Changing Your Mind? Dopamine

You are a robot, I told you…. Memes elicit an instant emotional coupling. People only react to what is constructed as reality, no matter the obvious, better advised outcome.


Got hungry, so bad that I go to the garage freezer, with a side plate, and pull FISH STIKS! Pathetic and full of GMOs and MSG. They lie in the plate, looking like frosty dog-biskets. I go   back to the kitchen. Into the microwave, 30 seconds, Flip-turn, 30 seconds, flip turn, 30 seconds, now its boiling inside. Get a hotpad and flip it into the toaster on a max-hot, foil lined pan. Watch them using a flashlight for 2 minutes, flip, and do it again, waiting, waiting…I can’t stand it, so, out they come into the rinsed saucer, sizzling. I pour a drink, add thick ranch dressing to the plate. Family-Guy’s on in my fort. The Cat wants to annoy me and I tell it to fuck-off. Bite. Crunch. Swallow. Result=happiness.

What can you learn about neuro-chemistry and endocrine systems from this? I will remember that (I have a whole Bag!)  the cat matters less, and I’ll be back. I’m safe, knowing to say screw to anyone who said that’s “bad for you” (-too late for them now, I know better).

Meme-makers should know Instincts work, that Food works, favors work, orgasms work, bribes work. and Fear triggers, promise motivates. I will adopt a promise of reward. How will we depict the indirect promise of a reward state in a Meme?

How targeted drug delivery using genetic targeting can be used to integrate nanotechnology capable of communicating with a computer device?

Targeted Drug

Targeted drug delivery, sometimes called smart drug delivery,[1] is a method of delivering medication to a patient in a manner that increases the concentration of the medication in some parts of the body relative to others. This means of delivery is largely founded on nanomedicine, which plans to employ nanoparticle-mediated drug delivery to combat the downfalls of conventional drug delivery. These nanoparticles would be loaded with drugs and targeted to specific parts of the body where there is solely diseased tissue, thereby avoiding interaction with healthy tissue. The goal of a targeted drug delivery system is to prolong, localize, target, and have a protected drug interaction with the diseased tissue. The conventional drug delivery system is the absorption of the drug across a biological membrane, whereas the targeted release system releases the drug in a dosage form. The advantages to the targeted release system is the reduction in the frequency of the dosages taken by the patient, having a more uniform effect of the drug, reduction of drug side-effects, and reduced fluctuation in circulating drug levels. The disadvantage of the system is high cost, which makes productivity more difficult and the reduced ability to adjust the dosages.

In passive targeting, the drug’s success is related to circulation time.[6] This is achieved by cloaking the nanoparticle with some sort of coating. Several substances can achieve this, with one of them being polyethylene glycol (PEG). By adding PEG to the surface of the nanoparticle, it is rendered hydrophilic, thus allowing water molecules to bind to the oxygen molecules on PEG via hydrogen bonding. The result of this bond is a film of hydration around the nanoparticle which makes the substance antiphagocytic. The particles obtain this property due to the hydrophobic interactions that are natural to the reticuloendothelial system (RES), thus the drug-loaded nanoparticle is able to stay in circulation for a longer period of time.[7] To work in conjunction with this mechanism of passive targeting, nanoparticles that are between 10 and 100 nanometers in size have been found to circulate systemically for longer periods of time.[8]

Active targeting of drug-loaded nanoparticles enhances the effects of passive targeting to make the nanoparticle more specific to a target site. There are several ways that active targeting can be accomplished. One way to actively target solely diseased tissue in the body is to know the nature of a receptor on the cell for which the drug will be targeted to.[9] Researchers can then utilize cell-specific ligands that will allow for the nanoparticle to bind specifically to the cell that has the complimentary receptor. This form of active targeting was found to be successful when utilizing transferrin as the cell-specific ligand.[9] The transferrin was conjugated to the nanoparticle to target tumor cells that possess transferrin-receptor mediated endocytosis mechanisms on their membrane. This means of targeting was found to increase uptake, as opposed to non-conjugated nanoparticles.

Active targeting can also be achieved by utilizing magnetoliposomes, which usually serves as a contrast agent in magnetic resonance imaging.[9] Thus, by grafting these liposomes with a desired drug to deliver to a region of the body, magnetic positioning could aid with this process.

