Brain-to-brain interfaces: A first step into an organic computer?

Neuroscientists at Duke University Medical Center recently make a lot of news describing brain-to-computer interfaces and thought-controlled robotics.

Few months ago, we saw quadriplegic patients using brain implants to control robot limbs. More recently, the researchers in the group of Miguel Nicolelis went a step further showing that rats were able to communicate through brain chips and collaborate on performing a task.
Who doesn’t immediately think of mind reading and mind control? Continue reading “Brain-to-brain interfaces: A first step into an organic computer?” »

Computer Scientist’s View on Graph Theory

‘Graph Theory’ plays a vital role in a vast number of algorithms in today’s computer and software engineering.

When computer scientists confer about ‘Graph Theory’ they most of the time think about anything from data relationships in a database to decision chains or network diagrams.

In principle, any graph has two essential elements: Continue reading “Computer Scientist’s View on Graph Theory” »

Barcoding and Sequencing the Connectome – New Ways in Neuroscience

With exponential innovative growth of computer and software tools during the last decade, genetic sequencing costs continuously drop and drive a new thinking in medical research.
One example is understanding the connectivity of neurons in the mammalian brain which is central part of neuroanatomical studies. Connectome projects, which focus on finding innovative ways to understand neural connections in the brain, add fundamental understanding the functionality of the brain and the development of diseases such as autism, schizophrenia or ADHD. Continue reading “Barcoding and Sequencing the Connectome – New Ways in Neuroscience” »

Computer Algorithms that “Read” Emotions

The visual expression of emotion is a complex process. Reading and interpreting the expressed emotions of others is puzzling and remains a mystery for many of us.
This may change soon by implementing emotion reader technology into our lives that may enable us to easily read and understand emotions of people around us.

Several research groups around the world created complex algorithms that allow training computer systems to read human emotions based on facial expressions, the tone of voice, body movement and lip patterns etc.

The MIT’s Media Lab and cooperation partners recently developed glasses that identify 24 feature points on a person’s face and interpret micro-expressions. This technology makes it possible to ‘read’ visual emotional signs.
Moreover, the user gets the information about what the other person is feeling using earphones and a green-red-light system on the lens of the glasses (good emotions=green, bad emotions=red).

Another group of the MIT Media Lab developed the “jerk-o-meter” – a technology that interprets gesture mirroring and variations of the tone of voice using an electronic badge around the neck. Among other factors, the audio sensors read the degree of aggressive behavior of the wearer, the pitch of the voice and the volume of vocal sound.
The data from the badge can be sent to other devices such as smartphones where it can be graphically displayed.

Most recently, a group at the International University in Selangor, Malaysia developed a genetic algorithm that interprets the shape of the human mouth displaying different emotions. Their research is based on the knowledge that lips are vital for the outward expression of emotion. Both, the upper and lower lips, are analyzed as two separate ellipses by the algorithm. The researchers used photos of persons to train a computer to identify six most common human emotions including happiness, sadness, fear, angry, disgust, surprise and a neutral expression.
One day, such an emotion detector may be helping persons with an insufficient ability of speech to interact more effectively with computer-based devices for information exchange and even allow the development of improved voice synthesizers that facilitate communication of disabled individuals.

The growing technologies are improving all aspects of interaction between humans and computers especially in the area of human emotion recognition. It will open up a new area how we interact with our devices, how our devices interact with us and even how we interact with each other.

Neural Networks IV – Medical Innovation of Tomorrow

Artificial Neural Networks (ANNs) are one of the “hot” topics in creating innovative medical diagnosis and treatment software for patient-centered medicine.
Neural networks are a class of pattern recognition methods to model the biological neuron to process non-linear or chaotic information where no algorithmic solution is possible or the solution is too complex for traditional techniques.

Recent ANN applications include the modeling of the human body recognizing patterns in scans such as MRI, CAT scans or X-rays but are also used for analyzing brain maturation, ultrasound pictures or cardiograms. They are applied to resolve different diagnostic problems such as detection and classification of cancer, cardiovascular diseases and the processing of EEG signals. Continue reading “Neural Networks IV – Medical Innovation of Tomorrow” »

Neuronal Networks in Business and Marketing

Artificial neural networks (ANN) imitate the principles of the biological brain and are progressively more applied across research, medical, engineering, social science and other fields. They also deliver substantial benefits in business applications.

Today’s world is multifaceted and there are countless inter-related variables that make predicting business outcomes very difficult. ANNs are the modern computational tools to study data and develop models to identify patterns and structures in data that offer Continue reading “Neuronal Networks in Business and Marketing” »

Artificial Neural Networks – Learning Smart

Both, the learning of human neurons and artificial neural networks (ANNs) occur in a training phase and an operation phase. Feed-forward ANNs are very steady and may range over multiple units without having any feedback connections present whereas recurrent ANNs, that do contain feedback connections and provide dynamic network components, that allow developing a dynamic behavior of output patterns of the neural network.
An ANN learns “off-line” if the learning and operation phases are separate and it learns “on-line” if learning and operating are performed at the same time Continue reading “Artificial Neural Networks – Learning Smart” »

Neural Networks I – Artificial Neural Networks

Artificial neural networks (ANNs) are an attempt to simulate the human brain within a computer system.

Both, biological and artificial neuronal networks are built from atomic modules – the ‘neurons’. The human brain has up to 150 billion neurons with hundreds of different classified subtypes. Each neuron usually has one axon, which expands off from a part of the cell body to conduct electrical signals to other cells. Thousands of interconnected neurons build separate groups that are called networks.
Biological neurons can be classified into ‘sensory neurons’ that provide all information for perception and motor coordination, ‘motor neurons’ which transfer signals to muscles and glands and ‘inter-neuronal neurons’ that are needed to relay information, protect other neurons or connect different parts of the brain. These neurons have linear and non-linear relationships to obtain and transfer information as well as to build memory. Continue reading “Neural Networks I – Artificial Neural Networks” »