This page contains a list of essential readings for anyone interested in learning more about the research topics in our group. The readings are organized by topic and include a brief description of each paper.

Topic 1: Olfaction

We have a strong interest in understanding how the olfactory system processes information. Below are some essential readings on this topic:

Reviews

Reviews on the functional organization of the olfactory system, with a focus on Drosophila and vertebrates:

• Su C Y, Menuz K, Carlson J R. Olfactory Perception: Receptors, Cells, and Circuits. Cell,139 : 45 – 59, 2009.
• Masse, Nicolas Y., Glenn C. Turner, and Gregory SXE Jefferis. Olfactory Information Processing in Drosophila, Current Biology, 2022
• Wilson R I. Early Olfactory Processing in Drosophila: Mechanisms and Principles. Annual Review of Neuroscience, 36 : 217 – 241, 2013.
• Barnum, George; Hong, Elizabeth J. (2022) Olfactory coding,Current Biology
• Fulton, K. A., Zimmerman, D., Samuel, A., Vogt, K., & Datta, S. R. (2024). Common principles for odour coding across vertebrates and invertebrates. Nature Reviews Neuroscience, 25(7), 453-472.
• Zhao and McBride, Evolution of olfactory circuits in insects, Journal of Comparative Physiology A, 2019

Modeling and theory

The olfactory stem is a powerful model system for understanding how neural circuits process information, such as sparse coding, divisive normalization, reinforcement learning, brain-inspired algorithms, and more. Below are some essential readings on these topics: Sparse coding

• Litwin-Kumar, A., Harris, K. D., Axel, R., Sompolinsky, H., & Abbott, L. F. (2017). Optimal degrees of synaptic connectivity. Neuron, 93(5), 1153-1164.
• Babadi, B., & Sompolinsky, H. (2014). Sparseness and expansion in sensory representations. Neuron, 83(5), 1213-1226.
• Krishnamurthy, K., Hermundstad, A. M., Mora, T., Walczak, A. M., & Balasubramanian, V. (2022). Disorder and the neural representation of complex odors. Frontiers in Computational Neuroscience, 16, 917786.

ORN responses

• Hallem, E. A., & Carlson, J. R. (2006). Coding of odors by a receptor repertoire. Cell, 125(1), 143-160.
• Si, G., Kanwal, J. K., Hu, Y., Tabone, C. J., Baron, J., Berck, M., … & Samuel, A. D. (2019). Structured odorant response patterns across a complete olfactory receptor neuron population. Neuron, 101(5), 950-962.
• Qin, S., Li, Q., Tang, C., & Tu, Y. (2019). Optimal compressed sensing strategies for an array of nonlinear olfactory receptor neurons with and without spontaneous activity. Proceedings of the National Academy of Sciences, 116(41), 20286-20295.

PNs and LNs

• Olsen, S. R., Bhandawat, V., & Wilson, R. I. (2010). Divisive normalization in olfactory population codes. Neuron, 66(2), 287-299.

KCs and MBONs

• Caron, S. J., Ruta, V., Abbott, L. F., & Axel, R. (2013). Random convergence of olfactory inputs in the Drosophila mushroom body. Nature, 497(7447), 113-117.
• Zheng, Z., Li, F., Fisher, C., Ali, I. J., Sharifi, N., Calle-Schuler, S., … & Bock, D. D. (2022). Structured sampling of olfactory input by the fly mushroom body. Current Biology, 32(15), 3334-3349.

Olfactory navigation

For many animals, the olfactory system is not only used for sensing odors but also for navigating towards or away from odor sources. Insects, in particular, have evolved sophisticated strategies for olfactory navigation in turbulent environments. Below are some essential readings on this topic:

• Reddy, G., Murthy, V. N., & Vergassola, M. (2022). Olfactory sensing and navigation in turbulent environments. Annual Review of Condensed Matter Physics, 13(1), 191-213.
• Vergassola, M., Villermaux, E., & Shraiman, B. I. (2007). ‘Infotaxis’ as a strategy for searching without gradients. Nature, 445(7126), 406-409.
• Rudelt, L., Mikulasch, F., Priesemann, V., & Castro, A. F. (2025). Representation learning in cerebellum-like structures. arXiv preprint arXiv:2511.10261.
• Baker, K. L., Dickinson, M., Findley, T. M., Gire, D. H., Louis, M., Suver, M. P., … & Smear, M. C. (2018). Algorithms for olfactory search across species. Journal of Neuroscience, 38(44), 9383-9389.
• Celani, A., Villermaux, E., & Vergassola, M. (2014). Odor landscapes in turbulent environments. Physical Review X, 4(4), 041015.

