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.
Y. Aso, R. Ray, X. Long, K. Cichewicz, T.-T. B. Ngo, C. Christoforou, B. Sharp, A. L. Lemire, 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. bioRxiv: 682815. [ bib | eprint ]
C. Eschbach, A. Fushiki, M. Winding, C. 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 (2019). Multilevel feedback architecture for adaptive regulation of learning in the insect brain. bioRxiv: 649731. [ bib | eprint ]
A. A. Zarin, B. Mark, A. Cardona, A. Litwin-Kumar & C. Q. Doe (2019). A Drosophila larval premotor/motor neuron connectome generating two behaviors via distinct spatio-temporal muscle activity. bioRxiv: 617977. [ bib | eprint ]
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.
[ bib |
journal | pdf ]
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.
[ 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.
Math 0290: Applied Differential Equations (University of Pittsburgh, Spring 2014)
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