This is the story of how I trained a simple neural network to solve a well-defined yet novel challenge in a real iOS app. The problem is unique, but most of what I cover should apply to any task in any iOS app. That’s the beauty of neural networks
## World Emulation via Neural Network 25 April 2025 I turned a forest trail near my apartment into a playable neural world. You can explore that world in your web browser by clicking right here : By "neural world", I mean that the entire thing is a neural network generating new images based on previous images + controls. There is no level geometry, no code for lighting or shadows, no scripted animation. Just a neural net in a loop. By "in your web browser" I mean this world runs locally, in your web br
Enhanced Preprints Neuroscience The interplay between homeostatic synaptic scaling and homeostatic structural plasticity maintains the robust firing rate of neural networks Department of Neuroanatomy, Institute of Anatomy and Cell Biology, Faculty of Medicine, University of Freiburg, Freiburg, Germany Center BrainLinks-BrainTools, University of Freiburg, Freiburg, Germany Forschungszentrum Jülich, Simulation Lab Neuroscience, Jülich Supercomputing Center, Institute for Advanced Simulation, Jülich Aachen
In humans and other mammals, the cerebral cortex is responsible for sensory, motor, and cognitive functions. A new study shows that the global architecture of the cortical networks in large-brained primates and small-brained rodents is organized by common principles. However, primate brains have weaker long-distance connections, which could explain why large brains are more susceptible to mental illnesses including schizophrenia and Alzheimer's disease
# Continuous Improvements in Neural Processing Units (NPU) in Mobile Phones 2026-08-14 Ryan Whitwam The neural processing unit (NPU) in your phone may not be doing much.Image Source: Aurich Lawson | Getty Images In recent years, almost all technological innovations have focused on one point: generative artificial intelligence. Many so-called revolutionary systems run on large, expensive servers in data centers, while chip manufacturers boast about the powerful capabilities of the neural processing units
Human cellular models of neurodegeneration require reproducibility and longevity, which is necessary for simulating age-dependent diseases. Such systems are particularly needed for TDP-43 proteinopathies1, which involve human-specific mechanisms2–5 that cannot be directly studied in animal models. Here, to explore the emergence and consequences of TDP-43 pathologies, we generated induced pluripotent stem cell-derived, colony morphology neural stem cells (iCoMoNSCs) via manual selection of neural
NeuralNetwork_lib latest Neural Networks: Neuroevolution: Convolution: Convolutional Neural Network General: NeuralNetwork_lib Docs » NeuralNetwork_lib Edit on GitHub NeuralNetwork_lib ¶ Neural Networks: Perceptron use momentum for training Setting extra Variables Neural Network use momentum for training Setting extra Variables Neuroevolution: Genetic Perceptron Genetic Neural Network Initializing a Genetic Neural Network Feeding Data through a Genetic Neural Network and receiving the Output Training a
Explains how power laws govern neural network scaling. Topics include log-log analysis, fitting techniques, and how to predict model performance at any scale
So far in our artificial neural network series, we have covered only neural networks that are using supervised learning. To be more precise, we only explored neural networks that have input and output data available to them during the learning process. Based on this information, this kind of neural networks change their weights and are able to
Deep neural networks have become increasingly popular under the name of deep learning recently due to their success in challenging machine learning tasks. Although the popularity is mainly due to recent successes, the history of neural networks goes as far back as 1958 when Rosenblatt presented a perceptron learning algorithm. Since then, various kinds of artificial neural networks have been proposed. They include Hopfield networks, self-organizing maps, neural principal component analysis, Boltzmann machin