IGI Reading Group

This project is maintained by IGITUGraz

IGI Reading Group

The IGI Reading Group is the informal Journal Club of IGI where we meet about once a week and discuss papers that are related to our research.

In the SS 2022, the journal club will take place every Tuesday at 10:00 online (or, if possible, in the IGI seminar room).

If you’re from outside IGI and would like to attend a particular session, contact ceca [at] igi.tugraz.at.

Current presentation cycle

Ceca, Christoph, Florian, Guozhang, Horst, Max, Ozan, Roland, Romain, Thomas


Please present papers that have a clear relation to our work: e.g. machine learning papers describing relevant developments in the field like new methods or architectures, or experimental papers discussing new data for our models or analysis methods for our experiments. Presented works should enhance our knowledge and provide inspiration for our research.

Start your presentation by giving a 5-minute overview before going into the details. The intro should include:

Presentations should convey the relevant findings from your selected paper with a focus on our group, i.e. also prepare the relevant background information, important concepts from the cited literature, etc. required to understand the main findings. You don’t need to prepare slides.

Upcoming meetings

Date Moderator Paper
??.05.2022 Romain tba


Feel free to add papers of interest.

Past meetings

Date Moderator Paper
20.05.2022 Thomas Flesch, Timo, et al. “Modelling continual learning in humans with Hebbian context gating and exponentially decaying task signals.” arXiv preprint arXiv:2203.11560 (2022).
10.05.2022 Roland Baevski, Alexei, et al. “Data2vec: A general framework for self-supervised learning in speech, vision and language.” arXiv preprint arXiv:2202.03555 (2022).
04.05.2022 Max Ito, Takuya, et al. “Constructing neural network models from brain data reveals representational transformations linked to adaptive behavior.” Nature communications 13.1 (2022): 1-16.
27.04.2022 Ozan Jain, Saachi, et al. “Missingness Bias in Model Debugging.” arXiv preprint arXiv:2204.08945 (2022).
05.04.2022 Horst Banino, Andrea, Jan Balaguer, and Charles Blundell. “Pondernet: Learning to ponder.” arXiv preprint arXiv:2107.05407 (2021).
29.03.2022 Guozhang Iyer, Abhiram, et al. “Avoiding Catastrophe: Active Dendrites Enable Multi-Task Learning in Dynamic Environments.” arXiv preprint arXiv:2201.00042 (2021).
22.03.2022 Florian Riihimäki, Henri. “Simplicial $ q $-connectivity of directed graphs with applications to network analysis.” arXiv preprint arXiv:2202.07307 (2022).
15.03.2022 Eben Krioukov, Dmitri, et al. “Hyperbolic geometry of complex networks.” Physical Review E 82.3 (2010): 036106.
08.03.2022 Christoph Kleyko, Denis, et al. “Vector symbolic architectures as a computing framework for nanoscale hardware.” arXiv preprint arXiv:2106.05268 (2021).
01.03.2022 Ceca Kutter, Esther F., et al. “Neuronal codes for arithmetic rule processing in the human brain.” Current Biology (2022).
09.02.2022 Florian About Monte-Carlo Markov Chains. Fosdick, Bailey K., et al. “Configuring random graph models with fixed degree sequences.” Siam Review 60.2 (2018): 315-355.
    Young, Jean-Gabriel, et al. “Construction of and efficient sampling from the simplicial configuration model.” Physical Review E 96.3 (2017): 032312.
    Artzy-Randrup, Yael, and Lewi Stone. “Generating uniformly distributed random networks.” Physical Review E 72.5 (2005): 056708.
