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Neuroscience

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The cannabis plant has been used for recreational and medicinal purposes for more than 4,000 years, but the scientific investigation into its effects has only recently yielded useful results. In this book, Linda Parker offers a review of the scientific evidence on the effects of cannabinoids on brain and behavioral functioning, with an emphasis on potential therapeutic uses.

New Perspectives on Mental Illness

Modern psychiatry is at a crossroads, as it attempts to balance neurological analysis with psychological assessment. Computational neuroscience offers a new lens through which to view such thorny issues as diagnosis, treatment, and integration with neurobiology. In this volume, psychiatrists and theoretical and computational neuroscientists consider the potential of computational approaches to psychiatric issues.

This textbook presents a wide range of subjects in neuroscience from a computational perspective. It offers a comprehensive, integrated introduction to core topics, using computational tools to trace a path from neurons and circuits to behavior and cognition. Moreover, the chapters show how computational neuroscience—methods for modeling the causal interactions underlying neural systems—complements empirical research in advancing the understanding of brain and behavior.

Retraining Subconscious Awareness

This is a book for readers who want to probe more deeply into mindfulness. It goes beyond the casual, once-in-awhile meditation in popular culture, grounding mindfulness in daily practice, Zen teachings, and recent research in neuroscience. In Living Zen Remindfully, James Austin, author of the groundbreaking Zen and the Brain, describes authentic Zen training—the commitment to a process of regular, ongoing daily life practice. This training process enables us to unlearn unfruitful habits, develop more wholesome ones, and lead a more genuinely creative life.

Before The Computational Brain was published in 1992, conceptual frameworks for brain function were based on the behavior of single neurons, applied globally. In The Computational Brain, Patricia Churchland and Terrence Sejnowski developed a different conceptual framework, based on large populations of neurons. They did this by showing that patterns of activities among the units in trained artificial neural network models had properties that resembled those recorded from populations of neurons recorded one at a time.

A Guide for the Practicing Neuroscientist

As neural data becomes increasingly complex, neuroscientists now require skills in computer programming, statistics, and data analysis. This book teaches practical neural data analysis techniques by presenting example datasets and developing techniques and tools for analyzing them. Each chapter begins with a specific example of neural data, which motivates mathematical and statistical analysis methods that are then applied to the data.

Philosophers from Descartes to Kripke have struggled with the glittering prize of modern and contemporary philosophy: the mind-body problem. The brain is physical. If the mind is physical, we cannot see how. If we cannot see how the mind is physical, we cannot see how it can interact with the body. And if the mind is not physical, it cannot interact with the body. Or so it seems.

Ancient Brains in a High-Tech World

Most of us will freely admit that we are obsessed with our devices. We pride ourselves on our ability to multitask—read work email, reply to a text, check Facebook, watch a video clip. Talk on the phone, send a text, drive a car. Enjoy family dinner with a glowing smartphone next to our plates. We can do it all, 24/7! Never mind the errors in the email, the near-miss on the road, and the unheard conversation at the table.

Learning Invariant Representations

The ventral visual stream is believed to underlie object recognition in primates. Over the past fifty years, researchers have developed a series of quantitative models that are increasingly faithful to the biological architecture. Recently, deep learning convolution networks—which do not reflect several important features of the ventral stream architecture and physiology—have been trained with extremely large datasets, resulting in model neurons that mimic object recognition but do not explain the nature of the computations carried out in the ventral stream.

Fifty years ago, neuroscientists thought that a mature brain was fixed like a fly in amber, unable to change. Today, we know that our brains and nervous systems change throughout our lifetimes. This concept of neuroplasticity has captured the imagination of a public eager for self-improvement—and has inspired countless Internet entrepreneurs who peddle dubious “brain training” games and apps.

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