Brain waves, Sharp-wave Ripples, SWRs from low intensity electrical stimulation

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/

Wow, Python Programming Language is Number Three

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.

Cannon Beach at night

Training the brain to think in new ways

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.

Cannon Beach, Oregon

Cannon Beach, Oregon

Our Brain and Music

Music and Piano

Music and Piano

“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

 

 

 

 

Video Processing by Neural Networks

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.

Computer go faster – GPU’s

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.

just a snowblower, not a CPU or GPU.

Convolutional Neural Networks

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

Music — Artificial Intelligence

wow, look at the ability for Machine Learning and Artificial Intelligence to learn your music rhythm and melody and play a duet on it.

Try it: Here!    How does it work: Here!

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

Piano in the sunlight

Piano in the sunlight

Artificial Intelligence – notes

With machine learning, Neural Networks are looking promising for deep learning. But there is the rise of Bayesians, which is researchers doing Artificial Intelligence (AI) through the scientific method starting with an hypothesis. There is lots of trial and error designing a Neural Network, lots of parameters to tweak as one tries to get the data to correlate. Can machine learning and neural networks help us design better neural networks?

Great video on Deep Learning  and Multilayer Neural Networks,  Deep Learning Summer School, Montreal 2015

Visual features: From Fourier to Gabor,  Deep Learning Summer School, Montreal 2015

What is a MOOC? —  a study course made available over the Internet for free to a large number of people

The Columbia River in the snow

The Columbia River in the snow

Creativity and more…

To be creative, one must start somewhere. Creativity is bringing things together. But just brain-storm and consider things even outside the box, things that are impossible to achieve (so we think).  Try a different mathematical solution, a different marketing or engineering solution.  Success sometimes comes by just tweaking things just a little.

Consider the configuration of artificial intelligence and deep learning neural networks. There are so many ways to configure and initialize them.  So many different types of neural nodes and ways to mathematically connect the nodes also called Perceptrons.

A person who never made a mistake never tried anything new — possibly said by Albert Einstein

Here is a great set of project results to consider for stimulating creativity in Machine Learning…
Higgs Boson Machine Learning Challenge

near the Columbia River in the snow

near the Columbia River in the snow