TY  - BOOK
AU  - Changizi,Mark A.
ED  - SpringerLink (Online service)
TI  - The Brain from 25,000 Feet: High Level Explorations of Brain Complexity, Perception, Induction and Vagueness 
T2  - Synthese Library, Studies in Epistemology, Logic, Methodology, and Philosophy of Science,
SN  - 9789401702935
AV  - QH359-425 
U1  - 576.8 23
PY  - 2003///
CY  - Dordrecht
PB  - Springer Netherlands, Imprint: Springer
KW  - Evolutionary biology
KW  - Artificial intelligence
KW  - Neurosciences
KW  - Philosophy
KW  - Optical data processing
KW  - Evolutionary Biology
KW  - Artificial Intelligence
KW  - Philosophy, general
KW  - Computer Imaging, Vision, Pattern Recognition and Graphics
N1  - Preface -- 1: Scaling in Nervous Networks. 1.1. The mammalian neocortex -- 1.2. Complexity in brain and behavior -- 1.3. The shape of limbed animals -- 2: Inevitability of Illusions -- 2.1. Visual inferences -- 2.2. A simple latency correction model -- 2.3. Explaining the geometrical illusions -- 2.4. Further directions for latency correction. 3: Induction and Innateness -- 3.1. Paradigm Theory -- 3.2. Applications -- 3.3. "Solution" to riddle and theory of innateness -- 4: Consequences of a Finite Brain -- 4.1. Vagueness, the phenomenon -- 4.2. Unseeable holes in our concepts -- 4.3. From theory to vagueness -- 4.4. Discussion -- Bibliography -- Index
N2  - In The Brain from 25,000 Feet, Mark A. Changizi defends a non-reductionist philosophy and applies it to a variety of problems in the brain sciences. Some of the key questions answered are as follows. Why do we see visual illusions, and why are illusions inevitable for any finite-speed vision machine? Why aren't brains universal learning machines, and what does the riddle of induction and its solution have to do with human learning and innateness? The author tackles such questions as why the brain is folded, and why animals have as many limbs as they do, explaining how these relate to principles of network optimality. He describes how most natural language words are vague and then goes on to explain the connection to the ultimate computational limits on machines. There is also a fascinating discussion of how animals accommodate greater behavioral complexity. This book is a must-read for researchers interested in taking a high-level, non-mechanistic approach to answering age-old fundamental questions in the brain sciences
UR  - https://doi.org/10.1007/978-94-017-0293-5
ER  -