Tag Archives: brain function
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). […]
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 […]
14 Jan Visual Input Processing
The visual cortex in the human brain is arguably the pattern after which most artificial neural networks were modeled: the flow of signals is directional through layers 1, 2 then 3; and large numbers of the cells are touched by the flow of action potentials through the system. The variations in the cells, however, contrasts with the artificial […]
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 […]
08 Jan E/I Electric Potential Curve
Challenge I’ve noticed two phenomena in computing that have often been compared to brain activity even though they don’t significantly resemble the behavior of electrical potential changes between neurons: Flip-Flop (the changing of a “register” from 0 to 1 Node Firing (The activation of a node in an artificial neural network) In this section of Understanding Context, I’ve been trying to […]
06 Jan Excitation and Inhibition
Most, if not all, neural information-processing functions involve the flow of action potential. Impulses in the nervous system are changes in the action potential or electrical charge of membranes. It is possible that, in addition to the membranes, the potential within the cytoplasm of the cell changes as a result of electrical flow in neurons. […]
03 Jan Synapses and Neurotransmitters
Synapse Structure Electrical impulses are transmitted between neurons either electrically or chemically, with chemical synapses being the most numerous by far. The synapse is where electrical transduction between neurons occurs, facilitating perception, thought and action. The gap between a synapse and its target is called the synaptic cleft (tagged in the center right of the illustration and […]
02 Jan Synapse Formation
Many neurons have only a few synapses. Others, like giant pyramidal and Purkinje cells, may have tens or even hundreds of thousands of synapses. At the conclusion of the complex growth process, called synaptogenesis, in which growth cones at the tips of spines, axons, and dendrites propel or draw the fiber through the crowded gray and white matter […]