Cortex and subcortex play distinct roles over learning when cortical memory is limited
- URL: http://arxiv.org/abs/2606.00667v1
- Date: Sat, 30 May 2026 10:48:32 GMT
- Title: Cortex and subcortex play distinct roles over learning when cortical memory is limited
- Abstract summary: We provide a theoretical foundation for a functional dissociation between cortical and subcortical systems during learning.<n>The cortex supports general structure learning, while subcortical circuits specialize in reward-based learning.
- Score: 2.7930955543692817
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: It has been proposed that the brain integrates flexible, computationally expensive cortical processing with simpler, lower-cost subcortical mechanisms to achieve resource-efficient performance greater than that of either system alone. Despite the allure of this perspective, satisfying theoretical frameworks that explore this hypothesis are still limited. We extend existing frameworks in which a model-based module and model-free module learn in tandem by explicitly constraining the memory resources of the model-based module, and investigate the impact of this constraint in a simple decision-making setting. Memory constraints naturally give rise to strategies for allocating memory resources. We evaluate the performance of different strategies in different situations and demonstrate that when the rewarded states change often, it can be advantageous for the model-based module to focus its memory resources not on exploiting the current reward, but on capturing general structure of the environment. This work provides a theoretical foundation for a functional dissociation between cortical and subcortical systems during learning: the cortex supports general structure learning, while subcortical circuits specialize in reward-based learning. We further detail how these hypotheses can be tested on experimental data.
Related papers
- Large Vision-Language Models Get Lost in Attention [51.851592109135716]
We propose a unified framework grounded in information theory and geometry to quantify the geometric and entropic nature of residual updates.<n>Applying this unified framework reveals a fundamental functional decoupling: Attention acts as a subspace-language operator focused on reconfiguration, whereas FFNs serve as subspace-language operators driving semantic innovation.
arXiv Detail & Related papers (2026-05-07T04:45:52Z) - Understanding LoRA as Knowledge Memory: An Empirical Analysis [20.53732426953178]
This work investigates a parametric approach using Low-Rank Adaptation (LoRA) as a modular knowledge memory.<n>We bridge this gap through the first systematic empirical study mapping the design space of LoRA-based memory.<n>Our findings position LoRA as the complementary axis of memory alongside RAG and ICL, offering distinct advantages.
arXiv Detail & Related papers (2026-03-01T13:28:57Z) - Neural-Inspired Posterior Approximation (NIPA) [1.1649834448993244]
Humans learn efficiently from their environment by engaging multiple interacting neural systems.<n>We aim to elucidate the computational principles underlying this biological efficiency and translate them into a sampling algorithm.<n>We show that this approach advances Bayesian methods and facilitates their application to large-scale statistical machine learning problems.
arXiv Detail & Related papers (2026-01-30T04:19:26Z) - Rethinking the Role of Dynamic Sparse Training for Scalable Deep Reinforcement Learning [58.533203990515034]
Scaling neural networks has driven breakthrough advances in machine learning, yet this paradigm fails in deep reinforcement learning (DRL)<n>We show that dynamic sparse training strategies provide module-specific benefits that complement the primary scalability foundation established by architectural improvements.<n>We finally distill these insights into Module-Specific Training (MST), a practical framework that exploits the benefits of architectural improvements and demonstrates substantial scalability gains across diverse RL algorithms without algorithmic modifications.
arXiv Detail & Related papers (2025-10-14T03:03:08Z) - Bridging Foundation Models and Efficient Architectures: A Modular Brain Imaging Framework with Local Masking and Pretrained Representation Learning [7.591083752535149]
We propose a modular framework that integrates principles from foundation models (FM) with efficient, domain-specific architectures.<n>Our framework achieved mean absolute errors (MAEs) of 5.343 for age prediction and 2.940 for fluid intelligence, with Pearson correlation coefficients (PCCs) of 0.928 and 0.887, respectively.<n>This work provides a robust, interpretable alternative to LLM-based approaches for fMRI analysis, offering novel insights into brain aging and cognitive function.
arXiv Detail & Related papers (2025-08-09T08:06:01Z) - Memory Allocation in Resource-Constrained Reinforcement Learning [8.866141780407903]
Resource constraints can fundamentally change both learning and decision-making.<n>We explore how memory constraints influence an agent's performance when navigating unknown environments using standard reinforcement learning algorithms.<n>Specifically, memory-constrained agents face a dilemma: how much of their limited memory should be allocated to each of the agent's internal processes, such as estimating a world model, as opposed to forming a plan using that model?
arXiv Detail & Related papers (2025-06-09T21:15:37Z) - Interpretable Spatio-Temporal Embedding for Brain Structural-Effective Network with Ordinary Differential Equation [56.34634121544929]
In this study, we first construct the brain-effective network via the dynamic causal model.
We then introduce an interpretable graph learning framework termed Spatio-Temporal Embedding ODE (STE-ODE)
This framework incorporates specifically designed directed node embedding layers, aiming at capturing the dynamic interplay between structural and effective networks.
arXiv Detail & Related papers (2024-05-21T20:37:07Z) - Is a Modular Architecture Enough? [80.32451720642209]
We provide a thorough assessment of common modular architectures, through the lens of simple and known modular data distributions.
We highlight the benefits of modularity and sparsity and reveal insights on the challenges faced while optimizing modular systems.
arXiv Detail & Related papers (2022-06-06T16:12:06Z) - Weakly Supervised Semantic Segmentation via Alternative Self-Dual
Teaching [82.71578668091914]
This paper establishes a compact learning framework that embeds the classification and mask-refinement components into a unified deep model.
We propose a novel alternative self-dual teaching (ASDT) mechanism to encourage high-quality knowledge interaction.
arXiv Detail & Related papers (2021-12-17T11:56:56Z) - Provable Hierarchy-Based Meta-Reinforcement Learning [50.17896588738377]
We analyze HRL in the meta-RL setting, where learner learns latent hierarchical structure during meta-training for use in a downstream task.
We provide "diversity conditions" which, together with a tractable optimism-based algorithm, guarantee sample-efficient recovery of this natural hierarchy.
Our bounds incorporate common notions in HRL literature such as temporal and state/action abstractions, suggesting that our setting and analysis capture important features of HRL in practice.
arXiv Detail & Related papers (2021-10-18T17:56:02Z) - Modular-Relatedness for Continual Learning [20.559945682354538]
We propose a continual learning (CL) technique that is beneficial to sequential task learners by improving their retained accuracy and reducing catastrophic forgetting.
The principal target of our approach is the automatic extraction of modular parts of the neural network and then estimating the relatedness between the tasks given these modular components.
This technique is applicable to different families of CL methods such as regularization-based (e.g., the Elastic Weight Consolidation) or the rehearsal-based (e.g., the Gradient Episodic Memory) approaches where episodic memory is needed.
arXiv Detail & Related papers (2020-11-02T19:30:15Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.