Related papers: High order synaptic learning in neuro-mimicking re…
Due to many limitations of Von Neumann architecture such as speed, memory bandwidth, efficiency of global interconnects and increase in the application of artificial neural network, researchers have been pushed to look into alternative…
This article presents a spiking neuroevolutionary system which implements memristors as plastic connections, i.e. whose weights can vary during a trial. The evolutionary design process exploits parameter self-adaptation and variable…
Neuromorphic computing aims to mimic both the function and structure of biological neural networks to provide artificial intelligence with extreme efficiency. Conventional approaches store synaptic weights in non-volatile memory devices…
We have calculated the key characteristics of associative (content-addressable) spatial-temporal memories based on neuromorphic networks with restricted connectivity - "CrossNets". Such networks may be naturally implemented in…
Brain-inspired computing and neuromorphic hardware are promising approaches that offer great potential to overcome limitations faced by current computing paradigms based on traditional von-Neumann architecture. In this regard, interest in…
Hardware spiking neural networks hold the promise of realizing artificial intelligence with high energy efficiency. In this context, solid-state and scalable memristors can be used to mimic biological neuron characteristics. However, these…
Thermodynamic-driven filament formation in redox-based resistive memory and the impact of thermal fluctuations on switching probability of emerging magnetic switches are probabilistic phenomena in nature, and thus, processes of binary…
Computation on a large volume of data at high speed and low power requires energy-efficient computing architectures. Spiking neural network (SNN) with bio-inspired spike-timing-dependent plasticity learning (STDP) is a promising solution…
Neuromorphic computing based on spiking neural networks has the potential to significantly improve on-line learning capabilities and energy efficiency of artificial intelligence, specially for edge computing. Recent progress in…
Amorphous insulators have localized wave functions that decay with the distance $r$ following exp($-r/\zeta$). Since nanoscale conduction is not excluded at $r<\zeta$, one may use amorphous insulators and take advantage of their size effect…
Neuromorphic computing architectures enable the dense co-location of memory and processing elements within a single circuit. This co-location removes the communication bottleneck of transferring data between separate memory and computing…
Memristors are promising next-generation memory candidates that are nonvolatile, possess low power requirements and are capable of nanoscale fabrication. In this article we physically realise and describe the use of organic memristors in…
In this research, we report the fabrication and characterization of a Ta/HfO2-x/Mo memristor. The synaptic behavior of a Ta/HfO2-x/Mo memristor has been investigated. HfO2-x (15 nm) was grown using the pulsed laser deposition (PLD) method.…
Resistive memory (RM) based neuromorphic systems can emulate synaptic plasticity and thus support continual learning, but they generally lack biologically inspired mechanisms for active forgetting, which are critical for meeting modern data…
The ever-increasing amount of data from ubiquitous smart devices fosters data-centric and cognitive algorithms. Traditional digital computer systems have separate logic and memory units, resulting in a huge delay and energy cost for…
The possibility to develop neuromorphic computing devices able to mimic the extraordinary data processing capabilities of biological systems spurs the research on memristive systems. Memristors with additional functionalities such as robust…
The emergence of nano-scale memristive devices encouraged many different research areas to exploit their use in multiple applications. One of the proposed applications was to implement synaptic connections in bio-inspired neuromorphic…
Future neuromorphic architectures will require millions of artificial synapses, making understanding the physical mechanisms behind their plasticity functionalities mandatory. In this work, we propose a simplified spin memristor, where the…
Organic neuromorphic device networks can accelerate neural network algorithms and directly integrate with microfluidic systems or living tissues. Proposed devices based on the bio-compatible conductive polymer PEDOT:PSS have shown high…
Neuromorphic computing is a brainlike information processing paradigm that requires adaptive learning mechanisms. A spiking neuro-evolutionary system is used for this purpose; plastic resistive memories are implemented as synapses in…