Philosophy of Intelligence:
Hutter, Marcus. “Can intelligence explode?.” Journal of Consciousness Studies 19.1-2 (2012): 143-166.
Bayesian Brain Hypothesis:
Kappel D., Habenschuss S., Legenstein R., Maass W. Network Plasticity as Bayesian Inference. PLoS Comput Biol 11(11) (2015): e1004485.
Free-Energy Theory:
Friston, Karl. “The free-energy principle: a unified brain theory?.” Nature Reviews Neuroscience 11.2 (2010): 127.
Modular Cell Biology:
Hartwell, Leland H., et al. “From molecular to modular cell biology.” Nature 402.6761supp (1999): C47.
Consciousness:
Neural Correlates of Consciousness (NCCs):
Rees, Geraint, Gabriel Kreiman, and Christof Koch. “Neural correlates of consciousness in humans.” Nature Reviews Neuroscience 3.4 (2002): 261.
Tononi, Giulio, and Christof Koch. “The neural correlates of consciousness.” Annals of the New York Academy of Sciences 1124.1 (2008): 239-261.
The Claustrum:
Crick, Francis, and Christof Koch. “Towards a neurobiological theory of consciousness.” Seminars in the Neurosciences. Vol. 2. Saunders Scientific Publications, 1990.
Crick, Francis, and Christof Koch. “A framework for consciousness.” Nature Neuroscience 6.2 (2003): 119.
Crick, Francis C., and Christof Koch. “What is the function of the claustrum?.” Philosophical Transactions of the Royal Society B: Biological Sciences 360.1458 (2005): 1271-1279.
Koubeissi, Mohamad Z., et al. “Electrical stimulation of a small brain area reversibly disrupts consciousness.” Epilepsy & Behavior 37 (2014): 32-35.
Reardon, Sara. “A giant neuron found wrapped around entire mouse brain.” Nature 543.7643 (2017): 14-15.
Anesthesia:
Purdon, Patrick L., et al. “Electroencephalogram signatures of loss and recovery of consciousness from propofol.” Proceedings of the National Academy of Sciences 110.12 (2013): E1142-E1151.
Alkire, Michael T., Anthony G. Hudetz, and Giulio Tononi. “Consciousness and anesthesia.” Science 322.5903 (2008): 876-880.
Neuron Models:
Gouwens, Nathan W., et al. “Systematic generation of biophysically detailed models for diverse cortical neuron types.” Nature communications 9.1 (2018): 710.
Sardi, Shira, et al. “New Types of Experiments Reveal that a Neuron Functions as Multiple Independent Threshold Units.” Scientific reports 7.1 (2017): 18036.
El Hady, Ahmed, and Benjamin B. Machta. “Mechanical surface waves accompany action potential propagation.” Nature communications 6 (2015): 6697.
Hodgkin-Huxley:
Hodgkin, Alan L., and Andrew F. Huxley. “A quantitative description of membrane current and its application to conduction and excitation in nerve.” The Journal of physiology 117.4 (1952): 500-544.
Adaptive Exponential Integrate-and-Fire:
Brette, Romain, and Wulfram Gerstner. “Adaptive exponential integrate-and-fire model as an effective description of neuronal activity.” Journal of neurophysiology 94.5 (2005): 3637-3642.
Izhikevich:
Izhikevich, Eugene M. “Simple model of spiking neurons.” IEEE Transactions on neural networks 14.6 (2003): 1569-1572.
Izhikevich, Eugene M. “Which model to use for cortical spiking neurons?.” IEEE transactions on neural networks 15.5 (2004): 1063-1070.
Neural Networks:
Hopfield, John J. “Neural networks and physical systems with emergent collective computational abilities.” Proceedings of the national academy of sciences 79.8 (1982): 2554-2558.
Hopfield, John J. “Neurons with graded response have collective computational properties like those of two-state neurons.” Proceedings of the national academy of sciences81.10 (1984): 3088-3092.
Hopfield, John J., and David W. Tank. ““Neural” computation of decisions in optimization problems.” Biological cybernetics52.3 (1985): 141-152.
Kohonen, Teuvo. “Essentials of the self-organizing map.” Neural networks 37 (2013): 52-65.
Backpropagation:
Rumelhart, David E., Geoffrey E. Hinton, and Ronald J. Williams. Learning internal representations by error propagation. No. ICS-8506. California Univ San Diego La Jolla Inst for Cognitive Science, 1985.
Rumelhart, David E., Geoffrey E. Hinton, and Ronald J. Williams. “Learning representations by back-propagating errors.” nature 323.6088 (1986): 533.
Convolutional Networks:
Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. “Imagenet classification with deep convolutional neural networks.” Advances in neural information processing systems. 2012.
Zeiler, Matthew D., and Rob Fergus. “Visualizing and understanding convolutional networks.” European conference on computer vision. Springer, Cham, 2014.
Simonyan, Karen, and Andrew Zisserman. “Very deep convolutional networks for large-scale image recognition.” arXiv preprint arXiv:1409.1556 (2014).
Szegedy, Christian, et al. “Going deeper with convolutions.” Cvpr, 2015.
Schmidhuber, Jürgen. “Deep learning in neural networks: An overview.” Neural networks 61 (2015): 85-117.
LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. “Deep learning.” nature 521.7553 (2015): 436.
He, Kaiming, et al. “Deep residual learning for image recognition.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
Gatys, Leon A., Alexander S. Ecker, and Matthias Bethge. “A neural algorithm of artistic style.” arXiv preprint arXiv:1508.06576 (2015).
Generative Adversarial Networks:
Goodfellow, Ian, et al. “Generative adversarial nets.” Advances in neural information processing systems. 2014.
Capsule Networks:
Sabour, Sara, Nicholas Frosst, and Geoffrey E. Hinton. “Dynamic routing between capsules.” Advances in Neural Information Processing Systems. 2017.
Hinton, Geoffrey, Nicholas Frosst, and Sara Sabour. “Matrix capsules with EM routing.” (2018).
Brain-Machine-Brain Interfaces (BMBIs):
O’Doherty, Joseph E., et al. “Active tactile exploration using a brain–machine–brain interface.” Nature 479.7372 (2011): 228.
Pais-Vieira, Miguel, et al. “A brain-to-brain interface for real-time sharing of sensorimotor information.” Scientific reports 3 (2013): 1319.
Death:
Dreier, Jens P., et al. “Terminal spreading depolarization and electric silence indeath of human cortex.” Annals of Neurology(2018).
Data Visualization:
Maaten, Laurens van der, and Geoffrey Hinton. “Visualizing data using t-SNE.” Journal of machine learning research9.Nov (2008): 2579-2605.