Are neural nets like the human brain?

Science ◽  
1989 ◽  
Vol 243 (4890) ◽  
pp. 481-482 ◽  
Author(s):  
L Roberts
Keyword(s):  
2021 ◽  
Vol 08 (01) ◽  
pp. 81-111
Author(s):  
Stephen L. Thaler

A novel form of neurocomputing allows machines to generate new concepts along with their anticipated consequences, all encoded as chained associative memories. Knowledge is accumulated by the system through direct experience as network chaining topologies form in response to various environmental input patterns. Thereafter, random disturbances to the connections joining these nets promote the formation of alternative chaining topologies representing novel concepts. The resulting ideational chains are then reinforced or weakened as they incorporate nets containing memories of impactful events or things. Such encodings of entities, actions, and relationships as geometric forms composed of artificial neural nets may well suggest how the human brain summarizes and appraises the states of nearly a hundred billion cortical neurons. It may also be the paradigm that allows the scaling of synthetic neural systems to brain-like proportions to achieve sentient artificial general intelligence (SAGI).


Author(s):  
Jack Copeland ◽  
Mark Sprevak

The theory that the whole universe is a computer is a bold and striking one. It is a theory of everything: the entire universe is to be understood, fundamentally, in terms of the universal computing machine that Alan Turing introduced in 1936. We distinguish between two versions of this grand-scale theory and explain what the universe would have to be like for one or both versions to be true. Spoiler: the question is in fact wide open—at the present stage of science, nobody knows whether it’s true or false that the whole universe is a computer. But the issues are as fascinating as they are important, so it’s certainly worth while discussing them. We begin right at the beginning: what exactly is a computer? To start with the obvious, your laptop is a computer. But there are also computers very different from your laptop—tiny embedded computers inside watches, and giant networked supercomputers like China’s Tianhe-2, for example. So what feature do all computers have in common? What is it that makes them all computers? Colossus was a computer, even though (as explained in Chapter 14) it did not make use of stored programs and could do very few of the things that a modern laptop can do (not even long multiplication). Turing’s ACE (see Chapters 21 and 22) was a computer, even though its design was unlike that of a laptop; for example, the ACE had no central processing unit (CPU), and moreover it stored its data and programs in the form of ‘pings’ of supersonic sound travelling along tubes of liquid. Turing’s artificial neural nets were also computers (Chapter 29), and so are the modern brain-mimicking ‘connectionist’ networks that Turing anticipated. In connectionist networks—as in a human brain, but unlike a laptop—there is no separation between memory and processing, and the very same ‘hardware’ that does the processing (the neurons and their connections) also functions as the memory. Even Babbage’s Analytical Engine (Chapter 24) was a computer, despite being built from mechanical rather than electrical parts.


2016 ◽  
Vol 39 ◽  
Author(s):  
Giosuè Baggio ◽  
Carmelo M. Vicario

AbstractWe agree with Christiansen & Chater (C&C) that language processing and acquisition are tightly constrained by the limits of sensory and memory systems. However, the human brain supports a range of cognitive functions that mitigate the effects of information processing bottlenecks. The language system is partly organised around these moderating factors, not just around restrictions on storage and computation.


Author(s):  
K.S. Kosik ◽  
L.K. Duffy ◽  
S. Bakalis ◽  
C. Abraham ◽  
D.J. Selkoe

The major structural lesions of the human brain during aging and in Alzheimer disease (AD) are the neurofibrillary tangles (NFT) and the senile (neuritic) plaque. Although these fibrous alterations have been recognized by light microscopists for almost a century, detailed biochemical and morphological analysis of the lesions has been undertaken only recently. Because the intraneuronal deposits in the NFT and the plaque neurites and the extraneuronal amyloid cores of the plaques have a filamentous ultrastructure, the neuronal cytoskeleton has played a prominent role in most pathogenetic hypotheses.The approach of our laboratory toward elucidating the origin of plaques and tangles in AD has been two-fold: the use of analytical protein chemistry to purify and then characterize the pathological fibers comprising the tangles and plaques, and the use of certain monoclonal antibodies to neuronal cytoskeletal proteins that, despite high specificity, cross-react with NFT and thus implicate epitopes of these proteins as constituents of the tangles.


Author(s):  
C. S. Potter ◽  
C. D. Gregory ◽  
H. D. Morris ◽  
Z.-P. Liang ◽  
P. C. Lauterbur

Over the past few years, several laboratories have demonstrated that changes in local neuronal activity associated with human brain function can be detected by magnetic resonance imaging and spectroscopy. Using these methods, the effects of sensory and motor stimulation have been observed and cognitive studies have begun. These new methods promise to make possible even more rapid and extensive studies of brain organization and responses than those now in use, such as positron emission tomography.Human brain studies are enormously complex. Signal changes on the order of a few percent must be detected against the background of the complex 3D anatomy of the human brain. Today, most functional MR experiments are performed using several 2D slice images acquired at each time step or stimulation condition of the experimental protocol. It is generally believed that true 3D experiments must be performed for many cognitive experiments. To provide adequate resolution, this requires that data must be acquired faster and/or more efficiently to support 3D functional analysis.


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