22 citations · 22 across the 6 of their papers we have counts for
7 papers
Impact of Neuron Models on Spiking Neural Networks performance. A Complexity Based Classification Approach
Zofia Rudnicka, Janusz Szczepanski, Agnieszka Pregowska
This study explores how the selection of neuron models and learning rules impacts the classification performance of Spiking Neural Networks (SNNs), with a focus on applications in…
Improving Liver Disease Diagnosis with SNNDeep: A Custom Spiking Neural Network Using Diverse Learning Algorithms
Zofia Rudnicka, Janusz Szczepanski, Agnieszka Pregowska
Purpose: Spiking neural networks (SNNs) have recently gained attention as energy-efficient, biologically plausible alternatives to conventional deep learning models. Their applicat…
Learning Internal Biological Neuron Parameters and Complexity-Based Encoding for Improved Spiking Neural Networks Performance
Zofia Rudnicka, Janusz Szczepanski, Agnieszka Pregowska
This study proposes a novel learning paradigm for spiking neural networks (SNNs) that replaces the perceptron-inspired abstraction with biologically grounded neuron models, jointly…
Integrating Complexity and Biological Realism: High-Performance Spiking Neural Networks for Breast Cancer Detection
Zofia Rudnicka, Januszcz Szczepanski, Agnieszka Pregowska
Spiking Neural Networks (SNNs) event-driven nature enables efficient encoding of spatial and temporal features, making them suitable for dynamic time-dependent data processing. Des…
Accuracy-Efficiency Trade-Offs in Spiking Neural Networks: A Lempel-Ziv Complexity Perspective on Learning Rules
Zofia Rudnicka, Janusz Szczepanski, Agnieszka Pregowska
Training spiking neural networks (SNNs) remains challenging due to temporal dynamics, non-differentiability of spike events, and sparse event-driven activations. This paper studies…
Artificial Intelligence-based algorithms in medical image scan seg-mentation and intelligent visual-content generation -- a concise overview
Zofia Rudnicka, Janusz Szczepanski, Agnieszka Pregowska
Recently, Artificial Intelligence (AI)-based algorithms have revolutionized the medical image segmentation processes. Thus, the precise segmentation of organs and their lesions may…
Does a larger neural network mean greater information transmission efficiency?
Bartosz Paprocki, Agnieszka Pregowska, Janusz Szczepanski
Realistic modeling of brain involves large number of neurons. The important question is how this size affects transmission efficiency? Here, these issue is studied in terms of Shan…