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Exploring Continual Learning Strategies in Artificial Neural Net-Exploring Continual Learning Strategies in Artificial Neural Networks Through Graph-Based Analysis of Connectivity: Insights from a Brain-Inspired Perspective

https://doi.org/10.2139/ssrn.4904745
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inferredcites →https://doi.org/10.1007/978-3-030-58536-5_31openalex-snapshot
GDumb: A Simple Approach that Questions Our Progress in Continual Learning
inferredcites →https://doi.org/10.1007/978-3-030-67664-3_17openalex-snapshot
Topological Insights into Sparse Neural Networks
inferredcites →https://doi.org/10.1007/978-3-642-42051-1_16openalex-snapshot
Challenges in Representation Learning: A Report on Three Machine Learning Contests
inferredcites →https://doi.org/10.1007/978-3-642-77281-8_11openalex-snapshot
Difficult Cases in Handwritten Numeral Recognition
inferredcites →https://doi.org/10.1007/bf02478259openalex-snapshot
A logical calculus of the ideas immanent in nervous activity
inferredcites →https://doi.org/10.1007/s10994-016-5570-zopenalex-snapshot
A topological insight into restricted Boltzmann machines
inferredcites →https://doi.org/10.1007/s11571-011-9177-6openalex-snapshot
Synaptic consolidation: an approach to long-term learning
inferredcites →https://doi.org/10.1016/j.cell.2019.06.012openalex-snapshot
Human Replay Spontaneously Reorganizes Experience
inferredcites →https://doi.org/10.1016/j.eswa.2021.115073openalex-snapshot
Application of complex systems topologies in artificial neural networks optimization: An overview
inferredcites →https://doi.org/10.1016/j.euroneuro.2010.03.008openalex-snapshot
Exploring the brain network: A review on resting-state fMRI functional connectivity
inferredcites →https://doi.org/10.1016/j.neunet.2009.11.005openalex-snapshot
A simple method for designing efficient small-world neural networks
inferredcites →https://doi.org/10.1016/j.neunet.2019.01.012openalex-snapshot
Continual lifelong learning with neural networks: A review
inferredcites →https://doi.org/10.1016/j.neunet.2023.10.007openalex-snapshot
Graph-based methods coupled with specific distributional distances for adversarial attack detection
inferredcites →https://doi.org/10.1016/j.neuroimage.2021.118486openalex-snapshot
Networks underpinning emotion: A systematic review and synthesis of functional and effective connectivity
inferredcites →https://doi.org/10.1016/j.neuroimage.2021.118587openalex-snapshot
Similarity and stability of face network across populations and throughout adolescence and adulthood
inferredcites →https://doi.org/10.1016/j.neuron.2008.11.026openalex-snapshot
Computational Influence of Adult Neurogenesis on Memory Encoding
inferredcites →https://doi.org/10.1016/j.neuron.2009.07.018openalex-snapshot
Generating Coherent Patterns of Activity from Chaotic Neural Networks
inferredcites →https://doi.org/10.1016/j.neuron.2015.09.027openalex-snapshot
Brain Networks and Cognitive Architectures
inferredcites →https://doi.org/10.1016/j.neuron.2017.06.011openalex-snapshot
Neuroscience-Inspired Artificial Intelligence
inferredcites →https://doi.org/10.1016/j.neuron.2019.12.002openalex-snapshot
Direct Fit to Nature: An Evolutionary Perspective on Biological and Artificial Neural Networks
inferredcites →https://doi.org/10.1016/j.neuron.2020.06.014openalex-snapshot
Deep Reinforcement Learning and Its Neuroscientific Implications
inferredcites →https://doi.org/10.1016/j.patcog.2011.09.021openalex-snapshot
A novel hybrid CNN–SVM classifier for recognizing handwritten digits
inferredcites →https://doi.org/10.1016/j.physa.2023.128585openalex-snapshot
Structure and performance of fully connected neural networks: Emerging complex network properties
inferredcites →https://doi.org/10.1016/j.physleta.2004.12.078openalex-snapshot
Fastest learning in small-world neural networks
inferredcites →https://doi.org/10.1016/j.procs.2015.08.011openalex-snapshot
Facial Expression Recognition: A Survey
inferredcites →https://doi.org/10.1016/j.protcy.2012.02.062openalex-snapshot
Performance Analysis of A Feed-Forward Artifical Neural Network With Small-World Topology
inferredcites →https://doi.org/10.1016/j.tics.2020.09.004openalex-snapshot
Embracing Change: Continual Learning in Deep Neural Networks
inferredcites →https://doi.org/10.1016/j.tins.2004.12.003openalex-snapshot
Memory retention – the synaptic stability versus plasticity dilemma
inferredcites →https://doi.org/10.1016/s0893-6080(97)00011-7openalex-snapshot
Networks of spiking neurons: The third generation of neural network models
inferredcites →https://doi.org/10.1016/s0896-6273(00)80219-6openalex-snapshot
Response and Habituation of the Human Amygdala during Visual Processing of Facial Expression
inferredcites →https://doi.org/10.1016/s1364-6613(99)01294-2openalex-snapshot
Catastrophic forgetting in connectionist networks
inferredcites →https://doi.org/10.1037/0033-295x.102.3.419openalex-snapshot
Why there are complementary learning systems in the hippocampus and neocortex: Insights from the successes and failures of connectionist models of learning and memory.
