Ashok Litwin-Kumar
Jerome L. Greene Science Center, rm. L6-077
ude [tod] aibmuloc [ta] ramuk-niwtil.a
I am part of the Center for Theoretical Neuroscience in the Department of Neuroscience and Zuckerman Institute. My group is supported in part by NSF grant #2443157.
Publications (also on Google scholar)
O. Marschall, D. G. Clark & A. Litwin-Kumar (2025). A theory of multi-task computation and task selection. [ bib | bioRxiv ]
H. Shan & A. Litwin-Kumar (2025). Graph embeddings for identifying symmetries in connectomes. [ bib | bioRxiv ]
F. Amin, J. T. Stone, C. König, N. Mancini, K. Murakami, S. S. Bidaye, M.-M. Heim, D. Owald, U. Majumder, I. C. G. Kadow, A. Pierzchlińska, A. Litwin-Kumar, O. Barnstedt & B. Gerber (2025). Avoidance engages dopaminergic punishment in Drosophila. [ bib | bioRxiv ]
A. Fink, S. Muscinelli, S. Wang, M. Hogan, D. English, R. Axel, A. Litwin-Kumar & C. Schoonover (2025). Experience-dependent reorganization of inhibitory neuron synaptic connectivity. [ bib | bioRxiv ]
M. Beiran & A. Litwin-Kumar (2025). Prediction of neural activity in connectome-constrained recurrent networks. Nature Neuroscience 28(12): 2561–2574. [ bib | journal | pdf ]
D. G. Clark, O. Marschall, A. van Meegen & A. Litwin-Kumar (2025). Connectivity structure and dynamics of nonlinear recurrent neural networks. Physical Review X 15(4): 041019. [ bib | journal | pdf ]
J. Lindsey, J. E. Markowitz, W. F. Gillis, S. R. Datta & A. Litwin-Kumar (2025). Dynamics of striatal action selection and reinforcement learning. eLife 13: RP101747. [ bib | journal | pdf ]
M. Garcia-Garcia, A. Kapoor, O. Akinwale, L. Takemaru, T. H. Kim, C. Paton,
A. Litwin-Kumar, M. J. Schnitzer, L. Luo & M. J. Wagner (2024).
A cerebellar granule cell-climbing fiber computation to learn to
track long time intervals.
Neuron 112(16): 2749–2764.
[ bib | journal |
pdf ]
Preview by R. Broerson & C. I. De Zeeuw.
J. Lindsey & A. Litwin-Kumar (2024). Selective consolidation of learning and memory via recall-gated plasticity. eLife 12: RP90793. [ bib | journal | pdf ]
I. Ganguly, E. L. Heckman, A. Litwin-Kumar, E. J. Clowney & R. Behnia (2024). Diversity of visual inputs to Kenyon cells of the Drosophila mushroom body. Nature Communications 15: 5698. [ bib | journal | pdf ]
K. E. Ellis, S. Bervoets, H. Smihula, I. Ganguly, E. Vigato, T. O. Auer, R. Benton, A. Litwin-Kumar & S. J. C. Caron (2024). Evolution of connectivity architecture in the Drosophila mushroom body. Nature Communications 15: 4872. [ bib | journal | pdf ]
K. Lakshminarasimhan, M. Xie, J. D. Cohen, B. Sauerbrei, A. W. Hantman, A. Litwin-Kumar & S. Escola (2024). Specific connectivity optimizes learning in thalamocortical loops. Cell Reports 43(4). [ bib | journal | pdf ]
D. G. Clark, L. F. Abbott & A. Litwin-Kumar (2023). Dimension of activity in random neural networks. Physical Review Letters 131(11): 118401. [ bib | journal | pdf ]
M. Xie, S. P. Muscinelli, K. D. Harris & A. Litwin-Kumar (2023). Task-dependent optimal representations for cerebellar learning. eLife 12: e82914. [ bib | journal | pdf ]
S. P. Muscinelli, M. J. Wagner & A. Litwin-Kumar (2023).
Optimal routing to cerebellum-like structures.
Nature Neuroscience 26(9): 1630–1641.
[ bib |
journal | pdf ]
Cover illustration by M. Farinella.
