item, and (iii) an interacting n-winners-take-all (WTA) rule that constrains the number of updated branches per item. The interacting rules substantially improve capacity by limiting variance in memory responses and decorrelating weights across branches -- even when inputs are strongly correlated. These results suggest that competition among dendrites, consistent with resource-limited plasticity mechanisms, can enhance single-cell memory beyond non-interacting schemes.

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Cai, F., Benna, M. K.. 2026-08-25. Learning with interacting dendrites improves neuronal familiarity detection. https://doi.org/10.64898/2026.08.20.746078

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