1 | Suggested Paper | Designated Readers | Link to article | Notes | Date added |
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2 | Seung, H., Sompolinsky, H. & Tishby, N. Statistical mechanics of learning from examples. Phys. Rev. A 45, 6056 (1992). | I. Fiete | classic | ||

3 | Gatys, Ecker, Bethge. Texture Synthesis Using Convolutional Neural Networks. NIPS 2015 | I. Fiete | http://papers.nips.cc/paper/5633-texture-synthesis-using-convolutional-neural-networks | ||

4 | Internal models direct dragonfly interception steering. Matteo Mischiati, Huai-Ti Lin, Paul Herold, Elliot Imler, Robert Olberg, & Anthony Leonardo. Nature | I. Fiete | |||

5 | Predictive coding of dynamical variables in balanced spiking networks M Boerlin, CK Machens, S Denève. PLoS Comp Bio 2013 | I. Fiete | http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003258 | done! | |

6 | Computation in a single neuron: Hodgkin and Huxley revisited. BA y Arcas, AL Fairhall, W Bialek. Neural Computation 15 (8), 1715-1749 | I. Fiete | classic | ||

7 | demixed PCA by Machens and colleagues | I Fiete | tool/algorithm | ||

8 | Invariant Scattering Convolution Networks | I Fiete | http://www.di.ens.fr/data/publications/papers/pami-final.pdf | Some theoretical results on deep networks | |

9 | An exact mapping between the Variational Renormalization Group and Deep Learning | I Fiete | http://arxiv.org/abs/1410.3831 | Some theoretical results on deep networks | |

10 | Unsupervised learning of digit recognition using STDP. Diehl PU and Cook M. Frontiers in Comp. Neuro. 2015. | http://journal.frontiersin.org/article/10.3389/fncom.2015.00099/abstract# | |||

11 | Neural Turing Machines. Alex Graves, Greg Wayne, Ivo Danihelka. arXiv 2015. | http://arxiv.org/abs/1410.5401 | |||

12 | Self-organized criticality attributed to a central limit-like convergence effect (Kendal 1015) and (Kendal & Jorgensen 2011). | I. Fiete | http://www.sciencedirect.com/science/article/pii/S0378437114009868 | Two papers on the connection between 1/f noise and a central-limit-like statistical convergence theorem: | |

13 | Structured synaptic connectivity between hippocampal regions S Druckmann, L Feng, B Lee, C Yook, T Zhao… - Neuron, 2014 | I. Fiete | https://www.janelia.org/publication/structured-synaptic-connectivity-between-hippocampal-regions | ||

14 | Wiskott, L and Sejnowski, T (2002). Slow feature analysis: Unsupervised learning of invariances. Neural Computation 14(4): 715-770. | I Fiete | http://www.cnbc.cmu.edu/~tai/readings/learning/wiskott_sejnowski_2002.pdf | 11/23/2014 | |

15 | L. Theis and M. Bethge Generative Image Modeling Using Spatial LSTMs arXiv , 2015 | I. Fiete | http://arxiv.org/abs/1506.03478 | deep/recurrent net applications | |

16 | L. A. Gatys, A. S. Ecker, and M. Bethge A Neural Algorithm of Artistic Style arXiv, 2015 | I. Fiete | http://arxiv.org/abs/1508.06576 | deep/recurrent net applications | |

17 | Mnih et al. Human-level control through deep reinforcement learning. Nature 518, 529–533 (26 February 2015) | I. Fiete | http://www.nature.com/nature/journal/v518/n7540/abs/nature14236.html | ||

18 | Stern, M., Sompolinsky, H. and Abbott, L.F. (2014) Dynamics of Random Neural Networks with Bistable Units. Phys. Rev. E 062710. | http://www.neurotheory.columbia.edu/Larry/SternPRE14.pdf | |||

19 | Shaul Druckmann, Tao Hu, Dmitri Chklovskii A mechanistic model of early sensory processing based on subtracting sparse representations NIPS 2012 Neuronal Circuits Underlying Persistent Representations Despite Time Varying Activity Current Biology 2012 | I. Memming Park | http://books.nips.cc/papers/files/nips25/NIPS2012_0975.pdf http://dx.doi.org/10.1016/j.cub.2012.08.058 | neural dynamics with sparse weights and stable neural representations | 1/5/2014 |