01.02.2022 Thomas Beniaguev, D., Shapira, S., Segev, I. & London, M. (n.d.). Multiple Synaptic Contacts combined with Dendritic Filtering enhance Spatio-Temporal Pattern Recognition capabilities of Single Neurons. https://doi.org/10.1101/2022.01.28.478132
28.01.2022 Romain About astrocytes. Bazargani, Narges, and David Attwell. “Astrocyte calcium signaling: the third wave.” Nature neuroscience 19.2 (2016): 182-189.
    Semyanov, Alexey, Christian Henneberger, and Amit Agarwal. “Making sense of astrocytic calcium signals—from acquisition to interpretation.” Nature Reviews Neuroscience 21.10 (2020): 551-564.
    Santello, Mirko, Nicolas Toni, and Andrea Volterra. “Astrocyte function from information processing to cognition and cognitive impairment.” Nature neuroscience 22.2 (2019): 154-166.
12.01.2022 Roland Lopes, Vasco, et al. “Guided Evolution for Neural Architecture Search.” arXiv preprint arXiv:2110.15232 (2021).
14.12.2021 Ozan Schott, Lukas, et al. “Visual representation learning does not generalize strongly within the same domain.” arXiv preprint arXiv:2107.08221 (2021).
01.12.2021 Max Beniaguev, David, Idan Segev, and Michael London. “Single cortical neurons as deep artificial neural networks.” Neuron 109.17 (2021): 2727-2739.
23.11.2021 Isabel Koay, Sue Ann, et al. “Sequential and efficient neural-population coding of complex task information.” bioRxiv (2021): 801654.
16.11.2021 Horst Kim, Timothy D., et al. “Inferring latent dynamics underlying neural population activity via neural differential equations.” International Conference on Machine Learning. PMLR, 2021.
09.11.2021 Guozhang Fischler-Ruiz, Walter, et al. “Olfactory landmarks and path integration converge to form a cognitive spatial map.” Neuron (2021).
02.11.2021 Special session  
  Florian Raussen, Martin. “Connectivity of spaces of directed paths in geometric models for concurrent computation.” arXiv preprint arXiv:2106.11703 (2021).
    Bronstein, Michael M., et al. “Geometric deep learning: Grids, groups, graphs, geodesics, and gauges.” arXiv preprint arXiv:2104.13478 (2021).
  Romain Losonczy, Attila, Judit K. Makara, and Jeffrey C. Magee. “Compartmentalized dendritic plasticity and input feature storage in neurons.” Nature 452.7186 (2008): 436-441.
  Ceca Flesch, Timo, et al. “Rich and lazy learning of task representations in brains and neural networks.” bioRxiv (2021).
  Thomas Lin, Stephanie, Jacob Hilton, and Owain Evans. “TruthfulQA: Measuring How Models Mimic Human Falsehoods.” arXiv preprint arXiv:2109.07958 (2021).
    Megatron-Turing NLG
    Power, Alethea, et al. “Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets.” ICLR MATH-AI Workshop. 2021.
    Grewal, K., et al. “Going Beyond the Point Neuron: Active Dendrites and Sparse Representations for Continual Learning.” (2021).
    Pinitas, Kosmas, Spyridon Chavlis, and Panayiota Poirazi. “Dendritic Self-Organizing Maps for Continual Learning.” arXiv preprint arXiv:2110.13611 (2021).
  Max Levi, Hila, and Shimon Ullman. “Multi-task learning by a top-down control network.” 2021 IEEE International Conference on Image Processing (ICIP). IEEE, 2021.
  Isabel Yap, Ee-Lynn, et al. “Bidirectional perisomatic inhibitory plasticity of a Fos neuronal network.” Nature 590.7844 (2021): 115-121.
  Titouan Engelhard, Ben, et al. “Specialized coding of sensory, motor and cognitive variables in VTA dopamine neurons.” Nature 570.7762 (2019): 509-513.
