Ashok Litwin-Kumar
Assistant Professor, Department of Neuroscience, Columbia University
Jerome L. Greene Science Center, rm. L6-077
ude [tod] aibmuloc [ta] ramuk-niwtil.a
I am an assistant professor in the Department of Neuroscience and a member of the Center for Theoretical Neuroscience and the Zuckerman Institute. Research in my group focuses on learning algorithms and their neural implementations. How do organisms use their past experiences to adapt their current behavior? How do these neural algorithms compare to those studied in machine learning and artificial intelligence? We approach these questions by working closely with experimental collaborators and building well-constrained models of learning and synaptic plasticity.
See here if you are interested in joining.
Publications
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 ]
K. Lakshminarasimhan, M. Xie, J. D. Cohen, B. Sauerbrei, A. W. Hantman, A. Litwin-Kumar & S. Escola (2022). Specific connectivity optimizes learning in thalamocortical loops. [ bib | bioRxiv ]
M. Xie, S. Muscinelli, K. D. Harris & A. Litwin-Kumar (2022). Task-dependent optimal representations for cerebellar learning. [ bib | bioRxiv ]
D. G. Clark, L. F. Abbott & A. Litwin-Kumar (2022). Dimension of activity in random neural networks. [ bib | arXiv ]
S. Muscinelli, M. Wagner & A. Litwin-Kumar (2022). Optimal routing to cerebellum-like structures. [ bib | bioRxiv ]
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. Ganguli, 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. Ganguli & 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 (2018). Feedback alignment in deep convolutional networks. [ bib | arXiv ]
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 | 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 ]
Publications also on Google scholar.
Code
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 I. Kahn; Columbia University, Spring 2022)
Introduction to Theoretical Neuroscience (with L. F. Abbott, K. Miller, S. Fusi; Columbia University, offered yearly)
Math 0290: Applied Differential Equations (University of Pittsburgh, Spring 2014)
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