相关论文: Temporal credit assignment for one-shot learning u…
Nonequilibrium phase transitions driven by light pulses represent a rapidly developing field in condensed matter physics. As one of the archetypal strongly correlated materials, vanadium dioxide (VO2) undergoes a structural phase transition…
Memristive devices represent a promising technology for building neuromorphic electronic systems. In addition to their compactness and non-volatility features, they are characterized by computationally relevant physical properties, such as…
Transimpedance amplifiers (TIAs) are essential in sensor electronics, converting input currents into output voltages. Conventional TIAs utilize fixed-gain resistors, which saturate under high input currents and consequently result in…
Optical tweezers revolutionized the manipulation of nanoscale objects. Typically, tunable manipulations of optical tweezers rely on adjusting either the trapping laser beams or the optical environment surrounding the nanoparticles. We…
Spiking neural networks (SNN) are artificial computational models that have been inspired by the brain's ability to naturally encode and process information in the time domain. The added temporal dimension is believed to render them more…
Materials with strong electronic Coulomb interactions play an increasing role in modern materials applications. "Thermochromic" systems, which exhibit thermally induced changes in their optical response, provide a particularly interesting…
We demonstrate an innovative multifunctional artificial material that combines exotic metamaterial properties and the environmentally responsive nature of phase change media. The tunable metamaterial is designed with the aid of two…
Spiking artificial neurons emulate the voltage spikes of biological neurons, and constitute the building blocks of a new class of energy efficient, neuromorphic computing systems. Antiferromagnetic materials can, in theory, be used to…
The computational efficiency of the human brain is believed to stem from the parallel information processing capability of neurons with integrated storage in synaptic interconnections programmed by local spike triggered learning rules such…
There is a growing demand for low-power, autonomously learning artificial intelligence (AI) systems that can be applied at the edge and rapidly adapt to the specific situation at deployment site. However, current AI models struggle in such…
We consider the information transmission problem in neurons and its possible implications for learning in neural networks. Our approach is based on recent developments in statistical physics and complexity science. Combining sensory…
The demand for cryogenic memory components is driven by the need for ultra-fast, low-power, and highly reliable computing systems. Phase slip-based devices promise to fulfill all these requirements, with potential applications in both…
Experimental insight in the nanoscale dynamics underlying switching in novel memristive devices is limited owing to the scarcity of techniques that can probe the electronic structure of these devices. Scattering scanning near-field optical…
Vanadium dioxide with metal-to-insulator transition (MIT) that is triggered by heat, current or light is a promising material for modern active THz/mid-IR metasurfaces and all-optical big data processing systems. Multilayer VO2-based active…
Mimicking the collective excitatory and inhibitory behaviors of biological neurons remains a critical challenge in the development of neuromorphic computing systems that rival the complexity and performance of the human brain. Volatile…
In this paper we present a simple microscopic stochastic model describing short term plasticity within a large homogeneous network of interacting neurons. Each neuron is represented by its membrane potential and by the residual calcium…
Advanced neural interfaces mediate a bio-electronic link between the nervous system and microelectronic devices, bearing great potential as innovative therapy for various diseases. Spikes from a large number of neurons are recorded leading…
Neuromorphic computing, inspired by the brain's parallel and energy-efficient processing, offers a transformative approach to artificial intelligence. In this study, we fabricated optimized spin-transfer torque nano-oscillators (STNOs) and…
The insulator-to-metal transition (IMT) in strongly correlated materials, such as vanadium dioxide (VO2), offers a transformative platform for next-generation adaptive electronics and neuromorphic computing. However, harnessing this…
Artificial neural networks and computational neuroscience models have made tremendous progress, allowing computers to achieve impressive results in artificial intelligence (AI) applications, such as image recognition, natural language…