A method comprising: receiving, by a processing device, raw image data corresponding to a time series of raw images; processing the raw image data with an encoder of the processing device to generate encoded data, where the encoder comprises one or more input/output models of retinal cells based on a linear nonlinear cascade model including a spatiotemporal transformation using a plurality of parameters optimized based on data obtained through experiments using mammalian retina, wherein the model mimics the input/output response of at least one retinal cell of the mammalian retina, wherein the encoder receives the time series of raw images as input and outputs the encoded data comprising a time series of encoded retinal images; processing, by the processing device, the encoded data to generate dimension reduced encoded data, wherein processing the encoded data comprises applying a dimension reduction algorithm comprising a trained convolutional neural network, CNN, to the encoded data, wherein the trained CNN is configured to compress the information contained in the encoded data, and extract a subset of features from the compressed information, the subset of features comprising motion data measured from the encoded data and corresponding to each of a plurality of encoded retinal image regions dividing each encoded retinal image of the encoded data into a grid for a specific machine vision task and ignores other features of the encoded data unnecessary for the specific machine vision task, wherein the motion data comprises speed data corresponding to each of the plurality of encoded retinal image regions; wherein processing, by the processing device, further comprises generating feature signature data from the subset of features, wherein the feature signature data comprises a vector having a number of components equal to the product of the number of encoded retinal image regions in the grid and a number, N, of multiple pairs of encoded retinal image frames from which the speed data is measured by the trained CNN, wherein the components comprise values of the speed data associated with respective regions of the plurality of retinal image regions, wherein the grid size of each of the plurality of encoded retinal image regions is selected so that each respective region captures motion in a single direction; and outputting generated feature signature data to the specific machine vision task trained on a training dataset comprising previously generated feature signature data, wherein the specific machine vision task comprises one or more from the group of: person recognition, face recognition, or object recognition. receiving, by a processing device, raw image data corresponding to a time series of raw images; processing the raw image data with an encoder of the processing device to generate encoded data, where the encoder comprises one or more input/output models of retinal cells based on a linear nonlinear cascade model including a spatiotemporal transformation using a plurality of parameters optimized based on data obtained through experiments using mammalian retina, wherein the model mimics the input/output response of at least one retinal cell of the mammalian retina, wherein the encoder receives the time series of raw images as input and outputs the encoded data comprising a time series of encoded retinal images; processing, by the processing device, the encoded data to generate dimension reduced encoded data, wherein processing the encoded data comprises applying a dimension reduction algorithm comprising a trained convolutional neural network, CNN, to the encoded data, wherein the trained CNN is configured to compress the information contained in the encoded data, and extract a subset of features from the compressed information, the subset of features comprising motion data measured from the encoded data and corresponding to each of a plurality of encoded retinal image regions dividing each encoded retinal image of the encoded data into a grid for a specific machine vision task and ignores other features of the encoded data unnecessary for the specific machine vision task, wherein the motion data comprises speed data corresponding to each of the plurality of encoded retinal image regions; wherein processing, by the processing device, further comprises generating feature signature data from the subset of features, wherein the feature signature data comprises a vector having a number of components equal to the product of the number of encoded retinal image regions in the grid and a number, N, of multiple pairs of encoded retinal image frames from which the speed data is measured by the trained CNN, wherein the components comprise values of the speed data associated with respective regions of the plurality of retinal image regions, wherein the grid size of each of the plurality of encoded retinal image regions is selected so that each respective region captures motion in a single direction; and outputting generated feature signature data to the specific machine vision task trained on a training dataset comprising previously generated feature signature data, wherein the specific machine vision task comprises one or more from the group of: person recognition, face recognition, or object recognition.