  Christoph Wightman, Ross, Hugo Touvron, and Hervé Jégou. “ResNet strikes back: An improved training procedure in timm.” arXiv preprint arXiv:2110.00476 (2021).
  Yujie Miconi, Thomas, et al. “Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity.” arXiv preprint arXiv:2002.10585 (2020).
  Guozhang Rumyantsev, Oleg I., et al. “Fundamental bounds on the fidelity of sensory cortical coding.” Nature 580.7801 (2020): 100-105.
19.10.2021 Eben Bordelon, Blake, and Cengiz Pehlevan. “Population Codes Enable Learning from Few Examples By Shaping Inductive Bias.” bioRxiv (2021).
12.10.2021 Christoph Pogodin, Roman, et al. “Towards Biologically Plausible Convolutional Networks.” arXiv preprint arXiv:2106.13031 (2021).
05.10.2021 Ceca Schuman, Catherine D., et al. “Non-traditional input encoding schemes for spiking neuromorphic systems.” 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 2019.
30.06.2021 Max Payeur, Alexandre, et al. “Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits.” Nature neuroscience (2021): 1-10.
23.06.2021 Isabel Bos, Hannah, Anne-Marie Oswald, and Brent Doiron. “Untangling stability and gain modulation in cortical circuits with multiple interneuron classes.” bioRxiv (2020).
16.06.2021 Eben Sezener, Eren, et al. “A rapid and efficient learning rule for biological neural circuits.” bioRxiv (2021).
09.06.2021 Titouan Rubin, Jonathan E., et al. “The credit assignment problem in cortico‐basal ganglia‐thalamic networks: A review, a problem and a possible solution.” European Journal of Neuroscience 53.7 (2021): 2234-2253. (pdf)
02.06.2021 Thomas Tyulmankov, Danil, Guangyu Robert Yang, and L. F. Abbott. “Meta-learning local synaptic plasticity for continual familiarity detection.” bioRxiv (2021).
26.05.2021 Špela Krasheninnikova, Elena, et al. “Reinforcement learning for pricing strategy optimization in the insurance industry.” Engineering applications of artificial intelligence 80 (2019): 8-19.
19.05.2021 Romain Rusch, T. Konstantin, and Siddhartha Mishra. “Coupled Oscillatory Recurrent Neural Network (coRNN): An accurate and (gradient) stable architecture for learning long time dependencies.” arXiv preprint arXiv:2010.00951 (2020).
12.05.2021 Roland Mangla, Puneet, et al. “Charting the right manifold: Manifold mixup for few-shot learning.” Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 2020.
05.05.2021 Ozan Xiao, Kai, et al. “Noise or signal: The role of image backgrounds in object recognition.” arXiv preprint arXiv:2006.09994 (2020).
28.04.2021 Michael Gidon, Albert, et al. “Dendritic action potentials and computation in human layer 2/3 cortical neurons.” Science 367.6473 (2020): 83-87.
21.04.2021 Horst Cross, Logan, et al. “Using deep reinforcement learning to reveal how the brain encodes abstract state-space representations in high-dimensional environments.” Neuron 109.4 (2021): 724-738.
14.04.2021 Guozhang van de Ven, Gido M., Hava T. Siegelmann, and Andreas S. Tolias. “Brain-inspired replay for continual learning with artificial neural networks.” Nature communications 11.1 (2020): 1-14.
31.03.2021 Franz Hyvärinen, Aapo, and Peter Dayan. “Estimation of non-normalized statistical models by score matching.” Journal of Machine Learning Research 6.4 (2005).
    Song, Yang, and Stefano Ermon. “Generative modeling by estimating gradients of the data distribution.” arXiv preprint arXiv:1907.05600 (2019).
    Ho, Jonathan, Ajay Jain, and Pieter Abbeel. “Denoising diffusion probabilistic models.” arXiv preprint arXiv:2006.11239 (2020).
