biology

Wulfram Gerstner

Wulfram Gerstner 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 Wulfram Gerstner rather than just read about it. In short: Wulfram Gerstner (born 1963 in Heilbronn) is a German and Swiss computational neuroscientist. His research focuses on neural spiking patterns in neural networks, and their connection to learning, spatial representation and navigation.

Key takeaways

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

Reference excerpt

Wulfram Gerstner (born 1963 in Heilbronn) is a German and Swiss computational neuroscientist. His research focuses on neural spiking patterns in neural networks, and their connection to learning, spatial representation and navigation. Since 2006 Gerstner has been a full professor of Computer Science and Life Sciences at École Polytechnique Fédérale de Lausanne (EPFL), where he also serves as a Director of the Laboratory of Computational Neuroscience.

Career Gerstner studied physics at the University of Tübingen and at LMU Munich. In 1989, he received his Master's degree with a thesis in experimental quantum optics. He then joined the theoretical biophysics group of William Bialek at University of California, Berkeley as a visiting researcher. He received his PhD in theoretical physics from the Technical University of Munich in 1993 under supervision from Leo van Hemmen. He did postdoctoral work at Brandeis University and at the Technical University of Munich, where he worked in theoretical neuroscience. In 1996, he was nominated as assistant professor and in February 2001, he was promoted as an associate professor with tenure at EPFL. In August 2006, Gerstner was appointed full professor at EPFL in both the School of Computer and Communication Sciences and the School of Life Sciences.

Research Gerstner's research is focused on models of spiking neurons, spike-timing-dependent plasticity (STDP), neuronal coding in single neurons and neuron populations. He also investigates models of the hippocampus and their application in the spatial representation for navigation of rat-like autonomous agents. He is also one of the initiators of The Deep Artificial Composer (DAC), a deep-learning algorithm that can generate melodies by imitating a given style of music.

Books Gerstner is the author of neuroscientific text books such as Spiking Neuron Models: Single neurons, populations, plasticity (Gerstner, W. and Kistler, W.M., 2002, Cambridge University Press) that introduced the field of spiking neural networks, and Neuronal dynamics: From single neurons to networks and models of cognition (Gerstner, W., Kistler, W.M., Naud, R. and Paninski, L., 2014, Cambridge University Press) on the field of computational neuroscience that was also published as an online version including exercises and video lectures.

Selected publications Gerstner, Wulfram (2000). "Population Dynamics of Spiking Neurons: Fast Transients, Asynchronous States, and Locking" (PDF). Neural Computation. 12 (1): 43–89. doi:10.1162/089976600300015899. PMID 10636933. S2CID 7832768. Clopath, Claudia; Büsing, Lars; Vasilaki, Eleni; Gerstner, Wulfram (2010). "Connectivity reflects coding: A model of voltage-based STDP with homeostasis" (PDF). Nature Neuroscience. 13 (3): 344–352. doi:10.1038/nn.2479. hdl:10044/1/21440. PMID 20098420. S2CID 8046538. Gerstner, Wulfram; Kempter, Richard; Van Hemmen, J. Leo; Wagner, Hermann (1996). "A neuronal learning rule for sub-millisecond temporal coding" (PDF). Nature. 383 (6595): 76–78. Bibcode:1996Natur.383...76G. doi:10.1038/383076a0. PMID 8779718. S2CID 4319500. Brette, Romain; Gerstner, Wulfram (2005). "Adaptive Exponential Integrate-and-Fire Model as an Effective Description of Neuronal Activity" (PDF). Journal of Neurophysiology. 94 (5): 3637–3642. doi:10.1152/jn.00686.2005. PMID 16014787. Kempter, Richard; Gerstner, Wulfram; Van Hemmen, J. Leo (1999). "Hebbian learning and spiking neurons" (PDF). Physical Review E. 59 (4): 4498–4514. Bibcode:1999PhRvE..59.4498K. doi:10.1103/PhysRevE.59.4498.

Distinctions Gerstner has been an editorial board member of journals such as Science, The Journal of Neuroscience, Network: Computation in Neural Systems, Journal of Computational Neuroscience, and Neural Computation. He is the recipient of the Valentino Braitenberg Award for Computational Neuroscience 2018 and in 2010 he was awarded an ERC Advanced Grant by the European Research Council. Gerstner is an elected member of the Academy of Sciences and Literature Mainz.

References

External links Wulfram Gerstner publications indexed by Google Scholar Web site of Laboratory of Computational Neuroscience Online version of the textbook Neuronal dynamics: From single neurons to networks and models of cognition

Illustrations

Worked examples

Example 1 — a first encounter with Wulfram Gerstner

Start with the simplest possible case. Write down what Wulfram Gerstner 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 Wulfram Gerstner 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 Wulfram Gerstner 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 Wulfram Gerstner

In research: Wulfram Gerstner 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 Wulfram Gerstner 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: Wulfram Gerstner is common in secondary-school and first-year university syllabi. It links to neighbouring topics 1963 births, Academic staff of the École Polytechnique Fédérale de Lausanne, Brandeis University alumni, so understanding it makes those chapters shorter.
In everyday life: Look for Wulfram Gerstner 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 Wulfram Gerstner in 20 minutes

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

Frequently asked questions

What is Wulfram Gerstner in simple terms?

Why does Wulfram Gerstner 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 Wulfram Gerstner?

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 Wulfram Gerstner.