By skyforbes Unlike current AI systems, brains can quickly and flexibly adapt to changing environments.
This is the topic of our new perspective in Nature MI (https://rdcu.be/eSeif), where we relate dynamical and plasticity mechanisms in the brain to in-context and continual learning in AI.
Key take-homes:
- Biological brains often quickly adapt to novel rules or task contingencies within just a few trials, often accompanied by sudden transitions in behavioral performance and neural population activity (e.g. https://www.nature.com/articles/s41467-025-60943-7).
- ynamical and plasticity mechanisms in the brain span a huge range of timescales, echoing the complex multiple time-scale dynamics inherent in our physical and biological world. ynamics in the brain mirrors dynamics in the real world, a property current AI systems fundamentally lack.
- Neuro-dynamical mechanisms are set up to work close to bifurcation (critical) points, allowing fast reconfiguration of (ghost-)attractor landscapes for novel situations through neuromodulators or short-term plasticity.
- Recently identified plasticity mechanisms, like behavioral time-scale plasticity, can quickly ingrain one-shot experiences in synaptic structure, enabling powerful new training algorithms (e.g.https://www.nature.com/articles/s41467-024-55563-6).
- Aligning cognitive task designs in neuroscience and AI, subjecting animals and AI to the same types of test procedures and benchmarks, could facilitate transfer of results and insights.
- ynamical systems reconstruction (SR) models trained on physiological and behavioral data may provide means to *directly* translate algorithms as implemented in the brain into AI architectures.
Please see paper for citations and links to original work on all these points. #NeuroAI