In 2008, the journalist Jonah Lehrer paid a visit to a lab in Lausanne, Switzerland, to profile Henry Markram, a world-renowned neuroscientist. Markram, a South African, had trained at a series of élite institutions in Israel, the United States, and Germany; in the nineties, he published foundational papers on neural connections and synaptic activity. Markram's work in the laboratory, which involved piercing neural membranes with what Lehrer described as an "invisibly sharp glass pipette," was known for its painstaking precision. Lehrer's visit, however, had been occasioned not by Markram's incremental contributions to the field--it's not easy to sell a colorful profile on the basis of such publications as "The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability"--but by Markram's pivot, in the early two-thousands, to brain simulation. Neuroscience, Markram declaimed to Lehrer, had reached an impasse. Researchers had generated an enormous wealth of fine-grained data, but the marginal returns had begun to diminish.

Here's a look at how HPC, AI, and other technologies are being used throughout the world by organizations to enhance healthcare research, drug development, public health, and patient outcomes. The ability to gather, process, and analyze data from genomics, bioinformatics, microscopy, medical imaging, and other areas in the life sciences has been supercharged with HPC systems and artificial intelligence (AI) algorithms. Researchers can sequence vast quantities of DNA data faster than ever before with supercomputer resources and use AI to identify patterns and make predictions. They can now use these available and affordable technologies to study genes and proteins, to predict health events, automate imaging analysis, and generate ideas for improving healthcare delivery. Here's a look at how HPC, AI, and other technologies are being used throughout the world by organizations to enhance healthcare research, drug development, public health, and patient outcomes.

To achieve this, the researchers link two types of deep learning networks. The feedback neuronal networks are responsible for "short-term memory," and recurrent modules filter out possible relevant information from the input signal and store it. A feed-forward network determines which of the relationships found are important for solving the current task. Relationships that are meaningless are filtered out, and the neurons only fire in those modules where relevant information has been found. This entire process is what leads to dramatic energy savings.

The AI revolution is upon us, creating a tsunami of change that is sweeping through virtually every sector of the economy, both public and private, and it's having a profound impact on our daily lives already. As computer scientist and venture capitalist Kai-Fu Lee told CBS back in 2018: "I believe [AI] is going to change the world more than anything in the history of mankind. PwC's recent global AI study predicts that AI's contribution to the global economy will exceed $15.7 trillion by 2030. AI is already changing the way you drive, communicate, shop for goods and services, and much more. As The New York Times noted, Boeing 777 pilots spend only about seven minutes manually flying the aircraft on a typical flight.

The nerve cells (or neurons) in the brain transmit information using short electrical pulses known as spikes. These spikes are triggered when a certain stimulus threshold is exceeded. Both the frequency with which a single neuron produces such spikes and the temporal sequence of the individual spikes are critical for the exchange of information. "The main difference of biological spiking networks to artificial neural networks is that, because they are using spike-based information processing, they can solve complex tasks such as image recognition and classification with extreme energy efficiency," states Julian Göltz, a doctoral candidate in Dr Petrovici's research group. Both the human brain and the architecturally similar artificial spiking neural networks can only perform at their full potential if the individual neurons are properly connected to one another. But how can brain-inspired -- that is, neuromorphic -- systems be adjusted to process spiking input correctly?

Summary: Combining deep learning algorithms with robotic engineering, researchers have developed a new robot able to combine vision and touch. On the new EBRAINS research infrastructure, scientists of the Human Brain Project have connected brain-inspired deep learning to biomimetic robots. How the brain lets us perceive and navigate the world is one of the most fascinating aspects of cognition. When orienting ourselves, we constantly combine information from all six senses in a seemingly effortless way–a feature that even the most advanced AI systems struggle to replicate. On the new EBRAINS research infrastructure, cognitive neuroscientists, computational modelers, and roboticists are now working together to shed new light on the neural mechanisms behind this, by creating robots whose internal workings mimic the brain.

Neural networks in both biological settings and artificial intelligence distribute computation across their neurons to solve complex tasks. New research now shows how so-called "critical states" can be used to optimize artificial neural networks running on brain-inspired neuromorphic hardware. The study was carried out by scientists from Heidelberg University working within the Human Brain Project, and the Max-Planck-Institute for Dynamics and Self-Organization (MPIDS). The results have been published in Nature Communications. Many computational properties are maximized when the dynamics of a network are at a "critical point", a state where systems can quickly change their overall characteristics in fundamental ways, transitioning e.g. between order and chaos or stability and instability. Therefore, the critical state is widely assumed to be optimal for any computation in recurrent neural networks, which are used in many AI applications.

The high energy consumption of artificial neural networks' learning activities is one of the biggest hurdles for the broad use of Artificial Intelligence (AI), especially in mobile applications. One approach to solving this problem can be gleaned from knowledge about the human brain. Although it has the computing power of a supercomputer, it only needs 20 watts, which is only a millionth of the energy of a supercomputer. One of the reasons for this is the efficient transfer of information between neurons in the brain. Neurons send short electrical impulses (spikes) to other neurons--but, to save energy, only as often as absolutely necessary.

The e-prop learning method developed at Graz University of Technology forms the basis for drastically more energy-efficient hardware implementations of Artificial Intelligence. The high energy consumption of artificial neural networks' learning activities is one of the biggest hurdles for the broad use of Artificial Intelligence (AI), especially in mobile applications. One approach to solving this problem can be gleaned from knowledge about the human brain. Although it has the computing power of a supercomputer, it only needs 20 watts, which is only a millionth of the energy of a supercomputer. One of the reasons for this is the efficient transfer of information between neurons in the brain.

The Helmholtz International BigBrain Analytics and Learning Laboratory (HIBALL) is a collaboration between McGill University and Forschungszentrum Jülich to develop next-generation high-resolution human brain models using cutting-edge Machine- and Deep Learning methods and high-performance computing. HIBALL is based on the high-resolution BigBrain model first published by the Jülich and McGill teams in 2013. Over the next five years, the lab will be funded with a total of up to 6 million Euro by the German Helmholtz Association, Forschungszentrum Jülich, and Healthy Brains, Healthy Lives at McGill University. In 2003, when Jülich neuroscientist Katrin Amunts and her Canadian colleague Alan Evans began scanning 7,404 histological sections of a human brain, it was completely unclear whether it would ever be possible to reconstruct this brain on the computer in three dimensions. At that time, there were no technical possibilities to cope with the huge amount of data.