2nd/10_Wiki/Topics/AI_and_ML/Bayesian-Brain-Hypothesis.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-bayesian-brain-hypothesis	Bayesian Brain Hypothesis	10_Wiki/Topics	verified	self	Bayesian brain predictive coding free energy principle active inference Friston	none	B	0.85	conceptual	neuroscience predictive-coding bayesian free-energy friston active-inference perception generative-model		2026-05-10	pending	language applicable_to neuroscience / cognitive science Active Inference Agents Perception Models World Models

Bayesian Brain Hypothesis

📌 한 줄 통찰

"매 brain = 매 inference engine". 매 incomplete sensor + 매 prior → 매 best guess (posterior). 매 Friston 의 Free Energy Principle 의 unify perception / action / learning. 매 modern world model + active inference 의 theoretical base.

📖 핵심

매 core claim

• 매 brain = 매 generative model.
• 매 perception = 매 Bayesian inference.
• 매 prior + likelihood → posterior.
• 매 surprise (prediction error) 의 minimize.

Bayes' theorem (perception version)

P(\text{cause} | \text{sensation}) = \frac{P(\text{sensation} | \text{cause}) \cdot P(\text{cause})}{P(\text{sensation})}

매 evidence

1. Optical illusion: 매 prior 의 dominate.
2. Multisensory integration: 매 weighted by reliability.
3. Cocktail party: 매 prior context 의 segregate.
4. Phantom limb: 매 prior 의 mismatch.
5. Schizophrenia: 매 prior weighting 의 broken.

매 핵심 개념

Predictive Coding

• 매 cortex 의 hierarchical prediction.
• 매 top-down prediction + bottom-up error.
• 매 error 만 의 propagate.
• 매 efficient (most signal 의 cancelled).

Free Energy Principle (Friston)

• 매 organism 의 environment 의 surprise 의 minimize.
• 매 free energy = upper bound on surprise.
• 매 perception (model 의 update) + action (world 의 change) 의 둘 다.

Active Inference

• 매 action = 매 prediction error 의 reduce 의 way.
• 매 motor 의 proprioception 의 prediction.
• 매 RL 의 reward 의 alternative.

Markov Blanket

• 매 system 의 외부 / 내부 의 boundary.
• 매 Friston 의 ontological foundation.

매 layer (cortical)

• 매 deep layer (5/6): 매 prediction (top-down).
• 매 superficial (2/3): 매 error (bottom-up).
• 매 NMDA / AMPA receptor 의 different role.

매 modern AI 의 응용

1. World models (Ha & Schmidhuber): 매 generative model 학습.
2. Active inference agent: 매 RL 의 alternative.
3. PILCO / Dreamer: 매 model-based RL.
4. Variational autoencoder (VAE): 매 generative + recognition.
5. Predictive coding networks (PredNet, Lotter): 매 NN 구현.
6. Self-supervised learning: 매 prediction-based.

매 disorder 의 explanation

• Autism: 매 high-precision prior (less plasticity).
• Schizophrenia: 매 low-precision prior + high error.
• Anxiety: 매 over-prediction of negative.
• Depression: 매 prior 의 negative bias.

매 critique

• Falsifiability: 매 거의 모든 것의 explain.
• Computational tractability: 매 brain 의 actual implementation.
• Strong vs weak: 매 metaphor vs 매 literal.

💻 패턴 (응용 — active inference / predictive coding)

Predictive coding network

import torch
import torch.nn as nn

class PredictiveCodingLayer(nn.Module):
    def __init__(self, dim):
        super().__init__()
        self.predictor = nn.Linear(dim, dim)  # 매 top-down
    
    def forward(self, top_down, bottom_up):
        prediction = self.predictor(top_down)
        error = bottom_up - prediction
        # 매 error 만 의 propagate up
        return error, prediction

class PredNet(nn.Module):
    def __init__(self, dims):
        super().__init__()
        self.layers = nn.ModuleList([PredictiveCodingLayer(d) for d in dims])
    
    def forward(self, x):
        # 매 hierarchical prediction + error propagation
        ...

Active inference (mountain car)

def active_inference_agent(observations, prior_belief):
    # 매 1. perception: state 의 infer
    posterior = bayes_update(prior_belief, observations)
    
    # 매 2. action selection: 매 expected free energy 의 minimize
    actions = enumerate_actions()
    efe = []
    for a in actions:
        # 매 epistemic value (information gain)
        info_gain = expected_kl(posterior_after(a), posterior)
        # 매 pragmatic value (preferred outcome)
        pragmatic = expected_log_prior(a)
        efe.append(-info_gain - pragmatic)
    
    return actions[np.argmin(efe)]

→ 매 reward X — 매 prediction error / preference.

Variational free energy

import torch.distributions as dist

def free_energy(q_phi, p_theta, observations):
    # F = E_q[log q] - E_q[log p(o, s)]
    s = q_phi.rsample()
    log_q = q_phi.log_prob(s)
    log_p_obs = p_theta.likelihood(observations, s)
    log_p_prior = p_theta.prior(s)
    return log_q - log_p_obs - log_p_prior

World model (Dreamer-like)

class WorldModel(nn.Module):
    def __init__(self):
        self.encoder = Encoder()        # 매 obs → state
        self.dynamics = RSSM()          # 매 state + action → next state
        self.decoder = Decoder()        # 매 state → obs (reconstruction)
        self.reward_pred = RewardHead()
    
    def imagine(self, state, policy, horizon):
        states, rewards = [], []
        for _ in range(horizon):
            action = policy(state)
            state = self.dynamics(state, action)
            states.append(state)
            rewards.append(self.reward_pred(state))
        return states, rewards

🤔 결정 기준

응용	Approach
Perception model	Predictive coding
RL agent (model-based)	Dreamer / world model
Sparse reward	Active inference
Generative + recognition	VAE
Hierarchical sensory	PredNet
Mental disorder modeling	Bayesian brain framework

기본값: 매 perception = predictive coding. 매 action = active inference (sparse reward) or RL (dense).

🔗 Graph

• 부모: Computational-Neuroscience-RL · Bayesian Inference
• 변형: Predictive-Coding · Free-Energy-Principle · Active-Inference
• 응용: World-Model · VAE
• 사상가: Karl-Friston
• Adjacent: Reinforcement-Learning · Generative-Model

🤖 LLM 활용

언제: 매 active inference agent design. 매 world model. 매 perception system. 매 sparse-reward RL. 언제 X: 매 specific neuroscience claim 의 substitute. 매 medical diagnosis.

❌ 안티패턴

• "매 brain literal": 매 metaphor 의 over-claim.
• No precision weighting: 매 prior / likelihood 의 same weight.
• Strong free energy 의 unfalsifiable: 매 모든 것 explain.
• Active inference 의 reward 의 conflate: 매 different objective.
• Hierarchical 의 ignore: 매 single-layer 의 limit.

🧪 검증 / 중복

• Verified (Friston, Rao-Ballard, Knill-Pouget).
• 신뢰도 B (active research).
• Related: Predictive-Coding · Free-Energy-Principle · World-Model · Active-Inference.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — Friston FEP + predictive coding + active inference + world model code