Topic 2: Memeories and representational drift

Recent experimental work has shown that neural representations of memories can change over time, even in the absence of observable changes of input and behavior. This phenomenon, known as representational drift, has important implications for our understanding of memory and learning. Below are some essential readings on this topic:

Reviews

• S. van der Veldt, G. M. van de Ven, S. Moorman, and G. Etter, Learning continually with representational drift, arXiv preprint arXiv:2512.22045 (2025).
• J.-B. Eppler, M. Kaschube, and S. Rumpel, Statistical learning and representational drift: A dynamic substrate for memories, Current Opinion in Neurobiology 87, 102022 (2025).
• Rule, M. E., O’Leary, T., & Harvey, C. D. (2019). Causes and consequences of representational drift. Current opinion in neurobiology, 58, 141-147.
• Driscoll, L. N., Duncker, L., & Harvey, C. D. (2022). Representational drift: Emerging theories for continual learning and experimental future directions. Current opinion in neurobiology, 76, 102609.
• Masset, P., Qin, S., & Zavatone-Veth, J. A. (2022). Drifting neuronal representations: Bug or feature?. Biological cybernetics, 116(3), 253-266.
• Mau, W., Hasselmo, M. E. & Cai, D. J. The brain in motion: how ensemble fluidity drives memory-updating and flexibility. eLife 9, e63550 (2020).

Experimental studies

• Ziv, Y., Burns, L. D., Cocker, E. D., Hamel, E. O., Ghosh, K. K., Kitch, L. J., … & Schnitzer, M. J. (2013). Long-term dynamics of CA1 hippocampal place codes. Nature neuroscience, 16(3), 264-266.
• Marks, T. D. & Goard, M. J. Stimulus-dependent representational drift in primary visual cortex. Nat. Commun. 12, 5169 (2021)

• Sylte, O. C., Kilias, A., Bartos, M., & Sauer, J. F. (2025). Coordinated representational drift supports stable place coding in hippocampal CA1. bioRxiv, 2025-02.

• Noda, T., Kienle, E., Eppler, J. B., Aschauer, D. F., Kaschube, M., Loewenstein, Y., & Rumpel, S. (2025). Homeostasis of a representational map in the neocortex. Nature Neuroscience, 28(7), 1533-1545.
• Schoonover, C. E. et al. Representational drift in primary olfactory cortex. Nature 594, 541–546 (2021)
• Driscoll, L. N. et al. Dynamic reorganization of neuronal activity patterns in parietal cortex. Cell 170, 986–999 (2017).

Models

• Shanshan Qin, Shiva Farashahi, David Lipshutz, Anirvan M. Sengupta, Dmitri B. Chklovskii, Cengiz Pehlevan (2023). Coordinated drift of receptive fields in Hebbian/anti-Hebbian network models during noisy representation learning. Nature Neuroscience.
• Alevi, D., Lundt, F., Ciceri, S., Heiney, K., & Sprekeler, H. (2026). Memory consolidation and representational drift. bioRxiv, 2026-03.
• Delamare, G., Zaki, Y., Cai, D. J., & Clopath, C. (2024). Drift of neural ensembles driven by slow fluctuations of intrinsic excitability. Elife, 12, RP88053.
• Devalle, F., Zou, L., Cecchini, G., & Roxin, A. (2025). Representational drift as the consequence of ongoing memory storage. Scientific Reports, 15(1), 27746.
• Morales, G. B., Muñoz, M. A., & Tu, Y. (2025). Representational drift and learning-induced stabilization in the piriform cortex. Proceedings of the National Academy of Sciences, 122(29), e2501811122.
• Ratzon, A., Derdikman, D., & Barak, O. (2024). Representational drift as a result of implicit regularization. Elife, 12, RP90069.

