How deeply does Sutskever's List cover neuron?

Explains weighted inputs, optional bias, and activation, then notes why counts become ambiguous in convolutional layers.

Neuron

As explained in Sutskever's List

A computational unit in a neural network that combines weighted inputs and applies an activation function.

A neuron is a computational block: it multiplies inputs by weights, may add a bias, then applies an activation function that rescales the output. A neuron alone is of little use; linked across layers, such units can learn complex patterns. section 2.5.1

AlexNet’s neuron count

AlexNet was described as having about 650,000 neurons and 60 million parameters. Parameter counts more directly indicate training and run costs and overfitting risk; neuron counts are harder to interpret in convolutional layers with shared parameters. section 2.5.1

The count also reflects a historical framing: in 2012, researchers used biological analogies to motivate network designs. AlexNet invoked neuron counts and lateral inhibition, while neural networks are now generally described in more engineering-focused terms. section 2.5.1

How Sutskever's List covers it

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Also explained in

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Natural Language Processing in Action, Second EditionAn artificial neuron is a parameterized computational unit that combines inputs to produce an output, typically through an activation function.
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Grokking AI Algorithms, Second EditionA neural-network node is a computational unit that combines weighted signals and transforms them into an activation.
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Related concepts

neural network layerpart of · A neural network layer is a computational stage or component that transforms input representations into outputs within a neural network.
language modelpart of · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.
activation functionuses · An activation function maps a neural-network unit’s or layer’s input to an output, often introducing nonlinearity into the network’s computation.
perceptronincludes · A perceptron is a single-layer computational model and linear classifier that combines weighted inputs with a bias and applies a threshold or activation rule.
deep learningpart of · Deep learning is a machine-learning approach that uses neural networks with multiple computational layers to learn representations from data.
neuroncontrasts with · A biological neuron is a nervous-system cell that receives, integrates, and transmits signals through electrical and chemical activity.

This explainer was generated by Manning's knowledge graph from the text of Sutskever's List. The section links point to the parts of the book it draws on.