Recommended Reading

Some recommended foundational papers and reviews to get acquainted with selected topics of interest to the lab

Sleep

• Sleep-dependent memory consolidation, Stickgold R, 2005, Nature
• The memory function of sleep, Diekelmann S & Born J, 2010, Nat Rev Neurosci
• Overlapping memory replay during sleep builds cognitive schemata, Lewis PA & Durrant SJ, 2011, Trends Cogn Sci
• About sleep’s role in memory, Rasch B & Born J, 2013, Physiol Rev
• Sleep and synaptic plasticity in the developing and adult brain, Frank MG, 2014, Curr Top Behav Neurosci
• Mechanisms of systems memory consolidation during sleep, Klinzing JG et al, 2019, Nat Neurosci
• Experience and sleep-dependent synaptic plasticity: from structure to activity, Sun L et al, 2020, Philos Trans R Soc B
• Sleep — a brain state serving systems memory consolidation, Brodt S et al, 2023, Neuron
• Sleep’s contribution to memory formation, Lutz ND et al, 2026, Physiol Rev

Memory consolidation

• Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory, McClelland JL et al, 1995, Psychol Rev

Artificial Neural Networks (ANNs)

• https://www.sciencedirect.com/science/article/pii/S0896627320307054

neuroAI

• https://www.nature.com/articles/s41583-023-00705-w
• https://www.nature.com/articles/s41593-019-0520-2

Replay in brains and ANNs

• https://www.cell.com/trends/neurosciences/abstract/S0166-2236(21)00144-2
• https://direct.mit.edu/neco/article-abstract/33/11/2908/107071/Replay-in-Deep-Learning-Current-Approaches-and
• https://www.tandfonline.com/doi/abs/10.1080/095400996116910
• https://www.nature.com/articles/s41467-020-17866-2

hippocampus

• https://pmc.ncbi.nlm.nih.gov/articles/PMC4648295/

Hippocampus/MEC - recent models

• The hippocampus as a predictive map, Stachenfeld KL et al, 2017, Nat Neurosci
• Vector-based navigation using grid-like representations in artificial agents, Banino A et al, 2018, Nature
• The Tolman-Eichenbaum Machine: Unifying Space and Relational Memory through Generalization in the Hippocampal Formation, Whittington JCR et al, 2020, Cell
• A model of egocentric to allocentric understanding in mammalian brains, Uria B et al, 2020, bioRxiv
• Place cells may simply be memory cells: Memory compression leads to spatial tuning and history dependence, Benna MK et al, 2021, Proc Natl Acad Sci U S A
• Predictive learning as a network mechanism for extracting low-dimensional latent space representations, Recanatesi S et al, 2021, Nat Commun
• Clone-structured graph representations enable flexible learning and vicarious evaluation of cognitive maps, George D et al, 2021, Nat Commun
• A unified theory for the computational and mechanistic origins of grid cells, Sorscher B et al, 2023, Neuron
• Self-Supervised Learning of Representations for Space Generates Multi-Modular Grid Cells, Schaeffer R et al, 2023, NeurIPS
• Episodic and associative memory from spatial scaffolds in the hippocampus, Chandra S et al, 2025, Nature

Neural population dynamics, manifolds, et al

• Mante et al., 2013, Nature. Context-dependent computation by recurrent dynamics in prefrontal cortex
• Sussillo et al, 2013, Neural Computation. Opening the Black Box: Low-Dimensional Dynamics in High-Dimensional Recurrent Neural Networks
• Sadtler et al, 2014, Nature. Neural constraints on learning
• Sussillo et al, 2015, Nature Neuroscience. A neural network that finds a naturalistic solution for the production of muscle activity
• Gallego et al, 2017, Neuron. Neural Manifolds for the Control of Movement
• Mastrogiuseppe & Ostojic, 2018, Neuron. Linking Connectivity, Dynamics, and Computations in Low-Rank Recurrent Neural Networks
• Saxena & Cunningham, 2019, Current Opinion in Neurobiology. Towards the neural population doctrine
• Inagaki et al, 2019, Nature. Discrete attractor dynamics underlies persistent activity in the frontal cortex
• Chaudhuri et al, 2019, Nature Neuroscience. The intrinsic attractor manifold and population dynamics of a canonical cognitive circuit across waking and sleep
• Yang et al, 2019, Nature Neuroscience. Task representations in neural networks trained to perform many cognitive tasks
• Vyas et al, 2020, Annual Review of Neuroscience. Computation Through Neural Population Dynamics
• Barack et al, 2021, Nat Rev Neurosci. Two views on the cognitive brain
• Kriegeskorte et al, 2021, Nature Reviews Neuroscience. Neural tuning and representational geometry
• Jazayeri & Ostojic, 2021, Current Opinion in Neurobiology. Interpreting neural computations by examining intrinsic and embedding dimensionality of neural activity 
• DePasquale et al, 2023, Neuron. The centrality of population-level factors to network computation is demonstrated by a versatile approach for training spiking networks
• Langdon et al, 2023, Nature Reviews Neuroscience. A unifying perspective on neural manifolds and circuits for cognition
• Driscoll et al, 2024, Nature Neuroscience. Flexible multitask computation in recurrent networks utilizes shared dynamical motifs
• Genkin et al, 2025, Nature. The dynamics and geometry of choice in the premotor cortex
• Langdon et al, 2025, Nature Neuroscience. Latent circuit inference from heterogeneous neural responses during cognitive tasks

General - scientific process

• https://web.stanford.edu/~fukamit/schwartz-2008.pdf
• https://www.nature.com/articles/s41587-023-02074-2

modeling and theory

• https://pubmed.ncbi.nlm.nih.gov/39257366/
• https://www.jstor.org/stable/184253