Ph.D., Humboldt University of Berlin, Germany, 2007
email@example.com | (516) 367-6902
My lab investigates how perception and cognition arise from changes in neural activity. We develop and apply computational methods to discover dynamic patterns in large-scale neural activity recordings. We then create mathematical models to explain how these activity changes emerge from signaling between neurons, ultimately driving behavior.
The brain’s activity is in constant motion: it ebbs and flows in big waves when we are in a deep slumber, turns into small ripples when we reawaken, and flows in orchestrated streams when we perceive, decide and remember. These complex dynamics are driven by intricate networks of microscopic interactions between hundreds thousands neurons and thus are only vaguely glimpsed in spike-trains of single neurons. Fortunately, recent advances in recording techniques enable us to monitor the activity of large neural populations in behaving animals, offering the opportunity to investigate how dynamic variations of collective neural-activity states translate into behavior. To gain insights from these large-scale recordings, we develop and apply computational methods for discovering collective neural dynamics from sparse, high-dimensional spike-train data. We also develop models and theory to explain how collective neural dynamics support specific network computations and how these dynamics are constrained by biophysical properties of neural circuits. In these endeavors, we employ and extend tools and ideas from diverse fields, including statistical mechanics, machine learning, dynamical systems theory, and information theory. Our work benefits from close collaborations with experimental neuroscience laboratories that are collecting neurophysiological data in animals engaged in sophisticated tasks, such as attention, decision making and learning.
How does math help us understand the brain?
January 31, 2019
We’ve all learned about math in school. For many of us, it calls to mind exercises like bisecting geometric shapes and cracking algebraic equations. But what does math have to do with researching the brain? Computational neuroscientists like Cold Spring Harbor Laboratory (CSHL) Assistant Professor Tatiana Engel use math to better understand how networks in...
Computational neuroscientist wins BRAIN grant
September 27, 2018
Data is crucial. But, without the proper tools to analyze it, data cannot be properly evaluated to reach credible conclusions. Cold Spring Harbor Laboratory (CSHL) Assistant Professor Tatiana Engel is helping build computational tools for data collected specifically from the brain, and has been awarded a Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative grant...
Portrait of a Neuroscience Powerhouse
April 27, 2018
At noon every Tuesday from September through June, scenes from a revolution in neuroscience are playing out at Cold Spring Harbor Laboratory. Week after week, over 100 scientists cram themselves into a ground-floor meeting room in the Beckman Laboratory. It’s standing-room only as everyone in the Neuroscience Program settles in to hear details of the...
Additional brain power
November 21, 2017
For neuroscientists, the brain presents an almost endless number of mysteries to be solved. Assistant Professor Tatiana Engel, the newest addition to CSHL’s Swartz Center for Computational Neuroscience, is focused on the dynamics of neural circuits. She wants to understand the role of changing neural activity patterns in decision-making and attention. While earning her doctorate...
Engel, T. A. and Steinmetz, N. A. and Gieselmann, M. A. and Thiele, A. and Moore, T. and Boahen, K. (2016) Selective modulation of cortical state during spatial attention. Science, 354(6316) pp. 1140-1144.
Engel, T. A. and Chaisangmongkon, W. and Freedman, D. J. and Wang, X. J. (2015) Choice-correlated activity fluctuations underlie learning of neuronal category representation. Nat Commun, 6 pp. 6454.
Rading, M. M. and Engel, T. A. and Lipowsky, R. and Valleriani, A. (2011) Stationary Size Distributions of Growing Cells with Binary and Multiple Cell Division. Journal of Statistical Physics, 145(1) pp. 1-22.
Engel, T. A. and Wang, X. J. (2011) Same or different? A neural circuit mechanism of similarity-based pattern match decision making. J Neurosci, 31(19) pp. 6982-96.
Engel, T. and Andrieux, D. (2010) Forget before you remember: dynamic mechanism of memory decay and retrieval. Front Neurosci, 4(3) pp. 3.Additional materials of the author at
CSHL Institutional Repository