Author: fzenke

Supervised learning in multi-layer spiking neural networks

We just put a conference paper version of “SuperSpike”, our work on supervised learning in multi-layer spiking neural networks to the arXiv https://arxiv.org/abs/1705.11146. As always I am keen to get your feedback.

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The temporal paradox of Hebbian learning and homeostatic plasticity

I am happy that our article on “The temporal paradox of Hebbian learning and homeostatic plasticity” was just published in Current Opinion in Neurobiology (full text). This article essentially concisely presents the main arguments for the existence of rapid compensatory

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Role of complex synapses in continual learning

Excited that our preprint “Improved multitask learning through synaptic intelligence” just went life on the arXiv (https://arxiv.org/abs/1703.04200). This article, by Ben Poole, Surya and myself, illustrates the benefits of complex synaptic dynamics on continual learning in neural networks. Here a

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Upcoming talk at COSYNE workshop “Learning in multi-layer spiking neural networks”

I am very much looking forward to presenting some recent work with Surya on learning in spiking neural networks at the CoSyNe workshop “Deep learning” and the brain (6.20–6.50p on Monday, 27 February 2017 in “Wasatch”). In my talk I

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Special Issue: “Integrating Hebbian and Homeostatic plasticity”

I recommend taking a look at the special issue  on ‘Integrating Hebbian and Homeostatic plasticity’ which was just published in Phil Trans of the Royal Society B. You can find the table of contents at http://rstb.royalsocietypublishing.org/content/372/1715. The issue is based

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Towards a post-journal world

I just enjoyed reading Romain Brette’s post about how to move towards a better scientific publication system. Maybe you will find it interesting too. My new year resolution : to help move science to the post-journal world  

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Auryn v0.8.0 stable released

The stable Auryn version 0.8 is available now. The new version comes with extensive refactoring under the hood an now supports complex synapse models and improved vectorization for neuron models. The new version is available on github

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What’s new in Auryn v0.8.0-alpha?

Last week I put up a release branch for Auryn v0.8 which is currently in alpha stage. The code can be found here https://github.com/fzenke/auryn/releases The main perks: Further increase of performance. Class-based state vectors for neuronal and synaptic states for ease

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Poster in London

I will have a poster at the Discussion Meeting “Integrating Hebbian and homeostatic plasticity” in London next week April 19–20, 2016 I am happy about the opportunity to present a poster which summarizes the key insights I gained during my

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Writing neuron models in Auryn …

… just got a lot easier with the new AurynVector class. Because Auryn originally used GSL vectors (which predates C++) it was still using non object oriented syntax for vector data types internally. That made writing code for new neuron

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