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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.

A collection of essays engaging with digital scholarship and new technologies.

Contributors to this volume engage with digital scholarship in several ways: by creating digital projects, often in multidisciplinary, collaborative environments; by applying digital methodologies and tools to explore research questions; and by speculating about the potential directions that digital scholarship can take to tackle existing research areas that could benefit from new perspectives. Together, the chapters demonstrate how various digital approaches—from network analysis to web mapping, VR and AR technologies, digital editions, databases, and archives—are all contributing in creative and effective ways to expand our knowledge of the past, to help ask and answer questions at a scale that was unimaginable before the digital turn, and to reshape early modern studies in the twenty-first century.