Results 41 to 50 of about 60,411 (286)
Spiking neural networks for computer vision [PDF]
Interface Focus, 2018 Abstract State-of-the-art computer vision systems use frame-based cameras that sample the visual scene as a series of high-resolution images. These are then processed using convolutional neural networks using neurons with continuous outputs.
Michael Hopkins +3 more
openaire +5 more sources
, 2017 Information in neural networks is represented as weighted connections, or synapses, between neurons. This poses a problem as the primary computational bottleneck for neural networks is the vector-matrix multiply when inputs are multiplied by the neural ...
Aimone, James B. +9 more
core +1 more source
Is Spiking Secure? A Comparative Study on the Security Vulnerabilities of Spiking and Deep Neural Networks [PDF]
, 2020 Spiking Neural Networks (SNNs) claim to present many advantages in terms of biological plausibility and energy efficiency compared to standard Deep Neural Networks (DNNs).
Hanif, Muhammad Abdullah +5 more
core +2 more sources
Neuromorphic Sentiment Analysis Using Spiking Neural Networks
Sensors, 2023 Over the past decade, the artificial neural networks domain has seen a considerable embracement of deep neural networks among many applications. However, deep neural networks are typically computationally complex and consume high power, hindering their ...
Raghavendra K. Chunduri +1 more
doaj +1 more source
Adaptive motor control and learning in a spiking neural network realised on a mixed-signal neuromorphic processor [PDF]
, 2018 Neuromorphic computing is a new paradigm for design of both the computing hardware and algorithms inspired by biological neural networks. The event-based nature and the inherent parallelism make neuromorphic computing a promising paradigm for building ...
Glatz, Sebastian +4 more
core +1 more source
Supervised Associative Learning in Spiking Neural Network [PDF]
, 2005 In this paper, we propose a simple supervised associative learning approach for spiking neural networks. In an excitatory-inhibitory network paradigm with Izhikevich spiking neurons, synaptic plasticity is implemented on excitatory to excitatory synapses
F. Crick +6 more
core +1 more source
Advancing Neural Networks: Innovations and Impacts on Energy Consumption
Advanced Electronic MaterialsThe energy efficiency of Artificial Intelligence (AI) systems is a crucial and actual issue that may have an important impact on an ecological, economic and technological level.
Alina Fedorova +9 more
doaj +1 more source
Research on Anti-Interference Performance of Spiking Neural Network Under Network Connection Damage
Brain SciencesBackground: With the development of artificial intelligence, memristors have become an ideal choice to optimize new neural network architectures and improve computing efficiency and energy efficiency due to their combination of storage and computing ...
Yongqiang Zhang +5 more
doaj +1 more source
Gut microbiome and aging—A dynamic interplay of microbes, metabolites, and the immune system
FEBS Letters, EarlyView.Age‐dependent shifts in microbial communities engender shifts in microbial metabolite profiles. These in turn drive shifts in barrier surface permeability of the gut and brain and induce immune activation. When paired with preexisting age‐related chronic inflammation this increases the risk of neuroinflammation and neurodegenerative diseases.
Aaron Mehl, Eran Blacher
wiley +1 more source
High-performance deep spiking neural networks with 0.3 spikes per neuron
Nature CommunicationsCommunication by rare, binary spikes is a key factor for the energy efficiency of biological brains. However, it is harder to train biologically-inspired spiking neural networks than artificial neural networks.
Ana Stanojevic +5 more
doaj +1 more source