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Recently, significant progress has been made regarding the statistical understanding of artificial neural networks (ANNs).
Competitive learning: From interactive activation to adaptive resonance
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Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs
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Synaptic modifications in cultured hippocampal neurons: Dependence on spike timing, synaptic strength, and postsynaptic cell type
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A critical window for cooperation and competition among developing retinotectal synapses
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Competitive Hebbian learning through spike-timing-dependent synaptic plasticity
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Error-backpropagation in temporally encoded networks of spiking neurons
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The Organization of Behavior: A Neuropsychological Theory (1st ed.)
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An introduction to the theory of point processes. Vol. I
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Learning in spiking neural networks by reinforcement of stochastic synaptic transmission
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Learning curves for stochastic gradient descent in linear feedforward networks
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Introduction to nonparametric estimation
Tsybakov, A. B · 2004
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A gradient descent rule for spiking neurons emitting multiple spikes
Booij, O., and tat Nguyen, H · 2005
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An introduction to the theory of point processes. Vol. II
Daley, D. J., and Vere-Jones, D · 2008
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Introduction to derivative-free optimization
Conn, A. R., Scheinberg, K., and Vicente, L. N · 2009
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STDP enables spiking neurons to detect hidden causes of their inputs
Nessler, B., Pfeiffer, M., and Maass, W · 2009
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Functional requirements for reward-modulated spike-timing-dependent plasticity
Frémaux, N., Sprekeler, H., and Gerstner, W · 2010
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Google Vizier: A Service for Black-Box Optimization
Golovin, D., Solnik, B., Moitra, S., Kochanski, G., Karro, J. E., and Sculley, D · 2017
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Random gradient-free minimization of convex functions
Nesterov, Y., and Spokoiny, V · 2017
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Equilibrium propagation: Bridging the gap between energy-based models and backpropagation
Scellier, B., and Bengio, Y · 2017
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An approximation of the error backpropagation algorithm in a predictive coding network with local Hebbian synaptic plasticity
Whittington, J. C. R., and Bogacz, R · 2017
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Assessing the scalability of biologically-motivated deep learning algorithms and architectures
Bartunov, S., Santoro, A., Richards, B., Marris, L., Hinton, G. E., and Lillicrap, T · 2018
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Gerstner, W., Kistler, W. M., Naud, R., and Paninski, L · 2014
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Optimal rates for zero-order convex optimization: The power of two function evaluations
Duchi, J. C., Jordan, M. I., Wainwright, M. J., and Wibisono, A · 2015
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Random synaptic feedback weights support error backpropagation for deep learning
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Brown, N., and Sandholm, T · 2018
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Derivative-free optimization methods
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Deep learning in spiking neural networks
Tavanaei, A., Ghodrati, M., Kheradpisheh, S. R., Masquelier, T., and Maida, A · 2019
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Theories of error back-propagation in the brain
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Backpropagation and the brain
Lillicrap, T. P., Santoro, A., Marris, L., Akerman, C. J., and Hinton, G · 2020
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A primer on zeroth-order optimization in signal processing and machine learning: Principals, recent advances, and applications
Liu, S., Chen, P.-Y., Kailkhura, B., Zhang, G., Hero III, A. O., and Varshney, P. K · 2020
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Predictive coding: a theoretical and experimental review
Millidge, B., Seth, A., and Buckley, C. L · 2021
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Predictive coding approximates backprop along arbitrary computation graphs
Millidge, B., Tschantz, A., and Buckley, C. L · 2022
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