Each student is encouraged to present a paper of her/his own interest. Just inform us by dropping an email to make sure that the paper is relevant to the seminar. A nice place to learn more about computational neuroscience is the Brain Inspired podcast, have a look at the bibliography section where you can find interesting papers. In case you do not have any favourite paper in mind, you can select one from the list below.
Pick an interesting chapter from here and explain it (including related papers mentioned in the chapter):
Consult the chosen chapter with us!!
Articles of Special Interest
Here are a few papers directly related to our ongoing projects that have to be reviewed as soon as possible.
- On the Binding Problem in Artificial Neural Networks by Greff et al. 2020.
- Inductive Biases for Deep Learning of Higher-Level Cognition by Goyal et al. 2020.
- Beyond dichotomies in reinforcement learning by Collins & Cockburn 2020.
- Learning task-state representations by Niv 2020.
- Embracing Change: Continual Learning in Deep Neural Networks by Hadsell et al. 2020.
- The Child as Hacker Rule et al. 2020.
- Cellular Mechanisms of Conscious Processing by Aru et al. 2020.
- A Theory of Natural Universal Computation Through RNA by Akhlaghpour 2020.
- Deep Learning and the Global Workspace Theory by VanRullen and Kanai 2020.
- Could a Neuroscientist Understand a Microprocessor? by Jonas & Kording, 2016.
- Predictive Maps in Rats and Humans for SpatialNavigation by de Cothi et al. 2020.
- Unsupervised deep learning identifies semantic disentanglement in single inferotemporal neurons by Higgins et al. 2020.
- Action and Perception as Divergence Minimization by Hafner et al. 2020.
- Placing language in an integrated understanding system: Next steps toward human-level performance in neural language models by McClelland et al. 2020.
- A solution to the learning dilemma for recurrent networks of spiking neurons by Bellec et al. 2019
- Evidence that recurrent circuits are critical to the ventral stream’s execution of core object recognition behavior by Kar et al. 2019.
- Deep Neuroethology of a Virtual Rodent Merel et al. 2020.
- The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence by Gary Marcus, 2020.
- Humans incorporate trial-to-trial working memory uncertainty into rewarded decisions by Honig et al., 2020
- Meta-Learning through Hebbian Plasticity in Random Networksby Najarro and Risi, 2020.
- Learning Human Objectives by Evaluating Hypothetical Behavior by Reddy et al. 2019.
- MEMO: A DEEP NETWORK FOR FLEXIBLE COMBINATION OF EPISODIC MEMORIES by Banuino et al. 2020.
- A distributional code for value in dopaminebased reinforcement learning by Dabney et al. 2020.
- Optimizing agent behavior over long time scales by transporting value by Hung et al. 2019
- A deep learning framework for neuroscience by Richards et al. 2019
- Learning without feedback: Direct random target projection as a feedback-alignment algorithm with layerwise feedforward training by Frenkel et al. 2019
- The Tolman-Eichenbaum Machine: Unifying space and relational memory through generalisation in the hippocampal formation by Whittington et al. 2019
- Theory of Minds: Understanding Behavior in Groups Through Inverse Planning Michael by Shum et al. 2019.
- Preferences Implicit in the State of the World by Shah et al. 2019.
- Designing neural networks through neuroevolution by Stanley et al. 2019.
- A Memory-Augmented Reinforcement Learning Model of Food Caching Behaviour in Birds by Brea et al. 2019.
- A neural network model of flexible grasp movement generation by Michaels et al. 2019.
- Open-ended Learning in Symmetric Zero-sum Games by Balduzzi et al. 2019.
- Unsupervised State Representation Learning in Atari by Anand et al. 2019.
- A common model explaining flexible decision making, grid fields and cognitive control by Piray & Daw 2019.
- Dendritic action potentials and computation in human layer 2/3 cortical neurons by Gidon et al. 2020.
- Backpropagation through time and the brain by Lillicrap et al. 2019.
- Generalisation of structural knowledge in the hippocampal system by Whittington, 2018.
- A Critique of Pure Learning: What Artificial Neural Networks can Learn from Animal Brains by Zador, 2019.
- Predicting visual stimuli on the basis of activity in auditory cortices by Kaspar Meyer et al., 2010
- Canonical Microcircuits for Predictive Coding by Andre M. Bastos et al., 2012
- Prefrontal cortex as a meta-reinforcement learning system by Jane X. Wang, Zeb Kurth-Nelson, Dharshan Kumaran, Dhruva Tirumala, Hubert Soyer, Joel Z. Leibo, Demis Hassabis and Matthew Botvinick.
- Flexible timing by temporal scaling of cortical responses by Jing Wang, Devika Narain, Eghbal A. Hosseini & Mehrdad Jazayeri;2017
- Dendritic error backpropagation in deep cortical microcircuits by João Sacramento, Rui Ponte Costa, Yoshua Bengio, Walter Senn;2017
- Spiking neurons can discover predictive features by aggregate-label learning by Robert Gütig, 2016 in Science
- Dynamical models of cortical circuits by Fred Wolf et al., 2014
Pure Deep Learning and AI
- MONet: Unsupervised Scene Decomposition and Representation by Burgess et al. 2019
- A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms by Bengio et al. 2019.
- Dense Associative Memory is Robust to Adversarial Inputs by Dmitry Krotov, John J Hopfield, 2016
Deep Learning Basic Algorithms
- Learning Internal Representations by Error Propagation by Rumelhart, David E ; Hinton, Geoffrey E ; Williams, Ronald J;1986
- Long Short-Term Memory by Sepp Hochreiter and Jürgen Schmidhuber;1997
- Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning by Ronald J. Williams;1992