astronomy

Panayiota Poirazi

Panayiota Poirazi is a astronomy 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 Panayiota Poirazi rather than just read about it. In short: Panayiota Poirazi is a neuroscientist known for her work in modelling dendritic computations. She is an elected member of the European Molecular Biology Organization (EMBO).

Key takeaways

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

Reference excerpt

Panayiota Poirazi is a neuroscientist known for her work in modelling dendritic computations. She is an elected member of the European Molecular Biology Organization (EMBO).

Education and career Poirazi studied at the University of Cyprus from 1992 until 1996. She earned an M.S. from the University of Southern California in 1998, and went on to earn her Ph.D. from there in 2000. Following her Ph.D., she worked at the Alexander Fleming Instite of Immunology in Greece until 2001, when she moved to the Foundation for Research and Technology-Hellas (FORTH) in Crete, Greece in 2004. As of 2021, she is the director of research at the Institute of Molecular Biology and Biotechnology (IMBB) at the Foundation for Research and Technology-Hellas (FORTH).

Research Poirazi is known for her work in neurobiology where she focuses on dendrites, the portion of a neuron that propagates signals. Her early research generated predictive models of how active dendrites and structural plasticity enhance storage capacity in single neurons. She has used biophysical models of pyramidal neurons to show that dendrites of these cells integrate inputs in a sigmoidal manner, enabling the neurons to act as two-layer neural network devices. Poirazi has built a circuit-level model of the hippocampus that shows how memories are linked through time. She has developed and applied biophysical models to explain how human neurons compute information, with a focus on solving the XOR problem.

Selected publications Poirazi, Panayiota; Brannon, Terrence; Mel, Bartlett W. (March 2003). "Pyramidal Neuron as Two-Layer Neural Network". Neuron. 37 (6): 989–999. doi:10.1016/s0896-6273(03)00149-1. PMID 12670427. S2CID 1680778. Poirazi, Panayiota; Mel, Bartlett W. (March 2001). "Impact of Active Dendrites and Structural Plasticity on the Memory Capacity of Neural Tissue". Neuron. 29 (3): 779–796. doi:10.1016/s0896-6273(01)00252-5. PMID 11301036. S2CID 14303314. Zhou, Yu; Won, Jaejoon; Karlsson, Mikael Guzman; Zhou, Miou; Rogerson, Thomas; Balaji, Jayaprakash; Neve, Rachael; Poirazi, Panayiota; Silva, Alcino J (November 2009). "CREB regulates excitability and the allocation of memory to subsets of neurons in the amygdala". Nature Neuroscience. 12 (11): 1438–1443. doi:10.1038/nn.2405. PMC 2783698. PMID 19783993. Poirazi, Panayiota; Brannon, Terrence; Mel, Bartlett W. (March 2003). "Arithmetic of Subthreshold Synaptic Summation in a Model CA1 Pyramidal Cell". Neuron. 37 (6): 977–987. doi:10.1016/s0896-6273(03)00148-x. PMID 12670426. S2CID 15742277. Richards, Blake A.; Lillicrap, Timothy P.; Beaudoin, Philippe; Bengio, Yoshua; Bogacz, Rafal; Christensen, Amelia; Clopath, Claudia; Costa, Rui Ponte; de Berker, Archy; Ganguli, Surya; Gillon, Colleen J.; Hafner, Danijar; Kepecs, Adam; Kriegeskorte, Nikolaus; Latham, Peter; Lindsay, Grace W.; Miller, Kenneth D.; Naud, Richard; Pack, Christopher C.; Poirazi, Panayiota; Roelfsema, Pieter; Sacramento, João; Saxe, Andrew; Scellier, Benjamin; Schapiro, Anna C.; Senn, Walter; Wayne, Greg; Yamins, Daniel; Zenke, Friedemann; Zylberberg, Joel; Therien, Denis; Kording, Konrad P. (November 2019). "A deep learning framework for neuroscience". Nature Neuroscience. 22 (11): 1761–1770. doi:10.1038/s41593-019-0520-2. PMC 7115933. PMID 31659335.

Awards and honors In 2017, Poirazi was elected a member of the European Molecular Biology Organization. In 2018, she received a Friedrich Wilhelm Bessel Research Award from the Alexander von Humboldt Foundation.

References

External links Panayiota Poirazi publications indexed by Google Scholar

Illustrations

Worked examples

Example 1 — a first encounter with Panayiota Poirazi

Start with the simplest possible case. Write down what Panayiota Poirazi claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In astronomy, 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 Panayiota Poirazi 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 Panayiota Poirazi 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 Panayiota Poirazi

In research: Panayiota Poirazi appears in astronomy 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 Panayiota Poirazi 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: Panayiota Poirazi is common in secondary-school and first-year university syllabi. It links to neighbouring topics 1974 births, Living people, University of Cyprus alumni, so understanding it makes those chapters shorter.
In everyday life: Look for Panayiota Poirazi 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 Panayiota Poirazi in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Panayiota Poirazi 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 Panayiota Poirazi out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Panayiota Poirazi in simple terms?

Panayiota Poirazi is a neuroscientist known for her work in modelling dendritic computations. She is an elected member of the European Molecular Biology Organization (EMBO).

Why does Panayiota Poirazi matter?

Because it connects several astronomy 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 Panayiota Poirazi?

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 Panayiota Poirazi.