Strong Allee effect synaptic plasticity rule in an unsupervised learning environment
Eddy Kwessi

TL;DR
This paper introduces a novel synaptic plasticity rule inspired by the Allee effect, demonstrating properties like normalization, competition, and stability, challenging traditional paradigms of brain plasticity.
Contribution
It proposes a new Allee effect-based plasticity rule that incorporates inhibition and bounded growth, offering a fresh perspective on synaptic dynamics in unsupervised learning.
Findings
The rule exhibits synaptic normalization and competition.
It demonstrates de-correlation potential and dynamic stability.
It models an absence of plasticity through the Allee effect.
Abstract
Synaptic plasticity or the ability of a brain to changes one or more of its functions or structures has generated and is sill generating a lot of interest from the scientific community especially neuroscientists. These interests especially went into high gear after empirical evidences were collected that challenged the established paradigm that human brain structures and functions are set from childhood and only modest changes were expected beyond. Early synaptic plasticity rules or laws to that regard include the basic Hebbian rule that proposed a mechanism for strengthening or weakening of synapses (weights) during learning and memory. This rule however did not account from the fact that weights must have bounded growth overtime. Thereafter, many other rules were proposed to complement the basic Hebbian rule and they also possess other desirable properties. In particular, a desirable…
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Taxonomy
TopicsAdvanced Memory and Neural Computing · Neural dynamics and brain function · Neural Networks and Applications