    Song, Yang, et al. “Score-Based Generative Modeling through Stochastic Differential Equations.” arXiv preprint arXiv:2011.13456 (2020).
24.03.2021 Florian Motta, Alessandro, et al. “Dense connectomic reconstruction in layer 4 of the somatosensory cortex.” Science 366.6469 (2019).
    Billeh, Yazan N., et al. “Systematic integration of structural and functional data into multi-scale models of mouse primary visual cortex.” Neuron 106.3 (2020): 388-403.
    Rees, Christopher L., Keivan Moradi, and Giorgio A. Ascoli. “Weighing the evidence in Peters’ rule: does neuronal morphology predict connectivity?.” Trends in neurosciences 40.2 (2017): 63-71. (pdf)
17.03.2021 Dominik Kato, Saul, et al. “Global brain dynamics embed the motor command sequence of Caenorhabditis elegans.” Cell 163.3 (2015): 656-669.
10.03.2021 Christoph Menick, Jacob, et al. “Practical Real Time Recurrent Learning with a Sparse Approximation to the Jacobian.” ICLR 2021 (2021).
03.03.2021 Ceca Kendall, Jack, et al. “Training End-to-End Analog Neural Networks with Equilibrium Propagation.” arXiv preprint arXiv:2006.01981 (2020).
26.01.2021 Thomas L. Radford, Alec, et al. “Learning Transferable Visual Models From Natural Language Supervision.” Image 2: T2.
19.01.2021 Florian Levina, Anna, J. Michael Herrmann, and Manfred Denker. “Critical branching processes in neural networks.” PAMM: Proceedings in Applied Mathematics and Mechanics. Vol. 7. No. 1. Berlin: WILEY‐VCH Verlag, 2007.
12.01.2020 Špela Young, Benjamin D., James A. Escalon, and Dennis Mathew. “Odors: from chemical structures to gaseous plumes.” Neuroscience & Biobehavioral Reviews 111 (2020): 19-29.
01.12.2020 Samuel Ly, Calvin, et al. “Psychedelics promote structural and functional neural plasticity.” Cell reports 23.11 (2018): 3170-3182.
24.11.2020 Roland Rajeswaran, Aravind, et al. “Meta-learning with implicit gradients.” Advances in Neural Information Processing Systems. 2019.
17.11.2020 Ozan Dapello, Joel, et al. “Simulating a primary visual cortex at the front of CNNs improves robustness to image perturbations.” Advances in Neural Information Processing Systems 33 (2020).
10.11.2020 Horst Sharma, Archit, et al. “Dynamics-aware unsupervised discovery of skills.” arXiv preprint arXiv:1907.01657 (2019).
03.11.2020 Franz Ramsauer, Hubert, et al. “Hopfield networks is all you need.” arXiv preprint arXiv:2008.02217 (2020).
27.10.2020 Florian Reimann, Michael W., et al. “Cliques of neurons bound into cavities provide a missing link between structure and function.” Frontiers in computational neuroscience 11 (2017): 48.
20.10.2020 Christoph Nieder, Andreas. “Neural constraints on human number concepts.” Current Opinion in Neurobiology 60 (2020): 28-36.
13.10.2020 Ceca Fitz, Hartmut, et al. “Neuronal spike-rate adaptation supports working memory in language processing.” Proceedings of the National Academy of Sciences 117.34 (2020): 20881-20889.
30.09.2020 Michael Frankland, Steven M., and Joshua D. Greene. “Concepts and compositionality: in search of the brain’s language of thought.” Annual review of psychology 71 (2020): 273-303. (pdf)
30.07.2020 Arjun Mittal, Sarthak, et al. “Learning to combine top-down and bottom-up signals in recurrent neural networks with attention over modules.” arXiv preprint arXiv:2006.16981 (2020).
    Goyal, Anirudh, et al. “Recurrent independent mechanisms.” arXiv preprint arXiv:1909.10893 (2019).
