Results 91 to 100 of about 208,150 (274)
Unsupervised Learning using Pretrained CNN and Associative Memory Bank
, 2018 Deep Convolutional features extracted from a comprehensive labeled dataset, contain substantial representations which could be effectively used in a new domain.
Liu, Qun, Mukhopadhyay, Supratik
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A CMOS Spiking Neuron for Brain-Inspired Neural Networks with Resistive Synapses and In-Situ Learning [PDF]
, 2015 Nanoscale resistive memories are expected to fuel dense integration of electronic synapses for large-scale neuromorphic system. To realize such a brain-inspired computing chip, a compact CMOS spiking neuron that performs in-situ learning and computing ...
Balagopal, Sakkarapani +3 more
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Overview of molecular signatures of senescence and associated resources: pros and cons
FEBS Open Bio, EarlyView.Cells can enter a stress response state termed cellular senescence that is involved in various diseases and aging. Detecting these cells is challenging due to the lack of universal biomarkers. This review presents the current state of senescence identification, from biomarkers to molecular signatures, compares tools and approaches, and highlights ...
Orestis A. Ntintas +6 more
wiley +1 more source
Nature CommunicationsLearning by experience that certain cues in the environment predict danger is crucial for survival. How dopamine (DA) circuits drive this form of associative learning is not fully understood.
Daphne Zafiri +4 more
doaj +1 more source
Integrative priming occurs rapidly and uncontrollably during lexical processing [PDF]
, 2009 Lexical priming, whereby a prime word facilitates recognition of a related target word (e.g., nurse ? doctor), is typically attributed to association strength, semantic similarity, or compound familiarity.
Estes, Zachary, Jones, Lara L.
core +2 more sources
Enzymatic degradation of biopolymers in amorphous and molten states: mechanisms and applications
FEBS Open Bio, EarlyView.This review explains how polymer morphology and thermal state shape enzymatic degradation pathways, comparing amorphous and molten biopolymer structures. By integrating structure–reactivity principles with insights from thermodynamics and enzyme engineering, it highlights mechanisms that enable efficient polymer breakdown.
Anđela Pustak, Aleksandra Maršavelski
wiley +1 more source
Individual and global adaptation in networks
, 2012 The structure of complex biological and socio-economic networks affects the selective pressures or behavioural incentives of components in that network, and reflexively, the evolution/behaviour of individuals in those networks changes the structure of ...
Watson, Richard
core
FEBS Open Bio, EarlyView.In this study, we developed a deep learning method for mitotic figure counting in H&E‐stained whole‐slide images and evaluated its prognostic impact in 13 external validation cohorts from seven different cancer types. Patients with more mitotic figures per mm2 had significantly worse patient outcome in all the studied cancer types except colorectal ...
Joakim Kalsnes +32 more
wiley +1 more source
Dense Associative Memory for Pattern Recognition
, 2016 A model of associative memory is studied, which stores and reliably retrieves many more patterns than the number of neurons in the network. We propose a simple duality between this dense associative memory and neural networks commonly used in deep ...
Hopfield, John J, Krotov, Dmitry
core
An associative memory for the on-line recognition and prediction of temporal sequences
, 2005 This paper presents the design of an associative memory with feedback that is capable of on-line temporal sequence learning. A framework for on-line sequence learning has been proposed, and different sequence learning models have been analysed according ...
Bose, J., Furber, S. B., Shapiro, J. L.
core +1 more source