Binary Spiking Neural Networks as Causal Models

• URL: http://arxiv.org/abs/2604.27007v1
• Date: Wed, 29 Apr 2026 09:09:48 GMT
• Title: Binary Spiking Neural Networks as Causal Models
• Abstract summary: We provide a causal analysis of Binary Spiking Neural Networks (BSNNs) to explain their behavior.<n>We are able to explain the output of the network by leveraging logic-based methods.
• Score: 13.300821692861271
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We provide a causal analysis of Binary Spiking Neural Networks (BSNNs) to explain their behavior. We formally define a BSNN and represent its spiking activity as a binary causal model. Thanks to this causal representation, we are able to explain the output of the network by leveraging logic-based methods. In particular, we show that we can successfully use a SAT as well as a SMT solver to compute abductive explanations from this binary causal model. To illustrate our approach, we trained the BSNN on the standard MNIST dataset and applied our SAT-based and SMT-based methods to finding abductive explanations of the network's classifications based on pixel-level features. We also compared the found explanations against SHAP, a popular method used in the area of explainable AI. We show that, unlike SHAP, our approach guarantees that a found explanation does not contain completely irrelevant features.

Related papers

• xDNN(ASP): Explanation Generation System for Deep Neural Networks powered by Answer Set Programming [1.1936305787902912]
  xDNN(ASP) is an explanation generation system for deep neural networks that provides global explanations.<n>We show that xDNN(ASP) maintains a high-level of accuracy in the prediction task.
  arXiv  Detail & Related papers  (2026-01-07T12:08:00Z)
• OMENN: One Matrix to Explain Neural Networks [2.397390211883228]
  One Matrix to Explain Neural Networks (OMENN) is a novel post-hoc method that represents a neural network as a single, interpretable matrix for each specific input.<n>We present a theoretical analysis of OMENN based on dynamic linearity property and validate its effectiveness with extensive tests on two XAI benchmarks.
  arXiv  Detail & Related papers  (2024-12-03T11:49:01Z)
• Explainable Graph Neural Networks Under Fire [69.15708723429307]
  Graph neural networks (GNNs) usually lack interpretability due to their complex computational behavior and the abstract nature of graphs. Most GNN explanation methods work in a post-hoc manner and provide explanations in the form of a small subset of important edges and/or nodes. In this paper we demonstrate that these explanations can unfortunately not be trusted, as common GNN explanation methods turn out to be highly susceptible to adversarial perturbations.
  arXiv  Detail & Related papers  (2024-06-10T16:09:16Z)
• Feature Attribution Explanations for Spiking Neural Networks [3.5824464725360077]
  Third-generation artificial neural networks, Spiking Neural Networks (SNNs), can be efficiently implemented on hardware. Their implementation on neuromorphic chips opens a broad range of applications, such as machine learning-based autonomous control and intelligent biomedical devices. We present textitTemporal Spike Attribution (TSA), a local explanation method for SNNs.
  arXiv  Detail & Related papers  (2023-11-02T21:30:56Z)
• Explaining Explainability: Towards Deeper Actionable Insights into Deep Learning through Second-order Explainability [70.60433013657693]
  Second-order explainable AI (SOXAI) was recently proposed to extend explainable AI (XAI) from the instance level to the dataset level. We demonstrate for the first time, via example classification and segmentation cases, that eliminating irrelevant concepts from the training set based on actionable insights from SOXAI can enhance a model's performance.
  arXiv  Detail & Related papers  (2023-06-14T23:24:01Z)
• Task-Agnostic Graph Explanations [50.17442349253348]
  Graph Neural Networks (GNNs) have emerged as powerful tools to encode graph structured data. Existing learning-based GNN explanation approaches are task-specific in training. We propose a Task-Agnostic GNN Explainer (TAGE) trained under self-supervision with no knowledge of downstream tasks.
  arXiv  Detail & Related papers  (2022-02-16T21:11:47Z)
• A Meta-Learning Approach for Training Explainable Graph Neural Networks [10.11960004698409]
  We propose a meta-learning framework for improving the level of explainability of a GNN directly at training time. Our framework jointly trains a model to solve the original task, e.g., node classification, and to provide easily processable outputs for downstream algorithms. Our model-agnostic approach can improve the explanations produced for different GNN architectures and use any instance-based explainer to drive this process.
  arXiv  Detail & Related papers  (2021-09-20T11:09:10Z)
• Parameterized Explainer for Graph Neural Network [49.79917262156429]
  We propose PGExplainer, a parameterized explainer for Graph Neural Networks (GNNs) Compared to the existing work, PGExplainer has better generalization ability and can be utilized in an inductive setting easily. Experiments on both synthetic and real-life datasets show highly competitive performance with up to 24.7% relative improvement in AUC on explaining graph classification.
  arXiv  Detail & Related papers  (2020-11-09T17:15:03Z)
• Interpreting Graph Neural Networks for NLP With Differentiable Edge Masking [63.49779304362376]
  Graph neural networks (GNNs) have become a popular approach to integrating structural inductive biases into NLP models. We introduce a post-hoc method for interpreting the predictions of GNNs which identifies unnecessary edges. We show that we can drop a large proportion of edges without deteriorating the performance of the model.
  arXiv  Detail & Related papers  (2020-10-01T17:51:19Z)
• Distilling neural networks into skipgram-level decision lists [4.109840601429086]
  We propose a pipeline to explain RNNs by means of decision lists (also called rules) over skipgrams. We find that our technique persistently achieves high explanation fidelity and qualitatively interpretable rules.
  arXiv  Detail & Related papers  (2020-05-14T16:25:42Z)
• Binarized Graph Neural Network [65.20589262811677]
  We develop a binarized graph neural network to learn the binary representations of the nodes with binary network parameters. Our proposed method can be seamlessly integrated into the existing GNN-based embedding approaches. Experiments indicate that the proposed binarized graph neural network, namely BGN, is orders of magnitude more efficient in terms of both time and space.
  arXiv  Detail & Related papers  (2020-04-19T09:43:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.