02.03.2020 Luca Hudson, Drew A., and Christopher D. Manning. “Compositional attention networks for machine reasoning.” arXiv preprint arXiv:1803.03067 (2018).
24.02.2020 Arjun Schrittwieser, Julian, et al. “Mastering atari, go, chess and shogi by planning with a learned model.” arXiv preprint arXiv:1911.08265 (2019).
03.02.2020 Ceca Introduction to ANOVA analysis (in Kass, Robert E., Uri T. Eden, and Emery N. Brown. Analysis of neural data. Vol. 491. New York: Springer, 2014.)
    Lindsay, Grace W., et al. “Hebbian learning in a random network captures selectivity properties of the prefrontal cortex.” Journal of Neuroscience 37.45 (2017): 11021-11036.
    Rigotti, Mattia, et al. “The importance of mixed selectivity in complex cognitive tasks.” Nature 497.7451 (2013): 585-590.
06.12.2019 Thomas Henaff, Mikael, et al. “Tracking the world state with recurrent entity networks.” arXiv preprint arXiv:1612.03969 (2016).
02.12.2019 Philipp Patel, Devdhar, et al. “Improved robustness of reinforcement learning policies upon conversion to spiking neuronal network platforms applied to ATARI games.” arXiv preprint arXiv:1903.11012 (2019).
25.11.2019 Special session  
  Florian Barrett, David GT, Sophie Deneve, and Christian K. Machens. “Optimal compensation for neuron loss.” Elife 5 (2016): e12454.
  Franz Voelker, Aaron R., and Chris Eliasmith. “Improving spiking dynamical networks: Accurate delays, higher-order synapses, and time cells.” Neural computation 30.3 (2018): 569-609.
    Voelker, Aaron, Ivana Kajić, and Chris Eliasmith. “Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks.” Advances in Neural Information Processing Systems. 2019.
  Arjun Frady, E. Paxon, and Friedrich T. Sommer. “Robust computation with rhythmic spike patterns.” Proceedings of the National Academy of Sciences 116.36 (2019): 18050-18059.
11.11.2019 Michael Habenschuss, Stefan, Zeno Jonke, and Wolfgang Maass. “Stochastic computations in cortical microcircuit models.” PLoS computational biology 9.11 (2013): e1003311.
    Berkes, Pietro, et al. “Spontaneous cortical activity reveals hallmarks of an optimal internal model of the environment.” Science 331.6013 (2011): 83-87.
29.10.2019 Darjan Nayebi, Aran, et al. “Task-Driven convolutional recurrent models of the visual system.” Advances in Neural Information Processing Systems. 2018.
27.09.2019 Horst Marblestone, Adam H., Greg Wayne, and Konrad P. Kording. “Toward an integration of deep learning and neuroscience.” Frontiers in computational neuroscience 10 (2016): 94.
07.06.2019 Franz Hung, Chia-Chun, et al. “Optimizing agent behavior over long time scales by transporting value.” arXiv preprint arXiv:1810.06721 (2018)
16.05.2019 Elias Frankle, Jonathan, and Michael Carbin. “The lottery ticket hypothesis: Finding sparse, trainable neural networks.” arXiv preprint arXiv:1803.03635 (2018).
09.05.2019 Rapid fire session  
  Anand Karnani, Mahesh M., et al. “A Blanket of Inhibition: Functional Inferences from Dense Inhibitory Connectivity”. Current Opinion in Neurobiology, 2014
    Okun, Michael, et al. “Diverse Coupling of Neurons to Populations in Sensory Cortex”. Nature, 2015.
  Darjan Bönstrup, Marlene, et al. “A Rapid Form of Offline Consolidation in Skill Learning.” Current Biology (2019).
    Triefenbach, Fabian, et al. “Phoneme recognition with large hierarchical reservoirs.” Advances in neural information
  Michael Saxe, Andrew M., et al. “On Random Weights and Unsupervised Feature Learning.” ICML. Vol. 2. No. 3. 2011.
