Lifeboat Foundation

And this feature distinguishes neuromorphic systems from conventional computing systems. The brain has evolved over billions of years to solve difficult engineering problems by using efficient, parallel, low-power computation. The goal of NE is to design systems capable of brain-like computation. Numerous large-scale neuromorphic projects have emerged recently. This interdisciplinary field was listed among the top 10 technology breakthroughs of 2014 by the MIT Technology Review and among the top 10 emerging technologies of 2015 by the World Economic Forum. NE has two-way goals: one, a scientific goal to understand the computational properties of biological neural systems by using models implemented in integrated circuits (ICs); second, an engineering goal to exploit the known properties of biological systems to design and implement efficient devices for engineering applications. Building hardware neural emulators can be extremely useful for simulating large-scale neural models to explain how intelligent behavior arises in the brain. The principal advantages of neuromorphic emulators are that they are highly energy efficient, parallel and distributed, and require a small silicon area. Thus, compared to conventional CPUs, these neuromorphic emulators are beneficial in many engineering applications such as for the porting of deep learning algorithms for various recognitions tasks. In this review article, we describe some of the most significant neuromorphic spiking emulators, compare the different architectures and approaches used by them, illustrate their advantages and drawbacks, and highlight the capabilities that each can deliver to neural modelers. This article focuses on the discussion of large-scale emulators and is a continuation of a previous review of various neural and synapse circuits (Indiveri et al., 2011). We also explore applications where these emulators have been used and discuss some of their promising future applications.

“Building a vast digital simulation of the brain could transform neuroscience and medicine and reveal new ways of making more powerful computers” (Markram et al., 2011). The human brain is by far the most computationally complex, efficient, and robust computing system operating under low-power and small-size constraints. It utilizes over 100 billion neurons and 100 trillion synapses for achieving these specifications. Even the existing supercomputing platforms are unable to demonstrate full cortex simulation in real-time with the complex detailed neuron models. For example, for mouse-scale (2.5 × 10⁶ neurons) cortical simulations, a personal computer uses 40,000 times more power but runs 9,000 times slower than a mouse brain (Eliasmith et al., 2012). The simulation of a human-scale cortical model (2 × 10¹⁰ neurons), which is the goal of the Human Brain Project, is projected to require an exascale supercomputer (10¹⁸ flops) and as much power as a quarter-million households (0.5 GW).

The electronics industry is seeking solutions that will enable computers to handle the enormous increase in data processing requirements. Neuromorphic computing is an alternative solution that is inspired by the computational capabilities of the brain. The observation that the brain operates on analog principles of the physics of neural computation that are fundamentally different from digital principles in traditional computing has initiated investigations in the field of neuromorphic engineering (NE) (Mead, 1989a). Silicon neurons are hybrid analog/digital very-large-scale integrated (VLSI) circuits that emulate the electrophysiological behavior of real neurons and synapses. Neural networks using silicon neurons can be emulated directly in hardware rather than being limited to simulations on a general-purpose computer. Such hardware emulations are much more energy efficient than computer simulations, and thus suitable for real-time, large-scale neural emulations.

An international team of researchers, led by Swinburne University of Technology, demonstrated what it claimed is the world’s fastest and most powerful optical neuromorphic processor for artificial intelligence (AI). It operates faster than 10 trillion operations per second (TeraOPs/s) and is capable of processing ultra-large scale data.

The researchers said this breakthrough represents an enormous leap forward for neural networks and neuromorphic processing in general. It could benefit autonomous vehicles and data-intensive machine learning tasks such as computer vision.

Artificial neural networks can ‘learn’ and perform complex operations with wide applications. Inspired by the biological structure of the brain’s visual cortex system, artificial neural networks extract key features of raw data to predict properties and behaviour with unprecedented accuracy and simplicity.

Vanderbilt researchers have developed a new nanoparticle that can more get drugs inside cells to boost the immune system and fight diseases such as cancer.

The research is led by John Wilson, associate professor of chemical and biomolecular engineering and biomedical engineering, as well as a corresponding author on the paper about the research that was recently published in the journal Nanoscale.

Wilson, who is Principal Investigator of the Immunoengineering Lab at Vanderbilt and a Chancellor Faculty Fellow, and his team created a polymeric nanoparticle that can penetrate cell membranes and get drugs into the cytosol—or liquid—inside cells.

Weak laser light confined at the apex of a scanning tunnelling microscope tip can drive the tautomerization of a free-base phthalocyanine with atomic-scale precision. The combination of tip-enhanced photoluminescence spectroscopy and hyperspectral mapping paired with theoretical modelling then unravel an excited-state mediated reaction.

We now have a standard model of cosmology, the current version of the Big Bang theory. Although it has proved very successful, its consequences are staggering. We know only 5% of the content of the universe, which is normal matter. The remaining 95% is made up of two exotic entities that have never been produced in the laboratory and whose physical nature is still unknown.

These are dark matter, which accounts for 25% of the content of the cosmos, and dark energy, which contributes 70%. In the standard model of cosmology, dark energy is the energy of empty space, and its density remains constant throughout the evolution of the universe.

According to this theory, sound waves propagated in the very early universe. In those early stages, the universe had an enormous temperature and density. The pressure in this initial gas tried to push the particles that formed it apart, while gravity tried to pull them together, and the competition between the two forces created sound waves that propagated from the beginning of the universe until about 400,000 years after the Big Bang.

It might sound like something from science fiction, but the holographic principle might help us answer the biggest problem in modern physics.

Unless we solve the problem of consciousness, the endeavour remains a dead end.

Replication Crisis.

Revisiting the self-refilling bowl of soup.

A replication of a study is called into question by scientific misconduct.

Simple rules in simple settings continue to puzzle mathematicians, even as they devise intricate tools to analyze them.