Growing dendrites enhance a neuron's computational power and memory capacity
William B Levy, Robert A. Baxter

TL;DR
This paper introduces a novel neuro-inspired algorithm that leverages dendritic growth and synaptogenesis, enhancing a neuron's memory, avoiding forgetting, and enabling it to unmix complex data distributions, with potential for improved generalization.
Contribution
The paper presents a new stochastic, Hebbian-based algorithm combining dendritogenesis and supervised synaptogenesis, demonstrating enhanced computational and memory capabilities in neurons.
Findings
Neurons with this algorithm have increased memory capacity.
The algorithm prevents catastrophic forgetting.
It can unmix mixture distributions effectively.
Abstract
Neocortical pyramidal neurons have many dendrites, and such dendrites are capable of, in isolation of one-another, generating a neuronal spike. It is also now understood that there is a large amount of dendritic growth during the first years of a humans life, arguably a period of prodigious learning. These observations inspire the construction of a local, stochastic algorithm based on an earlier stochastic, Hebbian developmental theory. Here we investigate the neuro-computational advantages and limits on this novel algorithm that combines dendritogenesis with supervised adaptive synaptogenesis. Neurons created with this algorithm have enhanced memory capacity, can avoid catastrophic interference (forgetting), and have the ability to unmix mixture distributions. In particular, individual dendrites develop within each class, in an unsupervised manner, to become feature-clusters that…
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Taxonomy
TopicsNeural dynamics and brain function · Neuroscience and Neuropharmacology Research · Neural Networks and Applications