  Philipp Frady, Edward & Sommer, Friedrich. “Robust computation with rhythmic spike patterns.” arxiv preprint: arXiv:1901.07718 (2019)
  Arjun Akrout, M., Wilson, C., Humphreys, P. C., Lillicrap, T., & Tweed, D. (2019). Using Weight Mirrors to Improve Feedback Alignment. arXiv preprint arXiv:1904.05391
  Ceca Behrens, Timothy E. J., et al. “What Is a Cognitive Map? Organising Knowledge for Flexible Behaviour.” 2018, doi:10.1101/365593.; LINK: https://www.cell.com/neuron/pdf/S0896-6273(18)30856-0.pdf
18.04.2019 Ceca O’Reilly, Randall C., Thomas E. Hazy, and Seth A. Herd. “The Leabra Cognitive Architecture: How to Play 20 Principles with Nature.​.” The Oxford handbook of cognitive science 91 (2016): 91-116.
11.04.2019 Darjan Krotov, Dmitry, and John J. Hopfield. “Unsupervised learning by competing hidden units.” Proceedings of the National Academy of Sciences (2019): 201820458.
20.03.2019 Arjun Koutnik, Jan, et al. “A clockwork rnn.” arXiv preprint arXiv:1402.3511 (2014).
    Chung, Junyoung, Sungjin Ahn, and Yoshua Bengio. “Hierarchical multiscale recurrent neural networks.” arXiv preprint arXiv:1609.01704 (2016).
14.03.2019 Anand Jaderberg, Max, et al. “Human-level performance in first-person multiplayer games with population-based deep reinforcement learning.” arXiv preprint arXiv:1807.01281 (2018).
17.12.2018 Thomas L. Beaulieu-Laroche, Lou, et al. “Enhanced Dendritic Compartmentalization in Human Cortical Neurons.” Cell 175.3 (2018): 643-651.
20.11.2018 Michael Kutter, Esther F., et al. “Single Neurons in the Human Brain Encode Numbers.” Neuron (2018).
    Quiroga, R. Quian, et al. “Invariant visual representation by single neurons in the human brain.” Nature 435.7045 (2005): 1102.
09.11.2018 Guillaume Wasmuht, Dante Francisco, et al. “Intrinsic neuronal dynamics predict distinct functional roles during working memory.” Nature communications 9.1 (2018): 3499.
19.10.2018 Franz Perich, Matthew G., Juan A. Gallego, and Lee E. Miller. “A neural population mechanism for rapid learning.” Neuron (2018).
12.10.2018 Darjan Zeng, Andy, et al. “Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement Learning.” arXiv preprint arXiv:1803.09956 (2018).
    Dubey, Rachit, et al. “Investigating Human Priors for Playing Video Games.” arXiv preprint arXiv:1802.10217 (2018).
05.10.2018 Ceca Rougier, Nicolas P., et al. “Prefrontal cortex and flexible cognitive control: Rules without symbols.” Proceedings of the National Academy of Sciences 102.20 (2005): 7338-7343.
21.09.2018 Arjun Palm, Rasmus Berg, Ulrich Paquet, and Ole Winther. “Recurrent Relational Networks.” arXiv preprint arXiv:1711.08028 (2018).
10.08.2018 Anand Franceschi, L., Frasconi, P., Salzo, S., Grazzi, R., & Pontil, M. (2018). Bilevel Programming for Hyperparameter Optimization and Meta-Learning. ArXiv:1806.04910 [Cs, Stat]. Retrieved from http://arxiv.org/abs/1806.04910 (ICML 2018)
03.08.2018 Darjan Siwani, Samer, et al. “OLMα2 cells bidirectionally modulate learning.” Neuron (2018).
20.07.2018 Arjun Henaff, Mikael, et al. “Tracking the world state with recurrent entity networks.” arXiv preprint arXiv:1612.03969 (2016).
13.07.2018 Franz Sabour, Sara, Nicholas Frosst, and Geoffrey E. Hinton. “Dynamic routing between capsules.” Advances in Neural Information Processing Systems. 2017.
23.04.2018 Ceca Glimcher, Paul W. “Understanding dopamine and reinforcement learning: the dopamine reward prediction error hypothesis.” Proceedings of the National Academy of Sciences 108.Supplement 3 (2011): 15647-15654.
