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Plasticity circuits in the brain are known to be influenced by the distribution of the synaptic weights through the mechanisms of synaptic integration and local regulation of synaptic strength.
Frenkel, C., Lefebvre, M. & Bol, D · 1909
Earlier work this paper cites.
Kinetik der invertinwirkung
Michaelis, L. & Menten, M · 1913
Earlier work this paper cites.
A logical calculus of the ideas immanent in nervous activity
McCulloch, W. S. & Pitts, W · 1943
Earlier work this paper cites.
A method for solving the convex programming problem with convergence rate o(1/k 2 )
Nesterov, Y · 1983
Earlier work this paper cites.
Learning rate schedules for faster stochastic gradient search
Darken, C., Chang, J. & Moody, J · 1992
Earlier work this paper cites.
Backpropagation: The Basic Theory , 1–34 (L. Erlbaum Associates Inc., USA, 1995)
Rumelhart, D. E., Durbin, R., Golden, R. & Chauvin, Y · 1995
Earlier work this paper cites.
Activity-dependent scaling of quantal amplitude in neocortical neurons
Turrigiano, G., Leslie, K., Desai, N., Rutherford, L. & Nelson, S · 1998
Earlier work this paper cites.
Catastrophic forgetting in connectionist networks
French, R. M · 1999
Earlier work this paper cites.
Is heterosynaptic modulation essential for stabilizing hebbian plasticity and memory?
Bailey, C., Giustetto, M., Huang, Y.-Y., Hawkins, R. & Kandel, E · 2000
Earlier work this paper cites.
Synaptic Integration (American Cancer Society, 2001)
Williams, S. R. & Stuart, G. J · 2001
Earlier work this paper cites.
Conservation of total synaptic weight through balanced synaptic depression and potentiation
Royer, S. & Paré, D · 2003
Earlier work this paper cites.
Highly nonrandom features of synaptic connectivity in local cortical circuits
Song, S., Sjöström, P. J., Reigl, M., Nelson, S. & Chklovskii, D. B · 2005
Earlier work this paper cites.
The computational limits of deep learning (2020)
Thompson, N. C., Greenewald, K., Lee, K. & Manso, G. F · 2007
Earlier work this paper cites.
Dendritic synaptic integration in central neurons
Williams, S. & Atkinson, S · 2008
Earlier work this paper cites.
The self-tuning neuron: Synaptic scaling of excitatory synapses
Turrigiano, G · 2008
Earlier work this paper cites.
Rapid synaptic scaling induced by changes in postsynaptic firing
Ibata, K., Sun, Q. & Turrigiano, G · 2008
Earlier work this paper cites.
Metaplasticity: Tuning synapses and networks for plasticity
Abraham, W · 2008
Earlier work this paper cites.
Pyramidal neurons: dendritic structure and synaptic integration
Spruston, N · 2008
Earlier work this paper cites.
Spiking neural networks
Ghosh-Dastidar, S. & Adeli, H · 2009
Earlier work this paper cites.
Synaptic integration
Etherington, S. J., Atkinson, S. E., Stuart, G. J. & Williams, S. R · 2010
Earlier work this paper cites.
Spike-time-dependent plasticity and heterosynaptic competition organize networks to produce long scale-free sequences of neural activity
Fiete, I. R., Senn, W., Wang, C. Z. H. & Hahnloser, R. H. R · 2010
Earlier work this paper cites.
Reward-Modulated Hebbian Learning of Decision Making
Pfeiffer, M., Nessler, B., Douglas, R. J. & Maass, W · 2010
Cited alongside, same era.
A reward-modulated hebbian learning rule can explain experimentally observed network reorganization in a brain control task
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Functional requirements for reward-modulated spike-timing-dependent plasticity
Frémaux, N., Sprekeler, H. & Gerstner, W · 2010
Cited alongside, same era.
MNIST handwritten digit database (2010)
LeCun, Y. & Cortes, C · 2010
Cited alongside, same era.
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Cireşan, D. C., Meier, U., Gambardella, L. M. & Schmidhuber, J · 2010
Cited alongside, same era.
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Later among the works it cites.
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Later among the works it cites.
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Xiao, H., Rasul, K. & Vollgraf, R · 2017
Later among the works it cites.
Emnist: Extending mnist to handwritten letters
Cohen, G., Afshar, S., Tapson, J. & van Schaik, A · 2017
Later among the works it cites.
Overcoming catastrophic forgetting in neural networks
Kirkpatrick, J. et al · 2017
Later among the works it cites.
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Cited alongside, same era.
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Cited alongside, same era.
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Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I. & Salakhutdinov, R · 2012
Cited alongside, same era.
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Cited alongside, same era.
The influence of synaptic weight distribution on neuronal population dynamics
Iyer, R., Menon, V., Buice, M., Koch, C. & Mihalas, S · 2013
Cited alongside, same era.
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Ba, J. & Caruana, R · 2014
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Measuring catastrophic forgetting in neural networks
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Deep learning with spiking neurons: Opportunities and challenges
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Theories of error back-propagation in the brain
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A deep learning framework for neuroscience
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Training neural networks with local error signals
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Unsupervised learning to overcome catastrophic forgetting in neural networks
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Effects of synaptic integration on the dynamics and computational performance of spiking neural network
Li, X., Luo, S. & Xue, F · 2020
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The synaptic scaling literature: A systematic review of methodologies and quality of reporting
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Heterosynaptic plasticity in cortical interneurons
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