Tag Archives: Neuromorphic Computing
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 […]
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. […]
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 […]
31 Dec Signal Transduction in Neurons
Wires in the Brain Electrical impulses jump from neuron to neuron in the brain through their branching nerve fibers. This movement of electrical potentials is called signal transduction, and significantly resembles the process of electrical flow in printed circuit boards and semiconductor chips. Nerve fibers (axons and dendrites) are filled with a fluid called axoplasm. […]
30 Dec Context Models
Building a Model The goals of the research that evolved into Understanding Context were twofold: to investigate human physiology/psychology for clues that would let us evaluate neuromorphic computational paradigms; and to explore the possibility of new computational models using context to correlate and associate concepts. Birds fly and they are lightweight. Building models of flight with lightweight materials works […]
31 Oct Modeling Biological Systems
Possible Mechanisms of Learning, Memory and Cognition In the first section of this blog, I talked about the brain, as a whole, to establish a framework for the discussion of natural intelligence. In this section, I have delved into the inner workings of neurons, themselves to ensure we understand how complex they are, and where we […]