"As data harvesting continues its exponential growth and computing power increases beyond what we can even imagine, AI and Big Data will be able to predict what we want for breakfast, what shoes we will buy, which political party we will vote for, and who we will fancy in a bar. Indeed, these deep-learning machines will know us better than we know ourselves. The day awaits when academia and big business will be able to quantify just how predictable each of us are. If indeed we are predictable like machines, to what extent are we alive, and under what definition? In The Human Test, quantum physicist Ron Folman unites findings from cognitive science, quantum physics, philosophy, and technology to offer a prescient look into this startling new era of human existence. If we are indeed found to be predictable, it will change everything we thought we knew about human nature. The Human Test strives for a new paradigm, and this new paradigm is found in human predictability. Existing monitors of general brain activity measure only very rudimentary aspects of the human experience, or what may be called human life; could the advent of a disruptive new technology bring about a much more insightful measure of who we are? While The Human Test describes a new and profound near-future impact of AI, it is rooted in a topic of paramount importance to humans: the enigma of consciousness and how it defines human life-or, what differentiates humans from all other life forms. Ultimately, The Human Test addresses some of the most fundamental questions about our species, starting with the most crucial: do we have free will, or are we merely machines? Fortunately, there is an antidote to predictability: creativity, that wondrous state of being original and inherently not predictable. Ultimately, Folman argues that if we can understand predictability, we can learn how to distance ourselves from it, and transcend to a purer idea of what human life should look like"-- Provided by publisher.

Methods in Neuronal Modeling is the first technical handbook on computational neuroscience. Written for researchers and theoreticians alike, it outlines methods and techniques used for simulating on digital computers the functional properties of single neurons from synapses, dendrites, single cells; and small invertebrate networks to large scale neural networks in the mammalian nervous system. The use of new experimental tools such as selective staining methods, membrane patch electrodes, voltage and calcium-dependent dyes, and multielectrode recordings, together with the, advent of universally available powerful computing, makes it possible to construct detailed and realistic models of neuronal systems. Methods in Neuronal Modeling addresses such questions as what can and should be simulated and what techniques should be used; what experimental parameters are crucial for such simulations, and whether these models may be verified experimentally. Chapters cover simulation of passive dendritic trees, compartmental models of single cells including neurons with a number of different ionic channels, calcium current dynamics, simulations of small invertebrate networks, simulations of the mammalian cortex, connectionists' models, and the use of parallel computers in modeling neural networks. Although the chapters were written by several authors, they are uniform in structure and notation. Detailed examples are given to clarify the different approaches. Each chapter concludes with a description of the model discussed and the details of its implementation on the computer. Christof Koch is an Assistant Professor of Computation and Neural Systems at the California Institute of Technology. Idan Segev is a Lecturer in Neurobiology at the Institute of Life Science, Hebrew University of Jerusalem. Methods in Neuronal Modeling inaugurates the new series in Computational Neuroscience, edited by Terrence J. Sejnowski and Tomaso Poggio. A Bradford Book.

0. 0 Psychology versus Complex Systems Science Over the last century, psychology has become much less of an art and much more of a science. Philosophical speculation is out; data collection is in. In many ways this has been a very positive trend. Cognitive science (Mandler, 1985) has given us scientific analyses of a variety of intelligent behaviors: short-term memory, language processing, vision processing, etc. And thanks to molecular psychology (Franklin, 1985), we now have a rudimentary understanding of the chemical processes underlying personality and mental illness. However, there is a growing feeling-particularly among non-psychologists (see e. g. Sommerhoff, 1990) - that, with the new emphasis on data collection, something important has been lost. Very little attention is paid to the question of how it all fits together. The early psychologists, and the classical philosophers of mind, were concerned with the general nature of mentality as much as with the mechanisms underlying specific phenomena. But the new, scientific psychology has made disappointingly little progress toward the resolution of these more general questions. One way to deal with this complaint is to dismiss the questions themselves. After all, one might argue, a scientific psychology cannot be expected to deal with fuzzy philosophical questions that probably have little empirical signifi cance. It is interesting that behaviorists and cognitive scientists tend to be in agreement regarding the question of the overall structure of the mind."