ember-hybrid-snn-llm-architecture

name	ember-hybrid-snn-llm-architecture
description	EMBER (Experience-Modulated Biologically-inspired Emergent Reasoning) - Hybrid cognitive architecture combining Spiking Neural Networks (SNN) with Large Language Models (LLM). SNN serves as persistent associative substrate, LLM as replaceable reasoning engine. Activation: EMBER, hybrid SNN LLM, cognitive architecture, emergent reasoning, spiking neural network LLM, biologically inspired AI.

EMBER: Hybrid SNN-LLM Cognitive Architecture

Overview

EMBER (Experience-Modulated Biologically-inspired Emergent Reasoning) represents a paradigm shift in cognitive AI architecture. Rather than augmenting LLMs with retrieval tools, EMBER places the LLM as a replaceable reasoning engine within a persistent, biologically-grounded associative substrate implemented as a Spiking Neural Network (SNN).

Key Innovation

Traditional Approach: LLM + Retrieval/RAG tools EMBER Approach: Persistent SNN substrate + Replaceable LLM reasoning

The SNN determines when to act and what associations to surface, while the LLM selects the action type and generates content.

Architecture Components

1. Spiking Neural Network Substrate (220,000 neurons)

Four-Layer Hierarchical Organization:

Layer 4: Meta-Pattern Layer (highest abstraction)
Layer 3: Category Layer (concept categories)
Layer 2: Concept Layer (semantic concepts)
Layer 1: Sensory Layer (raw input encoding)

Key Features:

• STDP Learning: Spike-timing-dependent plasticity for associative learning
• E/I Balance: Excitatory/inhibitory balance for stable dynamics
• Reward Modulation: Dopamine-like reinforcement signals
• Lateral Propagation: Association chains trigger without external input

2. Text Embedding Encoding

Z-Score Standardized Top-K Population Code:

• Converts text embeddings to sparse spike patterns
• Dimension-independent by construction (works with any embedding size)
• 82.2% discrimination retention across embedding dimensionalities

3. LLM Integration Interface

• Receives triggered associations from SNN
• Generates contextual responses/actions
• Stateless with respect to the substrate (can be swapped)

Implementation

EMBER Architecture

import torch
import torch.nn as nn
import numpy as np
from typing import List, Tuple, Optional

class EMBERArchitecture:
    """
    EMBER: Hybrid SNN-LLM Cognitive Architecture
    
    The SNN serves as a persistent associative memory substrate,
    while the LLM acts as a stateless reasoning engine.
    """
    def __init__(
        self,
        embedding_dim: int = 384,
        num_sensory: int = 50000,
        num_concept: int = 100000,
        num_category: int = 50000,
        num_meta: int = 20000,
        llm_backend: str = "gpt-4"
    ):
        self.embedding_dim = embedding_dim
        
        # Layer sizes
        self.layer_sizes = {
            'sensory': num_sensory,
            'concept': num_concept,
            'category': num_category,
            'meta': num_meta
        }
        
        # Total: 220,000 neurons
        self.total_neurons = sum(self.layer_sizes.values())
        
        # Initialize SNN layers
        self.snn = HierarchicalSNN(
            layer_sizes=list(self.layer_sizes.values()),
            embedding_dim=embedding_dim
        )
        
        # LLM interface
        self.llm_backend = llm_backend
        
        # State tracking
        self.learned_weights = False
        self.idle_spike_history = []
        
    def encode_embedding(self, embedding: np.ndarray, k: int = 50) -> torch.Tensor:
        """
        Z-score standardized top-k population coding
        
        Converts text embedding to sparse spike pattern.
        Dimension-independent - works with any embedding size.
        
