4 papers
Constrained Hebbian Learning Supports Efficient Representational Allocation under Structural Constraints
Patrick Inoue, Florian Röhrbein, Andreas Knoblauch
Introduction: Biological systems face anatomical and metabolic constraints, including costly synaptic maintenance and limited connectivity. These constraints favor neural codes tha…
Benchmarking local Hebbian learning rules for memory storage and prototype extraction
Anders Lansner, Andreas Knoblauch, Naresh B Ravichandran +1
Associative memory or content-addressable memory is an important component function in computer science and information processing, and at the same time a key concept in cognitive…
Guiding Sparse Neural Networks with Neurobiological Principles to Elicit Biologically Plausible Representations
Patrick Inoue, Florian Röhrbein, Andreas Knoblauch
While deep neural networks (DNNs) have achieved remarkable performance in tasks such as image recognition, they often struggle with generalization, learning from few examples, and…
Energy-Efficient Information Representation in MNIST Classification Using Biologically Inspired Learning
Patrick Stricker, Florian Röhrbein, Andreas Knoblauch
Efficient representation learning is essential for optimal information storage and classification. However, it is frequently overlooked in artificial neural networks (ANNs). This n…