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04 Jul Cognitive Multi-Processing

Layered Model

Today I’ll address parallel computing and models for breaking down computational problems. I will not address the question of autonomy today, but save the question of empowering independent agents for a future post. ANS and Multiprocessors Artificial Neural Systems (ANS) are probably the closest approximation of the mechanical brain paradigm, so it is useful to know how […]

11 May Thinking in Parallel

Parallel Lines

A Parallel Expert I once rode the Trans-Siberian Railway from Moscow to Ulan-Batar, Mongolia (not the picture at right – the engines were diesel). Several times along the journey we passed slower trains, and we were passed by faster ones. When people and freight are confined to a single lane, the speed of the slowest defines the speed of […]

14 Jan Segregating Layers of Intelligence

Layer Cake

Layered Architectures Layers appear regularly in my blog, whether it’s layers of the brain, layers of processing nodes in artificial neural networks or layers in systems architectures. Layering embodies important patterns in the inexorable move toward a knowledge economy with knowledge systems. In today’s post, I’m going to talk about what layering brings to enterprise […]

28 Oct Chemicals And Cognitive Performance

Lightning Brain

Outside Influences Mind-altering chemicals, stimulants, depressants and hallucinogens to name a few, affect the entire process of cognition, from receiving and processing input, through recognition and reasoning. They often even improve or impair our ability to act, affecting everything from muscle performance to language production and comprehension. Bacteria and viruses can also impact people. Things that come from outside […]

27 Oct The Nature of Innovative Thinking

Mental Exploration The shape of the world changed radically when folks from the eastern and western hemispheres became aware of one another and of their respective geographies. The Age of Exploration (AKA the Age of Discovery) was amazing – or should I say “it is amazing”? While the focus has changed from continents and cultures, to galaxies […]

14 Oct Knowledge in Non-Neural Models

Concept Graph

Non-Neural Models So far we have examined a number of models that are explicitly designed to be neuromorphic. This categorization is useful for two reasons: the apparent chaos or non-deterministic functioning of the brain is represented by these models; and neural networks explicitly use large numbers of distributed processors or neurodes that each contribute to […]

08 Sep Gnostic Learning Model

Hard Disk in Brain

In prior posts in this section, and periodically in other sections of my blog, I have been exploring how humans learn, and how we might replicate those processes in computer software or (less likely) hardware. The context of the learning, or knowledge acquisition, upon which I choose to focus is language learning. While knowledge acquisition is much broader, this is an […]

28 Aug Weight Control for Knowledge


Stochastic Models Data, information and knowledge may be stored in many different ways in computers. Most artificial neural models rely heavily on stochastic or probabilistic techniques for establishing the internal structure that represents the data. The generalized delta rule for adaptation is an example of this sort of technique. The generalized delta rule, developed by D.E. […]

25 Aug Determinacy in Neural Connections

Neural Net

For many years, researchers thought that it was wrong to assume that there was a cell or set of cells in the brain that stored the memory of Grandma’s face. Though the comparison with computer memory was appealing, it was thought to be too simplistic and incorrect. Now, more researchers in different academic disciplines are assuming […]

31 Jul Modeling Non-Random Synaptic Links

Random Hairdo

I have discussed the different meanings of “random” in “The Random Hamlet” and “That’s so Random!” in which the mathematical definition presumes there is some not yet known law that governs the phenomenon, where other definitions suggest that randomness means that the phenomenon is not governed by any law. Remember our reference to Rosenblatt’s early contributions in […]