Inferring the location of neurons within an artificial network from their activity

• Published in: Neural networks Vol. 157; pp. 160 - 175
• Main Authors: Dyer, Alexander J., Griffin, Lewis D.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.01.2023
• Subjects: Artificial neural networks; Correlation; Network inference; Neural Networks, Computer; Neurons; Supervised learning; Correlation; Supervised learning; Network inference; Artificial neural networks
• ISSN: 0893-6080; 1879-2782
• DOI: 10.1016/j.neunet.2022.10.012

Inferring the connectivity of biological neural networks from neural activation data is an open problem. We propose that the analogous problem in artificial neural networks is more amenable to study and may illuminate the biological case. Here, we study the specific problem of assigning artificial neurons to locations in a network of known architecture, specifically the LeNet image classifier. We evaluate a supervised learning approach based on features derived from the eigenvectors of the activation correlation matrix. Experiments highlighted that for an image dataset to be effective for accurate localisation, it should fully activate the network and contain minimal confounding correlations. No single image dataset was found that resulted in perfect assignment, however perfect assignment was achieved using a concatenation of features from multiple image datasets. •Inferring neural networks from activation data is an open problem.•We propose systematic exploration through a series of (artificial) inference tasks.•Performing the tasks on (controllable) ANNs is useful for algorithm development.•This work studies the first task of assigning neurons to known network locations.•Assignment is achieved using features based on eigenvectors of correlation matrix.