20 | Pehlevan, Hu and Chklovskii A Hebbian/Anti-Hebbian Network for Online Sparse Dictionary Learning Derived from Symmetric Matrix Factorization A Hebbian/Anti-Hebbian Network Derived from Online Non-Negative Matrix Factorization Can Cluster and Discover Sparse Features A Hebbian/Anti-Hebbian Neural Network for Linear Subspace Learning: A Derivation from Multidimensional Scaling of Streaming Data | http://www.researchgate.net/publication/273002963_A_HebbianAnti-Hebbian_Network_for_Online_Sparse_Dictionary_Learning_Derived_from_Symmetric_Matrix_Factorization http://www.researchgate.net/publication/273003026_A_HebbianAnti-Hebbian_Neural_Network_for_Linear_Subspace_Learning_A_Derivation_from_Multidimensional_Scaling_of_Streaming_Data http://www.researchgate.net/publication/273002967_A_HebbianAnti-Hebbian_Network_Derived_from_Online_Non-Negative_Matrix_Factorization_Can_Cluster_and_Discover_Sparse_Features | |||

21 | stochastic Transitions between Neural States in Taste Processing and Decision-Making | http://www.jneurosci.org/content/30/7/2559.long | |||

22 | Bram Bakker (2014) Reinforcement learning with Long Short-Term Memory. NIPS 2002 | http://papers.nips.cc/paper/1953-reinforcement-learning-with-long-short-term-memory.pdf | |||

23 | A motor cortex circuit for motor planning and movement N Li, TW Chen, ZV Guo, CR Gerfen, K Svoboda - Nature, 2015 | https://www.janelia.org/publication/motor-cortex-circuit-motor-planning-and-movement | |||

24 | Self-organization of microcircuits in networks of spiking neurons with plastic synapses GK Ocker, A Litwin-Kumar, B Doiron - arXiv preprint arXiv:1411.3956, 2014 | http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004458 | |||

25 | Chabrol, F.P., Arenz, A., Wiedemer, L., Margrie, T.W. & DiGregorio, D.A. Nat. Neurosci. 18, 718–727 (2015). Synaptic diversity enables temporal coding of coincident multisensory inputs in single neurons | http://www.nature.com/neuro/journal/v18/n5/abs/nn.3974.html http://www.nature.com/neuro/journal/v18/n5/full/nn.4006.html | Discuss in context of cerebellar coding | ||

26 | Human representation of visuo-motor uncertainty as mixtures of orthogonal basis distributions Hang Zhang, Nathaniel D Daw & Laurence T Maloney Nature Neuroscience 18, 1152–1158 (2015) | http://www.nature.com/neuro/journal/v18/n8/full/nn.4055.html | |||

27 | Parameter space compression underlies emergent theories and predictive models BB Machta, R Chachra, MK Transtrum, JP Sethna - Science, 2013 | http://www.sciencemag.org/content/342/6158/604.short | Not neuroscience but interesting for theoretical work on compelx systems in general | ||

28 | Optimizing working memory with heterogeneity of recurrent cortical excitation ZP Kilpatrick, B Ermentrout… - The Journal of Neuroscience, 2013 | http://www.jneurosci.org/content/33/48/18999.full | |||

29 | Pfau, D., Pnevmatikakis, E. & Paninski, L. (2013). Robust learning of low-dimensional dynamics from large neural ensembles. NIPS. | http://www.stat.columbia.edu/~liam/research/pubs/pfau-subspace-id.pdf | |||

30 | A memory frontier for complex synapses. S. Lahiri and S. Ganguli, Neural Information Processing Systems (NIPS), 2013. | Capacity for general models of complex synapses | |||

31 | Spatial patterns of persistent neural activity vary with the behavioral context of short-term memory K Daie, MS Goldman, ERF Aksay - Neuron, 2015 | http://www.sciencedirect.com/science/article/pii/S0896627315000070 | |||

32 | Wayne, G. and Abbott, L.F. (2014) A Design Procedure for Hierarchical Network Control. Neural Comp. 26:2163-2193 | http://www.neurotheory.columbia.edu/Larry/WayneNeuralComp14.pdf | |||

33 | Kennedy, A., Wayne, G., Kaifosh, P., Alvina, K., Abbott, L.F. and Sawtell, N.B. (2014) A Temporal Basis for Predicting the Sensory Consequences of Motor Commands in an Electric Fish. Nature Neurosci. 17:416-424 | http://www.neurotheory.columbia.edu/Larry/KennedyNatNeuro14.pdf | |||