12.03.2018 Franz Houthooft, Rein, et al. “Vime: Variational information maximizing exploration.” Advances in Neural Information Processing Systems. 2016.
    Blundell, Charles, et al. “Weight uncertainty in neural networks.” arXiv preprint arXiv:1505.05424 (2015).
02.03.2018 Anand Finn, Chelsea, Pieter Abbeel, and Sergey Levine. “Model-agnostic meta-learning for fast adaptation of deep networks.” arXiv preprint arXiv:1703.03400 (2017).
23.02.2018 Michael Mostafa, Hesham, Vishwajith Ramesh, and Gert Cauwenberghs. “Deep supervised learning using local errors.” arXiv preprint arXiv:1711.06756 (2017).
09.02.2018 Anand Costa, R., Assael, Y., Shillingford, B., de Freitas, N. & Vogels, Ti. Cortical microcircuits as gated-recurrent neural networks. in Advances in Neural Information Processing Systems 30 (eds. Guyon, I. et al.) 271–282 (Curran Associates, Inc., 2017).
02.02.2018 Guillaume Bahdanau, Dzmitry, et al. “End-to-end attention-based large vocabulary speech recognition.” Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on. IEEE, 2016.
    Graves, Alex, Abdel-rahman Mohamed, and Geoffrey Hinton. “Speech recognition with deep recurrent neural networks.” Acoustics, speech and signal processing (icassp), 2013 ieee international conference on. IEEE, 2013.
    Graves, Alex, et al. “Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks.” Proceedings of the 23rd international conference on Machine learning. ACM, 2006.
    Amodei, Dario, et al. “Deep speech 2: End-to-end speech recognition in english and mandarin.” International Conference on Machine Learning. 2016.
    Hannun, Awni, et al. “Deep speech: Scaling up end-to-end speech recognition.” arXiv preprint arXiv:1412.5567 (2014).
26.01.2018 Darjan Mishra, Nikhil, et al. “Meta-learning with temporal convolutions.” arXiv preprint arXiv:1707.03141 (2017). https://arxiv.org/abs/1707.03141
19.01.2018 Arjun Jaderberg, Max, et al. “Population Based Training of Neural Networks.” arXiv preprint arXiv:1711.09846 (2017).
12.01.2018 Thomas B. Wang, Peng, et al. “Multi-attention network for one shot learning.” 2017 IEEE conference on computer vision and pattern recognition, CVPR. 2017.
05.01.2018 Thomas L. Jaderberg, Max, et al. “Decoupled neural interfaces using synthetic gradients.” arXiv preprint arXiv:1608.05343 (2016).
07.12.2017 Franz Graves. “Adaptive Computation Time for Recurrent Neural Networks.” arXiv:1603.08983 (2016). https://arxiv.org/abs/1603.08983
01.12.2017 Guillaume Sussillo, Stavisky, Kao, Ryu, Shenoy. “Making brain-machine interfaces robust to future neural variability” nature communications
    Panzeri, Harvey, Piasini, Latham, Fellin “Cracking the Neural Code for Sensory Perception by Combining Statistics, Intervention and Behavior”
    Lee, Delbruck, and Pfeiffer “Training Deep Spiking Neural Networks Using Backpropagation”
24.11.2017 Anand Xu, Yan, Xiaoqin Zeng, and Shuiming Zhong. “A new supervised learning algorithm for spiking neurons.” Neural computation 25.6 (2013): 1472-1511.
    Ponulak, Filip, and Andrzej Kasiński. “Supervised learning in spiking neural networks with ReSuMe: sequence learning, classification, and spike shifting.” Neural Computation 22.2 (2010): 467-510.
17.11.2017 Michael Hadji, Isma, and Richard P. Wildes. “A Spatiotemporal Oriented Energy Network for Dynamic Texture Recognition.” arXiv preprint arXiv:1708.06690 (2017). https://arxiv.org/abs/1708.06690
