Deep learning in neural networks: An overview
J Schmidhuber - Neural networks, 2015 - Elsevier
In recent years, deep artificial neural networks (including recurrent ones) have won
numerous contests in pattern recognition and machine learning. This historical survey …
numerous contests in pattern recognition and machine learning. This historical survey …
A survey of neuromorphic computing and neural networks in hardware
Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices,
and models that contrast the pervasive von Neumann computer architecture. This …
and models that contrast the pervasive von Neumann computer architecture. This …
Experimental demonstration of associative memory with memristive neural networks
YV Pershin, M Di Ventra - Neural networks, 2010 - Elsevier
Synapses are essential elements for computation and information storage in both real and
artificial neural systems. An artificial synapse needs to remember its past dynamical history …
artificial neural systems. An artificial synapse needs to remember its past dynamical history …
Neuromorphic, digital, and quantum computation with memory circuit elements
YV Pershin, M Di Ventra - Proceedings of the IEEE, 2011 - ieeexplore.ieee.org
Memory effects are ubiquitous in nature and the class of memory circuit elements—which
includes memristive, memcapacitive, and meminductive systems—shows great potential to …
includes memristive, memcapacitive, and meminductive systems—shows great potential to …
Challenges for large-scale implementations of spiking neural networks on FPGAs
The last 50 years has witnessed considerable research in the area of neural networks
resulting in a range of architectures, learning algorithms and demonstrative applications. A …
resulting in a range of architectures, learning algorithms and demonstrative applications. A …
Compact hardware liquid state machines on FPGA for real-time speech recognition
B Schrauwen, M D'Haene, D Verstraeten… - Neural networks, 2008 - Elsevier
Hardware implementations of Spiking Neural Networks are numerous because they are well
suited for implementation in digital and analog hardware, and outperform classic neural …
suited for implementation in digital and analog hardware, and outperform classic neural …
An FPGA platform for real-time simulation of spiking neuronal networks
In the last years, the idea to dynamically interface biological neurons with artificial ones has
become more and more urgent. The reason is essentially due to the design of innovative …
become more and more urgent. The reason is essentially due to the design of innovative …
Implementing spiking neural networks for real-time signal-processing and control applications: A model-validated FPGA approach
In this paper, we present two versions of a hardware processing architecture for modeling
large networks of leaky-integrate-and-flre (LIF) neurons; the second version provides …
large networks of leaky-integrate-and-flre (LIF) neurons; the second version provides …
Bioinspired and low-power 2D machine vision with adaptive machine learning and forgetting
A Dodda, D Jayachandran… - ACS …, 2022 - ACS Publications
Natural intelligence has many dimensions, with some of its most important manifestations
being tied to learning about the environment and making behavioral changes. In primates …
being tied to learning about the environment and making behavioral changes. In primates …
An all-in-one bioinspired neural network
In spite of recent advancements in artificial neural networks (ANNs), the energy efficiency,
multifunctionality, adaptability, and integrated nature of biological neural networks remain …
multifunctionality, adaptability, and integrated nature of biological neural networks remain …