Tag Archives: understanding
04 Feb Body Language in Understanding
How much can you hear without a word being spoken? How often does something about a person’s face, posture or hand gestures completely contradict the “normal interpretation” of the words they speak, creating a sense of sarcasm or other indirect message? To what extent are the academy awards influenced by an actor’s ability to use […]
31 Jan Feature Selectivity in Vision
This post is another in the series on specialization, in which the author stresses the need for very heterogeneous models for imitating brain capabilities with computers. An important discovery of neurophysiological and cybernetic research is that many neurons, particularly those in areas of the brain that specialize in processing perceptual data, are feature selective. Vision processing is […]
27 Jan Go With the Flow
Modeling Neural Electrical Flow Patterns From looking at possible mechanisms for information storage, we move back to its movement. It may be important to understand the patterns of electrical flow in the brain to define good models for artificial systems that attempt to match human competence in cognitive processing tasks. This is what neural network and […]
25 Jan The Chromophore as Digital Bit
I have opined in prior posts that the skeletal components that give structure to axons and dendrites, especially microtubules, may play a larger role in cognition than previously thought. The illustration of microtubule structure at right shows how the alpha and beta tubulin dimers string themselves together to make protofilaments, which further join one another […]
18 Jan Multi-Layered Perceptron
Multi-Layered Perceptron In prior posts we introduced the concept of the artificial neural network and the perceptron model as a simple implementation of a neural network. We showed the structure, including an input layer and an output layer. Let’s look at one of the typical approaches for processing input to derive the output. The net output of […]
17 Jan Perceptrons and Weighted Schemes
Perceptrons In the late 1600’s, John Locke expounded an associationist theory in which neurons or “bundles” of neurons came to represent certain ideas and associations between ideas. Rosenblatt‘s work seems a logical extension of associationist theory. Perceptrons can perform linear discrimination, thus enabling them to model the cognitive function of recognition (or, in computational terms, pattern classification). […]
16 Jan Roots of Neural Nets
Roots of Neural Nets The concept of the modern Artificial Neural Systems (ANS) has its roots in the work of psychologists and philosophers as well as computer scientists. As mentioned in prior posts, Aristotelian theories on cognition and logic influenced the development of automata theory and associationism, spawning connectionism or parallel distributed processing (PDP) theory. Connectionism is the […]
14 Jan Timing is Everything: Asynchronicity of E/I
Time marches straight on in a never-ending rhythm, never stopping to rest – never missing a beat. After memory fails, and all the mainsprings unwind, time strides forward. Coincidence and synchronicity of events in the physical universe are time bound, but not necessarily meaning bound. In the human brain, time loses relevance and thought flows in lesser […]
13 Jan Harmonic Convergence of Light
Light waves diverge and converge and bend on their journey to places where they are perceived. We choose to focus, perceived light waves enter us through the portals of our eyes, then flow through the visual cortex and resonate in the brain until they trigger recognition, often very quickly. The illustration shows a lens that […]
11 Jan The Pedantic Querulous Shrinking Violet
How much do you know about a person when you first meet them? What can you learn from speaking with them, even through an hour-long interview? Are first impressions valid in any way? Socially, we understand that it takes a variety of experiences in different contexts to really get to know a person, yet our ability […]