22 citations · 35 across the 14 of their papers we have counts for
15 papers
Implicit neural representations as a coordinate-based framework for continuous environmental field reconstruction from sparse ecological observations
Agnieszka Pregowska, Hazem M. Kalaji
Reconstructing continuous environmental fields from sparse and irregular observations remains a central challenge in environmental modelling and biodiversity informatics. Many ecol…
Continuous ageing trajectory representations for knee-aware lifetime prediction of lithium-ion batteries across heterogeneous dataset
Agnieszka Pregowska, Stefan Marynowicz
Accurate assessment of lithium-ion battery ageing is challenged by cell-to-cell variability, heterogeneous cycling protocols, and limited transferability of data-driven models acro…
Implicit neural representations for larval zebrafish brain microscopy: a reproducible benchmark on the MapZebrain atlas
Agnieszka Pregowska
Implicit neural representations (INRs) offer continuous coordinate-based encodings for atlas registration, cross-modality resampling, sparse-view completion, and compact sharing of…
On Stealing Graph Neural Network Models
Marcin Podhajski, Jan Dubiński, Franziska Boenisch +3
Current graph neural network (GNN) model-stealing methods rely heavily on queries to the victim model, assuming no hard query limits. However, in reality, the number of allowed que…
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…
Efficient Model-Stealing Attacks Against Inductive Graph Neural Networks
Marcin Podhajski, Jan Dubiński, Franziska Boenisch +3
Graph Neural Networks (GNNs) are recognized as potent tools for processing real-world data organized in graph structures. Especially inductive GNNs, which allow for the processing…
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…
Signal Fluctuations and the Information Transmission Rates in Binary Communication Channels
Agnieszka Pregowska
In nervous system information is conveyed by sequence of action potentials (spikes-trains). As MacKay and McCulloch proposed, spike-trains can be represented as bits sequences comi…
How far can neural correlations reduce uncertainty? Comparison of Information Transmission Rates for Markov and Bernoulli processes
Agnieszka Pregowska, Ehud Kaplan, Janusz Szczepanski
The nature of neural codes is central to neuroscience. Do neurons encode information through relatively slow changes in the emission rates of individual spikes (rate code), or by t…
Temporal code versus rate code for binary Information Sources
Agnieszka Pregowska, Janusz Szczepanski, Eligiusz Wajnryb
Neuroscientists formulate very different hypotheses about the nature of neural code. At one extreme, it has been argued that neurons encode information in relatively slow changes o…