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.
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 ]
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.
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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.
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Commentary by D. J. E. Green & A. C. Lin.
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
Nature 548(7666): 175–182.
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News Feature on circuit mapping.
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 ]
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 (2012).
Slow dynamics and high variability in balanced cortical networks with
Nature Neuroscience 15(11): 1498–1505.
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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.
Math 0290: Applied Differential Equations (University of Pittsburgh, Spring 2014)
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