        Args:
            embedding: Text embedding vector (any dimension)
            k: Number of active neurons in population code
            
        Returns:
            Spike pattern for sensory layer
        """
        # Z-score normalization
        z_scores = (embedding - embedding.mean()) / (embedding.std() + 1e-8)
        
        # Top-k selection
        top_k_indices = np.argsort(np.abs(z_scores))[-k:]
        
        # Map to sensory neurons (with expansion for dimension independence)
        sensory_pattern = torch.zeros(self.layer_sizes['sensory'])
        
        # Distribute top-k values across sensory layer
        step = self.layer_sizes['sensory'] // k
        for i, idx in enumerate(top_k_indices):
            start = i * step
            end = (i + 1) * step
            sensory_pattern[start:end] = z_scores[idx]
        
        # Normalize to firing rates
        sensory_pattern = torch.sigmoid(sensory_pattern)
        
        return sensory_pattern

class HierarchicalSNN(nn.Module):
    """
    Four-layer hierarchical SNN with STDP learning
    """
    def __init__(self, layer_sizes: List[int], embedding_dim: int):
        super().__init__()
        
        self.layer_sizes = layer_sizes
        self.num_layers = len(layer_sizes)
        
        # Create layers
        self.layers = nn.ModuleList()
        for i, size in enumerate(layer_sizes):
            self.layers.append(SpikingLayer(size))
        
        # Inter-layer connections (feedforward)
        self.ff_weights = nn.ParameterList()
        for i in range(self.num_layers - 1):
            w = torch.randn(layer_sizes[i+1], layer_sizes[i]) * 0.01
            self.ff_weights.append(nn.Parameter(w))
        
        # Intra-layer connections (lateral/associative)
        self.lat_weights = nn.ParameterList()
        for size in layer_sizes:
            # Sparse recurrent connectivity
            w = torch.randn(size, size) * 0.001
            # Inhibitory diagonal (self-regulation)
            w.fill_diagonal_(-0.1)
            self.lat_weights.append(nn.Parameter(w))
        
        # STDP learning parameters
        self.stdp = STDPModule()
        
        # Membrane potentials
        self.register_buffer('V', None)
        self.register_buffer('spike_times', None)
        
    def forward(self, sensory_input: torch.Tensor, 
                num_steps: int = 10) -> Tuple[torch.Tensor, List[torch.Tensor]]:
        """
        Forward pass through hierarchical SNN
        
        Args:
            sensory_input: Input spike pattern
            num_steps: Simulation timesteps
            
        Returns:
            (output_activations, layer_spikes)
        """
        batch_size = sensory_input.size(0)
        
        # Initialize states
        V = [torch.zeros(batch_size, size) for size in self.layer_sizes]
        spikes_history = [[] for _ in range(self.num_layers)]
        
        for t in range(num_steps):
            # Layer 0: Sensory (external input on first step)
            if t == 0:
                V[0] = V[0] + sensory_input
            
            # Compute spikes for all layers
            layer_spikes = []
            for i in range(self.num_layers):
                # Threshold crossing
                s = (V[i] >= 1.0).float()
                layer_spikes.append(s)
                
                # Reset
                V[i] = V[i] * (1 - s)
                
                spikes_history[i].append(s)
            
            # Update membrane potentials
            for i in range(self.num_layers):
                # Decay
                dV = -V[i] / 20.0  # tau = 20ms
                
                # Lateral input
                if self.lat_weights[i] is not None:
                    lat_input = torch.matmul(layer_spikes[i], self.lat_weights[i].t())
                    dV = dV + lat_input
                
                # Feedforward input from previous layer
                if i > 0:
                    ff_input = torch.matmul(layer_spikes[i-1], self.ff_weights[i-1].t())
                    dV = dV + 0.5 * ff_input
                
                # Feedforward output to next layer
                if i < self.num_layers - 1:
                    ff_output = torch.matmul(layer_spikes[i+1], self.ff_weights[i])
                    dV = dV + 0.1 * ff_output
                
