Summer break
4 September 2025 (TBD): TBD
18 September 2025 (TBD): TBD
2 October 2025 (TBD): TBD
16 October 2025 (TBD): TBD
30 October 2025 (TBD): TBD
13 November 2025 (TBD): TBD
27 November 2025: no meeting (Thanksgiving)
11 December 2025 (TBD): TBD
25 December 2025 (TBD): no meeting (winter break)
12 June 2025: Paper blitz!
29 May 2025 (Zaki): Vollan et al., (2025). Left–right-alternating theta sweeps in entorhinal–hippocampal maps of space. Nature.
15 May 2025 (Darrell): Rebhuhn-Glanz et al., (2025). Spatial periodicity in grid cell firing is explained by a neural sequence code of 2-D trajectories. eLife.
1 May 2025 (Dianna): Pospisil et al., (2024). The fly connectome reveals a path to the effectome. Nature.
17 April 2025 (Albert): Pjanovic et al., (2025). Combining Sampling Methods with Attractor Dynamics in Spiking Models of Head-Direction Systems. arXiv.
3 April 2025: no meeting (Cosyne)
20 March 2025 (Caleb): Cao et al., (2021). Identifiability in inverse reinforcement learning. NeurIPS.
6 March 2025 (John): Kamb & Ganguli (2024). An analytic theory of creativity in convolutional diffusion models. arXiv
20 February 2025 (Camille): Safavi et al. (2024). "Signatures of criticality in efficient coding networks." PNAS.
6 February 2025 (Byron): Leone et al. (2025). "Noise Correlations in Balanced Networks with Unreliable Synapses." bioRxiv
23 January 2025 (Brooks): Steinemann et al. (2024). "Direct observation of the neural computations underlying a single decision." eLife.
9 January 2025 (Zach): Kumar et al. (2024). “A Model of Place Field Reorganization During Reward Maximization,” bioRxiv doi:10.1101/2024.12.12.627755
26 December 2024: no meeting
12 December 2024 (Jan): Schmutz, Brea & Gerstner (2024). Emergent rate-based dynamics in duplicate-free populations of spiking neurons. arXiv:303.05174 [q-bio.NC]
28 November 2024: no meeting (Thanksgiving)
14 November 2024 (Asem): Brunel et al. (2004). Optimal information storage and the distribution of synaptic weights: Perceptron vs. Purkinje cell. Neuron, 43(5), 745-757.
31 October 2024 (Albert): Tian et al. (2024). Neuronal firing rate diversity lowers the dimension of population covariability. bioRxiv.
17 October 2024 (Zaki): Dan et al. (2024). A neural circuit architecture for rapid learning in goal-directed navigation. Neuron
3 October 2024 (Darrell): Hermundstad & Młynarski (2024). A theory of rapid behavioral inferences under the pressure of time. bioRxiv.
19 September 2024 (Brooks): Sani et al. (2024). Dissociative and prioritized modeling of behaviorally relevant neural dynamics using recurrent neural networks. Nature Neuroscience.
Summer break
27 June 2024 (Jan): Stroud et al. (2023). Optimal information loading into working memory explains dynamic coding in the prefrontal cortex. PNAS, 120(40), e2307991120.
13 June 2024 (Satpreet): Ostrow et al. (2024). Beyond Geometry: Comparing the Temporal Structure of Computation in Neural Circuits with Dynamical Similarity Analysis. NeurIPS.
30 May 2024 (Caleb): Yin (2020). The crisis in neuroscience. In W. Mansell (Ed.), The interdisciplinary handbook of perceptual control theory: Living control systems IV.
16 May 2024: postponed
2 May 2024: cancelled
25 April 2024 (Albert): Han & Wei (2024), A unifying theory explains seemingly contradictory biases in perceptual estimation. Nature Neuroscience
4 April 2024 (Byron): Wen et al., (2023), One-shot entorhinal maps enable flexible navigation in novel environments. bioRxiv
21 March 2024 (Zach): Lyle et al., (2022), Learning Dynamics and Generalization in Deep Reinforcement Learning. ICML.
7 March 2024: no meeting (Cosyne)
22 February 2024 (Darrell): Fang et al., (2023), Neural learning rules for generating flexible predictions and computing the successor representation. eLife.
8 February 2024: no meeting
25 January 2024 (Binxu): Guth et al., (2023), A Rainbow in Deep Network Black Boxes. arXiv.
11 January 2024 (Siyan): Perez-Cruz (2008), Kullback-Leibler divergence estimation of continuous distributions. IEEE ISIT.
