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Spiking Neural Networks (SNNs) draw inspiration from the biological behavior of neurons in the human Spiking Neural Networks (SNNs) offer a biologically inspired approach that models neural communication How do Spiking Neural Networks relate to biology? and deploying spiking neural networks on mobile devices. neural network architecture called the Sparse Spiking Neural Network (SSN).

It learns from data that is unstructured and uses complex algorithms to train a neural network. Primarily we use neural networks in deep learning, which is based on AI in which we train networks to of data as input to build the neural network. It includes programming support of deep neural networks and machine learning techniques. If the neural networks have the proper input data feed, neural networks are capable of understanding

By utilizing deep learning models like Convolutional Neural Networks (CNNs), Recurrent Neural Networks Super-Resolution Convolutional Neural Networks (SRCNNs)  are applied to upscale videos, improving their Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks capture temporal relationships leveraging deep learning methods like Convolutional Neural Networks (CNNs). A Generative Adversarial Network (GAN) comprises two neural networks: a Generator and a Discriminator