How neurons transform inputs into outputs is a fundamental building block of brain computation. Here, we measure neurons’ IO functions in the awake and intact brain, where ongoing network activity ...
Abstract: This article introduces output prediction methods for two types of systems containing sinusoidal-input uniformly convergent (SIUC) elements. The first method considers these elements in ...
Biological systems can process information without expending energy, and the limit to what can be achieved in this way is known as a Hopfield barrier. We characterize this barrier for the sharpness of ...
Activation functions for neural networks are an essential part of deep learning since they decide the accuracy and efficiency of the training model used to create or split a large-scale neural network ...
Linear and nonlinear functions are the building blocks of algebra. They are essential to the understanding of graphs, equations, and the principles that govern the study of mathematics beyond the ...
[Transformer Tutorial] Why is the activation function of the output layer linear instead of softmax?
Thank you very much for providing such a detailed and comprehensive tutorial. I have successfully reconstructed a similar model on my own and successfully trained it ...
Abstract: Structural identifiability is a property of a differential model with parameters that allows for the parameters to be determined from the model equations in the absence of noise. The method ...
What is an operational transconductance amplifier? Operational transconductance amplifiers (OTAs) have become essential building blocks of many modern analog and mixed signal circuits. OTAs are used ...
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