We present (Experience-Modulated Biologically-inspired Emergent Reasoning), a hybrid cognitive architecture that reorganises the relationship between large language models (LLMs) and memory: rather than augmenting an LLM with retrieval tools, we place the LLM as a replaceable reasoning engine within a persistent, biologically-grounded associative substrate. The architecture centres on a 220,000-neuron spiking neural network (SNN) with spike-timing-dependent plasticity (STDP), four-layer hierarchical organisation (sensory/concept/category/meta-pattern), inhibitory E/I balance, and reward-modulated learning. Text embeddings are encoded into the SNN via a novel z-score standardised top-k population code that is dimension-independent by construction, achieving 82.2\% discrimination retention across embedding dimensionalities. We show that STDP lateral propagation during idle operation can trigger and shape LLM actions without external prompting or scripted triggers: the SNN determines when to act and what associations to surface, while the LLM selects the action type and generates content. In one instance, the system autonomously initiated contact with a user after learned person-topic associations fired laterally during an 8-hour idle period. From a clean start with zero learned weights, the first SNN-triggered action occurred after only 7 conversational exchanges (14 messages).

翻译：我们提出 EMBER（Experience-Modulated Biologically-inspired Emergent Reasoning，经验调制生物启发式涌现推理），这是一种混合认知架构，重新组织了大语言模型（LLM）与记忆之间的关系：不同于用检索工具增强LLM，我们将LLM作为可替换的推理引擎，嵌入一个持久化、具有生物学基础的关联基质中。该架构核心是一个拥有22万个神经元的脉冲神经网络（SNN），具备脉冲时序依赖可塑性（STDP）、四层层次化组织（感觉/概念/类别/元模式）、抑制-兴奋（E/I）平衡以及奖励调制学习能力。文本嵌入通过一种新颖的z-score标准化top-k群体编码（该编码天然具有维度无关性）被映射到SNN中，在不同嵌入维度下实现了82.2%的判别保持率。我们发现，空闲操作期间的STDP侧向传播可以在无需外部提示或脚本触发的情况下，触发并塑造LLM的行为：SNN决定何时行动以及呈现哪些关联，而LLM则选择行动类型并生成内容。在一个实例中，系统在学习到的人-主题关联在8小时空闲期间发生侧向放电后，自主启动了与用户的交互。从零学习权重的初始状态开始，首次SNN触发的行动仅出现在7轮对话交流（14条消息）之后。