Klaus Richard Pawelzik

This paper presents a spiking neural network model that learns to recognize spatiotemporal spike patterns sequentially using biological synaptic mechanisms, addressing the stability-plasticity dilemma in neural learning.

The paper introduces a spiking neural network framework called SbS that matches CNN performance on clean data and shows superior robustness to unseen noise.

The paper shows that a combination of synaptic plasticity mechanisms can self-organize neurons to detect repeating spatio-temporal spike patterns, supporting the idea of precise temporal coding in the brain.

The paper introduces a constrained sparse coding model that reproduces contextual effects and predicts laminar neural dynamics in visual cortex by incorporating local and long-range interactions.

The paper designs an open-source closed-loop system for neuro-prosthetic research that can stimulate and measure neuronal activity with high precision and scalability.

This paper develops a learning rule for deep Spike-by-Spike networks and demonstrates its effectiveness on MNIST classification with 99.3% accuracy.

The paper develops FPGA hardware for Spike-By-Spike networks, demonstrating efficient parallelization and optimization for spiking neural network computations.

This paper introduces a framework of spiking neurons with local learning rules that achieve performance comparable to artificial deep networks in image classification.