Transient bursts of activity such as hippocampal ripple oscillations, amygdala high-frequency oscillations, and prefrontal cortical ripple oscillations are used in the brain to make memory “stick” to current memories. See the article at Memory Is Formed Through Rewiring of Global Network Among Pre-existing Local Neuronal Ensembles
For many years researchers have said that DNA and RNA do not fluoresce without help from other chemicals. But now some researchers are discovering how to make DNA (and RNA) to fluoresce naturally.
In this research article, they add infrared (IR) to enhance the florescence. Dark State-Modulated Fluorescence Correlation Spectroscopy For Quantitative Signal Recovery – DNA
Some brain waves look like Sharp-wave Ripples, these are sometimes called SWR’s.
SWRs can be evoked by low intensity electrical stimulation, and at lower intensities than those needed to evoke detectable population spikes. In addition, evoked SWRs show large variability in amplitude, similar to that of spontaneous events. See more at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5862867/
Hippocampal sharp-wave ripples (SPW-Rs) support consolidation of recently acquired episodic memories and planning future actions by generating ordered neuronal sequences of previous or future experiences. See more at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6310484/
I just started learning Python (a computer programming language) two years ago when I look an Artificial Intelligence Class. It was required to program to Tensor Flow, one of the top AI programming frameworks by Google.
Now I hear that Python is considered the number three in popularity among current computer programmers.
Sometimes a better strategy can win over more intelligence. For example, making a smarter wagon wheel is nice, but is it practical? Having a better strategy and a broader set of mental models can help discover something new or help to solve a mathematical or scientific puzzle.
To a person who uses hammers and nails to solve most problems, this person will use this model for almost any problem. Someone expert in mathematics will try to apply mathematics to solve a social issue, but this may not work.
These are some of my comments from an article: Mental Models: How to Train Your Brain to Think in New Ways. This article takes many examples from how Richard Feynman solved hard problems.
“This is the power of music. And as you’ve now seen, it goes far beyond an ability to enhance our moods. In fact, research has also shown it can reduce everyday stress, boost memory and creativity, enhance blood vessel function and even give an added boost to your immune system.” — from https://www.tonyrobbins.com/mind-meaning/music-brain
Check out these links where there is some success in processing video with neural networks using a Convolutional Neural Network (CNN) followed by a Recursive Neural Network (RNN) using LSTM’s (Long Short-term Memory) modules.
- Large-scale Video Classification with Convolutional Neural Networks
- Deep Learning for Video Classification and Captioning
- Continuous online video classification with TensorFlow, Inception and a Raspberry Pi
- Five video classification methods implemented in Keras and TensorFlow
- Continuous video classification with TensorFlow, Inception and Recurrent Nets
Some machine learning or deep learning data sets can take a lot of CPU time, their doing a lot of matrix arithmetic operations. There is a lot of number crunching here.
If you want to write code for GPUs then I suggest you to try the following Udacity course. Introduction to Parallel Programming With CUDA
So the abbreviations are crazy, CPU stands for central processing unit, GPU means graphics processing unit. What is more crazy is that almost all computers have a CPU and a GPU. The CPU does almost all computing to run the various applications (programs) on the computer. The GPU runs the graphics card, but not all GPU’s are easy to use for math calculations beside its graphics.
Then Google is working on a variety of custom machine learning chips that are even faster.
Convolutional Neural Networks got inspired from the way our human visual cortex works (human eye) and classifies images like dog, cat, man, women, car, jet, or computer.
Convolutional Neural Networks are used for the classification of images.They do a great job because the training of the neural network breaks up the image into component parts and does classification based on various parts of the image. So a dog or cat or numerical number is learned by the various properties of the image. Here are some great links:
best tutorial: Convolutional Neural Networks (CNNs / ConvNets)
Google’s TensorFlow neural networks
Visualizing and Understanding Deep Neural Networks by Matt Zeiler
Visualizing and Understanding Convolutional Networks
Deep Learning Reading List
Google’s TensorFlow: Convolutional Neural Networks
wow, look at the ability for Machine Learning and Artificial Intelligence to learn your music rhythm and melody and play a duet on it.
It is here based on Google’s TensorFlow Machine Learning open source software. Check out this link about “Make Music and Art Using Machine Learning” at magenta.tensorflow.org Then look at Learning from A.I. Duet . Some real possibilities for expanding music and allowing software to help accompany the musician. Yes this will not make one a better musician, but it sure helps to make more music. The software is part of the work of the Google Brain team who work on Magenta project in collaboration with the Google Creative Lab team.
Now to take this to the next level, consider this note on
Style-conditioned music generation