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Bio-Inspired Approaches for Deep Learning: Hebbian Learning, Spiking Neural Networks, and Backprop Alternatives
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Silicon Chip for Electronic Communication Between Nerve Cells by Non‐invasive Interfacing and Analog–Digital Processing
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Information Processing with Frequency-Dependent Synaptic Connections
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Learning Feature Representations with K-Means
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Generalizations of principal component analysis, optimization problems, and neural networks
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Contrastive Hebbian Learning in the Continuous Hopfield Model
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STDP-based spiking deep convolutional neural networks for object recognition
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Deep learning in spiking neural networks
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Biologically plausible deep learning — But how far can we go with shallow networks?
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Hebbian semi-supervised learning in a sample efficiency setting
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Advancing the Biological Plausibility and Efficacy of Hebbian Convolutional Neural Networks
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Learning by the Dendritic Prediction of Somatic Spiking
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Online local learning algorithms for linear discriminant analysis
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Population-wide distributions of neural activity during perceptual decision-making
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Embracing Change: Continual Learning in Deep Neural Networks
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Equivariant nonstationary source separation
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Objective function formulation of the BCM theory of visual cortical plasticity: Statistical connections, stability conditions
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Principal components, minor components, and linear neural networks
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Networks of spiking neurons: The third generation of neural network models
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Conditionally independent component analysis for supervised feature extraction
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Computational Consequences of Temporally Asymmetric Learning Rules: I. Differential Hebbian Learning
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Second Order Nonstationary Source Separation
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Emergence of simple-cell receptive field properties by learning a sparse code for natural images
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A neuronal learning rule for sub-millisecond temporal coding
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Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects
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Competitive Hebbian learning through spike-timing-dependent synaptic plasticity
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Multiple forms of synaptic plasticity triggered by selective suppression of activity in individual neurons
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The Chronotron: A Neuron That Learns to Fire Temporally Precise Spike Patterns
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Differential Hebbian-type learning algorithms fordecorrelationand independent component analysis
inferredcites →https://doi.org/10.1073/pnas.1820458116openalex-snapshot
Unsupervised learning by competing hidden units
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Formation of receptive fields in realistic visual environments according to the Bienenstock, Cooper, and Munro (BCM) theory.
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A synaptic basis for memory storage in the cerebral cortex
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Development of low entropy coding in a recurrent network
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Natural image statistics and efficient coding
inferredcites →https://doi.org/10.1098/rstb.2005.1622openalex-snapshot
A theory of cortical responses
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Homeostatic Synaptic Plasticity: Local and Global Mechanisms for Stabilizing Neuronal Function
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Adaptive blind signal processing-neural network approaches
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Analysis of the convergence properties of topology preserving neural networks
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A nonlinear discriminant algorithm for feature extraction and data classification
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Equivariant adaptive source separation
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A blind source separation technique using second-order statistics
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Blind separation of mixture of independent sources through a quasi-maximum likelihood approach
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Infomax and maximum likelihood for blind source separation
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Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
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Temporal Spiking Recurrent Neural Network for Action Recognition
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Spiking Neural Networks: A Survey
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Optimization theory of Hebbian/anti-Hebbian networks for PCA and whitening
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ImageNet: A large-scale hierarchical image database
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Manifold Discriminant Analysis
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A neural network learning algorithm for adaptive principal component extraction (APEX)
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Deep discriminative manifold learning
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Things you haven't heard about the self-organizing map
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Kernelized discriminative canonical correlation analysis
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Review of stability properties of neural plasticity rules for implementation on memristive neuromorphic hardware
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Review and unification of learning framework in Cog Ex Machina platform for memristive neuromorphic hardware
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Stable spike-timing dependent plasticity rule for multilayer unsupervised and supervised learning
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Neurogrid: A Mixed-Analog-Digital Multichip System for Large-Scale Neural Simulations
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Loihi: A Neuromorphic Manycore Processor with On-Chip Learning
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Surrogate Gradient Learning in Spiking Neural Networks: Bringing the Power of Gradient-Based Optimization to Spiking Neural Networks
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A reinforcement learning algorithm for spiking neural networks
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Simple model of spiking neurons
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Learning Robust and Discriminative Subspace With Low-Rank Constraints
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Modularizing Deep Learning via Pairwise Learning With Kernels
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Locally linear discriminant analysis for multimodally distributed classes for face recognition with a single model image
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Discriminative Learning and Recognition of Image Set Classes Using Canonical Correlations
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Attention Spiking Neural Networks
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Incremental Linear Discriminant Analysis for Classification of Data Streams
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$rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
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A silent eligibility trace enables dopamine‐dependent synaptic plasticity for reinforcement learning in the mouse striatum
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A quantitative description of membrane current and its application to conduction and excitation in nerve
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A million spiking-neuron integrated circuit with a scalable communication network and interface
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A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
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Local Fisher discriminant analysis for supervised dimensionality reduction
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Least squares linear discriminant analysis
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A least squares formulation for canonical correlation analysis
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Exploring Neuromorphic Computing Based on Spiking Neural Networks: Algorithms to Hardware
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Spike-Driven Synaptic Plasticity: Theory, Simulation, VLSI Implementation
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Generalization in Interactive Networks: The Benefits of Inhibitory Competition and Hebbian Learning
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Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations
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Equivalence of Backpropagation and Contrastive Hebbian Learning in a Layered Network
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An Information-Maximization Approach to Blind Separation and Blind Deconvolution
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Biologically Plausible Error-Driven Learning Using Local Activation Differences: The Generalized Recirculation Algorithm
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Sparse Coding via Thresholding and Local Competition in Neural Circuits
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On the Asymptotic Equivalence Between Differential Hebbian and Temporal Difference Learning
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A Hebbian/Anti-Hebbian Neural Network for Linear Subspace Learning: A Derivation from Multidimensional Scaling of Streaming Data
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An Approximation of the Error Backpropagation Algorithm in a Predictive Coding Network with Local Hebbian Synaptic Plasticity
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SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks
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Prediction by Supervised Principal Components
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Unsupervised Learning of Visual Features through Spike Timing Dependent Plasticity
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Somato-dendritic Synaptic Plasticity and Error-backpropagation in Active Dendrites
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Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex
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Backpropagation of Physiological Spike Trains in Neocortical Pyramidal Neurons: Implications for Temporal Coding in Dendrites
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Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detection
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Energy and Policy Considerations for Modern Deep Learning Research
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Going Deeper With Directly-Trained Larger Spiking Neural Networks
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Analysis of correlation based dimension reduction methods
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The Diversity of Cortical Inhibitory Synapses
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Eligibility Traces and Plasticity on Behavioral Time Scales: Experimental Support of NeoHebbian Three-Factor Learning Rules
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Unsupervised learning of digit recognition using spike-timing-dependent plasticity
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Equilibrium Propagation: Bridging the Gap between Energy-Based Models and Backpropagation
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BindsNET: A Machine Learning-Oriented Spiking Neural Networks Library in Python
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Deep Learning With Spiking Neurons: Opportunities and Challenges
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Brian: a simulator for spiking neural networks in Python
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Improvement of Heterogeneous Transfer Learning Efficiency by Using Hebbian Learning Principle
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Brain and high metabolic rate organ mass: contributions to resting energy expenditure beyond fat-free mass
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Towards deep learning with segregated dendrites
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