Results 111 to 120 of about 16,068 (283)
, 2019 Spiking neural networks (SNNs) equipped with latency coding and spike-timing dependent plasticity rules offer an alternative to solve the data and energy bottlenecks of standard computer vision approaches: they can learn visual features without ...
Bilasco, Ioan Marius +4 more
core +1 more source
Shaping of STDP curve by interneuron and Ca2+dynamics [PDF]
BMC Neuroscience, 2007 Spike-timing-dependent-plasticity (STDP)[1,2] is a special form of Hebbian learning [3] where the relative timing of post- and presynaptic activity determines the change in synaptic weight. More familiarly, the postsynaptic and presynaptic activity correspond respectively to the derivative of the membrane potential Vm and the NMDA channel activation [4]
Florentin Wörgötter +3 more
openaire +3 more sources
Digital Implementation of a Spiking Convolutional Neural Network for Tumor Detection [PDF]
Informacije MIDEM, 2020 A. Adineh-vand, G. Karimi, M. Khazaei
doaj +1 more source
Frontiers in Computational Neuroscience, 2020 Spiking Neural Networks (SNNs) are considered as the third generation of artificial neural networks, which are more closely with information processing in biological brains.
Dongcheng Zhao +4 more
semanticscholar +1 more source
VIEW, EarlyView.Existing clinical approaches for assessing anesthesia depth often rely on physiological and behavioral indicators, which can be unreliable due to uncertain correlation with patient consciousness. This study presents a neuromorphic SNN with STDP, implemented on an field‐programmable gate array (FPGA) platform, enabling real‐time iEEG‐based assessment of
Ke Chen +7 more
wiley +1 more source
ASP: Learning to Forget with Adaptive Synaptic Plasticity in Spiking Neural Networks
, 2018 A fundamental feature of learning in animals is the "ability to forget" that allows an organism to perceive, model and make decisions from disparate streams of information and adapt to changing environments.
Allred, Jason M. +3 more
core +1 more source
Learning weights with STDP to build prototype images for classification
2019 14th International Conference on Design & Technology of Integrated Systems In Nanoscale Era (DTIS), 2019 The combination of Spike Timing Dependent Plasticity (STDP) and latency coding used in a spiking neural network has been shown to learn hierarchical features. In this paper we propose a new way to classify images using an SVM. Prototype images are built from the weights learned in an unsupervised manner using STDP.
Vasudevan, Ajay +2 more
openaire +3 more sources
Temporal Changes in Tasmanian Devil Genetic Diversity at Sites With and Without Supplementation
Molecular Ecology, EarlyView.ABSTRACT Management interventions for threatened species are well documented with genetic data now playing a pivotal role in informing their outcomes. However, in situ actions like supplementations (releasing individuals into an existing population) are often restricted to a singular site. Considerable research and management effort have been dedicated
Andrea L. Schraven +7 more
wiley +1 more source
Plant Biology, EarlyView.Steroidal glycoside diversity among organs across ontogeny in relation to candidate gene expression in two Solanum dulcamara chemotypes. Abstract Solanaceous plants, such as Solanum dulcamara, produce steroidal glycosides (SGs). Leaf SG profiles vary among S.
R. A. Anaia +5 more
wiley +1 more source
Voltage and spike timing interact in STDP - a unified model [PDF]
Frontiers in Synaptic Neuroscience, 2010 A phenomenological model of synaptic plasticity is able to account for a large body of experimental data on spike-timing-dependent plasticity (STDP). The basic ingredient of the model is the correlation of presynaptic spike arrival with postsynaptic voltage.
Wulfram Gerstner +2 more
openaire +5 more sources