Functional activation-based analysis of deep neural networks

A method for analyzing an artificial neural network, comprising:

• accessing input data with a computer system, wherein the input data comprise a first input dataset and a second input dataset;
• accessing a pretrained neural network with the computer system, the pretrained neural network comprising a plurality of layers;
• forming a block-sequence of the input data that defines an order to apply the first input data set and the second input dataset to the pretrained neural network;
• generating a time-series of layer output values by:
• applying the input data to the pretrained neural network according to the block-sequence;
• storing an output of each of the plurality of layers generated when applying the input data to the pretrained neural network according to the block-sequence, wherein the output of each of the plurality of layers are ordered based on the block-sequence and define the time-series of layer output values;
• and
• generating neural network activation data by processing the time-series of layer output values with the computer system, wherein the neural network activation data indicate a degree of activation of each of the plurality of layers of the pretrained neural network in response to at least one of the first input dataset or the second input dataset.