Models of Neural Plasticity
Our work on basing models of neural plasticity on cellular principles continues to advance. With the help of Michael Wheeldon, B.Sc., as the current recipient of a research scholarship we anticipate two publications at the beginning of the new year. One publication will focus on outlining a new type of memory model using both horizontal Read more..
Read MorePharmacodynamics: Problems and Pitfalls
A systematic overview of qualitative and quantitative model evaluation methods with many detailed references. This is applied and substantiated with case studies, and most interestingly, with an analysis of what can go wrong. Dynamic models are highly sensitive to uncertainties and we need to be aware of the difficulties that can arise from that. S. Read more..
Read MorePersonalized Classifiers from Ensemble learning with Gaussian Process
Personalized Federated Learning with Gaussian Processes Idan Achituve, Aviv Shamsian, Aviv Navon, Gal Chechik, Ethan Fetaya This is a theoretical work for personalized learning with limited data. It was shown that the disadvantage of restricted exposure for each “person” or client can be remediated by learning a shared kernel function across all clients. This is Read more..
Read MoreResearch 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 MorePercolation on an autonomous network
Percolation on the gene regulatory network Giuseppe Torrisi, Reimer Kühn, and Alessia Annibale King’s College London, UK A theoretical analysis, adapting percolation theory to directed bipartite graphs (such as coupled dynamics of transcription factors and genes), investigating conditions under which genetic networks can support a multiplicity of stable gene expression patterns, as required in stable 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
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