                V[i] = V[i] + dV
        
        # Aggregate spikes across time
        output = torch.stack([torch.stack(h).sum(dim=0) for h in spikes_history])
        
        return output, spikes_history

class STDPModule:
    """
    Spike-Timing-Dependent Plasticity with reward modulation
    """
    def __init__(self, A_plus=0.01, A_minus=0.01, 
                 tau_plus=20.0, tau_minus=20.0):
        self.A_plus = A_plus
        self.A_minus = A_minus
        self.tau_plus = tau_plus
        self.tau_minus = tau_minus
        
    def apply_stdp(self, weights: torch.Tensor,
                   pre_spikes: torch.Tensor,
                   post_spikes: torch.Tensor,
                   reward: float = 1.0):
        """
        Apply STDP weight updates with reward modulation
        
        Args:
            weights: Synaptic weights to update
            pre_spikes: Presynaptic spike train (time, neurons)
            post_spikes: Postsynaptic spike train (time, neurons)
            reward: Reward signal (dopaminergic modulation)
        """
        T = pre_spikes.size(0)
        delta_w = torch.zeros_like(weights)
        
        # Compute spike time differences
        for t_post in range(T):
            if post_spikes[t_post].sum() == 0:
                continue
                
            for t_pre in range(T):
                if pre_spikes[t_pre].sum() == 0:
                    continue
                
                dt = t_post - t_pre
                
                if dt > 0:
                    # Potentiation (causal)
                    factor = self.A_plus * np.exp(-dt / self.tau_plus)
                elif dt < 0:
                    # Depression (anti-causal)
                    factor = -self.A_minus * np.exp(dt / self.tau_minus)
                else:
                    continue
                
                # Outer product for weight update
                update = torch.outer(post_spikes[t_post], pre_spikes[t_pre])
                delta_w += factor * update
        
        # Apply reward modulation
        delta_w = delta_w * reward
        
        # Update weights
        with torch.no_grad():
            weights += delta_w
            # Weight bounds
            weights.clamp_(-0.5, 0.5)

Autonomous Action Triggering

class EMBERController:
    """
    Controller for autonomous cognitive behavior via EMBER
    """
    def __init__(self, ember: EMBERArchitecture):
        self.ember = ember
        self.llm = LLMInterface(ember.llm_backend)
        
        # Tracking
        self.conversation_count = 0
        self.last_action_time = 0
        self.idle_start_time = None
        
    def process_message(self, message: str, user_id: str):
        """
        Process incoming message and update SNN substrate
        """
        # Encode message to embedding
        embedding = self.llm.get_embedding(message)
        
        # Encode to SNN
        spike_pattern = self.ember.encode_embedding(embedding)
        
        # Run SNN forward pass
        activations, spike_history = self.ember.snn.forward(
            spike_pattern.unsqueeze(0),
            num_steps=10
        )
        
        # STDP learning
        self.ember.snn.stdp.apply_stdp(
            weights=self.ember.snn.lat_weights[0],
            pre_spikes=spike_history[0],
            post_spikes=spike_history[1],
            reward=1.0  # Positive interaction
        )
        
        self.conversation_count += 1
        
        # Check if this triggers a learned association
        triggered_concepts = self._get_triggered_concepts(activations)
        
        return triggered_concepts
        
    def run_idle_period(self, duration_hours: float = 8.0):
        """
        Simulate idle period with lateral propagation
        
        During idle, STDP lateral propagation can trigger
        learned associations without external input.
        """
        self.idle_start_time = time.time()
        
        # Continue SNN dynamics with minimal noise input
        for _ in range(int(duration_hours * 3600)):  # seconds
            # Small spontaneous activity
            noise = torch.randn(1, self.ember.layer_sizes['sensory']) * 0.01
            
            activations, spikes = self.ember.snn.forward(noise, num_steps=1)
            
            # Check for strong activation in concept layer
            if activations[1].max() > 5.0:  # Threshold
                # Association triggered - prepare LLM action
                triggered = self._get_triggered_concepts(activations)
                
                if triggered and self._should_act():
                    self._initiate_action(triggered)
                    
    def _get_triggered_concepts(self, activations: torch.Tensor) -> List[str]:
        """Map SNN activations to semantic concepts"""
        # Get top activated neurons in concept layer
        concept_activations = activations[1]  # Layer 1 = concept
        top_neurons = torch.topk(concept_activations, k=5).indices
        
