Interpretable neural network architecture using continued fractions

A computer program product comprising a computer readable storage medium having computer readable instructions stored therein, wherein the computer readable instructions, when executed on a computing device, causes the computing device to:

• receive, as input to a neural network, input data;
• train the input data through a plurality of continued fractions layers of the neural network to generate output data, wherein the input data is provided to each of the continued fractions layers;
• and
• output, from the neural network, the output data, wherein each continued fractions layer of the continued fractions layers is configured to calculate one or more linear functions of its respective input and to generate an output that is used as the input for a subsequent continued fractions layer.

An artificial neural network comprising:

• a plurality of inputs of the artificial neural network, each input configured to receive an input signal having an input value in a form of a continued fraction;
• a plurality of synapses, wherein each synapse is connected to one of the plurality of inputs and includes a plurality of corrective weights, wherein each corrective weight is defined by a weight value;
• a set of neurons, wherein each neuron has at least one output and is connected with at least one of the plurality of inputs via one of the plurality of synapses, and wherein each neuron is configured to add up the weight values of the corrective weight selected from each synapse connected to the respective neuron to generate a neuron sum;
• and
• a layers architecture configured to distribute linear functions of the continued fractions to continued fractions layers where the set of neurons reside.