Show pageBacklinksCite current pageExport to PDFBack to top This page is read only. You can view the source, but not change it. Ask your administrator if you think this is wrong. ====== Adaptive behaviour ====== ====== Multi-Timescale Reinforcement Learning and Adaptive Behaviour: A Critical Neurosurgical Perspective (Nature, 2025) ====== **Reference:** Masset P, Tano P, Kim HG, Malik AN, Pouget A, Uchida N. *Multi-timescale reinforcement learning in the brain*. Nature. 2025 Jun;642(8068):682–690. doi: 10.1038/s41586-025-08929-9. **Study Type:** Basic neuroscience study using mouse electrophysiology, behavioral tasks, and computational reinforcement learning models. ===== 🧠 Definition: Adaptive Behaviour ===== **Adaptive behaviour** is the capacity of a biological or artificial agent to **change its actions or strategies in response to environmental changes**, in order to improve survival, performance, or reward acquisition. In neuroscience, adaptive behaviour is driven by **cortical and subcortical circuits** that monitor outcomes and adjust decisions over time. This includes: * Prefrontal cortex — for executive control and planning * Basal ganglia and dopaminergic systems — for learning from reward and error * Cerebellum — for sensory-motor adaptation Adaptive behaviour occurs over **multiple timescales**: from immediate reflexes to lifelong strategy shifts. ===== 📄 Summary of the Article ===== The authors propose that individual dopaminergic neurons in mice encode **reward prediction errors** using **different temporal discounting rates**, implying that the brain learns at **multiple timescales simultaneously**. They combine: * Computational models showing the advantages of multi-timescale learning * In vivo recordings in mice during behavioral tasks * Cross-task correlation of neuronal discount rates, suggesting cell-specific properties ===== 🔬 Critical Evaluation ===== While the concept is intellectually appealing, the paper suffers from serious limitations: * **Overinterpretation of noisy signals**: Dopaminergic neuron activity is context-sensitive and multifactorial; inferring stable cell-specific discount rates is speculative. * **Lack of causal or translational relevance**: No lesion, disease model, or human data is presented. No functional outcome is measured. * **Buzzword-driven AI overreach**: The authors imply impact on reinforcement learning algorithms without any concrete implementation. * **Misuse of adaptive behaviour**: The paper attempts to link multi-timescale learning to "adaptive behaviour" broadly, but provides **no functional demonstration** of actual adaptive decision-making improved by this heterogeneity. ===== 🧠 Neurosurgical Perspective ===== For neurosurgeons, particularly in the fields of **functional neurosurgery**, **DBS**, or **cognitive rehabilitation**, this article lacks: * A surgical target * A biomarker or physiological readout applicable to patients * Any proposed change in therapy or diagnostics The supposed insight into dopaminergic diversity has **no practical application in patient care or device programming**. ===== 🧨 Final Critique ===== This article exemplifies **high-concept theoretical neuroscience with no clinical traction**. It misuses the term “adaptive behaviour” as a rhetorical bridge between **neural noise** and **AI aspiration**, without establishing functional evidence at either end. > Neurosurgeons should read it only as a case study in academic overreach — not as a guide to brain function or intervention. adaptive_behaviour.txt Last modified: 2025/06/19 11:15by administrador