28 December 2023: no meeting (winter break)
14 December 2023: no meeting (winter break)
30 November 2023 (Brooks): Barbosa et al. (2023), Early selection of task-relevant features through population gating. Nature Communications.
16 November 2023 (Asem): Clark et al. (2023), Dimension of Activity in Random Neural Networks. Physical Review Letters.
2 November 2023 (John): Liang et al. (2023), Causal Component Analysis. arXiv.
19 October 2023 (Caleb): Sharma et al. (2023). Assembly theory explains and quantifies selection and evolution. Nature, 622, 321-328.
5 October 2023: no meeting
21 September 2023 (Jan): Park, Sagodi & Sokol (2023). Persistent learning signals and working memory without continuous attractors. arXiv:2308.12585.
7 September 2023 (Albert; exceptionally in Goldenson 357): Muscinelli et al., (2023). Optimal routing to cerebellum-like structures. Nature Neuroscience.
Summer break
15 June 2023 (Binxu): Schneider, Lee & Mathis. (2023). Learnable latent embeddings for joint behavioural and neural analysis, Nature.
1 June 2023: no meeting (Neurobiology Department retreat)
18 May 2023 (Zach): Sanborn et al. (2023). Bispectral Neural Networks, ICLR.
4 May 2023 (Siyan): Whittington et al. (2022). Relating transformers to models and neural representations of the hippocampal formation, ICLR.
20 April 2023: no meeting
6 April 2023 (Albert): Kessler et al. (2022). "A Dynamic Bayesian Actor Model explains Endpoint Variability in Homing Tasks." bioRxiv.
23 March 2023 (Brooks): Salmasi & Sahani (2022). "Learning neural codes for perceptual uncertainty." IEEE.
9 March 2023: no meeting (Cosyne)
23 February 2023 (Asem): Pereira-Obilinovic et al. (2023). Forgetting Leads to Chaos in Attractor Networks. Phys. Rev. X
9 February 2023 (Tejas): Hiratani & Fukai (2018). "Redundancy in synaptic connections enables neurons to learn optimally." PNAS.
26 January 2023: no meeting
12 January 2023 (John): Masset & Zavatone-Veth et al. (2022). Natural gradient enables fast sampling in spiking neural networks. NeurIPS.
29 December 2022: no meeting
15 December 2022: no meeting
1 December 2022 (Siyan): Whittington (2017). An Approximation of the Error Backpropagation Algorithm in a Predictive Coding Network with Local Hebbian Synaptic Plasticity. Neural Computation
17 November 2022 (Caleb): Bialek (2022). On the dimensionality of behavior. PNAS
3 November 2022 (Zach): Jensen et al. (2020). Manifold GPLVMs for discovering non-Euclidean latent structure in neural data. NeurIPS
20 October 2022 (Binxu): Whittington et al. (2022). Disentangling with Biological Constraints: A Theory of Functional Cell Types. arXiv
6 October 2022 (Shih-Yi): Langdon & Engel (2022). Latent circuit inference from heterogeneous neural responses during cognitive tasks. bioRxiv
22 September 2022 (Albert): Gokcen et al. (2022). Disentangling the flow of signals between populations of neurons. Nature Computational Science 2, 512-525.
8 September 2022 (Jan): Młynarski & Hermundstad (2021) Efficient and adaptive sensory codes. Nature Neuroscience 24, 998-1009.
Pandemic break
24 June 2021 (Anna): Boyd-Meredith et al. (2021) Stable choice coding during changes of mind. bioRxiv
10 June 2021 (Shih-Yi): Sorscher et al. (2021) The Geometry of Concept Learning. bioRxiv
27 May 2021 (Johannes): Stewart & Plotkin (2021) The natural selection of good science. Nature Human Behaviour
13 May 2021 (Qiao): Duncker et al. (2020) Organizing recurrent network dynamics by task-computation to enable continual learning. NeurIPS
29 April 2021 (Jan): Aitchison et al. (2021) Synaptic plasticity as Bayesian inference. Nature Neuroscience
15 April 2021 (Johannes): Klos et al. (2020) Dynamical Learning of Dynamics. Physical Review Letters
1 April 2021 (Luke): Chua et al. (2018) Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models. NeurIPS
18 March 2021 (Shih-Yi): Glaser et al. (2020) Recurrent Switching Dynamical Systems Models for Multiple Interacting Neural Populations. NeurIPS
4 March 2021 (Qiao): Ashwood et al. (2020). Inferring learning rules from animal decision-making. NeurIPS
18 February 2021 (Anna): Ramírez-Ruiz & Moreno-Bote (2021). Optimal allocation of finite sampling capacity in accumulator models of multi-alternative decision making. arXiv:2102.01597.