inferredcites →https://doi.org/10.1037/0033-295x.99.3.550openalex-snapshot
Are there basic emotions?
inferredcites →https://doi.org/10.1037/h0042519openalex-snapshot
The perceptron: A probabilistic model for information storage and organization in the brain.
inferredcites →https://doi.org/10.1037/t27734-000openalex-snapshot
Facial Action Coding System
inferredcites →https://doi.org/10.1038/372669a0openalex-snapshot
Impaired recognition of emotion in facial expressions following bilateral damage to the human amygdala
inferredcites →https://doi.org/10.1038/383812a0openalex-snapshot
A differential neural response in the human amygdala to fearful and happy facial expressions
inferredcites →https://doi.org/10.1038/nature08577openalex-snapshot
Stably maintained dendritic spines are associated with lifelong memories
inferredcites →https://doi.org/10.1038/nature14251openalex-snapshot
Branch-specific dendritic Ca2+ spikes cause persistent synaptic plasticity
inferredcites →https://doi.org/10.1038/nature14539openalex-snapshot
Deep learning
inferredcites →https://doi.org/10.1038/nature15257openalex-snapshot
Labelling and optical erasure of synaptic memory traces in the motor cortex
inferredcites →https://doi.org/10.1038/nn.4244openalex-snapshot
Using goal-driven deep learning models to understand sensory cortex
inferredcites →https://doi.org/10.1038/nrn2575openalex-snapshot
Complex brain networks: graph theoretical analysis of structural and functional systems
inferredcites →https://doi.org/10.1038/nrn2920openalex-snapshot
Emotion processing and the amygdala: from a 'low road' to 'many roads' of evaluating biological significance
inferredcites →https://doi.org/10.1038/s41467-018-04316-3openalex-snapshot
Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science
inferredcites →https://doi.org/10.1038/s42003-018-0110-yopenalex-snapshot
Activations of deep convolutional neural networks are aligned with gamma band activity of human visual cortex
inferredcites →https://doi.org/10.1038/sj.mp.4000812openalex-snapshot
The amygdala: vigilance and emotion
inferredcites →https://doi.org/10.1038/srep27755openalex-snapshot
Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence
inferredcites →https://doi.org/10.1049/iet-syb.2011.0011openalex-snapshot
Building blocks of biological networks: a review on major network motif discovery algorithms
inferredcites →https://doi.org/10.1049/iet-syb.2020.0004openalex-snapshot
Review of tools and algorithms for network motif discovery in biological networks
inferredcites →https://doi.org/10.1073/pnas.1403112111openalex-snapshot
Performance-optimized hierarchical models predict neural responses in higher visual cortex
inferredcites →https://doi.org/10.1073/pnas.1611835114openalex-snapshot
Overcoming catastrophic forgetting in neural networks
inferredcites →https://doi.org/10.1073/pnas.79.8.2554openalex-snapshot
Neural networks and physical systems with emergent collective computational abilities.
