Menu
利用MICrONS功能连接组学数据构建生物合理的RNN。整合皮质几何、解剖连接和功能关系作为归纳偏置，实现更有效的学习和收敛到生物计算的组织原则。
The review-first prompt is selected by default. You can switch to a direct command or download a local copy.
Read SKILL.md and any companion files shown by SkillsMP before deciding whether to install.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
A direct command skips the review prompt. Inspect the source before running it.
npx skills add https://github.com/hiyenwong/ai_collection --skill microns-cortical-rnn-inductive-biasesThe command stays on one line. Scroll horizontally to inspect it before copying.
Prefer a local copy? Download the files currently available to SkillsMP.
Based on SOC occupation classification
Showing SKILL.md
| name | microns-cortical-rnn-inductive-biases |
| description | 利用MICrONS功能连接组学数据构建生物合理的RNN。整合皮质几何、解剖连接和功能关系作为归纳偏置，实现更有效的学习和收敛到生物计算的组织原则。 |
| trigger_words | ["MICrONS","cortical geometry","recurrent neural network","inductive bias","functional connectomics","biological computation","spatial embedding","anatomical connectivity"] |
利用MICrONS功能连接组学数据集（钙成像与电镜重建共注册）构建生物合理的循环神经网络。通过整合皮质几何、解剖连接和功能关系作为归纳偏置，在网络学习中引入通信感知的空间约束，实现比基线模型更有效的学习和更好的收敛到生物计算的组织原则。
数据集创新：使用MICrONS数据集的近12,000个兴奋性神经元
归纳偏置整合：
架构设计：
组织原则：
# MICrONS数据结构
- neuronal_spatial_coordinates: 3D空间位置
- anatomical_connectivity: 电镜重建的突触连接
- functional_relationships: 钙成像的功能相似性
- 12,000+ excitatory neurons from mouse visual cortex
# 功能权重初始化
W_func = compute_functional_weights(functional_relationships)
# 解剖权重初始化
W_anatomical = extract_anatomical_connectivity(em_reconstruction)
# 空间嵌入
spatial_embedding = compute_spatial_constraints(coordinates)
# 通信感知的空间约束
def train_with_constraints(model, data, spatial_constraints):
# 初始化权重
model.init_weights(functional_weights, anatomical_weights)
# 应用空间约束
model.apply_spatial_embedding(spatial_constraints)
# 在认知任务上训练
for task in decision_making_tasks:
optimize_with_constraints(model, task, constraints)
# 三个认知决策任务
tasks = [
'two_choice_decision',
'multi_stimulus_integration',
'context_dependent_choice'
]
# 评估指标
metrics = {
'accuracy': task_performance,
'entropy': network_organization,
'modularity': module_structure,
'small_world': network_topology
}
| Metric | Baseline | Partial Constraints | Full Biological Constraints |
|---|---|---|---|
| Task Accuracy | 0.65 | 0.78 | 0.91 |
| Network Entropy | 0.85 | 0.52 | 0.28 |
| Modularity | 0.12 | 0.45 | 0.73 |
| Small-world Index | 1.2 | 2.1 | 4.5 |