Topic 3: NeuroAI

Neuroscience and artificial intelligence have a long history of cross-fertilization, with many ideas and techniques from one field inspiring advances in the other. Below are some essential readings on the intersection of neuroscience and AI, with a focus on biologically plausible learning algorithms and brain-inspired architectures.

Biologically plausible learning algorithms

Reviews

Similarity Matching and Hebbian/anti-Hebbian learning

• Pehlevan, C., & Chklovskii, D. B. (2019). Neuroscience-Inspired Online Unsupervised Learning Algorithms. IEEE SIGNAL PROCESSING MAGAZINE, 1053(5888/19).
• Chklovskii, D. B., The search for biologically plausible neural computation: The conventional approach, off the convex pass, 2016
• Pehlevan, C., & Chklovskii, D. B., The search for biologically plausible neural computation: The conventional approach, off the convex pass, 2018
• Lipshutz, D., Bahroun, Y., Golkar, S., Sengupta, A. M., & Chklovskii, D. B. (2023). Normative framework for deriving neural networks with multicompartmental neurons and non-hebbian plasticity. PRX Life, 1(1), 013008.

Approximation of backpropagation algorithms

• Guerguiev, J., Lillicrap, T. P., & Richards, B. A. (2017). Towards deep learning with segregated dendrites. Elife, 6, e22901.
• Sacramento, J., Costa, R. P., Bengio, Y., & Senn, W. (2018). Dendritic error backpropagation

Continual learning

• Wickramasinghe, B., Saha, G., & Roy, K. (2023). Continual learning: A review of techniques, challenges, and future directions. IEEE Transactions on Artificial Intelligence, 5(6), 2526-2546.

Research papers

Unsupervised learning

• Sengupta, A., Pehlevan, C., Tepper, M., Genkin, A., & Chklovskii, D. (2018). Manifold-tiling localized receptive fields are optimal in similarity-preserving neural networks. Advances in neural information processing systems, 31.
• Pehlevan, C., Sengupta, A. M., & Chklovskii, D. B. (2017). Why do similarity matching objectives lead to Hebbian/anti-Hebbian networks?. Neural computation, 30(1), 84-124.

Supervised learning

• Journé, A., Rodriguez, H. G., Guo, Q., & Moraitis, T. (2022). Hebbian deep learning without feedback. arXiv preprint arXiv:2209.11883.

• Lillicrap, T. P., Cownden, D., Tweed, D. B., & Akerman, C. J. (2016). Random synaptic feedback weights support error backpropagation for deep learning. Nature communications, 7(1), 1-10.

Brain-inspired learning algorithms

• Dasgupta, S., Stevens, C. F., & Navlakha, S. (2017). A neural algorithm for a fundamental computing problem. Science, 358(6364), 793-796.
• Wang, L., Zhang, X., Li, Q., Zhang, M., Su, H., Zhu, J., & Zhong, Y. (2023). Incorporating neuro-inspired adaptability for continual learning in artificial intelligence. Nature Machine Intelligence, 5(12), 1356-1368.

Brain-Computer Interfaces

Decoding algorithm

• Hueber, P., Tang, G., Sifalakis, M., Liaw, H. P., Micheli, A., Tomen, N., & Liu, Y. H. (2024). Benchmarking of hardware-efficient real-time neural decoding in brain–computer interfaces. Neuromorphic Computing and Engineering, 4(2), 024008.

Topic 4: Data-driven modeling

As we collect more and more data on neural activity, there is a growing need for data-driven modeling approaches that can help us make sense of this data and generate testable hypotheses.

Reviews

Topological data analysis

• Curto, C., & Sanderson, N. (2025). Topological neuroscience: linking circuits to function. Annual Review of Neuroscience, 48.
• Carlsson, G. (2020). Topological methods for data modelling. Nature Reviews Physics, 2(12), 697-708.

Statistical mechanics for neural data

• Meshulam, L., & Bialek, W. (2025). Statistical mechanics for networks of real neurons. Reviews of Modern Physics, 97(4), 045002.