Efforts to design devices emulating complex cognitive abilities and response processes of
biological systems have long been a coveted goal. Recent advancements in flexible …

Neuromorphic computing technologies will be important for the future of computing, but
much of the work in neuromorphic computing has focused on hardware development. Here …

The brain has effectively proven a powerful inspiration for the development of computing
architectures in which processing is tightly integrated with memory, communication is event …

Though neuromorphic computers have typically targeted applications in machine learning
and neuroscience ('cognitive'applications), they have many computational characteristics …

The potential of neuromorphic computing to bring about revolutionary advancements in
multiple disciplines, such as artificial intelligence (AI), robotics, neurology, and cognitive …

The growth of machine learning, combined with the approaching limits of conventional
digital computing, are driving a search for alternative and complementary forms of …

In the past 20 years, microelectronics technology has seen a tapering off of Moore's law (the
exponential growth of transistor density on computer chips) and an end to Dennard scaling …

Neuromorphic computing faces long-standing challenges in handling unknown situations
beyond the preset boundaries, resulting in catastrophic information loss and model failure …

Probabilistic computing using random number generators (RNGs) can leverage the inherent
stochasticity of nanodevices for system-level benefits. Device candidates for this application …

Background: Cuproptosis is a recently discovered form of programmed cell death.
Ferredoxin 1 (FDX1) is a key gene that mediates this process. However, the role of FDX1 in …