ember-hybrid-snn-llm-cognitive-architecture

name	ember-hybrid-snn-llm-cognitive-architecture
description	EMBER hybrid cognitive architecture combining LLM reasoning with persistent biologically-grounded SNN memory substrate. Autonomous cognitive behavior via STDP-based lateral propagation.

EMBER: Hybrid SNN-LLM Cognitive Architecture

Methodology from paper: "EMBER: Autonomous Cognitive Behaviour from Learned Spiking Neural Network Dynamics in a Hybrid LLM Architecture" (arXiv:2604.12167, 2026-04-14)

Core Concept

EMBER (Experience-Modulated Biologically-inspired Emergent Reasoning) reorganizes the LLM-memory relationship:

Traditional: LLM + retrieval tools EMBER: LLM as replaceable reasoning engine within persistent SNN substrate

Architecture

SNN Substrate (220,000 neurons)

• Spike-Timing-Dependent Plasticity (STDP): Online learning
• Four-Layer Hierarchy:
  1. Sensory layer
  2. Concept layer
  3. Category layer
  4. Meta-pattern layer
• E/I Balance: Inhibitory-excitatory balance mechanisms
• Reward-Modulated Learning: Associative learning with reinforcement

Text Encoding

Z-score Standardized Top-K Population Code:

• Dimension-independent by construction
• 82.2% discrimination retention across embedding sizes
• Converts embeddings to sparse population codes

Key Innovation: Autonomous Action

STDP Lateral Propagation

• Idle Operation: Continuous internal dynamics
• Autonomous Triggering: SNN determines when to act
• Association Surfacing: Learned associations fire laterally
• LLM Integration: LLM selects action type and generates content

Real-World Example

• 8-hour idle period → learned person-topic associations fired
• System autonomously initiated contact with user
• No external prompting or scripted triggers

Performance

• Learning Speed: First SNN-triggered action after only 7 conversations (14 messages) from clean start
• Discrimination: 82.2% retention across embedding dimensionalities
• Autonomy: True self-directed behavior without explicit triggers

Applications

• Autonomous Agents: Self-directed AI assistants
• Proactive Systems: Anticipating user needs
• Long-Term Memory: Persistent associative memory
• Cognitive Architectures: Biologically-inspired AI

Implementation Notes

class EMBERArchitecture:
    def __init__(self, llm_engine, snn_config):
        self.llm = llm_engine  # Replaceable reasoning engine
        self.snn = SpikingNeuralNetwork(snn_config)
        self.encoder = ZScoreTopKEncoder()
        
    def encode_input(self, text):
        """Convert text to SNN population code"""
        embedding = self.llm.embed(text)
        normalized = (embedding - embedding.mean()) / embedding.std()
        top_k_indices = torch.topk(normalized.abs(), k=K).indices
        population_code = torch.zeros_like(embedding)
        population_code[top_k_indices] = normalized[top_k_indices]
        return population_code
        
    def process_conversation(self, messages):
        for msg in messages:
            code = self.encode_input(msg['content'])
            self.snn.inject_spikes(code)
        
        self.snn.run_steps(duration='idle_period')
        
        if self.snn.detect_strong_association():
            associations = self.snn.retrieve_active_associations()
            action = self.llm.generate_action(associations)
            return action
        return None

References

• Savage, W. (2026). EMBER: Autonomous Cognitive Behaviour from Learned Spiking Neural Network Dynamics in a Hybrid LLM Architecture. arXiv:2604.12167.
• arXiv: https://arxiv.org/abs/2604.12167

Activation Keywords

• EMBER architecture
• hybrid SNN LLM
• autonomous cognitive behavior
• STDP lateral propagation
• biologically-grounded memory
• proactive AI agents