biology

Tatyana Sharpee

Tatyana Sharpee is a biology topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Tatyana Sharpee rather than just read about it. In short: Tatyana Sharpee is an American neuroscientist. She is a Professor at the Salk Institute for Biological Studies, where she spearheads a research group at the Computational Neurobiology Laboratory, with the support from Edwin Hunter Chair in Neurobiology.

Tatyana Sharpee — illustration

Key takeaways

Tatyana Sharpee belongs to biology; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Tatyana Sharpee to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Tatyana Sharpee from memory before moving on to harder problems.

Reference excerpt

Tatyana Sharpee is an American neuroscientist. She is a Professor at the Salk Institute for Biological Studies, where she spearheads a research group at the Computational Neurobiology Laboratory, with the support from Edwin Hunter Chair in Neurobiology. She is also an Adjunct Professor at the Department of Physics at University of California, San Diego. She was elected a fellow of American Physical Society in 2019.

Early life and education Sharpee was interested in science from childhood, and was encouraged by her grandfather. She obtained her BS from Taras Shevchenko National University of Kyiv in Ukraine, and moved on to Michigan State University for her PhD in theoretical physics. Following her PhD, she was a Sloan-Swartz post-doctoral fellow at the University of California, San Francisco. In 2015, she received a National Science Foundation grant to study feature selectivity and invariance in deep neural architectures. Her work has been focused on probing how our brains represent complex transformations of the same object to recognize it. Sharpee uses state-of-the-art deep learning algorithms to understand where the algorithms are failing at complex object transformations. The NSF grant also enabled her lab to study how the auditory system of the brain works based on large scale simulations of neural networks. This latter work not only has potential to improve the current hearing aid technology, but also could reveal therapeutic paths to treat a number of attention deficit and psychiatric disorders which depend on the corresponding system.

Career and research Following graduate school, Sharpee worked at the University of California, San Francisco from 2001 to 2007 as a Sloan-Swartz fellow, where the bulk of her work revolved around computational neuroscience. She then joined UCSD and the Salk Institute as faculty and has been working there since, supervising a number of graduate students in physics, neuroscience and quantitative biology. In 2018, Sharpee was elected as a Fellow of the American Physical Society for her work in Physics.

Selected publications Jeanne, J.M., Sharpee, T.O., Gentner, T.Q. Associative learning enhances population coding by inverting interneuronal correlation patterns. (2013) Neuron. 78(2):352-63. DOI: 10.1016/j.neuron.2013.02.023 Atencio, C.A., Sharpee, T.O., Schreiner, C.E. Cooperative nonlinearities in auditory cortical neurons. (2008) Neuron. 58(6):956-66. DOI: 10.1016/j.neuron.2008.04.026 Clifford, C.W.G., Webster, M.A., Stanley, G.B., et al. Visual adaptation: Neural, psychological and computational aspects. Vision Research. 2007; 47(25): 3125-3131, https://doi.org/10.1016/j.visres.2007.08.023 Sharpee, T.O., Sugihara, H., Kurgansky, A.V., Rebrik, S.P., Stryker, M.P., Miller, K.D. Adaptive filtering enhances information transmission in visual cortex. (2006) Nature. 439(7079):936-42. DOI: 10.1038/nature04519 Sharpee, T., Rust, N.C., Bialek, W. Analyzing neural responses to natural signals: maximally informative dimensions. (2004) Neural Computation. 16(2):223-50. DOI: 10.1162/089976604322742010

Awards and honors DeLano Award for Computational Biosciences (2022) Fellow of the American Physical Society (2018) McKnight Scholar (2009) Searle Scholar (2008) Sloan Research Fellowship (2008)

References

External links Tatyana Sharpee publications indexed by Google Scholar

Illustrations

Worked examples

Example 1 — a first encounter with Tatyana Sharpee

Start with the simplest possible case. Write down what Tatyana Sharpee claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In biology, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Tatyana Sharpee before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Tatyana Sharpee ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Tatyana Sharpee

In research: Tatyana Sharpee appears in biology research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Tatyana Sharpee in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Tatyana Sharpee is common in secondary-school and first-year university syllabi. It links to neighbouring topics 21st-century American women, American neuroscientists, American women neuroscientists, so understanding it makes those chapters shorter.
In everyday life: Look for Tatyana Sharpee outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Tatyana Sharpee in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Tatyana Sharpee means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Tatyana Sharpee out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Tatyana Sharpee in simple terms?

Why does Tatyana Sharpee matter?

Because it connects several biology ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Tatyana Sharpee?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Tatyana Sharpee.