D. Yamada, D. Bushey, F. Li, K. L. Hibbard, M. Sammons, J. Funke, A. Litwin-Kumar, T. Hige & Y. Aso (2023). Hierarchical architecture of dopaminergic circuits enables second-order conditioning in Drosophila. eLife 12: e79042. [ bib | journal | pdf ]
J. Lindsey & A. Litwin-Kumar (2022). Action-modulated midbrain dopamine activity arises from distributed control policies. Advances in Neural Information Processing Systems 35: 5535–5548. [ bib | pdf | link ]
T. T. Hayashi, A. J. MacKenzie, I. Ganguly, K. E. Ellis, H. M. Smihula, M. S. Jacob, A. Litwin-Kumar & S. J. C. Caron (2022). Mushroom body input connections form independently of sensory activity in Drosophila melanogaster. Current Biology 32(18): 4000–4012. [ bib | journal | pdf ]
I. Ganguly & A. Litwin-Kumar (2022). Connectomics: Relating synaptic connectivity to physiology [commentary]. Current Biology 32(3): R118–R120. [ bib | journal | pdf ]
L. Jiang & A. Litwin-Kumar (2021). Models of heterogeneous dopamine signaling in an insect learning and memory center. PLOS Computational Biology 17(8): e1009205. [ bib | journal | pdf ]
B. Mark, S. Lai, A. A. Zarin, L. Manning, H. Q. Pollington, A. Litwin-Kumar, A. Cardona, J. W. Truman & C. Q. Doe (2021). A developmental framework linking neurogenesis and circuit formation in the Drosophila CNS. eLife 10: e67510. [ bib | journal | pdf ]
F. Li, J. W. Lindsey, E. C. Marin, N. Otto, M. Dreher, G. Dempsey, I. Stark, A. S. Bates, M. W. Pleijzier, P. Schlegel, A. Nern, S. Takemura, N. Eckstein, T. Yang, A. Francis, A. Braun, R. Parekh, M. Costa, L. K. Scheffer, Y. Aso, G. S. X. E. Jefferis, L. F. Abbott, A. Litwin-Kumar, S. Waddell & G. M. Rubin (2020). The connectome of the adult Drosophila mushroom body provides insights into function. eLife 9: e62576. [ bib | journal | pdf ]
J. Lindsey & A. Litwin-Kumar (2020). Learning to learn with feedback and local plasticity. Advances in Neural Information Processing Systems 33: 21213–21223. [ bib | pdf | link ]
Z. Wu, A. Litwin-Kumar, P. Shamash, A. Taylor, R. Axel & M. N. Shadlen (2020).
Context-dependent decision making in a premotor circuit.
Neuron 106(2): 316–328.
[ bib | journal | pdf ]
Preview by D. H. Gire
C. Eschbach, A. Fushiki, M. Winding, C. M. Schneider-Mizell, M. Shao, R. Arruda, K. Eichler, J. Valdes-Aleman, T. Ohyama, A. S. Thum, B. Gerber, R. D. Fetter, J. W. Truman, A. Litwin-Kumar, A. Cardona & M. Zlatic (2020). Recurrent architecture for adaptive regulation of learning in the insect brain. Nature Neuroscience 23: 544–555. [ bib | journal | pdf ]
A. A. Zarin, B. Mark, A. Cardona, A. Litwin-Kumar & C. Q. Doe (2019). A multilayer circuit architecture for the generation of distinct motor behaviors in Drosophila. eLife 8: e51781. [ bib | journal | pdf ]
Y. Aso, R. Ray, X. Long, D. Bushey, K. Cichewicz, T.-T. B. Ngo, B. Sharp,
C. Christoforou, A. Hu, A. L. Lemire, P. Tillberg, J. Hirsh, A. Litwin-Kumar
& G. M. Rubin (2019).
Nitric oxide acts as a cotransmitter in a subset of dopaminergic
neurons to diversify memory dynamics.
eLife 8: e49257.
[ bib | journal | pdf ]
Commentary by D. J. E. Green & A. C. Lin.
A. Litwin-Kumar & S. C. Turaga (2019). Constraining computational models using electron microscopy wiring diagrams [review]. Current Opinion in Neurobiology 58: 94–100. [ bib | journal | pdf ]
T. H. Moskovitz, A. Litwin-Kumar & L. F. Abbott (2019). Feedback alignment in deep convolutional networks. [ bib | arXiv | pdf ]
S. R. Bittner, R. C. Williamson, A. C. Snyder, A. Litwin-Kumar, B. Doiron, S. M. Chase, M. A. Smith & B. M. Yu (2017). Population activity structure of excitatory and inhibitory neurons. PLOS ONE 12(8): e0181773. [ bib | journal | pdf ]
K. Eichler, F. Li, A. Litwin-Kumar, Y. Park, I. Andrade, C. M.
Schneider-Mizell, T. Saumweber, A. Huser, C. Eschbach, B. Gerber, R. D.
