How Singular? How singular is the Singularity? Will it happen in an instant? Lets consider again the derivation of the word. In mathematics a singularity is a value that is beyond any limit - in essence, infinity. The Singularity as we have discussed it in this book, does not achieve infinite levels of computation, memory, as any other other measurable attribute. But it certainly achieves vast levels of all these qualities, including intelligence. With the reverse engineering of the human brain we will be able to apply the parallel, self-organizing, chaotic, algorithms of human intelligence to enormously powerful computational substrates. This intelligence will then be in a position to improve its own design, both hardware and software, in a rapidly accelerating iterative process.

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.