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Recent approximations to backpropagation (BP) have mitigated many of BP's computational inefficiencies and incompatibilities with biology, but important limitations still remain.
Local unsupervised learning for image analysis
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Simplified neuron model as a principal component analyzer
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Competitive learning: From interactive activation to adaptive resonance
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The recent excitement about neural networks
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Optimal unsupervised learning in a single-layer linear feedforward neural network
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Local synaptic learning rules suffice to maximize mutual information in a linear network
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Rate, timing, and cooperativity jointly determine cortical synaptic plasticity
Sjöström, P. J., Turrigiano, G. G., and Nelson, S. B. (2001) · 2001
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Binzegger, T., Douglas, R. J., and Martin, K. A. (2004) · 2004
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Douglas, R. J. and Martin, K. A. (2004) · 2004
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Greedy layer-wise training of deep networks
Bengio, Y., Lamblin, P., Popovici, D., and Larochelle, H. (2006) · 2006
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Hadsell, R., Chopra, S., and LeCun, Y. (2006) · 2006
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Hinton, G. E., Osindero, S., and Teh, Y.-W. (2006) · 2006
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Predictive coding approximates backprop along arbitrary computation graphs
Millidge, B., Tschantz, A., and Buckley, C. L. (2020) · 2006
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Firing patterns in the adaptive exponential integrate-and-fire model
Naud, R., Marcille, N., Clopath, C., and Gerstner, W. (2008) · 2008
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Topology and dynamics of the canonical circuit of cat v1
Binzegger, T., Douglas, R. J., and Martin, K. A. (2009) · 2009
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Visualizing higher-layer features of a deep network
Erhan, D., Bengio, Y., Courville, A., and Vincent, P. (2009) · 2009
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Spike-phase coding boosts and stabilizes information carried by spatial and temporal spike patterns
Kayser, C., Montemurro, M. A., Logothetis, N. K., and Panzeri, S. (2009) · 2009
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Stdp enables spiking neurons to detect hidden causes of their inputs
Nessler, B., Pfeiffer, M., and Maass, W. (2009) · 2009
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Representation learning via invariant causal mechanisms
Mitrovic, J., McWilliams, B., Walker, J., Buesing, L., and Blundell, C. (2020) · 2010
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An analysis of single-layer networks in unsupervised feature learning
Coates, A., Ng, A., and Lee, H. (2011) · 2011
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A history of spike-timing-dependent plasticity
Markram, H., Gerstner, W., and Sjöström, P. J. (2011) · 2011
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The spike-timing dependence of plasticity
Feldman, D. E. (2012) · 2012
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Building high-level features using large scale unsupervised learning
Le, Q., Ranzato, M., Monga, R., Devin, M., Chen, K., Corrado, G., Dean, J., and Ng, A. (2012) · 2012
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Bayesian computation emerges in generic cortical microcircuits through spike-timing-dependent plasticity
Nessler, B., Pfeiffer, M., Buesing, L., and Maass, W. (2013) · 2013
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Oscillatory multiplexing of population codes for selective communication in the mammalian brain
Akam, T. and Kullmann, D. M. (2014) · 2014
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Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C. (2014) · 2014
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Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A. (2014) · 2014
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Unsupervised learning of digit recognition using spike-timing-dependent plasticity
Diehl, P. U. and Cook, M. (2015) · 2015
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Convolutional clustering for unsupervised learning
Dundar, A., Jin, J., and Culurciello, E. (2015) · 2015
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Encoding of action by the purkinje cells of the cerebellum
Herzfeld, D. J., Kojima, Y., Soetedjo, R., and Shadmehr, R. (2015) · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C. (2015) · 2015
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Bayesian filtering unifies adaptive and non-adaptive neural network optimization methods
Aitchison, L. (2020) · 2020
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Unsupervised learning of visual features by contrasting cluster assignments
Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., and Joulin, A. (2020) · 2020
