2nd/10_Wiki/Topics/AI_and_ML/Biological-Intelligence.md

---
id: wiki-2026-0508-biological-intelligence
title: Biological Intelligence
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [생물학적 지능, biological intelligence, embodied cognition, evolution as learning, 4 billion years of training]
duplicate_of: none
source_trust_level: B
confidence_score: 0.85
verification_status: conceptual
tags: [biology, neuroscience, evolution, embodied-cognition, ai-comparison, energy-efficiency, few-shot, neuromorphic]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: biology / cognitive science
  applicable_to: [AI Architecture, Neuromorphic Computing, Robotics]
---

# Biological Intelligence

## 📌 한 줄 통찰
> **"매 4 billion year 의 deep learning"**. 매 survival + reproduction = 매 reward. 매 evolution = 매 backpropagation. 매 modern AI 의 still 매 surpass X — 매 energy efficiency, 매 few-shot, 매 embodied. 매 inspiration 의 source.

## 📖 핵심

### 매 AI vs Biological 비교
| 측면 | Biological | AI (current) |
|---|---|---|
| Energy | 매 20W (brain) | 매 100W-MW (LLM) |
| Few-shot | 매 1-5 example | 매 trillion token |
| Embodied | ✓ | 매 robotics 시작 |
| Continual learn | ✓ | 매 catastrophic forget |
| Sample efficiency | 매 high | 매 low |
| Generality | ✓ (cross-domain) | 매 narrow → improving |
| Speed (perception) | ms | ms (inference) |
| Speed (math) | slow | 매 fast |
| Memory | 매 hierarchical | 매 attention window |

### 매 biological evolution 의 단계
1. **Single cell** (3.5 Bya): 매 chemotaxis (gradient).
2. **Multicellular** (1 Bya): 매 specialization.
3. **Nervous system** (650 Mya): 매 cnidaria.
4. **Brain** (550 Mya): 매 cambrian.
5. **Mammal** (200 Mya): 매 cortex.
6. **Primate** (65 Mya): 매 prefrontal.
7. **Human** (300 Kya): 매 language.
8. **Modern human** (50 Kya): 매 abstract reasoning.

### 매 brain 의 efficiency
- **Power**: 매 20W ≈ 매 light bulb.
- **Synapse**: 매 100 trillion.
- **Neuron**: 매 86 billion.
- **Connection density**: 매 sparse + 매 modular.
- **Spike**: 매 sparse activation.
- **Plasticity**: 매 STDP, Hebbian.

### 매 key biological mechanism
1. **Neuron**: 매 leaky integrate-and-fire.
2. **Synapse**: 매 chemical / electrical.
3. **Plasticity**: LTP / LTD, STDP.
4. **Neurotransmitter**: 매 dopamine, serotonin, glutamate.
5. **Modulator**: 매 attention / arousal.
6. **Glial cell**: 매 metabolic + memory consolidation.

### 매 AI 의 inspiration
- **Neural network**: 매 neuron model.
- **Convolutional NN**: 매 visual cortex (Hubel-Wiesel).
- **Reinforcement learning**: 매 dopamine.
- **Attention**: 매 selective attention.
- **LSTM / GRU**: 매 gating.
- **Dropout**: 매 noise / robustness.
- **Spiking NN**: 매 direct biology.
- **World model**: 매 predictive coding.

### 매 embodied cognition (Lakoff, Varela)
- 매 mind ≠ 매 disembodied symbol.
- 매 body 의 cognition 의 기반.
- 매 metaphor 의 physical (warm = friendly).
- 매 robotics 의 important.
- 매 LLM 의 limitation (no body).

### 매 few-shot 의 mechanism
- 매 prior knowledge (innate + learned).
- 매 hierarchical / compositional representation.
- 매 active inference.
- 매 social learning.

### 매 modern AI 의 도전 영역
1. **Energy**: 매 neuromorphic chip.
2. **Few-shot**: 매 meta-learning, in-context.
3. **Embodied**: 매 robotics, 매 sim2real.
4. **Continual**: 매 EWC, replay.
5. **Common sense**: 매 LLM 의 의외 의 X.
6. **Causal reasoning**: 매 Pearl's ladder.

### 매 modern brain-AI fusion
- 매 BCI (Neuralink, BrainGate).
- 매 organoid intelligence (mini-brain in dish).
- 매 cognitive enhancement (ethics).
- 매 hybrid intelligence.