4 February 2021 (Jan): Wu et al. (2020). Rational thoughts in neural codes. PNAS, 117(47), 29311-29320.
21 January 2021 (Emma): Masis et al. (2020). Rats strategically manage learning during perceptual decision making. bioRxiv
17 December 2020 (Jan): Domingos (2020). Every Model Learned by Gradient Descent Is Approximately a Kernel Machine. arXiv
3 December 2020 (Qiao): Mancoo et al. (2020). Understanding spiking networks through convex optimization. NeurIPS
19 November 2020 (Shih-Yi): Whittington et al. (2020). The Tolman-Eichenbaum Machine: Unifying Space and Relational Memory through Generalization in the Hippocampal Formation. Cell
5 November 2020 (Anna): Sadeh & Clopath (2020) Theory of neuronal perturbome in cortical networks. PNAS
22 October 2020 (Jan): Maheswaranathan et al. (2019). Universality and individuality in neural dynamics across large populations of recurrent networks. NeurIPS 2019.
8 October 2020 (Emma): de Cothi et al. (2020). Predictive Maps in Rats and Humans for Spatial Navigation. bioRxiv
24 September 2020 (Johannes): Mann (2020). Collective decision-making by rational agents with differing preferences. PNAS
10 September 2020 (Luke): Titsias et al. (2019). Functional Regularisation for Continual Learning with Gaussian Processes. arXiv
20 August 2020 (Shih-Yi): Mattar & Daw (2018). Prioritized memory access explains planning and hippocampal replay. Nature Neuroscience.
6 August 2020 (Anna): Schaeffer et al. (2020). Reverse-engineering Recurrent Neural Network solutions to a hierarchical inference task for mice. bioRxiv, 2020.06.09.142745.
23 July 2020 (Luke): Toth et al. (2020). Hamiltonian Generative Networks. ICLR 2020 & arXiv:1909.13789.
9 July 2020 (Jan): Piray & Daw (2020). A simple model for learning in volatile environments. PLoS Computational Biology 16(7):e1007963.
25 June 2020 (Johannes): Neklyudov et al. (2019). Variance Networks: When Expectation Does Not Meet Your Expectations. ICLR
11 June 2020 (Emma): Khalvati et al. (2019). Modeling other minds: Bayesian inference explains human choices in group decision-making. Science Advances, 5(11), eaax8783.
28 May 2020 (Qiao): Blakeman & Mareschal (2020). A complementary learning systems approach to temporal difference learning. Neural Networks, 122, 218-230.
14 May 2020: no meeting
30 April 2020 (Luke): Koay et al. (2020). Sequential and efficient neural-population coding of complex task information. bioRxiv.
16 April 2020 (Jan): Brendel et al. (2020). Learning to represent signals spike by spike. PLoS Computational Biology 16(3): e1007692.
2 April 2020: No meeting
19 March 2020 (Anna): Zhao, et al. (2019). Streaming Variational Monte Carlo arXiv
5 March 2020 (Chong): Jacot, Gabriel, & Hongler (2018) Neural Tangent Kernel: Convergence and Generalization in Neural Networks NeurIPS
20 February 2020 (Qiao): Bergomi et al. (2019) Towards a topological–geometrical theory of group equivariant non-expansive operators for data analysis and machine learning Nature Machine Intelligence
6 February 2020 (Johannes): Goldt, et al. (2019) Dynamics of stochastic gradient descent for two-layer neural networks in the teacher-student setup. Advances in Neural Information Processing Systems
23 January 2020 (Emma): Gupta et al. (2018) Meta-Reinforcement Learning of Structured Exploration Strategies. NeurIPS
9 January 2020: No meeting
26 December 2019: No meeting
12 December 2019 (Chong): Belkin, Hsu & Xu (2019) Two models of double descent for weak features. arXiv
28 November 2019: No meeting (Thanksgiving)
14 November 2019 (Johannes): Acerbi, Ma, & Vijayakumar (2014) A Framework for Testing Identifiability of Bayesian Models of Perception. NeurIPS
31 October 2019: No meeting
17 October 2019 (Qiao): Dold et al. (2018) Stochasticity from function -- why the Bayesian brain may need no noise. arXiv
3 October 2019 (Luke): Daptardar, Schrater & Pitkow (2019) Inverse Rational Control with Partially Observable Continuous Nonlinear Dynamics. arXiv.
19 September 2019 (Anna): Lin, Tegmark & Rolnick (2017) Why does deep and cheap learning work so well? Journal of Statistical Physics, 168(6), 1223-1247.