Electrical Stimulation of Neural Stem Cells Mediated by Humanized Carbon Nanotube Composite Made with Extracellular Matrix Protein

Design and evolution of modular neural network architectures


To investigate the relations between structure and function in both artificial and natural neural networks, we present a series of simulations and analyses with modular neural networks. We suggest a number of design principles in the form of explicit ways in which neural modules can cooperate in recognition tasks. These results may supplement recent accounts of the relation between structure and function in the brain. The networks used consist of several modules, standard subnetworks that serve as higher order units with a distinct structure and function. The simulations rely on a particular network module called the categorizing and learning module. This module, developed mainly for unsupervised categorization and learning, is able to adjust its local learning dynamics. The way in which modules are interconnected is an important determinant of the learning and categorization behavior of the network as a whole. Based on arguments derived from neuroscience, psychology, computational learning theory, and hardware implementation, a framework for the design of such modular networks is presented. A number of small-scale simulation studies shows how intermodal connectivity patterns implement “neural assemblies” that induce a particular category structure in the network. Learning and categorization improves because the induced categories are more compatible with the structure of the task domain. In addition to structural compatibility, two other principles of design are proposed that underlie information processing in interactive activation networks: replication and recurrence.

Because a general theory for relating network architectures to specific neural functions does not exist, we extend the biological metaphor of neural networks, by applying genetic algorithms (a biocomputing method for search and optimization based on natural selection and evolution) to search for optimal modular network architectures for learning a visual categorization task. The best performing network architectures seemed to have reproduced some of the overall characteristics of the natural visual system, such as the organization of course and fine processing of stimuli in separate pathways. A potentially important result is that a genetically defined initial architecture cannot only enhance learning and recognition performance, but it can also induce a system to better generalize its learned behavior to instances never encountered before. This may explain why for many vital learning tasks in organisms only a minimal exposure to relevant stimuli is necessary.

ARTMAP: Supervised real-time learning and classification of nonstationary data by a self-organizing neural network

Deep brain stimulation to illicit fear and extreme anger.

Nanotube radio.

The integration of computers with the brain.The human-computer interface can be described as the point of communication between the human user and the computer. The flow of information between the human and computer is defined as the loop of interaction. The loop of interaction has several aspects to it, including:

    Visual Based :The visual based human computer inter-action is probably the most widespread area in HCI(Human Computer Interaction) research.

    Audio Based : The audio based interaction between a computer and a human is another important area of in HCI systems. This area deals with information acquired by different audio signals.

    Task environment: The conditions and goals set upon the user.

    Machine environment: The environment that the computer is connected to, e.g. a laptop in a college student’s dorm room.

    Areas of the interface: Non-overlapping areas involve processes of the human and computer not pertaining to their interaction. Meanwhile, the overlapping areas only concern themselves with the processes pertaining to their interaction.

    Input flow: The flow of information that begins in the task environment, when the user has some task that requires using their computer.

    Output: The flow of information that originates in the machine environment.

    Feedback: Loops through the interface that evaluate, moderate, and confirm processes as they pass from the human through the interface to the computer and back.

    Fit: This is the match between the computer design, the user and the task to optimize the human resources needed to accomplish the task.

A brain–computer interface (BCI), sometimes called a mind-machine interface (MMI), direct neural interface (DNI), or brain–machine interface (BMI), is a direct communication pathway between an enhanced or wired brain and an external device. BCIs are often directed at researching, mapping, assisting, augmenting, or repairing human cognitive or sensory-motor functions.[1]

Research on BCIs began in the 1970s at the University of California, Los Angeles (UCLA) under a grant from the National Science Foundation, followed by a contract from DARPA.[2][3] The papers published after this research also mark the first appearance of the expression brain–computer interface in scientific literature.

In 1980s a report was given on control of a physical object, a mobile robot, using EEG signals [S. Bozinovski, M. Sestakov, L. Bozinovska: Using EEG alpha rhythm to control a mobile robot, In G. Harris, C. Walker (eds.) Proc IEEE Annual Conference on Medical and Biological Society, New Orleans, p. 1515-1516,1988][S. Bozinovski: Mobile robot trajectory control: From fixed rails to direct bioelectric control, In O. Kayniak (ed.) Proc. IEEE Workshop on Intelligent Motion Control, p. 63-67, 1990].