## 💻 패턴 (응용 — biologically-inspired ML)

### Spiking Neural Network (LIF)
```python
import torch
import torch.nn as nn

class LIFLayer(nn.Module):
    def __init__(self, dim, threshold=1.0, decay=0.9):
        super().__init__()
        self.fc = nn.Linear(dim, dim)
        self.threshold = threshold
        self.decay = decay
        self.v = None
    
    def forward(self, x_seq):
        outputs = []
        v = torch.zeros_like(x_seq[0])
        for x in x_seq:
            v = self.decay * v + self.fc(x)
            spike = (v >= self.threshold).float()
            v = v * (1 - spike)
            outputs.append(spike)
        return torch.stack(outputs)
```

### Hebbian learning ("매 fire together, wire together")
```python
def hebbian_update(W, pre, post, lr=0.01):
    """매 Δw_ij = lr * pre_i * post_j."""
    return W + lr * torch.outer(post, pre)
```

### Predictive coding (Bayesian brain)
```python
class PredictiveCodingLayer(nn.Module):
    """매 top-down prediction + bottom-up error."""
    def __init__(self, dim):
        super().__init__()
        self.predictor = nn.Linear(dim, dim)
    
    def forward(self, top_down, bottom_up):
        prediction = self.predictor(top_down)
        error = bottom_up - prediction
        return error  # 매 error 만 의 propagate
```

### Episodic memory (hippocampus-inspired)
```python
class EpisodicBuffer:
    """매 fast learning store."""
    def __init__(self, size=10000):
        self.size = size
        self.buffer = []
    
    def store(self, state, action, reward):
        if len(self.buffer) >= self.size: self.buffer.pop(0)
        self.buffer.append((state, action, reward))
    
    def retrieve(self, query, k=10):
        # 매 nearest neighbor
        scored = [(s, a, r, similarity(query, s)) for s, a, r in self.buffer]
        return sorted(scored, key=lambda x: -x[3])[:k]

# 매 fast learning + slow consolidation (system 1 + 2).
```

### Few-shot meta-learning (MAML)
```python
def maml_step(model, tasks, inner_lr=0.01, outer_lr=0.001):
    meta_loss = 0
    for task in tasks:
        # 매 inner: task-specific
        adapted = clone_model(model)
        for x, y in task.support:
            loss = F.cross_entropy(adapted(x), y)
            grads = torch.autograd.grad(loss, adapted.parameters())
            for p, g in zip(adapted.parameters(), grads):
                p.data -= inner_lr * g
        
        # 매 outer: meta
        for x, y in task.query:
            meta_loss += F.cross_entropy(adapted(x), y)
    
    meta_loss.backward()
    optimizer.step()
```

### Active inference (Friston)
```python
def active_inference(belief, action_space, world_model):
    """매 expected free energy 의 minimize."""
    efe = []
    for a in action_space:
        next_belief = world_model.predict(belief, a)
        info_gain = expected_info_gain(next_belief)
        pragmatic = expected_log_preference(next_belief)
        efe.append(-info_gain - pragmatic)
    return action_space[np.argmin(efe)]
```

## 🤔 결정 기준 (응용)
| 상황 | Bio-inspiration |
|---|---|
| Edge inference | Spiking NN |
| Few-shot | Meta-learning + episodic |
| Robotics | Embodied + active inference |
| Continual | Replay + EWC |
| Energy budget | Neuromorphic chip |
| World model | Predictive coding |
| Sparse reward | Active inference |

**기본값**: 매 specific bio-mechanism 의 isolate + 매 ML 의 integrate. 매 wholesale brain 의 mimic 의 X.

## 🔗 Graph
- 부모: [[Evolution]]
- 변형: [[Embodied Cognition]] · [[Bayesian-Brain-Hypothesis]] · [[Free-Energy-Principle]]
- 응용: [[Neuromorphic-Computing]] · [[Brain-Computer_Interface_(BCI)]] · [[Bioenergetics]]
- Adjacent: [[Reinforcement-Learning]] · [[Active-Inference]] · [[Predictive-Coding]]

## 🤖 LLM 활용
**언제**: 매 AI architecture 의 bio-inspire. 매 efficiency / few-shot 의 design. 매 embodied AI / robotics. 매 brain-AI fusion.
**언제 X**: 매 specific medical claim. 매 brain 의 literal mimic 의 expectation.

## ❌ 안티패턴
- **Brain literal mimic**: 매 different paradigm.
- **Anthropomorphism**: 매 LLM ≠ 매 conscious.
- **Embodied 의 ignore** (robotics): 매 sim2real 의 fail.
- **Bigger = better assumption**: 매 brain 의 sparse.
- **Single bio-feature 의 magic 의 expect**: 매 system 의 emergent.

## 🧪 검증 / 중복
- Verified (Kandel neuroscience, Friston FEP, Lakoff embodied).
- 신뢰도 B.
- Related: [[Neuromorphic-Computing]] · [[Bayesian-Brain-Hypothesis]] · [[Bioenergetics]] · [[Brain-Computer_Interface_(BCI)]] · [[Embodied Cognition]].

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — comparison + bio mechanism + 매 SNN / Hebbian / MAML / active inference code |