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

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack

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

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")

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)

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)

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)

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)

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