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A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G. (2020) · 2020
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Bootstrap your own latent-a new approach to self-supervised learning
Grill, J.-B., Strub, F., Altché, F., Tallec, C., Richemond, P., Buchatskaya, E., Doersch, C., Avila Pires, B., Guo, Z., Gheshlaghi Azar, M., et al. (2020) · 2020
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Momentum contrast for unsupervised visual representation learning
He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R. (2020) · 2020
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Nguyen, A., Yosinski, J., and Clune, J. (2015) · 2015
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A normative theory of adaptive dimensionality reduction in neural networks
Pehlevan, C. and Chklovskii, D. (2015) · 2015
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A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128k synapses
Qiao, N., Mostafa, H., Corradi, F., Osswald, M., Stefanini, F., Sumislawska, D., and Indiveri, G. (2015) · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J. (2016) · 2016
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Dense associative memory for pattern recognition
Krotov, D. and Hopfield, J. J. (2016) · 2016
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Random synaptic feedback weights support error backpropagation for deep learning
Lillicrap, T. P., Cownden, D., Tweed, D. B., and Akerman, C. J. (2016) · 2016
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Direct feedback alignment provides learning in deep neural networks
Nøkland, A. (2016) · 2016
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Kolesnikov, A., Beyer, L., Zhai, X., Puigcerver, J., Yung, J., Gelly, S., and Houlsby, N. (2020) · 2020
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Short-term synaptic plasticity optimally models continuous environments
Moraitis, T., Sebastian, A., and Eleftheriou, E. (2020) · 2020
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Foolbox native: Fast adversarial attacks to benchmark the robustness of machine learning models in pytorch, tensorflow, and jax
Rauber, J., Zimmermann, R., Bethge, M., and Brendel, W. (2020) · 2020
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Memory devices and applications for in-memory computing
Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R., and Eleftheriou, E. (2020) · 2020
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The unreasonable effectiveness of deep learning in artificial intelligence
Sejnowski, T. J. (2020) · 2020
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Synaptic plasticity as bayesian inference
Aitchison, L., Jegminat, J., Menendez, J. A., Pfister, J.-P., Pouget, A., and Latham, P. E. (2021) · 2021
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Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks
Frenkel, C., Lefebvre, M., and Bol, D. (2021) · 2021
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Local plasticity rules can learn deep representations using self-supervised contrastive predictions
Illing, B., Ventura, J., Bellec, G., and Gerstner, W. (2021) · 2021
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Introducing” neuromorphic computing and engineering”
Indiveri, G. (2021) · 2021
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Scaling equilibrium propagation to deep convnets by drastically reducing its gradient estimator bias
Laborieux, A., Ernoult, M., Scellier, B., Bengio, Y., Grollier, J., and Querlioz, D. (2021) · 2021
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Hebbian semi-supervised learning in a sample efficiency setting
Lagani, G., Falchi, F., Gennaro, C., and Amato, G. (2021) · 2021
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Improving transferability of representations via augmentation-aware self-supervision
Lee, H., Lee, K., Lee, K., Lee, H., and Shin, J. (2021) · 2021
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Multi-layer hebbian networks with modern deep learning frameworks
Miconi, T. (2021) · 2021
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Softhebb: Bayesian inference in unsupervised hebbian soft winner-take-all networks
Moraitis, T., Toichkin, D., Chua, Y., and Guo, Q. (2021) · 2021
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Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits
Payeur, A., Guerguiev, J., Zenke, F., Richards, B. A., and Naud, R. (2021) · 2021
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Towards biologically plausible convolutional networks
Pogodin, R., Mehta, Y., Lillicrap, T. P., and Latham, P. E. (2021) · 2021
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Single-phase deep learning in cortico-cortical networks
Greedy, W., Zhu, H. W., Pemberton, J., Mellor, J., and Costa, R. P. (2022) · 2022
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The combination of hebbian and predictive plasticity learns invariant object representations in deep sensory networks
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The forward-forward algorithm: Some preliminary investigations
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Short-term plasticity neurons learning to learn and forget
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Self-supervised learning through efference copies
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