Neural Networks

Ranjodh Singh Dhaliwal, Théo LePage-Richer, Lucy Suchman

• Publisher: Univ Of Minnesota Press
• Year: 2024
• eBook ISBN: 9781452970493
• Subtopic: Artificial Intelligence (AI) & Semantics

A critical examination of the figure of the neural network as it mediates neuroscientific and computational discourses and technical practices

Neural Networks proposes to reconstruct situated practices, social histories, mediating techniques, and ontological assumptions that inform the computational project of the same name. If so-called machine learning comprises a statistical approach to pattern extraction, then neural networks can be defined as a biologically inspired model that relies on probabilistically weighted neuron-like units to identify such patterns. Far from signaling the ultimate convergence of human and machine intelligence, however, neural networks highlight the technologization of neurophysiology that characterizes virtually all strands of neuroscientific and AI research of the past century. Taking this traffic as its starting point, this volume explores how cognition came to be constructed as essentially computational in nature, to the point of underwriting a technologized view of human biology, psychology, and sociability, and how countermovements provide resources for thinking otherwise.

Table of contents

• Cover
• Title Page
• Copyright Page
• Contents
• Series Foreword
• Introduction: Rendering the Neural Network
• [ 1 ] Neural Media
• [ 2 ] On Parascientific Mediations: Science Fictions, Educational Platforms, and Other Substrates That Think Neural Networks
• [ 3 ] The Neural Network at Its Limits
• Authors