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Deep learning has led to significant advances in artificial intelligence, in part, by adopting strategies motivated by neurophysiology.
Neuronal oscillations in cortical networks
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Activity-Dependent Modulation of Layer 1 Inhibitory Neocortical Circuits by Acetylcholine
Brombas, A., Fletcher, L. N., and Williams, S. R. (2014) · 1932
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Dropout: A simple way to prevent neural networks from overfitting
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Natural Waking and Sleep States: A View From Inside Neocortical Neurons
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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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Extrastriate feedback to primary visual cortex in primates: a quantitative analysis of connectivity
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Gradient-based learning applied to document recognition
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A new cellular mechanism for coupling inputs arriving at different cortical layers
Larkum, M. E., Zhu, J. J., and Sakmann, B. (1999) · 1999
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Synaptic plasticity and memory: an evaluation of the hypothesis
Martin, S. J., Grimwood, P. D., and Morris, R. G. M. (2000) · 2000
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NMDA spikes in basal dendrites of cortical pyramidal neurons
Schiller, J., Major, G., Koester, H. J., and Schiller, Y. (2000) · 2000
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Supervised and Unsupervised Learning with Two Sites of Synaptic Integration
Körding, K. P. and König, P. (2001) · 2001
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Cortical Region Interactions and the Functional Role of Apical Dendrites
Spratling, M. W. (2002) · 2002
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Spike timing-dependent plasticity of neural circuits
Dan, Y. and Poo, M.-M. (2004) · 2004
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LTP and LTD
Malenka, R. C. and Bear, M. F. (2004) · 2004
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A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., and Teh, Y.-W. (2006) · 2006
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Learning rules for spike timing-dependent plasticity depend on dendritic synapse location
Letzkus, J. J., Kampa, B. M., and Stuart, G. J. (2006) · 2006
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A Cooperative Switch Determines the Sign of Synaptic Plasticity in Distal Dendrites of Neocortical Pyramidal Neurons
Sjöström, P. J. and Häusser, M. (2006) · 2006
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A feedback model of perceptual learning and categorization
Spratling, M. W. and Johnson, M. H. (2006) · 2006
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Scaling learning algorithms towards AI
Bengio, Y. and LeCun, Y. (2007) · 2007
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Dendritic spikes in apical dendrites of neocortical layer 2/3 pyramidal neurons
Larkum, M. E., Waters, J., Sakmann, B., and Helmchen, F. (2007) · 2007
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Stability of the fittest: organizing learning through retroaxonal signals
Harris, K. D. (2008) · 2008
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Visualizing data using t-SNE
Maaten, L. v. d. and Hinton, G. (2008) · 2008
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Synaptic Integration in Tuft Dendrites of Layer 5 Pyramidal Neurons: A New Unifying Principle
Larkum, M. E., Nevian, T., Sandler, M., Polsky, A., and Schiller, J. (2009) · 2009
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Dendritic encoding of sensory stimuli controlled by deep cortical interneurons
Murayama, M., Perez-Garci, E., Nevian, T., Bock, T., Senn, W., and Larkum, M. E. (2009) · 2009
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Reinforcement learning in populations of spiking neurons
Urbanczik, R. and Senn, W. (2009) · 2009
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Scinet: Lessons learned from building a power-efficient top-20 system and data centre
Loken, C., Gruner, D., Groer, L., Peltier, R., Bunn, N., Craig, M., Henriques, T., Dempsey, J., Yu, C.-H., Chen, J., Dursi, L. J., Chong, J., Northrup, S., Pinto, J., Knecht, N., and Zon, R. V. (2010) · 2010
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Making memories last: the synaptic tagging and capture hypothesis
Redondo, R. L. and Morris, R. G. M. (2011) · 2011
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
He, K., Zhang, X., Ren, S., and Sun, J. (2015) · 2015
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Deep learning
LeCun, Y., Bengio, Y., and Hinton, G. (2015) · 2015
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Difference target propagation
Lee, D.-H., Zhang, S., Fischer, A., and Bengio, Y. (2015) · 2015
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How Important is Weight Symmetry in Backpropagation?