5 September 2019 (Jan): Echeveste et al. (2019) Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inference. bioRXiv.
Summer break
20 Jun 2019 (Chong): Belghazi et al. (2018) Mutual Information Neural Estimation. ICML.
6 June 2019 (Daniel): Alemi et al. (2017) Deep Variational Information Bottleneck. ICLR.
23 May 2019 (Selmaan): Linderman et al. (2019) Hierarchical recurrent state space models reveal discrete and continuous dynamics of neural activity in C. elegans bioRxiv.
9 May 2019 (Luke): Mathieu et al. (2019) Hierarchical Representations with Poincaré Variational Auto-Encoders arXiv.
25 April 2019 (Jan): Johnston, Palmer & Freedman (2019). Nonlinear mixed selectivity supports reliable neural computation. bioRxiv.
11 April 2019 (Emma): Bouchacourt and Buschman. (2018) A flexible model of working memory. bioRxiv.
28 March 2019 (Johannes): Darlington et al. (2018) Neural implementation of Bayesian inference in a sensorimotor behavior. Nature Neuroscience, 21, 1442-1451.
14 March 2019 (Chong): Chen et al. (2018) Neural Ordinary Differential Equations. NeurIPS.
14 February 2019 (Till): Bashivan et al. (2018). Neural Population Control via Deep ANN Image Synthesis. bioRxiv.
31 January 2019 (Selmaan): Stringer et al. (2018) High-dimensional geometry of population responses in visual cortex. bioRxiv.
17 January 2019 (Daniel): Rajalingham et al. (2018). Large-Scale, High-Resolution Comparison of the Core Visual Object Recognition Behavior of Humans, Monkeys, and State-of-the-Art Deep Artificial Neural Networks. J Neurosci.
6 December 2018 (Luke): Lakshminarasimhan et al (2018). Inferring decoding strategies for multiple correlated neural populations, PLoS Comp Bio 14(9):e1006371.
22 November 2018: No meeting (Thanksgiving)
15 November 2018 (Jan): Chandrasekaran et al. (2018). Brittleness in model selection analysis of single neuron firing rates. bioRXiv.
25 October 2018 (Emma): Pandarinath et al (2018). Inferring single-trial neural population dynamics using sequential auto-encoders, Nature Methods 15, 805-815.
11 October 2018 : No meeting ("Bridging Theory and Data in Neuroscience" HBI event)
27 September 2018 (Johannes): Wilting & Priesemann (2018). Inferring collective dynamical stated from widely unobserved systems. Nature Communications 9:2325.
13 September 2018 (Chong): Memmesheimer et al. (2014) Learning Precisely Timed Spikes. Neuron 82, 925-938.
Summer break
19 July 2018 (Emma): Ocko et al. (2018). Emergent elasticity in the neural code for space. bioRXiv.
5 July 2018 (Chong): Lilicrap et al. (2016). Random synaptic feedback weights support error backpropagation for deep learning. Nature Communications 7 (13276); and Nøkland (2016). Direct Feedback Alignment Provides Learning in Deep Neural Networks. arXiv:1609.01596.
21 June 2018 (Stephen): Pereira & Brunel (2018). Attractor Dynamics in Networks with Learning Rules Inferred from In Vivo Data. Neuron 99(1), 227-238.
7 June 2018 (Till): Banino et al. (2018). Vector-based navigation using grid-like representations in artificial agents. Nature 557, 429-433.
24 May 2018 (Luke): Wei & Stocker (2017). Lawful relation between perceptual bias and discriminability. PNAS 114(38), 10244-10249. (and Sims (2018). Efficient coding explains the universal law of generalization in human perception. Science 360(6389), 652-656.)
10 May 2018 (Daniel): Chalk, Maree & Tkacik (2018). Towards a unified theory of efficient, predictive, and sparse coding. PNAS 115(1), 186-191.
26 April 2018 (Selmaan): Latimer, Rieke & Pillow (2018). Inferring synaptic inputs from spikes with a conductance-based neural encoding model. BioRXiv.
12 April 2018 (Johannes): Grabska-Barwińska et al. (2016). A probabilistic approach to demixing odors. Nature Neuroscience 20(1).
29 March 2018 (Matthias): Stachenfeld, Botvinick & Gershman (2017). The hippocampus as a predictive map. Nature Neuroscience 20, 1643-1653.
15 March 2018 (Emma): Russo et al. (2018). Motor cortex embeds muscle-like commands in an untangled population response. Neuron 97(4). 953-966.
