arXiv:2603.12286q-bio.NCcs.AI2026-03被引 1

提出统一神经架构DIME,整合感知、记忆、估值与意识机制。

The DIME Architecture: A Unified Operational Algorithm for Neural Representation, Dynamics, Control and Integration

  • 构建检测-整合-标记-执行的四组件循环框架
  • 实现感知、记忆、估值与意识的动态协同运作
  • 适合神经科学与类脑智能研究者参考

现代神经科学积累了大量关于感知、记忆、预测、估值和意识的证据,但仍缺乏一个能将这些现象整合到统一计算框架中的明确操作架构。现有理论分别关注神经功能的特定方面:预测编码与主动推理强调层级推断与预测误差最小化;痕迹理论通过分布式细胞集合解释记忆;神经调制理论聚焦价值依赖的可塑性与行为调节;全局工作空间或大规模网络模型则研究意识访问机制。尽管各有解释力,但这些方法在架构层面仍不完整整合。本文提出DIME(Detect-Integrate-Mark-Execute)架构,将感知、记忆、估值与意识访问纳入共同操作周期。该框架包含四个交互组件:痕迹(engrams),支持多重激活轨迹的分布式递归神经结构;执行线程(execution threads),实现神经过程的时空轨迹;标记系统(marker systems),调控增益、可塑性与轨迹选择的神经调制与边缘机制;超痕迹(hyperengrams),与操作性意识访问相关的大规模整合状态。该框架与海马索引、皮层递归处理、重放现象、大规模网络整合及神经调制调节等实证证据一致。在抽象计算层面,DIME可为人工智能与机器人提供统一机制模板,使表征、估值与时间序列从同一机制中涌现。完整理论阐述见附录专著(Zenodo)。

原文摘要 · Abstract (English)

Modern neuroscience has accumulated extensive evidence on perception, memory, prediction, valuation, and consciousness, yet still lacks an explicit operational architecture capable of integrating these phenomena within a unified computational framework. Existing theories address specific aspects of neural function: predictive coding and active inference emphasize hierarchical inference and prediction error minimization; engram theories explain memory through distributed cell assemblies; neuromodulatory accounts focus on value-dependent regulation of plasticity and behaviour; and global workspace or large-scale network models investigate mechanisms underlying conscious access. Despite their explanatory power, these approaches remain only partially integrated at the architectural level. This work introduces DIME (Detect-Integrate-Mark-Execute), a neural architecture organizing perception, memory, valuation, and conscious access within a common operational cycle. The framework includes four interacting components: engrams, distributed recurrent neural structures supporting multiple activation trajectories; execution threads, spatiotemporal trajectories implementing neural processes; marker systems, neuromodulatory and limbic mechanisms regulating gain, plasticity, and trajectory selection; and hyperengrams, large-scale integrative states associated with operational conscious access. The framework is consistent with empirical evidence from hippocampal indexing, recurrent cortical processing, replay phenomena, large-scale network integration, and neuromodulatory regulation. Formulated at an abstract computational level, DIME may also inform artificial intelligence and robotics by providing an architectural template in which representation, valuation, and temporal sequencing emerge from a unified mechanism. An extended theoretical exposition is available in a companion monograph on Zenodo.

神经架构意识机制类脑智能

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