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The Engine of Reason, the Seat of the Soul: A Philosophical Journey into the Brain
| Content Provider | Semantic Scholar |
|---|---|
| Author | Freidman, David P. |
| Copyright Year | 1996 |
| Abstract | The past two decades have seen explosive growth in knowledge about animal nervous systems. At the same time, interest in computers and computing systems has evolved beyond a sequential, von Neuman approach, especially for dealing with ill-posed and “fuzzy” realworld computational problems. Finally, a new generation of cognitive scientists is more amenable to incorporating biological data into their work. Neural networks and neurocomputing represent a confluence of these three disciplines and has enjoyed tremendous recent growth, as readers of this journal are, no doubt, aware. Anderson’s introduction to this area far surpasses the three or four available competitors. Introduction to Neural Networks evolved over several years from course notes, and the integration to topics, perspectives, examples, and applications all suggest a carefully considered approach. Many introductory books in neural nets are designed for engineering students and, therefore, concentrate almost exclusively on a mathematical analysis of available networks and algorithms. Anderson skillfully interweaves a biologically relevant message about realworld processes and how a computational approach can enhance an understanding of the underlying neuroscience. The book concentrates primarily on those approaches that have proven “useful”-primarily for pattern recognition. Anderson limits his mathematical emphasis not so much on the formal analysis of networks as on the use of algorithms. However, a student without some calculus, algebra, familiarity with vectors, and some programming experience may find parts of this text daunting. Some neuroscience and cognitive science experience would also provide a useful background for any prospective student, although the chapters on neuron and synaptic physiology as they pertain to networks are quite clear. Anderson begins the history of network modeling with Rosenblatt’s Perceptron and early gradient descent algorithms such as Widrow and Hoff s ADALJNE. Hopfield networks and Boltzmann machines are extremely well covered. Kohonen’s adaptive map-forming algorithm is nicely contrasted with a section entitled “Biological Caveat, ” which contrasts the biological complexity with the “simplicity” suggested by simple adaptive algorithms. Nonlinear Autoassociative neural networks are repre- |
| Starting Page | 383 |
| Ending Page | 385 |
| Page Count | 3 |
| File Format | PDF HTM / HTML |
| DOI | 10.1162/jocn.1996.8.4.383b |
| PubMed reference number | 23971509 |
| Journal | Medline |
| Volume Number | 8 |
| Alternate Webpage(s) | http://csrnet.co.uk/the/engine/the_engine_of_reason_the_seat_of_the_soul_a_philosophical_journey_into_the_brain.pdf |
| Alternate Webpage(s) | http://oururl.co.uk/the/engine/the_engine_of_reason_the_seat_of_the_soul_a_philosophical_journey_into_the_brain.pdf |
| Alternate Webpage(s) | https://doi.org/10.1162/jocn.1996.8.4.383b |
| Journal | Journal of Cognitive Neuroscience |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
