A method including: receiving image data corresponding to a series of images; applying a spatiotemporal transformation to the image data to generate retinal output cell response values, wherein applying the spatiotemporal transformation comprises applying a single stage spatiotemporal transformation that comprises a set of weights generated from experimental data, wherein the experimental data is generated using a stimulus that includes natural scene images, wherein applying the spatiotemporal transformation includes convolving the series of images with a spatiotemporal kernel to generate one or more spatially-temporally transformed images, and applying a nonlinear function to the spatially-temporally transformed images to generate the set of response values, wherein the convolving comprises convolving with a linear filter that is parametrized as a product of a spatial function and a temporal function, the generated retinal output cell response values constituting encoded data; generating a series of retinal images based on the encoded data; determining pixel values in the retinal images based on the encoded data; and applying a first machine vision algorithm to the series of retinal images to generate output data; determining, by a controller, performance information indicative of a performance of the first machine vision algorithm based on the output data; adjusting, by the controller, one or more characteristics of the first machine vision algorithm based on the performance information; iteratively repeating the determining and adjusting operations until a selected performance criteria is reached; where determining pixel values in the retinal images based on the encoded data includes determining a pixel intensity or color based on encoded data indicative of a retinal cell response; and where the data indicative of a retinal cell response is indicative of a retinal cell firing rate. receiving image data corresponding to a series of images; applying a spatiotemporal transformation to the image data to generate retinal output cell response values, wherein applying the spatiotemporal transformation comprises applying a single stage spatiotemporal transformation that comprises a set of weights generated from experimental data, wherein the experimental data is generated using a stimulus that includes natural scene images, wherein applying the spatiotemporal transformation includes convolving the series of images with a spatiotemporal kernel to generate one or more spatially-temporally transformed images, and applying a nonlinear function to the spatially-temporally transformed images to generate the set of response values, wherein the convolving comprises convolving with a linear filter that is parametrized as a product of a spatial function and a temporal function, the generated retinal output cell response values constituting encoded data; generating a series of retinal images based on the encoded data; determining pixel values in the retinal images based on the encoded data; and applying a first machine vision algorithm to the series of retinal images to generate output data; determining, by a controller, performance information indicative of a performance of the first machine vision algorithm based on the output data; adjusting, by the controller, one or more characteristics of the first machine vision algorithm based on the performance information; iteratively repeating the determining and adjusting operations until a selected performance criteria is reached; where determining pixel values in the retinal images based on the encoded data includes determining a pixel intensity or color based on encoded data indicative of a retinal cell response; and <EOS>