Liao, Q., Leibo, J. Z., and Poggio, T. (2015) · 2015
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A top-down cortical circuit for accurate sensory perception
Manita, S., Suzuki, T., Homma, C., Matsumoto, T., Odagawa, M., Yamada, K., Ota, K., Matsubara, C., Inutsuka, A., Sato, M., Ohkura, M., Yamanaka, A., Yanagawa, Y., Nakai, J., Hayashi, Y., Larkum, M., and Murayama, M. (2015) · 2015
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Human-level control through deep reinforcement learning
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A Sparse Coding Model with Synaptically Local Plasticity and Spiking Neurons Can Account for the Diverse Shapes of V1 Simple Cell Receptive Fields
Zylberberg, J., Murphy, J. T., and DeWeese, M. R. (2011) · 2011
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Depression-Biased Reverse Plasticity Rule Is Required for Stable Learning at Top-Down Connections
Burbank, K. S. and Kreiman, G. (2012) · 2012
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012) · 2012
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Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude
Tieleman, T. and Hinton, G. (2012) · 2012
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Top-down influences on visual processing
Gilbert, C. D. and Li, W. (2013) · 2013
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A cellular mechanism for cortical associations: an organizing principle for the cerebral cortex
Larkum, M. (2013) · 2013
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Inhibition of inhibition in visual cortex: the logic of connections between molecularly distinct interneurons
Pfeffer, C. K., Xue, M., He, M., Huang, Z. J., and Scanziani, M. (2013) · 2013
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Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., and Hassabis, D. (2015) · 2015
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Experience-dependent spatial expectations in mouse visual cortex
Fiser, A., Mahringer, D., Oyibo, H. K., Petersen, A. V., Leinweber, M., and Keller, G. B. (2016) · 2016
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Dopamine neurons encode performance error in singing birds
Gadagkar, V., Puzerey, P. A., Chen, R., Baird-Daniel, E., Farhang, A. R., and Goldberg, J. H. (2016) · 2016
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Opening holes in the blanket of inhibition: Localized lateral disinhibition by VIP interneurons
Karnani, M. M., Jackson, J., Ayzenshtat, I., Hamzehei Sichani, A., Manoocheri, K., Kim, S., and Yuste, R. (2016) · 2016
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Deep neural networks as a computational model for human shape sensitivity
Kubilius, J., Bracci, S., and Op de Beeck, H. P. (2016) · 2016
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Very deep neural network for handwritten digit recognition
Li, Y., Li, H., Xu, Y., Wang, J., and Zhang, Y. (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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Towards an integration of deep learning and neuroscience
Marblestone, A., Wayne, G., and Kording, K. (2016) · 2016
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Towards a biologically plausible backprop
Scellier, B. and Bengio, Y. (2016) · 2016
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Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., Dieleman, S., Grewe, D., Nham, J., Kalchbrenner, N., Sutskever, I., Lillicrap, T., Leach, M., Kavukcuoglu, K., Graepel, T., and Hassabis, D. (2016) · 2016
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Active cortical dendrites modulate perception
Takahashi, N., Oertner, T. G., Hegemann, P., and Larkum, M. E. (2016) · 2016
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Cortical Feedback Regulates Feedforward Retinogeniculate Refinement
Thompson, A., Picard, N., Min, L., Fagiolini, M., and Chen, C. (2016) · 2016
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Using goal-driven deep learning models to understand sensory cortex
Yamins, D. L. K. and DiCarlo, J. J. (2016) · 2016
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Segregated-dendrite-deep-learning
Guergiuev, J. (2017) · 2017
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Chrna2-Martinotti Cells Synchronize Layer 5 Type A Pyramidal Cells via Rebound Excitation
Hilscher, M. M., Leão, R. N., Edwards, S. J., Leão, K. E., and Kullander, K. (2017) · 2017
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View-Tolerant Face Recognition and Hebbian Learning Imply Mirror-Symmetric Neural Tuning to Head Orientation
Leibo, J. Z., Liao, Q., Anselmi, F., Freiwald, W. A., and Poggio, T. (2017) · 2017
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Layer-specific modulation of neocortical dendritic inhibition during active wakefulness
Muñoz, W., Tremblay, R., Levenstein, D., and Rudy, B. (2017) · 2017
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Context- and Output Layer-Dependent Long-Term Ensemble Plasticity in a Sensory Circuit
Yamada, Y., Bhaukaurally, K., Madarász, T. J., Pouget, A., Rodriguez, I., and Carleton, A. (2017) · 2017
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