Research Stipend on Neural Plasticity
We are offering a research stipend to investigate theories of memorization in neural plasticity. The focus is a critical evaluation of the role of LTP/LTD and synaptic plasticity in memory. This position is virtual and could be done part-time, or full-time for three months. The ideal candidate should have solid knowledge of neurobiology, especially plasticity Read more..
Read MoreNeural ensembles – local information compression
A biorxiv preprint: Johann Schumann and Gabriele Scheler The issue of memory is difficult for standard neural network models. Ubiquitous synaptic plasticity introduces the problem of interference, which limits pattern recall and introduces conflation errors. We present a lognormal recurrent neural network, load patterns into it (MNIST), and test the resulting neural representation for information Read more..
Read MoreBoolean Neural Networks
Neural Networks, which are the foundation of every human brain, are very peculiar structures. Their functioning can produce amazing results. Learning about them and the way they work, however, is an uphill task.To respond to this challenge, several models that represent the functionality of the neural networks were created, for instance, [6] and [14] . Read more..
Read MoreDesign and Analysis of a novel Boolean neuron model
Sergey Nasonov’s master’s thesis on Boolean dendrites has been finished and will soon appear.
Read MoreTwo Step Adaptation as a Learning Principle
Our work with Carl Correns researcher Florian Dietz has resulted in the specification of a new, two-step adaptation algorithm for neural tissue models, which is highly compatible with biological observations. This is a major step forward beyond current synaptic plasticity models, where each processing step produces a learning event. We are now implementing and experimenting Read more..
Read MoreThesis on novel neuron model
The CCF sponsors a thesis to develop a new neuron model at the Technical University Munich: goo.gl/1yEm87
Read MoreFirst CCF Grant Proposal on Mathematical Oncology
First CCF Grant Proposal on Mathematical Oncology: Tiling of RNAseq derived graphs. PI: Bachmann, Scheler.
Read MoreSponsored Lecture Series at the Technical University of Munich Starts
Sponsored Lecture Series at the Technical University of Munich starts with talks about Neurorobotics and the Sense of Gravity, Big Data and Ion Channels, and Network Theory in Deep Learning.
Read MorePaper on Logarithmic Distribution of Neuronal Gain published
Paper published: Logarithmic distributions prove that intrinsic learning is Hebbian. Scheler, G. https://www.ncbi.nlm.nih.gov/pubmed/29071065.2
Read MorePaper on Astrocyte-Neuron Interactions published
Paper published: From in silico astrocyte cell models to neuron-astrocyte network models: A review. Obermayer, K. https://www.ncbi.nlm.nih.gov/pubmed/28189516
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