Among the most promising frontiers in artificial intelligence, today is a field inspired by one of the brain’s most remarkable capabilities: neuroplasticity. In human biology, neuroplasticity is the brain’s ability to reorganize itself by growing new neural pathways, pruning ineffective ones, and reshaping its structure in response to learning and experience. This flexible, self-modifying nature allows us to adapt throughout our lives. For AI researchers, it represents an ideal worth emulating.
Traditional AI systems, by contrast, are built on fixed architectures. Once trained, their neural networks remain largely static, requiring costly retraining or fine-tuning to incorporate new information. As one expert recently noted, “Humans don’t keep the same exact number of neurons for life, so why should an AI model be forced to keep the same number of artificial ‘neurons’ or ‘weights’?” This question lies at the heart of efforts to build more dynamic, plastic AI systems—technologies capable of evolving not just through data but by reshaping themselves.
The idea of adaptable neural networks isn’t new. As early as the 1990s, researchers explored dynamic architectures. Some algorithms added new neurons during training, while others pruned unnecessary ones to streamline performance. These early models provided proof that self-modification could be both possible and beneficial.
What has changed is the momentum. Recent developments have begun weaving together growth and pruning techniques to create AI systems that self-organize in real-time. These models are capable of expanding computational capacity when needed, scaling back to preserve resources, and adjusting their internal structure as the task or environment demands. The result is a potential leap from static computation to systems that learn and grow continually.
This concept is particularly compelling when applied to large language models (LLMs). Currently, LLMs rely on massive pretraining followed by fine-tuning, often consuming an enormous amount of time and energy. By contrast, neuroplastic AI systems could introduce lifelong learning. Models update themselves on the fly, absorbing new knowledge without retraining from scratch. Instead of being retrained periodically, these models could evolve continuously.
Several innovations point in this direction. Techniques like “dropout,” which temporarily disables neurons during training to improve generalization, have long been standard. More recently, “drop-in” approaches have emerged, enabling networks to add new neurons dynamically as new demands arise. Together, these tools offer a framework for building adaptive systems that refine their architecture as they operate.
The benefits of such plasticity extend well beyond efficiency. These systems could mitigate issues like catastrophic forgetting, a problem where models overwrite previous knowledge when learning new tasks. By retaining structural memory, they could preserve old insights while incorporating new ones. Personalization would also advance: AI could restructure itself to better align with individual users’ preferences and habits.
Imagine an AI assistant that compresses itself when operating in a low-power environment, then expands its capabilities when resources become available. Or a medical diagnostic system that adapts its internal structure to specialize in rare diseases based on patient population data. These examples illustrate how neuroplasticity could enable AI to operate fluidly across contexts.
The implications grow even more intriguing with the development of neuromorphic hardware—computer chips designed to mimic the brain’s structure. Unlike conventional chips, neuromorphic systems are built to support physical adaptation. Their architecture aligns naturally with plastic neural networks, allowing for on-chip learning and reconfiguration. This convergence could yield machines that are not only faster or more efficient but fundamentally more brain-like in how they learn and evolve.
Still, challenges remain. Researchers must find reliable methods for determining when to grow or prune neurons and how to balance adaptability with stability. Dynamic models are also harder to interpret, raising questions about transparency and accountability. As AI systems become more autonomous in how they modify themselves, the boundaries of human oversight must be carefully defined.
Ethical considerations are equally pressing. If an AI system continually rewires itself, does it remain the same entity over time? How do we monitor evolving biases or ensure alignment with human values as models adapt? These questions are not just theoretical—they touch on the very nature of identity, responsibility, and trust in intelligent systems.
Yet the pursuit of neuroplastic AI is about more than mirroring the brain. It’s about building machines that can thrive in a world that is constantly changing. Systems that do not merely react to data but grow through experience. Intelligence that is not static, but alive with potential.
In education, research, healthcare, and beyond, these emerging systems could become partners in learning and innovation—tools that reshape themselves as we reshape our understanding of what intelligence can be.
About Rick Inatome
Rick Inatome is the Managing Director of Collegio Partners and a transformative business and education leader whose legacy includes being an architect of the digital age.
