Finish "recommended-reading" list by the end of 2018

[nagataka]

Introduction to Computational Neuroscience

• Friston, Karl. "The free-energy principle: a unified brain theory?." Nature Reviews Neuroscience 11.2 (2010): 127-138. http://www.nature.com/nrn/journal/v11/n2/pdf/nrn2787.pdf
• Rafal Bogacz, A tutorial on the free-energy framework for modelling perception and learning, Journal of Mathematical Psychology, Volume 76, Part B, February 2017, Pages 198-211, ISSN 0022-2496. https://doi.org/10.1016/j.jmp.2015.11.003
• Douglas, Rodney J., and Kevan AC Martin. "Mapping the matrix: the ways of neocortex." Neuron 56.2 (2007): 226-238. http://www.sciencedirect.com/science/article/pii/S0896627307007787#
• Chklovskii, Dmitri B., B. W. Mel, and K. Svoboda. "Cortical rewiring and information storage." Nature 431.7010 (2004): 782. https://search.proquest.com/docview/204560208?pq-origsite=gscholar
• Poggio, Tomaso, and Emilio Bizzi. "Generalization in vision and motor control." Nature 431.7010 (2004): 768. http://cbcl.csail.mit.edu/publications/ps/nature03014.pdf
• [ON] Dayan, Peter, and Laurence F. Abbott. Theoretical neuroscience. Vol. 806. Cambridge, MA: MIT Press, 2001.
• [ON] Gerstner, Wulfram, et al. Neuronal dynamics: From single neurons to networks and models of cognition. Cambridge University Press, 2014. (Available online: http://neuronaldynamics.epfl.ch/)

Machine learning and Neuroscience

• [ON] Hassabis, Demis, et al. "Neuroscience-Inspired Artificial Intelligence." Neuron 95.2 (2017): 245-258. http://www.sciencedirect.com/science/article/pii/S0896627317305093 ​* Marblestone, Adam H., Greg Wayne, and Konrad P. Kording. Marblestone, A. H., Wayne, G., & Kording, K. P. (2016). Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience, 10. http://journal.frontiersin.org/article/10.3389/fncom.2016.00094/full
• Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2016). Building machines that learn and think like people. Behavioral and Brain Sciences, 1-101. https://arxiv.org/pdf/1604.00289

Neuromorphic Engineering:

• Douglas, Rodney, Misha Mahowald, and Carver Mead. "Neuromorphic analogue VLSI." Annual review of neuroscience18.1 (1995): 255-281. http://authors.library.caltech.edu/1497/1/DOUarn95.pdf
• A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128K synapses http://journal.frontiersin.org/article/10.3389/fnins.2015.00141/full
• P. Lichtsteiner, C. Posch and T. Delbruck, "A 128× 128 120 dB 15 μs Latency Asynchronous Temporal Contrast Vision Sensor," in IEEE Journal of Solid-State Circuits, vol. 43, no. 2, pp. 566-576, Feb. 2008. http://ieeexplore.ieee.org/abstract/document/4444573/
• A million spiking-neuron integrated circuit with a scalable communication network and interface http://www.sciencemag.org/content/345/6197/668.short
• Memory and information processing in neuromorphic systems http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7159144
• Indiveri, Giacomo, et al. "Neuromorphic silicon neuron circuits." Frontiers in neuroscience 5 (2011). http://www.ncbi.nlm.nih.gov/pmc/articles/pmc3130465/
• Azghadi, Mostafa Rahimi, et al. "Spike-based synaptic plasticity in silicon: design, implementation, application, and challenges." Proceedings of the IEEE 102.5 (2014): 717-737. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3130465/pdf/fnins-05-00073.pdf
• Finding a roadmap to achieve large neuromorphic hardware systems http://journal.frontiersin.org/article/10.3389/fnins.2013.00118/full
• Furber, Steve B., et al. "The spinnaker project." Proceedings of the IEEE 102.5 (2014): 652-665. http://ieeexplore.ieee.org/iel7/5/6807530/06750072.pdf

Memristors and Nanotechnologies for Neuromorphic Hardware

• Querlioz, Damien, et al. "Bioinspired programming of memory devices for implementing an inference engine." Proceedings of the IEEE 103.8 (2015): 1398-1416. https://pdfs.semanticscholar.org/f791/b5bd796eaa10fd623fc842db5db561f45b2b.pdf

Spiking neural networks:

• Vreeken, Jilles. "Spiking neural networks, an introduction." (2003). https://dspace.library.uu.nl/bitstream/handle/1874/24416/vreeken_03_spikingneuralnetworks.pdf?sequence=2
• Neftci, Emre O., et al. "Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines." Frontiers in Neuroscience 11 (2017). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5478701/
• STICK: Spike Time Interval Computational Kernel, a Framework for General Purpose Computation Using Neurons, Precise Timing, Delays, and Synchrony http://www.mitpressjournals.org/doi/abs/10.1162/NECO_a_00783#.VlYQAnVBTEU
• Nengo and the neural engineering framework: A large-scale model of the functioning brain http://science.sciencemag.org/content/338/6111/1202
• Real-time classification and sensor fusion with a spiking deep belief network. http://www.frontiersin.org/neuromorphic_engineering/10.3389/fnins.2013.00178/abstract.
• Convolutional Networks for Fast, Energy-Efficient Neuromorphic Computing http://arxiv.org/pdf/1603.08270
• Vogels, Tim P., and Larry F. Abbott. "Signal propagation and logic gating in networks of integrate-and-fire neurons." Journal of neuroscience 25.46 (2005): 10786-10795. http://www.jneurosci.org/content/25/46/10786.full.pdf
• Abbott, L. F., and Wade G. Regehr. "Synaptic computation." Nature431.7010 (2004): 796. http://www.ee.columbia.edu/~aurel/nature%20insight04/synaptic%20computation04.pdf

Learning in Neural Circuits

• Song, Sen, Kenneth D. Miller, and Larry F. Abbott. "Competitive Hebbian learning through spike-timing-dependent synaptic plasticity." Nature neuroscience 3.9 (2000): 919-926. https://www.nature.com/neuro/journal/v3/n9/abs/nn0900_919.html
• Abbott, Larry F., and Sacha B. Nelson. "Synaptic plasticity: taming the beast." Nature neuroscience 3.11s (2000): 1178. http://search.proquest.com/openview/6779c1043e8fa25f397a1cdedfb67051/1?pq-origsite=gscholar&cbl=44706
• Bi, Guo-qiang, and Mu-ming Poo. "Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type." Journal of neuroscience 18.24 (1998): 10464-10472. http://www.jneurosci.org/content/18/24/10464.short
• Graupner, Michael, and Nicolas Brunel. "Calcium-based plasticity model explains sensitivity of synaptic changes to spike pattern, rate, and dendritic location." Proceedings of the National Academy of Sciences 109.10 (2012): 3991-3996. http://www.pnas.org/content/109/10/3991.short
• Sutton, Richard S., and Andrew G. Barto. Reinforcement learning: An introduction. Vol. 1. No. 1. Cambridge: MIT press, 1998. http://www.academia.edu/download/38529120/9780262257053_index.pdf
• Markram, Henry, Wulfram Gerstner, and Per Jesper Sjöström. "Spike-timing-dependent plasticity: a comprehensive overview." Frontiers in synaptic neuroscience 4 (2012). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3395004/
• Fusi, Stefano, and L. F. Abbott. "Limits on the memory storage capacity of bounded synapses." Nature neuroscience 10.4 (2007): 485-493. https://www.nature.com/articles/nn1859
• Gütig, Robert, and Haim Sompolinsky. "The tempotron: a neuron that learns spike timing-based decisions." Nature neuroscience 9.3 (2006): 420. https://capocaccia.ethz.ch/capo/raw-attachment/wiki/2010/ifslwta10/Gutig_Sompolinsky06.pdf

Neural Circuit Dynamics

• Wong, K., & Wang, X. (2006). A recurrent network mechanism of time integration in perceptual decisions. J. Neurosci., 26 (4), 1314. http://www.jneurosci.org/content/26/4/1314.long
• Durstewitz, Daniel, and Gustavo Deco. "Computational significance of transient dynamics in cortical networks." European Journal of Neuroscience 27.1 (2008): 217-227. http://onlinelibrary.wiley.com/doi/10.1111/j.1460-9568.2007.05976.x/full

Bayesian Brain

• Rao, Rajesh PN, and Dana H. Ballard. "Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects." Nature neuroscience 2.1 (1999).
• Shriki, Oren, David Hansel, and Haim Sompolinsky. "Rate models for conductance-based cortical neuronal networks." Neural computation 15.8 (2003): 1809-1841.
• Rao, Rajesh PN. "Bayesian computation in recurrent neural circuits." Neural computation 16.1 (2004): 1-38.
• Ma, Wei Ji, et al. "Bayesian inference with probabilistic population codes." Nature neuroscience 9.11 (2006): 1432-1438.
• Nessler, Bernhard, et al. "Bayesian computation emerges in generic cortical microcircuits through spike-timing-dependent plasticity." PLoS computational biology 9.4 (2013): e1003037.

Artificial Neural Networks and Deep Learning

• Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep learning. MIT press, 2016. http://www.deeplearningbook.org/
• These resources are excellent starting points:
  • http://neuralnetworksanddeeplearning.com/
  • http://colah.github.io/
  • http://cs231n.github.io/
• The Google TensorFlow Tutorial is a great one too: https://www.tensorflow.org/tutorials/
• For high-level deep learning, check out Keras:https://keras.io/
• "Representation Learning: A Review and New Perspectives, Yoshua Bengio" University of Montreal, Montreal ; Aaron Courville ; Pascal Vincent, 2013
  • This is a review paper written by the deep learning trio Hinton, Lecun, Bengio:
• Glorot and Bengio, "Understanding the difficulty of training deep feedforward neural networks", JMLR 2010 http://proceedings.mlr.press/v9/glorot10a.html