15 February 2018 (Till): Berardino et al. (2017). Eigen-Distortions of Hierarchical Representations. NIPS.
1 February 2018 (Stephen): Elsayed & Cunningham (2017). Structure in neural population recordings: an expected byproduct of simpler phenomena? Nature Neuroscience, 20(9), 1310-1318. See also News & Views.
18 January 2018 (Matthias): Shwartz-Ziv & Tishby (2017). Opening the Black Box of Deep Neural Networks via Information. arXiv:1703.00810. See also open review.
7 December 2017 (Jan): Brinkman et al. (2017). Predicting how and when hidden neurons skew measured synaptic interactions. arXiv:1702.00865.
23 November 2017: No meeting (Thanksgiving)
9 November 2017 (Mehdi): Tootoonian & Latham (2017). Sparse connectivity in MAP inference for linear models using sister mitral cells. arXiv:1709.01437.
26 October 2017 (Selmaan): Hennequin, Vogels & Gerstner (2014). Optimal control of transient dynamics in balanced networks supports generation of complex movements. Neuron 82(6), 1394-1406.
12 October 2017 (Luke): Harel, Meir & Opper (2015). A tractable approximation to optimal point process filtering: application to neural encoding. NIPS.
28 September 2017 (Daniel): Charles et al. (2017). Dethroning the Fano factor: a flexible, model-based approach to partitioning neural variability. bioRXiv.
14 September 2017 (Chong): Pehlevan, Sengupta & Chklovskii (2017). Why do similarity matching objectives lead to Hebbian/anti-Hebbian networks? arXiv:1703.07914.
Summer break
22 June 2017 (Till): McIntosh et al. (2016). Deep learning models of the retinal response to natural scenes. NIPS.
8 June 2017 (Mehdi): Pritchett & Murray (2015). Classification images reveal decision variables and strategies in forced choice tasks. PNAS 112(23).
25 May 2017 (Tonino): Voss (2016). The Leaky Integrator with Recurrent Inhibition as a Predictor. Neural Computation 28, 1498-1502; Voss (2016). Signal prediction by anticipatory relaxation dynamics. Phys. Rev. E 93, 030201(R).
11 May 2017 (Stephen): Kappel et al. (2015). Synaptic sampling: a Bayesian approach to neural network plasticity and rewiring. NIPS; Yu et al. (2016). CaMKII activation supports reward-based neural network optimization through Hamiltonian sampling. arXiv:1606.00157.
27 April 2017 (Luke): Heeger (2017). Theory of cortical function. PNAS, 114(8).
13 April 2017 (Emma): Wang et al. (2017). Learning to reinforcement learn. arXiv:1611.05763.
30 March 2017 (Laura): Zenke, Poole & Ganguli (2017). Improved multitask learning through synaptic intelligence. arXiv:1703.04200 (see also Kirkpatrick et al. (2017). Overcoming catastrophic forgetting in neural networks. PNAS).
16 March 2017 (Selmaan): Zhang et al. (2017). Understanding deep learning requires rethinking generalization. ICLR. (reviews)
2 March 2017 (Chong): Sussillo et al. (2016). LFADS - Latent factor analysis via dynamical systems. arXiv:1608.06315; and Zhao et al. (2016). Interpretable nonlinear dynamic modeling of neural trajectories. NIPS
16 Feb 2017 (Matthias): Gao et al. (2016). Linear dynamical neural population models through nonlinear embeddings. NIPS.
2 Feb 2017 (Daniel): Haefner et al. (2016). Perceptual decision-making as probabilistic inference by neural sampling. Neuron 90(3).
19 Jan 2017 (Camille): Rosenbaum et al. (2016). The spatial structure of correlated neuronal variability. Nature Neuroscience 20(1). (see also Latham (2016). Correlations demystified. Nature Neuroscience 20(1)).
8 Dec 2016 (Chong): Boerlin & Deneve (2011). Spike-based population coding and working memory. PLoS Computational Biology 7(2): e1001080.
22 Nov 2016 (Jan): Ma et al. (2006). Bayesian inference with probabilistic population codes. Nature Neuroscience 9, 1432-1438.
9 Nov 2016 (Laura): Orban et al. (2016). Neural variability and sampling-based probabilistic representations in the visual cortex. Neuron 92(2), 530-543.
27 Oct 2016 (Stephen): Advani & Ganguli (2016). Statistical mechanics of optimal convex inference in high dimensions. Physical Review X 6, 031034.
13 Oct 2016 (Camille): Moreno-Bote et al. (2014). Information-limiting correlations. Nature Neuroscience 17, 1410-1417.
29 Sept 2016 (Jan): Zylberberg et al. (2016). Robust information propagation through noisy neural circuits. arXiv:1608.05706.