Brain-like
LLM Recursive
Database

// Neuron — the fundamental memory unit
type Neuron struct {
    ID          NeuronID
    Content     string
    
    // Spatial position in N-dimensional matrix
    Position    []float64
    
    // Activation dynamics
    Energy      float64   // Current activation (0.0 - 1.0)
    BaseEnergy  float64   // Resting energy level
    
    // Depth in memory hierarchy
    // 0 = surface/hot, higher = deeper/consolidated
    Depth       int
    
    // Temporal markers
    CreatedAt   time.Time
    LastFiredAt time.Time
    AccessCount uint64
}

// Fire activates the neuron, boosting its energy
func (n *Neuron) Fire() {
    n.Energy = min(1.0, n.Energy + 0.3)
    n.LastFiredAt = time.Now()
    n.AccessCount++
}

// Decay reduces energy based on time elapsed
func (n *Neuron) Decay(rate float64) {
    elapsed := time.Since(n.LastFiredAt).Seconds()
    n.Energy = max(n.BaseEnergy, n.Energy - rate*elapsed/3600)
}

// Search with spreading activation
func (e *Engine) Search(query string, depth int, limit int) []*Neuron {
    // Start with lexical/vector matches
    seeds := e.findSeeds(query)
    
    // Spread activation through synaptic connections
    activated := make(map[NeuronID]float64)
    for _, seed := range seeds {
        e.spreadActivation(seed.ID, seed.Energy, depth, activated)
    }
    
    // Collect and rank by accumulated activation
    results := e.collectByActivation(activated, limit)
    
    // Fire all accessed neurons (Hebbian reinforcement)
    for _, n := range results {
        n.Fire()
        e.hebbian.OnNeuronFired(n.ID)
    }
    
    return results
}

// Recursive traversal through neural graph
func (e *Engine) spreadActivation(
    id NeuronID, 
    energy float64, 
    depth int, 
    acc map[NeuronID]float64,
) {
    if depth <= 0 || energy < 0.01 { return }
    
    acc[id] += energy
    
    // Spread to connected neurons via synapses
    for _, neighborID := range e.matrix.Adjacency[id] {
        synapse := e.getSynapse(id, neighborID)
        e.spreadActivation(neighborID, energy*synapse.Weight*0.7, depth-1, acc)
    }
}