AINEApr 14

EMBER: Autonomous Cognitive Behaviour from Learned Spiking Neural Network Dynamics in a Hybrid LLM Architecture

arXiv:2604.121678.5
AI Analysis

For AI researchers, this work presents a novel integration of SNNs and LLMs that enables emergent, autonomous behavior without external prompting, though it is an incremental step combining existing techniques.

The paper introduces EMBER, a hybrid cognitive architecture that integrates a 220,000-neuron spiking neural network (SNN) with an LLM, enabling autonomous, biologically-plausible reasoning. The system achieved 82.2% discrimination retention across embedding dimensionalities and autonomously initiated user contact after 8 idle hours, with the first SNN-triggered action occurring after only 7 conversational exchanges.

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).

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