This valuable study describes a simple and robust approach for estimating information-limiting noise by splitting neural populations and comparing estimator values. The authors report more accurate ...

This repository is a fork from https://github.com/carpentries-incubator/machine-learning-neural-python, an excellent introduction to ML for images. This half-day ...

intrinsic-neural-timescales/ ├── data/ │ └── example_spike_data.mat # example dataset ├── Matlab/ │ ├── README.md # MATLAB-specific docs │ ├── timescale_analysis.m # main MATLAB pipeline │ └── ...

Researchers at Meta’s FAIR lab have released NeuralSet, a Python framework designed to eliminate one of the most persistent bottlenecks in Neuro-AI research: the painful, fragmented process of getting ...

Abstract: Network configuration synthesis promises to increase the efficiency of network management by reducing human involvement. However, despite significant advances in this field, existing ...

You are free to share (copy and redistribute) this article in any medium or format and to adapt (remix, transform, and build upon) the material for any purpose, even commercially within the parameters ...

Massive computing systems are required to train neural networks. The prodigious amount of consumed energy makes the creation of AI applications significant polluters ...

A distinguishing feature of the neural network models used in Physics and Chemistry is that they must obey basic underlying symmetries, such as symmetry to translations, rotations, and the exchange of ...

Abstract: In this paper, we consider the design of model predictive control (MPC) algorithms based on deep operator neural networks (DeepONets) (Lu et al. 2021). These neural networks are capable of ...

Neural networks are computing systems designed to mimic both the structure and function of the human brain. Caltech researchers have been developing a neural network made out of strands of DNA instead ...