binary-spiking-causal-models

name	binary-spiking-causal-models
description	Causal analysis of Binary Spiking Neural Networks (BSNNs) using logic-based explainable AI methods. Formally defines BSNNs as binary causal models and provides tractable algorithms for computing abductive explanations.

Binary Spiking Neural Networks as Causal Models

Research methodology from paper "Binary Spiking Neural Networks as Causal Models" (2026-04-29).

Core Idea

Formally represents Binary Spiking Neural Networks (BSNNs) as binary causal models, enabling logic-based explanations of network behavior using methods from explainable AI (XAI).

Key Contributions

1. Formal BSNN Definition

• Binary spiking activity mapped to causal model variables
• Each neuron's spike/non-spike state as a binary variable
• Causal dependencies defined by network connectivity

2. Causal Representation

• Spiking activity represented as structural causal model
• Input-output relationships captured through causal pathways
• Enables formal reasoning about network decisions

3. Abductive Explanations

• Abductive explanation: minimal set of input features that suffice to explain the output
• Problem proven computationally tractable under certain conditions
• Algorithms provided for computing explanations efficiently

4. Logic-Based XAI Integration

• Leverages existing XAI literature for logic-based explanations
• Bridges spiking neural networks and interpretable AI
• Formal guarantees on explanation correctness

When to Use

• Need interpretable SNN decisions (medical, safety-critical applications)
• Analyzing BSNN behavior and decision pathways
• Comparing SNN explanations with ANN explanation methods
• Formal verification of spiking network behavior

Implementation Pattern

# Conceptual causal explanation for BSNN
class BSNNCausalExplainer:
    def __init__(self, bsnn_model):
        self.model = bsnn_model
        self.causal_graph = self.build_causal_graph()
    
    def build_causal_graph(self):
        """Map BSNN connectivity to causal graph."""
        # Each neuron = binary variable
        # Synaptic weights = causal strengths
        pass
    
    def abductive_explanation(self, input_spikes, output):
        """Find minimal input subset explaining output."""
        # Tractable under certain network conditions
        pass
    
    def necessary_causes(self, output):
        """Find inputs that are necessary for the output."""
        pass

Theoretical Results

• Finding abductive explanations is tractable for BSNNs under specific structural conditions
• Complexity depends on network depth and connectivity patterns
• Provides formal guarantees missing from post-hoc explanation methods

Related Skills

• spiking-neural-network-analysis
• quantization-spiking-neural-networks-beyond-accuracy
• snn-universal-approximation-theory

Paper Reference

• arXiv: 2604.27007
• Authors: Aditya Kar, Emiliano Lorini, Timothee Masquelier
• Date: 2026-04-29
• Categories: cs.AI