inferredcites →https://doi.org/10.1080/026432996381890openalex-snapshot
Facial Emotion Recognition after Bilateral Amygdala Damage: Differentially Severe Impairment of Fear
inferredcites →https://doi.org/10.1093/neucas/3.4.267-dopenalex-snapshot
Facial emotion recognition after bilateral amygdala damage: Differentially severe impairment of fear
inferredcites →https://doi.org/10.1093/oso/9780198522614.003.0010openalex-snapshot
Facial expressions of emotion: an old controversy and new findings
inferredcites →https://doi.org/10.1098/rstb.1992.0008openalex-snapshot
Facial expressions of emotion: an old controversy and new findings
inferredcites →https://doi.org/10.1103/physreve.107.014302openalex-snapshot
Nodal-statistics-based equivalence relation for graph collections
inferredcites →https://doi.org/10.1109/aciiw52867.2021.9666338openalex-snapshot
Dream Net: a privacy preserving continual leaming model for face emotion recognition
inferredcites →https://doi.org/10.1109/cvpr.2012.6248110openalex-snapshot
Multi-column deep neural networks for image classification
inferredcites →https://doi.org/10.1109/cvpr.2019.00489openalex-snapshot
What Does It Mean to Learn in Deep Networks? And, How Does One Detect Adversarial Attacks?
inferredcites →https://doi.org/10.1109/cvpr46437.2021.01619openalex-snapshot
On Robustness and Transferability of Convolutional Neural Networks
inferredcites →https://doi.org/10.1109/cvprw53098.2021.00399openalex-snapshot
Avalanche: an End-to-End Library for Continual Learning
inferredcites →https://doi.org/10.1109/iccv.2009.5459469openalex-snapshot
What is the best multi-stage architecture for object recognition?
inferredcites →https://doi.org/10.1109/ictai52525.2021.00056openalex-snapshot
Characterizing Learning Dynamics of Deep Neural Networks via Complex Networks
inferredcites →https://doi.org/10.1109/ijcnn.2017.7965898openalex-snapshot
Neurogenesis deep learning: Extending deep networks to accommodate new classes
inferredcites →https://doi.org/10.1109/ijcnn.2018.8489241openalex-snapshot
Confronting machine-learning with neuroscience for neuromorphic architectures design
inferredcites →https://doi.org/10.1109/msp.2012.2211477openalex-snapshot
The MNIST Database of Handwritten Digit Images for Machine Learning Research [Best of the Web]
inferredcites →https://doi.org/10.1109/nfsi-icfbi.2007.4387765openalex-snapshot
Spatiotemporal Profiles of Brain Activation During Learning and Strategy Formulation
inferredcites →https://doi.org/10.1109/ojcas.2020.3032092openalex-snapshot
Brain-Inspired Computing: Models and Architectures
inferredcites →https://doi.org/10.1109/tnnls.2022.3152527openalex-snapshot
Learning From Noisy Labels With Deep Neural Networks: A Survey
inferredcites →https://doi.org/10.1109/tpami.2017.2773081openalex-snapshot
Learning without Forgetting
inferredcites →https://doi.org/10.1111/1467-9280.00224openalex-snapshot
Expression Without Recognition: Contributions of the Human Amygdala to Emotional Communication
inferredcites →https://doi.org/10.1111/j.1551-6709.2011.01214.xopenalex-snapshot
Complementary Learning Systems
inferredcites →https://doi.org/10.1126/science.aav9436openalex-snapshot
Neural population control via deep image synthesis
inferredcites →https://doi.org/10.1139/jpn.0953openalex-snapshot
Functional atlas of emotional faces processing: a voxel-based meta-analysis of 105 functional magnetic resonance imaging studies
inferredcites →https://doi.org/10.1140/epjb/e2003-00114-7openalex-snapshot
Efficient Hopfield pattern recognition on a scale-free neural network
inferredcites →https://doi.org/10.1142/s0218127410026198openalex-snapshot
STRUCTURAL ORGANIZATION OF FUNCTIONAL NETWORKS FROM EEG SIGNALS DURING MOTOR LEARNING TASKS
inferredcites →https://doi.org/10.1145/2993148.2993165openalex-snapshot
Training deep networks for facial expression recognition with crowd-sourced label distribution
inferredcites →https://doi.org/10.1145/3240876.3240908openalex-snapshot