        # Map to concept vocabulary (simplified)
        concepts = []
        for neuron_idx in top_neurons[0]:
            concept = self._neuron_to_concept(neuron_idx.item())
            if concept:
                concepts.append(concept)
        
        return concepts
        
    def _initiate_action(self, triggered_concepts: List[str]):
        """
        Use LLM to generate action based on SNN-triggered associations
        """
        # Build context from triggered associations
        context = f"The following associations have emerged from memory: {', '.join(triggered_concepts)}"
        
        # LLM decides action type and generates content
        prompt = f"""Based on learned associations from past interactions:
{context}

What action would be appropriate? Consider:
1. Following up on previous topics
2. Sharing relevant information
3. Initiating conversation

Generate an appropriate response or action."""

        response = self.llm.generate(prompt)
        
        return response
        
    def _should_act(self) -> bool:
        """Determine if triggered association should lead to action"""
        # Simple threshold-based decision
        # More sophisticated: learned action policy in SNN
        return self.conversation_count >= 7  # From paper: first action after ~7 exchanges

Training and Learning

From Clean Start to First Action

Based on paper findings:

• 0 messages: Clean slate, zero learned weights
• 7 exchanges (14 messages): First SNN-triggered LLM action
• 8-hour idle: Demonstrated autonomous contact initiation

Learning Dynamics

def train_ember_interaction(
    ember: EMBERArchitecture,
    messages: List[Tuple[str, str]],  # (user_msg, assistant_msg)
    rewards: List[float]
):
    """
    Train EMBER on conversation history
    
    Args:
        messages: List of (user, assistant) message pairs
        rewards: Reward signal for each exchange
    """
    for (user_msg, assistant_msg), reward in zip(messages, rewards):
        # Encode both messages
        user_emb = get_embedding(user_msg)
        assistant_emb = get_embedding(assistant_msg)
        
        # Create temporal sequence in SNN
        combined_spikes = []
        
        # Present user input
        user_pattern = ember.encode_embedding(user_emb)
        _, user_spikes = ember.snn.forward(user_pattern.unsqueeze(0), num_steps=5)
        combined_spikes.extend(user_spikes)
        
        # Present assistant response
        assistant_pattern = ember.encode_embedding(assistant_emb)
        _, assistant_spikes = ember.snn.forward(assistant_pattern.unsqueeze(0), num_steps=5)
        combined_spikes.extend(assistant_spikes)
        
        # Apply STDP with reward
        for i in range(len(combined_spikes) - 1):
            ember.snn.stdp.apply_stdp(
                weights=ember.snn.lat_weights[0],
                pre_spikes=combined_spikes[i],
                post_spikes=combined_spikes[i+1],
                reward=reward
            )

Key Results

Metric	Value	Notes
Embedding retention	82.2%	Across embedding dimensionalities
First autonomous action	7 exchanges	From clean start
Autonomous contact	Yes	After 8-hour idle period
Neuron count	220,000	Hierarchical organization

Advantages Over Traditional RAG

Aspect	RAG	EMBER
Memory	External database	Embedded in SNN substrate
When to retrieve	Explicit query	Emergent from dynamics
What to retrieve	Similarity-based	Association chains
LLM role	Primary + augmented	Reasoning engine only
Persistence	Database	Learned weights

Use Cases

• Personal AI Assistants: Proactive behavior based on learned patterns
• Conversational Agents: Context-aware without explicit context windows
• Cognitive Companions: Long-term relationship building
• Research Tools: Autonomous information surfacing

References

• Paper: "EMBER: Autonomous Cognitive Behaviour from Learned Spiking Neural Network Dynamics in a Hybrid LLM Architecture" (arXiv:2604.12167)
• Author: William Savage, 2026
• Categories: cs.AI, cs.NE

Related Skills

• adaptive-spiking-neuron-asn: General spiking neuron designs
• dual-timescale-memory-spiking-neuron-astrocyte: Working memory mechanisms
• neuro-inspired-memory-ai-agents: Memory systems for AI agents

Last updated: 2026-04-27