What Neuroscience Can Teach AI About Learning in Continuously Changing Environments

Abstract: Modern AI models, such as LLMs, are usually trained once on a huge corpus of data, potentially fine-tuned for a specific task, and then deployed with fixed parameters. Their training is costly, slow, and gradual, requiring billions of repetitions. In stark contrast, animals continuously adapt to the ever-changing contingencies in their environments. This is particularly important for social species, where behavioral policies and reward outcomes may frequently change in interaction with peers. The underlying computational processes are often marked by rapid shifts in an animal's behaviour and rather sudden transitions in neuronal population activity. Such computational capacities are of growing importance for AI systems operating in the real world, like those guiding robots or autonomous vehicles, or for agentic AI interacting with humans online. Can AI learn from neuroscience? This Perspective explores this question, integrating the literature on continual and in-context learning in AI with the neuroscience of learning on behavioral tasks with shifting rules, reward probabilities, or outcomes. We will outline an agenda for how specifically insights from neuroscience may inform current developments in AI in this area, and - vice versa - what neuroscience may learn from AI, contributing to the evolving field of NeuroAI.

• The paper demonstrates that unlike gradient descent-based AI, biological systems achieve rapid, one-shot adaptation in non-stationary environments.
• It introduces dynamical systems theory and synaptic plasticity as methods to enable efficient, low-sample learning in AI models.
• The study advocates for new AI architectures and benchmarks modeled on animal learning to improve robustness and real-time adaptation.

Integrating Neuroscience Insights into AI for Learning in Non-Stationary Environments

The paper "What Neuroscience Can Teach AI About Learning in Continuously Changing Environments" (2507.02103) presents a comprehensive analysis of the limitations of current AI learning paradigms in the context of non-stationary environments and proposes a research agenda for integrating mechanisms from neuroscience to address these challenges. The authors contrast the slow, resource-intensive, and largely static learning processes of modern AI systems with the rapid, flexible, and continual adaptation observed in animal brains, particularly in dynamic and unpredictable real-world settings.

Summary of Key Arguments

The central thesis is that while AI has made significant progress in continual and in-context learning, its mechanisms remain fundamentally distinct from those employed by biological systems. The paper identifies two primary adaptation strategies in AI:

• Continual (In-Weights) Learning: Involves parameter updates via gradient descent, with methods to mitigate catastrophic forgetting (e.g., regularization, modularity, experience replay, partial resets). Despite advances, these methods are slow, require extensive data, and are primarily focused on memory retention rather than rapid adaptation.
• In-Context Learning (ICL): Foundation models, especially LLMs, can perform few-shot or zero-shot generalization by leveraging large context windows. However, ICL is limited by the training distribution, is computationally expensive, and lacks the rapid, one-shot learning capabilities of biological systems.

In contrast, animal learning is characterized by:

• Rapid adaptation to novel rules or contingencies, often within a few trials.
• Continuous exploration and updating of behavioral policies, even in stable environments.
• Sudden transitions in behavior and neural representations, rather than gradual changes.
• Robust memory retention, with the ability to suppress rather than erase outdated behaviors.

Neurobiological Mechanisms Relevant to AI

The paper highlights two classes of mechanisms from neuroscience that could inform AI research:

1. Neuro-Dynamical Mechanisms

• Dynamical Systems Theory (DST): The brain is conceptualized as a high-dimensional dynamical system, with computation implemented via trajectories in state space. Attractors (fixed points, manifolds, ghost attractors) provide substrates for memory, context, and flexible adaptation.
• Manifold Attractors: Enable long short-term memory and context retention without parameter updates, offering a potential alternative to the resource-intensive context windows in transformers.
• Ghost Attractors and Bifurcations: Support rapid, qualitative changes in behavior and neural activity, mirroring the sudden performance jumps observed in animals. These mechanisms allow for flexible reconfiguration of computational motifs in response to environmental changes.
• Multiple Timescales: The brain operates across a hierarchy of timescales, enabling both rapid responses and long-term integration, a feature largely absent in current AI architectures.

2. Synaptic Plasticity Mechanisms

• Diverse Plasticity Rules: Synaptic changes occur on multiple timescales (from milliseconds to years), are often local and unsupervised, and can be rapid (one-shot learning via mechanisms like Behavioral Time Scale Plasticity, BTSP).
• Complementary Learning Systems: The hippocampus and neocortex serve distinct roles in fast episodic memory and slow integration, respectively, minimizing catastrophic forgetting and supporting generalization.
• Meta-Plasticity: The plasticity of plasticity itself, modulated by neuromodulators and developmental trajectories, enables adaptive learning rates and modularity.
• Experience Replay: Biological replay mechanisms inspire AI experience replay but are more tightly integrated with memory consolidation and schema formation in the brain.

Numerical Results and Empirical Claims

The paper references empirical findings from both neuroscience and AI:

• Animals often require only a few trials to adapt to new rules, in contrast to the thousands or millions of iterations needed for AI models.
• Sudden behavioral and neural transitions are statistically correlated and can be detected via change point analysis.
• BTSP-based models demonstrate robust one-shot learning and content-addressable memory, outperforming traditional Hebbian mechanisms in certain regimes.
• Manifold attractor-regularized RNNs outperform LSTMs on long-range dependency tasks.

Implications for AI Research

The integration of neurobiological principles into AI has several practical and theoretical implications:

• Resource Efficiency: Dynamical and plasticity-based mechanisms could enable rapid, low-sample adaptation, reducing the computational and data requirements of current AI systems.
• Robustness and Flexibility: Attractor-based memory and modular plasticity may provide more robust solutions to catastrophic forgetting and support flexible recombination of learned skills.
• Temporal Reasoning: Incorporating multiple timescales and dynamical priors could improve AI performance in time series forecasting, robotics, and agentic AI operating in real-time environments.
• Benchmarking and Task Design: The paper advocates for closer alignment between AI benchmarks and ecologically valid neuroscience tasks, facilitating more meaningful evaluation of continual and adaptive learning.

Future Directions

The authors propose several avenues for future research:

• Development of AI architectures that explicitly incorporate dynamical systems principles, such as manifold attractors and ghost attractor chains.
• Implementation of rapid, local, and unsupervised plasticity rules, inspired by BTSP and STDP, to enable one-shot and few-shot learning.
• Adoption of complementary learning systems and meta-plasticity for modular, hierarchical, and lifelong learning.
• Use of dynamical systems reconstruction (DSR) models to directly transfer neurophysiological data and computational motifs into AI architectures.
• Design of new benchmarks and tasks that reflect the complexity and non-stationarity of real-world environments, informed by animal learning paradigms.

Conclusion

This paper provides a rigorous and detailed roadmap for bridging the gap between AI and neuroscience in the domain of learning under non-stationarity. By moving beyond gradient descent and static architectures, and embracing the rich repertoire of dynamical and plasticity mechanisms evolved in biological systems, AI research stands to make significant advances in adaptability, efficiency, and robustness. The proposed integration of DST, multi-timescale plasticity, and complementary learning systems represents a promising direction for the development of next-generation agentic AI capable of thriving in continuously changing environments.