Ph.D., Humboldt University of Berlin, Germany, 2007
firstname.lastname@example.org | (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.
NIH BRAIN Initiative invests $9.7 million in CSHL scientists
December 29, 2020
CSHL scientists received grants to broaden our knowledge of the human brain and how to treat neurological disorders.
How to figure out what you don’t know
November 30, 2020
Cold Spring Harbor Laboratory Assistant Professor Tatiana Engel discusses how a model like Ptolemy’s seems to explain the world and yet is wrong.
NeuroAI program connects AI experts with neuroscientists
November 2, 2020
The CSHL NeuroAI program is training researchers to be fluent in neuroscience and AI to expedite the development of next-generation AI.
How to figure out what you don’t know
October 26, 2020
Sometimes, what seems like a good way to understand the world turns out to be wrong. A new machine learning tool lets scientists find better answers.
Tatiana Engel named 2020 Sloan Fellow
February 12, 2020
Assistant Professor Tatiana Engel is named a 2020 Sloan Fellow for her work on computational models of decision-making.
How does math help us understand the brain?
January 31, 2019
An exploration of how computational neuroscientist Tatiana Engel uses math to understand how the brain makes decisions.
Discussing your brain, the computing machine, with Tatiana Engel – A Cocktails and Chromosomes talk
December 5, 2018
Dr. Tatiana Engel explains why scientists study optical illusions, how learning configures the computing machine that is your brain, and more.
Computational neuroscientist wins BRAIN grant
September 27, 2018
Assistant Professor Tatiana Engel earns grant to build tools that will help build better models of the brain
Portrait of a Neuroscience Powerhouse
April 27, 2018
A relatively small neuroscience group at CSHL is having an outsized impact on a dynamic and highly competitive field
Additional brain power
November 21, 2017
For neuroscientists, the brain presents an almost endless number of mysteries to be solved.