Fetter, J. W. Truman, C. E. Priebe, L. F. Abbott, A. S. Thum, M. Zlatic &
A. Cardona (2017).
The complete connectome of a learning and memory centre in an insect
brain.
Nature 548(7666): 175–182.
[ bib | journal | pdf ]
News Feature on circuit mapping.
A. Litwin-Kumar, K. D. Harris, R. Axel, H. Sompolinsky & L. F. Abbott (2017). Optimal degrees of synaptic connectivity. Neuron 93(5): 1153–1164. [ bib | journal | pdf ]
R. C. Williamson, B. R. Cowley, A. Litwin-Kumar, B. Doiron, A. Kohn, M. A. Smith & B. M. Yu (2016). Scaling properties of dimensionality reduction for neural populations and network models. PLOS Computational Biology 12(12): e1005141. [ bib | journal | pdf ]
A. Litwin-Kumar, R. Rosenbaum & B. Doiron (2016). Inhibitory stabilization and visual coding in cortical circuits with multiple interneuron subtypes. Journal of Neurophysiology 115(3): 1399–1409. [ bib | journal | pdf ]
B. Doiron, A. Litwin-Kumar, R. Rosenbaum, G. Ocker & K. Josić (2016). The mechanics of state dependent neural correlations [review]. Nature Neuroscience 19(3): 383–393. [ bib | journal | pdf ]
G. Ocker, A. Litwin-Kumar & B. Doiron (2015). Self-organization of microcircuit structure in networks of spiking neurons with plastic synapses. PLOS Computational Biology 11(8): e1004458. [ bib | journal | pdf ]
A. Litwin-Kumar & B. Doiron (2014). Formation and maintenance of neuronal assemblies through synaptic plasticity. Nature Communications 5(5319). [ bib | journal | pdf ]
B. Doiron & A. Litwin-Kumar (2014). Balanced neural architecture and the idling brain. Frontiers in Computational Neuroscience 8(56). [ bib | journal | pdf ]
A. Litwin-Kumar (2013). Relationship between neuronal architecture and variability in cortical circuits. Ph.D. thesis, Carnegie Mellon University. [ bib | pdf | link ]
A. Litwin-Kumar & B. Doiron (2012).
Slow dynamics and high variability in balanced cortical networks with
clustered connections.
Nature Neuroscience 15(11): 1498–1505.
[ bib | journal | pdf ]
News & Views by M. M. Churchland & L. F. Abbott.
A. Litwin-Kumar, M. J. Chacron & B. Doiron (2012). The spatial structure of stimuli shapes the timescale of correlations in population spiking activity. PLOS Computational Biology 8(9): e1002667. [ bib | journal | pdf ]
A. Litwin-Kumar, A. M. Oswald, N. N. Urban & B. Doiron (2011). Balanced synaptic input shapes the correlation between neural spike trains. PLOS Computational Biology 7(12): e1002305. [ bib | journal | pdf ]
Code
2023: Drosophila connectome analysis tools. [ github ]
2023: Model of cerebellar routing from Muscinelli, Wagner & Litwin-Kumar. [ download ]
2019: Data analysis and recurrent network model from Zarin et al. [ github ]
2017: Algorithms for computation of dimension and error rate from Litwin-Kumar, Harris, Axel, Sompolinsky & Abbott. [ download ]
2016: Spiking network with multiple inhibitory interneuron subtypes from Litwin-Kumar, Rosenbaum & Doiron. [ download ]
2014: Balanced spiking network with synaptic plasticity from Litwin-Kumar & Doiron. [ download ]
2012: Balanced spiking network with clustered connections from Litwin-Kumar & Doiron. [ download ]
Code is written in Julia and Python.
Courses
Analysis for Neuroscientists (with M. Churchland; Columbia University, offered yearly)
Introduction to Theoretical Neuroscience (with L. F. Abbott, K. Miller, S. Fusi, L. Duncker, K. Stachenfeld; Columbia University, offered yearly)
Math 0290: Applied Differential Equations (University of Pittsburgh, Spring 2014)
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