Abstract: In accordance with some embodiments, systems, methods, and media for generating and using neural networks having improved efficiency for analyzing video are provided. In some embodiments, the method comprises: providing image data to a trained neural network; receiving, at a neuron, a delta-based input ?in from a previous layer; generating an output g(?in) of a linear transform g; generating an updated state variable a based on g(?in) and a current a; generating an output f(a) of an activation function f based on updated a; generating an updated state variable d based on a current d, a state variable b, and f(a); generating an updated b based on output f(a); transmitting d to a next layer based on a transmission policy and subtracting the value from d; and receiving an output from the trained neural network that represents a prediction based on the image data.

Abstract: In accordance with some embodiments, systems, methods, and media for generating digital images using low bit depth image sensor data are provided. In some embodiments, the system comprises: an image sensor; a processor programmed to: receive, from the image sensor, a series of low bit depth frames; provide low bit depth image information to a trained machine learning model comprising: a 3D convolutional layer; a 2D convolutional LSTM layer; a concatenation layer configured to generate a tensor that includes an output of the 2D convolutional LSTM layer and the low bit depth image information; and a 2D convolutional layer configured to generate an output based on the tensor; and generate a high bit depth image of a scene based on an output of the two-dimensional convolutional layer.

Abstract: In accordance with some embodiments, systems, methods, and media for generating and using spiking neural networks with improved efficiency are provided. In some embodiments, a method comprises: receiving image data; providing the image data to a trained spiking neural network (SNN), the SNN comprising a plurality of neurons, each of the plurality of neurons associated with a respective initialization value V0 of a plurality of initialization values, wherein a first layer of the trained SNN comprises a first subset of the plurality of neurons, and a second layer of the trained SNN comprises a second subset of the plurality of neurons, and wherein a mean of the plurality of initialization values is about 0.5, and a standard deviation of the initialization values is at least 0.