06.10.2017 Guillaume Song Han et al. 2017 - SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size https://arxiv.org/abs/1602.07360 (submitted to ICLR 2017)
    Song Han et al. 2017 - ESE: Efficient Speech Recognition Engine with Sparse LSTM on FPGA https://arxiv.org/abs/1612.00694
    Collins et al. 2014 - Memory bounded neural network https://arxiv.org/pdf/1412.1442.pdf
    Song Han et al. 2015 - Learning both weights and connections https://arxiv.org/pdf/1506.02626.pdf (appeared in NIPS)
29.10.2017 Jian Dvorkin R, Ziv NE (2016) Relative Contributions of Specific Activity Histories and Spontaneous Processes to Size Remodeling of Glutamatergic Synapses. PLoS Biol 14(10): e1002572. https://doi.org/10.1371/journal.pbio.1002572
    Rubinski A, Ziv NE (2015) Remodeling and Tenacity of Inhibitory Synapses: Relationships with Network Activity and Neighboring Excitatory Synapses. PLoS Comput Biol 11(11): e1004632. https://doi.org/10.1371/journal.pcbi.1004632
    Statman A, Kaufman M, Minerbi A, Ziv NE, Brenner N (2014) Synaptic Size Dynamics as an Effectively Stochastic Process. PLoS Comput Biol 10(10): e1003846. https://doi.org/10.1371/journal.pcbi.1003846
10.08.2017 Franz Zoph, Barret, and Quoc V. Le. “Neural architecture search with reinforcement learning.” arXiv preprint arXiv:1611.01578 (2016).
02.08.2017 David Friston K. and Kiebel S. “Predictive coding under the free-energy principle.” Phil. Trans. R. Soc. B (2009) 364, 1211–1221.
    Friston K. “Variational filtering.” NeuroImage (2008) 41, 747-766.
26.07.2017 Anand Spratling, M. W. “A review of predictive coding algorithms.” Brain and cognition 112 (2017): 92-97.
    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): 79-87.
    PredNet: Lotter, William, Gabriel Kreiman, and David Cox. “Deep predictive coding networks for video prediction and unsupervised learning.” arXiv preprint arXiv:1605.08104 (2016).
26.07.2017 Michael M. Dosovitskiy, Alexey, and Vladlen Koltun. “Learning to act by predicting the future.” arXiv preprint arXiv:1611.01779 (2016).
13.06.2017 Guillaume Lillicrap, Timothy P., et al. “Random synaptic feedback weights support error backpropagation for deep learning.” Nature Communications 7 (2016).
25.04.2017 Guillaume Salimans, Tim, et al. “Evolution Strategies as a Scalable Alternative to Reinforcement Learning.” arXiv preprint arXiv:1703.03864 (2017).
25.04.2017 Anand Whittington, James CR, and Rafal Bogacz. “An Approximation of the Error Backpropagation Algorithm in a Predictive Coding Network with Local Hebbian Synaptic Plasticity.” Neural Computation (2017).
25.04.2017 Arjun Orhan, A. Emin, and Wei Ji Ma. “Efficient Probabilistic Inference in Generic Neural Networks Trained with Non-Probabilistic Feedback.” arXiv preprint arXiv:1601.03060 (2016). APA
25.04.2017 David Schiess, Mathieu, Robert Urbanczik, and Walter Senn. “Somato-dendritic synaptic plasticity and error-backpropagation in active dendrites.” PLoS Comput Biol 12.2 (2016): e1004638.
18.04.2017 Anand Goodfellow, Ian, et al. “Generative adversarial nets.” Advances in neural information processing systems. 2014.
14.04.2017 David Variational Auto-encoders
    Kingma, Diederik P., and Max Welling. “Auto-encoding variational bayes.” arXiv preprint arXiv:1312.6114 (2013).
04.04.2017 Guillaume and David Variational Inference
    Mnih, Andriy, and Karol Gregor. “Neural variational inference and learning in belief networks.” arXiv preprint arXiv:1402.0030 (2014).
28.03.2017 Arjun Dirichlet Distributions
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