Circuits of the MindIn this groundbreaking work, computer scientist Leslie G. Valiant details a promising new computational approach to studying the intricate workings of the human brain. Focusing on the brain's enigmatic ability to quickly access a massive store of accumulated information during reasoning processes, the author asks how such feats are possible given the extreme constraints imposed by the brain's finite number of neurons, their limited speed of communication, and their restricted interconnectivity. Valiant proposes a "neuroidal model" that serves as a vehicle to explore these fascinating questions. While embracing the now classical theories of McCulloch and Pitts, the neuroidal model also accommodates state information in the neurons, more flexible timing mechanisms, a variety of assumptions about interconnectivity, and the possibility that different areas perform different functions. Programmable so that a wide range of algorithmic theories can be described and evaluated, the model provides a concrete computational language and a unified framework in which diverse cognitive phenomena--such as memory, learning, and reasoning--can be systematically and concurrently analyzed. Requiring no specialized knowledge, Circuits of the Mind masterfully offers an exciting new approach to brain science for students and researchers in computer science, neurobiology, neuroscience, artificial intelligence, and cognitive science. |
Contents
The Approach | 1 |
Biological Constraints | 9 |
Computational Laws | 25 |
Copyright | |
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A₁ Algorithm 8.1 allocated areas assume attributes axonal branching basic behavior Boolean algebra Boolean functions brain caused to fire cell Chapter circuit cognitive commonsense reasoning complex computational computational learning theory concepts conjunction connections consider corresponding cortex defined described descriptional complexity disjunction edges expected number expression fraction frontier Hence Horn clause human hypothesis implemented inductive learning initial input interactions knowledge representation L-expressions layer mechanism mode negative examples neighbors neocortex neural neuroidal model neuroidal system neuroids representing neurons nodes fire number of synapses objects pac learning pair parameters perceptron perform peripherals phenomena positive example predicates preprogrammed presented probability problem programs prompted PSPs pyramidal cells random access tasks random graph relation relay nodes scene semantic sequence simple reflex specific spikes supervised supervised learning target nodes theory tions transitions unsupervised memorization update variables vicinal algorithms weights zero