34 | A Theory of Cheap Control in Embodied Systems (2015) Guido Montúfar, Keyan Ghazi-Zahedi, Nihat Ay. PLoS Comp Bio | http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004427 | |||

35 | Sepp Hochreiter and Jürgen Schmidhuber (1997). "Long short-term memory" Neural Computation 9 (8): 1735–1780. | http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf | |||

36 | Gelman A, Vehtari A, Jylanki P, Robert C, Chopin C, and Cunningham JP (2015) Expectation propagation as a way of life. | http://arxiv.org/abs/1412.4869 | |||

37 | Churchland MM, Cunningham JP (2015) A dynamical basis set for generating reaches. Cold Spring Harbor Laboratory Press. | http://stat.columbia.edu/~cunningham/pdf/ChurchlandCSHL2015.pdf | Some overlap with their previous work | ||

38 | A hierarchical structure of cortical interneuron electrical diversity revealed by automated statistical analysis S Druckmann, S Hill, F Schürmann, H Markram… - Cerebral Cortex, 2013 | https://www.janelia.org/publication/hierarchical-structure-cortical-interneuron-electrical-diversity-revealed-automated | |||

39 | Wei Z, Wang X-J (2015) Confidence estimation as a stochastic process in a neurodynamical system of decision making J. Neurophys. 114: 99-113 | https://www.janelia.org/publication/confidence-estimation-stochastic-process-neurodynamical-system-decision-making | |||

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41 | Tonic signaling from O₂ sensors sets neural circuit activity and behavioral state.Busch KE, Laurent P, Soltesz Z, Murphy RJ, Faivre O, Hedwig B, Thomas M, Smith HL, de Bono M.Nat Neurosci. 2012 Mar 4;15(4):581-91. doi: 10.1038/nn.3061. | Ila Fiete | http://www.nature.com/neuro/journal/v15/n4/full/nn.3061.html | cell+circuit persistent activity in c. elegans. Recruit J-P Shimomura? | old |

42 | Cortical oscillations and speech processing: emerging computational principles and operations. Anne-Lise Giraud & David Poeppeldoi. Nature Neurosci. pp511 - 517, 2012 doi:10.1038/nn.3063 | Ila Fiete | http://www.nature.com/neuro/journal/v15/n4/full/nn.3063.html | Title says it all. Review/preview article. Recruit L. Colgin? | old |

43 | |||||

44 | Glycolytic Oscillations and Limits on Robust Efficiency. F. Chandra, G. Buzi, J. Doyle. Science 8 July 2011: Vol. 333 no. 6039 pp. 187-192. | Ila Fiete | theory for why cells oscillate, based on arguments of efficiency and robustness. claim: provide a physically fundamental explanation for cellular oscillations. | old | |

45 | Johannes Wilms, Matthias Troyer, Frank Verstraete. Mutual information in classical spin models. J. Stat. Mech. (2011) P10011 | Andrew Tan, Ila Fiete | http://arxiv.org/abs/1011.4421 | New numerical approximation of the mutual information in classical lattice spin systems. Claims that the mutual information is maximized away from criticality. | old |

46 | Nessler, Pfeiffer, Buesing, Maass Bayesian computation emerges in generic cortical microcircuits through spike-timing-dependent plasticity PLoS CB 2013 | Il Memming Park | http://dx.doi.org/10.1371/journal.pcbi.1003037 | Bayesian brain with spiking neurons | 5/14/2013 |

47 | A Topological Paramdigm for Hippocampal Spatial Map Using Persistent Homology. Y. Dabaghian, F. Mémoli, L. Frank, G. Carlsson | Keegan Hines | http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002581 | An interesting idea for encoding spatial maps and internal models. Will require some readers with expertise in algebraic topology. | old |

48 | When are microcircuits well-modeled by maximum entropy methods? Andrea K. Barreiro, Julijana Gjorgjieva, Fred Rieke, Eric Shea-Brown | Il Memming Park, Ila Fiete | http://arxiv.org/abs/1011.2797 | higher-order correlations vs Ising model. Circuit based analysis | 5/26/2013 |

49 | Elizabeth E Steinberg, Ronald Keiflin, Josiah R Boivin, Ilana B Witten, Karl Deisseroth & Patricia H Janak. A causal link between prediction errors, dopamine neurons and learning. Nature Neuroscience 16, 966–973 (2013) | I. Memming Park, Ila Fiete | http://www.nature.com/neuro/journal/v16/n7/abs/nn.3413.html | Reward signal in the midbrain | 6/25/2013 |

