dual-axis-zebrafish-circuits

name	dual-axis-zebrafish-circuits
description	斑马鱼被盖微电路双轴归因方法论。基于斑马鱼视网膜被盖微电路的双轴功能归因（能量高效信息处理和鲁棒稳定）方法，将生物学电路组织转化为类脑神经网络架构设计。适用于脉冲神经网络、类脑架构设计、电路归因。触发词：dual-axis, 双轴归因, zebrafish tectal, microcircuit attribution, energy efficiency, robustness, ns_TIN, superficial_TIN

Dual-Axis Zebrafish Tectal Microcircuit Attribution Methodology

斑马鱼被盖微电路的双轴功能归因方法，将生物学电路结构转化为类脑神经网络设计模式。

Source Paper

Title: Dual-axis attribution of zebrafish tectal microcircuits for energy-efficient and robust neurocomputing arXiv: 2605.13924 Date: 2026-05-15 Authors: Ningping Li, Hao Zhang, Yi Zhou Category: cs.NE (Neural and Evolutionary Computing) URL: https://arxiv.org/abs/2605.13924

Core Concept

生物神经网络包含支持不同计算功能的特化子结构，但许多类脑神经网络借用生物基序而不识别其电路级起源。本方法提出沿两个计算轴对斑马鱼被盖微电路进行功能归因：

1. Energy-Efficient Information Processing（能量高效信息处理）
2. Robustness-Preserving Stabilization（鲁棒保存稳定）

Key Innovations

1. Directed Retinotectal Microcircuit Graph Reconstruction

• 从斑马鱼视网膜被盖系统重建有向微电路图
• 通过动态仿真验证视网膜被盖信号传播
• 建立从生物结构到计算图的映射

2. Dual-Axis Attribution Framework

通过 LIF (Leaky Integrate-and-Fire) 脉冲神经网络作为非线性扰动测试台：

Energy Sensitivity Index (ESI)

$$ESI_i = \frac{|E_{baseline} - E_{ablated}|}{E_{baseline}}$$

• 测量子电路 $i$ 被消融后的能量敏感性
• 低 ESI 但显著影响预测误差 → 能量高效信息门

Robustness Sensitivity Index (RSI)

$$RSI_i = \frac{|R_{baseline} - R_{ablated}|}{R_{baseline}}$$

• 测量子电路 $i$ 被消融后的鲁棒性敏感性
• 高 RSI → 反馈式系统稳定性维护

3. Functional Dissociation Findings

Subcircuit	Spike Footprint	Sensitivity	Role
ns_TIN	Low	Prediction error (ESI)	Spike-efficient internal information gate
superficial_TIN	Higher	System robustness (RSI)	Feedback-like stability maintenance

4. Transfer to Artificial Neural Networks

将归因功能转移到 ResNet18 架构中：

• ns_TIN-inspired module: 在推理预算缩减下提升性能保持
• superficial_TIN-inspired module: 在输入噪声下提升鲁棒性

Methodology Steps

Step 1: Microcircuit Reconstruction

import networkx as nx

def build_retinotectal_graph():
    """Construct directed microcircuit graph from zebrafish tectal connectivity."""
    G = nx.DiGraph()
    
    # Add nodes (cell types)
    G.add_nodes_from([
        'RGC',           # Retinal Ganglion Cells
        'ns_TIN',        # Non-superficial Tectal Interneurons  
        'superficial_TIN', # Superficial Tectal Interneurons
        'TPC',           # Tectal Projection Cells
        'Pyr',           # Pyramidal neurons
    ])
    
    # Add edges (known connectivity)
    G.add_edges_from([
        ('RGC', 'ns_TIN'),
        ('RGC', 'superficial_TIN'),
        ('ns_TIN', 'TPC'),
        ('superficial_TIN', 'TPC'),
        ('superficial_TIN', 'ns_TIN'),  # lateral connection
    ])
    
    return G

Step 2: LIF-SNN Ablation Testing

def ablation_test(G, target_subcircuit, model):
    """Selectively ablate a subcircuit and measure sensitivity."""
    
    # Baseline performance
    baseline_pred = model.predict(G)
    baseline_energy = compute_spike_footprint(model)
    baseline_robust = evaluate_robustness(model, noise_level=0.1)
    
    # Ablated performance
    G_ablated = G.copy()
    G_ablated.remove_nodes_from(target_subcircuit)
    ablated_pred = model.predict(G_ablated)
    ablated_energy = compute_spike_footprint(model)
    ablated_robust = evaluate_robustness(model, noise_level=0.1)
    
    # Compute indices
    ESI = abs(baseline_energy - ablated_energy) / baseline_energy
    RSI = abs(baseline_robust - ablated_robust) / baseline_robust
    
    return {'ESI': ESI, 'RSI': RSI}

Step 3: Functional Transfer

class DualAxisModule(nn.Module):
    """Dual-axis inspired neural architecture module."""
    
    def __init__(self, in_channels, out_channels, mode='energy'):
        super().__init__()
        self.mode = mode
        
        if mode == 'energy':  # ns_TIN-inspired
            # Lightweight gating for efficient computation
            self.gate = nn.Sequential(
                nn.Linear(in_channels, in_channels // 4),
                nn.ReLU(),
                nn.Linear(in_channels // 4, in_channels),
                nn.Sigmoid()
            )
        elif mode == 'robustness':  # superficial_TIN-inspired
            # Feedback stabilization module
            self.stabilizer = nn.Sequential(
                nn.LayerNorm(in_channels),
                nn.Linear(in_channels, in_channels),
                nn.GELU(),
                nn.Dropout(0.1),
            )
    
    def forward(self, x):
        if self.mode == 'energy':
            # Sparse gating for energy efficiency
            gate = self.gate(x.mean(dim=-1))
            return x * gate.unsqueeze(-1)
        else:
            # Stabilization feedback
            return self.stabilizer(x) + x

Design Patterns

Pattern 1: Energy-Efficient Information Gate (ns_TIN)

Input → [Lightweight Gate] → Sparse Activation → Output
              ↓
         Computation Budget
         Reduction Module

Use when: Need to reduce computation while preserving key information flow

Pattern 2: Robustness Stabilizer (superficial_TIN)

Input → [Normalization] → [Feedback Path] → [+ Residual] → Output
              ↓                ↓
         Stabilizer       Robustness
         Module           Enhancement

Use when: Need to improve robustness against noise/perturbations

Application Guidelines

1. Architecture Design: Use dual-axis modules in hybrid networks

   • Front layers: ns_TIN for efficient early processing
   • Deep layers: superficial_TIN for robust feature stabilization

2. Resource-Constrained Deployment: ns_TIN-inspired gating for edge deployment

3. Safety-Critical Systems: superficial_TIN-inspired stabilization for robust operation

Verification

• Test ESI/RSI tradeoff on target architecture
• Validate on CIFAR-10 with budget reduction and noise corruption
• Compare spike footprint vs. accuracy preservation
• Measure robustness improvement under adversarial conditions

Related Skills

• spiking-neural-network-analysis
• brain-inspired-snn-pattern-analysis
• brain-inspired-intelligence-paradigm
• energy-regularized-neural-mpc

Activation Keywords

• dual-axis attribution
• 双轴归因
• zebrafish tectal microcircuit
• microcircuit attribution
• energy efficient neurocomputing
• robustness-preserving stabilization
• ns_TIN
• superficial_TIN
• retinotectal circuit
• biological circuit transfer
• LIF-SNN ablation
• circuit-level bio-inspiration