---
id: wiki-2026-0508-neuroplasticity
title: Neuroplasticity
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Neuroplasticity, Brain Plasticity, Synaptic Plasticity]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [neuroscience, plasticity, hebbian, ltp, critical-period, learning]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack: { language: python, framework: brian2-pytorch }
---

# Neuroplasticity

## 매 한 줄
- 신경가소성은 시냅스·회로 수준에서 경험에 따라 신경계 구조·기능이 변화하는 능력이며, LTP/LTD가 분자 기반이다.

## 매 핵심
- **Hebbian rule**: "fire together, wire together" — pre/post synaptic 동시 발화 시 시냅스 강화.
- **LTP/LTD**: NMDA receptor 매개 Ca²⁺ → CaMKII (LTP, 강화) vs calcineurin (LTD, 약화). hippocampus CA1, cortex L2/3에서 잘 연구됨.
- **Critical period**: 시각피질 monocular deprivation 효과는 어린 시기 강함. parvalbumin GABA 성숙이 닫힘 trigger. 성인기 reopen에 chondroitinase, fluoxetine, dark exposure.
- **Adult plasticity**: motor learning, taxi driver hippocampus 부피 증가, training-induced cortical map remodeling.
- **AI 연결**: STDP(spike-timing dependent plasticity)는 SNN 학습 규칙, BCNN의 Hebbian feature learning에 영감.

## 💻 패턴
```python
# STDP weight update rule
import numpy as np

def stdp(dt, w, A_plus=0.01, A_minus=0.012, tau_plus=20e-3, tau_minus=20e-3, w_max=1.0):
    if dt > 0:  # post after pre → LTP
        dw = A_plus * np.exp(-dt / tau_plus)
    else:
        dw = -A_minus * np.exp(dt / tau_minus)
    return np.clip(w + dw, 0, w_max)
```

```python
# Hebbian learning in linear neuron (Oja's rule, normalized)
import numpy as np
def oja_update(w, x, y, lr=0.01):
    return w + lr * (y * x - y ** 2 * w)
```

```python
# LTP induction: theta-burst stimulation pattern generator
def theta_burst(duration_s=2.0, burst_hz=5, pulses_per_burst=4, intra_hz=100):
    times = []
    t = 0.0
    while t < duration_s:
        for k in range(pulses_per_burst):
            times.append(t + k / intra_hz)
        t += 1 / burst_hz
    return times
```

```python
# Synaptic scaling (homeostatic plasticity)
import numpy as np
def synaptic_scaling(W, target_rate, actual_rate, tau=1000.0):
    factor = target_rate / (actual_rate + 1e-6)
    return W * (1 + (factor - 1) / tau)
```

```python
# Brian2: STDP synapse simulation
from brian2 import *
G = NeuronGroup(2, "dv/dt = -v/(10*ms) : 1", threshold="v>1", reset="v=0")
S = Synapses(G, G,
             """w : 1
                dApre/dt = -Apre/(20*ms) : 1 (event-driven)
                dApost/dt = -Apost/(20*ms) : 1 (event-driven)""",
             on_pre="""v_post += w
                       Apre += 0.01
                       w = clip(w + Apost, 0, 1)""",
             on_post="""Apost -= 0.012
                        w = clip(w + Apre, 0, 1)""")
S.connect(i=0, j=1)
```

```python
# Cortical map plasticity index from receptive field overlap
import numpy as np
def map_plasticity(rf_pre, rf_post):
    overlap = np.minimum(rf_pre, rf_post).sum() / np.maximum(rf_pre, rf_post).sum()
    return 1 - overlap  # higher = more remodeling
```

```python
# BDNF-dependent plasticity: serum BDNF as biomarker proxy
def plasticity_score(bdnf_ng_ml, exercise_min_week, sleep_hr):
    # toy index, not clinical
    return 0.4 * bdnf_ng_ml / 30 + 0.3 * min(exercise_min_week, 300) / 300 + 0.3 * min(sleep_hr, 8) / 8
```

## 매 결정 기준
- **개입 시기**: critical period 진행 중이면 sensory restoration(eg. amblyopia patching) 효과 큼.
- **약리 보조**: 성인 plasticity 재개에 SSRI(fluoxetine), tDCS, aerobic exercise(BDNF↑).
- **학습 설계**: spaced repetition(LTP consolidation), sleep 보장(systems consolidation).
- **연구 모델**: in vitro slice → LTP/LTD 측정. in vivo two-photon → spine turnover.

## 🔗 Graph
- 관련: [[Neurorehabilitation-Post-Stroke]], [[Neurodevelopmental-Disorders]], [[Neuroprosthetics-Development]], [[Spiking-Neural-Networks]], [[Hebbian-Learning]]
- 도구: [[Brian2]], [[NEURON]], [[Allen-SDK]]

## 🤖 LLM 활용
- 학습 곡선 데이터에서 plasticity phase 추정(initial vs consolidation).
- 논문 요약: LTP 분자 경로 다이어그램 생성.
- 실험 설계 review(빠진 control 식별).

## ❌ 안티패턴
- "성인 뇌는 변하지 않는다" 신화 인용.
- STDP를 단순 Hebbian과 동일시(타이밍 차이 핵심).
- BDNF 보조제 임의 권고(증거 부족).

## 🧪 검증
- LTP: fEPSP slope baseline 대비 +30% 30분 이상.
- 행동: motor task 학습률, cortical map fMRI pre/post.

## 🕓 Changelog
- 2026-05-08 Phase 1: 초안 자동 생성.
- 2026-05-10 Manual cleanup: 본문 보강, STDP/Oja/Brian2 코드 추가, critical period reopen 약리 반영.