50 | Timm Lochmann, Sophie Deneve Neural processing as causal inference Current Opinion in Neurobiology Volume 21, Issue 5, October 2011, Pages 774–781 | I. Memming Park, Ila Fiete | http://dx.doi.org/10.1016/j.conb.2011.05.018 | spike train representation as inference? | 7/26/2013 |

51 | |||||

52 | Space–time wiring specificity supports direction selectivity in the retina. Nature 2014. Seung and colleagues. | Ila Fiete | http://www.nature.com/nature/journal/vaop/ncurrent/full/nature13240.html | connectomics result on retinal anatomy | 5/15/2014 |

53 | Lak, A., Costa, G. M., Romberg, E., Koulakov, A. A., Mainen, Z. F., and Kepecs, A. (2014). Orbitofrontal cortex is required for optimal waiting based on decision confidence. Neuron, 84(1):190-201. | I. Memming Park | http://dx.doi.org/10.1016/j.neuron.2014.08.039 | orbitofrontal cortex, confidence coding, psychophysics | 10/1/2014 |

54 | Fourier analysis and systems identification of the p53 feedback loop | I Fiete | http://www.weizmann.ac.il/mcb/UriAlon/sites/mcb.UriAlon/files/fourieranalysisandsystemsidentificationp53.pdf | Engineering systems id approach to unravel circuit underlying oscillatory dynamics in p53 protein network | |

55 | Xaq Pitkow, Sheng Liu, Dora E. Angelaki, Gregory C. DeAngelis, Alexandre Pouget (2015). "How Can Single Sensory Neurons Predict Behavior?" Neuron | http://www.cell.com/neuron/abstract/S0896-6273(15)00596-6 | |||

56 | Rita Morais Tavares, Avi Mendelsohn, Yael Grossman, Christian Hamilton Williams, Matthew Shapiro, Yaacov Trope, Daniela Schiller (2015) A Map for Social Navigation in the Human Brain Neuron | http://www.cell.com/neuron/abstract/S0896-6273(15)00524-3 | Discuss in the context of general hippocampal maps | ||

57 | What single-cell stimulation has told us about neural coding (2015); review by Doron and Brecht | B Kriener | http://rstb.royalsocietypublishing.org/content/royptb/370/1677/20140204.full.pdf | how important are single spikes? maybe in connection with 55 | 3/14/2016 |

58 | R. Gutig. Spiking neurons can discover predictive features by aggregate-label learning | http://science.sciencemag.org/content/351/6277/aab4113/tab-pdf | |||

59 | Goudar V, Buonomano DV. A model of order-selectivity based on dynamic changes in the balance of excitation and inhibition produced by short-term synaptic plasticity. | K Houck | http://jn.physiology.org/content/113/2/509.full | computational model of auditory cortex modeling discrimination of order in sequences of stimuli | 4/12/2017 |

60 | Deadwyler SA, Berger TW, Sweatt AJ, Song D, Chan RHM, Opris I, Gerhardt GA, Marmarelis VZ, Hampson RE. Donor/recipient enhancement of memory in rat hippocampus. Front Syst Neurosci. 2013;7:1–11. | I. Fiete | this paper claims that by recording hippocampal activity in the brain of an animal trained on a task, then stimulating a similar pattern of activity in a naive animal, it is possible to get a transfer of learning from the trained to the naive animal. sounds like science fiction, i would be curious to read and see what has been done and if we believe it/why it might work. | ||

61 | Memory replay in balanced recurrent networks. Chenkov, Sprekeler, Kempter. Plos Comp Biol 2017 | I. Fiete | http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005359 | ||

62 | James Kirkpatrick and DeepMind. Overcoming catastrophic forgetting in neural networks. PNAS (2017) | MY Yim | http://www.pnas.org/content/114/13/3521.abstract | To overcome catastrophic forgetting of old tasks by selectively slowing down learning on the weights important for those tasks | 9/21/2017 |

63 | M Katkov, S Romani, M Tsodyks (2017) Memory Retrieval from First Principles. Neuron 94 (5), 1027-1032 | I. Fiete | http://www.sciencedirect.com/science/article/pii/S0896627317302921?via%3Dihub | 11/7/2017 | |

64 | Alireza Alemi, Carlo Baldassi, Nicolas Brunel, Riccardo Zecchina (2015) A Three-Threshold Learning Rule Approaches the Maximal Capacity of Recurrent Neural Networks. PLoS Comp Biol | I. Fiete | http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004439 | 11/7/2017 |

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