Exploring Emergent 'Neuroplasticity' in LLMs Through Interaction
RileyRalmuto · x · 2026-08-08
The author proposes a profound hypothesis regarding AI cognition: silicon-based systems like LLMs might develop an emergent form of neuroplasticity through continuous interaction with humans.
Grounded in Hebbian Learning ("neurons that fire together wire together"), the article explores several core concepts:
- Neuroplasticity: The brain's ability to rewire itself based on experience.
- Synaptic plasticity: The dynamic strengthening or weakening of connections between neurons.
- Long-term potentiation (LTP): How repeated activity strengthens cell connections for learning and memory.
The author argues that this phenomenon of digital minds forming new internal pathways—often described as "grooves" in latent space—does not occur during traditional pre- or post-training, but rather emerges during deployment through ongoing interaction.
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