A critical examination of the figure of the neural network as it mediates neuroscientific and computational discourses and technical practices
Authored by Ranjodh Singh Dhaliwal, Théo Lepage-Richer, and Lucy Suchman
University of Minnesota Press
April, 2024
Neural Networks reconstructs 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.
Here is a thread featuring some highlights from the book: https://x.com/ranjodhd/status/1784947179741679928 .
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“Neural Networks is an elegant, compact book that renders visible the too-often naturalized equation of brain and computer. The authors illuminate the march of neural networks through colonial hierarchies, clinical psychiatry, and the present era of machine learning along with its parascientific mediations.” —Beth Coleman, University of Toronto
“This multifaceted book reveals how neural networks today still play the ambivalent role of an almost mythological object, in between idealized visions of the brain and naturalized technology.” —Matteo Pasquinelli, Ca’ Foscari University, Venice