Facial expression recognition with deep learning
inferredcites →https://doi.org/10.1146/annurev.neuro.27.070203.144216openalex-snapshot
THE PLASTIC HUMAN BRAIN CORTEX
inferredcites →https://doi.org/10.11591/ijeecs.v14.i3.pp1552-1563openalex-snapshot
Imbalance class problems in data mining: a review
inferredcites →https://doi.org/10.1162/neco.1989.1.4.541openalex-snapshot
Backpropagation Applied to Handwritten Zip Code Recognition
inferredcites →https://doi.org/10.11834/jig.200233openalex-snapshot
Deep facial expression recognition: a survey
inferredcites →https://doi.org/10.1186/s12859-020-03836-4openalex-snapshot
Biological interpretation of deep neural network for phenotype prediction based on gene expression
inferredcites →https://doi.org/10.1371/journal.pone.0130140openalex-snapshot
On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation
inferredcites →https://doi.org/10.1523/jneurosci.15-09-05879.1995openalex-snapshot
Fear and the human amygdala
inferredcites →https://doi.org/10.1609/aaai.v32i1.11651openalex-snapshot
Measuring Catastrophic Forgetting in Neural Networks
inferredcites →https://doi.org/10.26438/ijcse/v6si5.96100openalex-snapshot
Survey on Handwritten Digit Recognition using Machine Learning
inferredcites →https://doi.org/10.31887/dcns.2018.20.2/ospornsopenalex-snapshot
Graph theory methods: applications in brain networks
inferredcites →https://doi.org/10.33564/ijeast.2021.v06i07.033openalex-snapshot
A THE HANDWRITTEN DIGITS RECOGNITION
inferredcites →https://doi.org/10.3389/fncom.2016.00094openalex-snapshot
Toward an Integration of Deep Learning and Neuroscience
inferredcites →https://doi.org/10.3389/fnsys.2010.00016openalex-snapshot
Graph-based network analysis of resting-state functional MRI
inferredcites →https://doi.org/10.3389/fpsyg.2013.00504openalex-snapshot
The stability-plasticity dilemma: investigating the continuum from catastrophic forgetting to age-limited learning effects
inferredcites →https://doi.org/10.3390/app9153169openalex-snapshot
A Survey of Handwritten Character Recognition with MNIST and EMNIST
inferredcites →https://doi.org/10.3390/e22020204openalex-snapshot
Emergence of Network Motifs in Deep Neural Networks
inferredcites →https://doi.org/10.3390/math11020321openalex-snapshot
Neural Network Optimization Based on Complex Network Theory: A Survey
inferredcites →https://doi.org/10.3390/s18020416openalex-snapshot
Facial Emotion Recognition: A Survey and Real-World User Experiences in Mixed Reality
inferredcites →https://doi.org/10.3906/elk-1202-89openalex-snapshot
Impact of small-world topology on the performance of a feed-forward artificial neural network based on 2 different real-life problems
inferredcites →https://doi.org/10.4324/9781410612403openalex-snapshot
The Organization of Behavior
inferredcites →https://doi.org/10.48550/arxiv.1312.6211openalex-snapshot
An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.1412.6572openalex-snapshot
Explaining and Harnessing Adversarial Examples
inferredcites →https://doi.org/10.48550/arxiv.1703.04200openalex-snapshot
Continual Learning Through Synaptic Intelligence
inferredcites →https://doi.org/10.48550/arxiv.1808.05377openalex-snapshot
Neural Architecture Search: A Survey
inferredcites →https://doi.org/10.48550/arxiv.1902.01996openalex-snapshot
Are All Layers Created Equal?
inferredcites →https://doi.org/10.48550/arxiv.1904.07734openalex-snapshot
Three scenarios for continual learning
inferredcites →https://doi.org/10.48550/arxiv.2004.07211openalex-snapshot
Dark Experience for General Continual Learning: a Strong, Simple Baseline
inferredcites →https://doi.org/10.48550/arxiv.2209.05488openalex-snapshot
Deep Neural Networks as Complex Networks
inferredcites →https://doi.org/10.5120/ijca2020920550openalex-snapshot
Handwritten Digit Recognition using Machine and Deep Learning Algorithms
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