In the realm of artificial intelligence, particularly with modern large language models, a common practice is to train these systems on extensive datasets, fine-tune them for specific tasks, and then deploy them with fixed parameters. This process, however, is often resource-intensive, requiring significant computational power and time, as it demands billions of iterations to ensure effective learning. In contrast, biological systems, particularly animals, exhibit remarkable agility in their learning processes, allowing them to adapt continuously to the shifting dynamics of their environments. This illustrates a fundamental difference between AI and natural intelligence, begging the question: Can artificial intelligence glean insights from the realms of neuroscience?
Research has demonstrated that social species—those organisms that thrive within intricate interpersonal networks—exhibit behavioral adaptations based on real-time interactions with peers. These adaptations are imperative as the rewards and penalties associated with various behaviors can fluctuate. For example, in a group of social animals, an observed behavior may yield different outcomes based on immediate context or the behavior of other individuals in the group. This fluidity in behavioral strategy highlights a profound layer of complexity that is often missing in traditional AI frameworks, which do not typically adjust or learn after their initial training phase.
Neuroscience offers a wealth of information regarding how living organisms navigate and adapt their behaviors in response to changes in their environment. An extensive body of research outlines how animals learn in conditions where rules, reward structures, and expected outcomes are neither fixed nor predictable. This contrasts starkly with the conventional training paradigm of AI systems, which are often siloed in their learning strategies, unable to evolve after deployment. As AI technologies advance and begin to intertwine with the fabric of daily life—guiding everything from autonomous vehicles to personal assistants—there is a compelling imperative to re-evaluate the rigidity of these systems through a neuroscientific lens.
Consider the intricacies of how animals learn within social environments. For instance, studies on primates and other social mammals reveal that group dynamics can instigate shifts in learning behaviors, adapting strategies based on the behaviors of others and altering outcomes in real-time. Such observations provide valuable insights into the malleability of learning. By leveraging principles derived from neuroscience, AI could potentially be engineered to adjust dynamically to such nuanced scenarios, paving the way for more intelligent and responsive systems capable of real-world application.
The concept of continual learning—iterations of learning that adapt over time—has gained traction in recent AI research. However, the prevailing models typically struggle with ‘catastrophic forgetting,’ a phenomenon where the introduction of new information leads to the deterioration of previously acquired knowledge. Utilizing insights from neuroscience could provide strategies to overcome these limitations, enhancing AI systems’ abilities to retain learned information while also adapting to new data. The dynamic nature of animal learning can inspire frameworks that allow AI to develop more resilient architectures better equipped for real-world applications.
The mechanisms through which animals encode and recall information about their environment are profound areas of inquiry within neuroscience. Recent studies on neural activity in various species illustrate the way neuronal populations transition rapidly in response to changing inputs, demonstrating the brain’s ability to encode multidimensional tasks efficiently. Emulating these neural mechanisms could greatly enhance machine-learning algorithms, allowing them to process changing information in a manner akin to how biological entities operate.
Moreover, behavioral experiments indicate that animals often engage in explorative behavior to ascertain new rules or rewards within their environment, a process that is facilitated by neuroplasticity—the brain’s ability to reorganize itself by forming new neural connections. Such exploration strategies can inform AI frameworks that would encourage agents to seek out novel data and experiences actively, fostering an environment of continuous learning and adaptation. By merging neuroscience principles with AI architectures, the path towards developing more autonomous and capable systems opens up, effectively bridging the gap between artificial learning and natural intelligence.
In pursuit of this interdisciplinary dialogue, researchers are called upon to integrate established knowledge from neuroscience into the evolving field of AI, promoting mutual understanding and innovation. Collaborative efforts can lead to rich exchanges of ideas that bolster the growth of both domains, creating a synergy that advances our comprehension of learning and adaptability. AI systems robust enough to mimic animal-like learning capabilities could positively impact various applications, including robotics, healthcare, and user interface design.
As advancements in AI continue to accelerate, the necessity for systems characterized by adaptive learning becomes paramount. The ability to adjust based on tangible experiences and interactions not only enhances performance but also has profound ethical implications as AI begins to operate in sensitive domains such as healthcare or security. By understanding how animals regulate behavior based on social context and environmental feedback, AI researchers can better equip systems for ethical reasoning and decision-making in the complex tapestry of human interaction.
The exploration of how neuroscience can inform the development of AI serves as an intriguing frontier for investigation, pushing the boundaries of our understanding of both realms. As such, there is an urgent call to further this research agenda, creating robust collaborative frameworks that would allow scientists, engineers, and ethicists to work together in conceiving AI systems that learn and adapt continuously. This endeavor is not merely an academic pursuit; it embodies our intrinsic desire to understand intelligence—both artificial and natural—ultimately enriching our grasp over the technologies that shape our future.
As the discourse surrounding the intersection of AI and neuroscience grows, it will undoubtedly illuminate new pathways towards intelligent systems that are more nuanced, adaptable, and intertwined with human experiences. The journey involves embracing complexity, allowing AI to learn not just through vast data collections but through intelligent interaction, mirroring the remarkable learning capabilities seen in the animal kingdom. As we navigate this interdisciplinary convergence, we stand at the threshold of a revolution in how technology can evolve, potentially transforming the fabric of our relationship with machines and redefining our expectations of intelligent behavior.
Subject of Research: The intersection of neuroscience and artificial intelligence in understanding learning in dynamic environments.
Article Title: What neuroscience can tell AI about learning in continuously changing environments.
Article References:
Durstewitz, D., Averbeck, B. & Koppe, G. What neuroscience can tell AI about learning in continuously changing environments.
Nat Mach Intell (2025). https://doi.org/10.1038/s42256-025-01146-z
Image Credits: AI Generated
DOI: https://doi.org/10.1038/s42256-025-01146-z
Keywords: NeuroAI, artificial intelligence, neuroscience, continual learning, adaptive systems, dynamic environments
Tags: adaptive learning in AIAI learning from neurosciencecomputational power in AI trainingcontinuous learning in AIdifferences between AI and natural intelligencedynamic learning environmentsfixed parameters in AIfluidity in behavioral strategiesinsights from biological systemsneuroscience and artificial intelligencereal-time interaction learningsocial species behavior adaptations
