Conveners
Neural Networks: Part I
- Jeremi Ochab (Jagiellonian University)
Neural Networks: Part II
- Zuzanna Szymańska (ICM, University of Warsaw)
Across vertebrate species, sleep states are known to cycle consistently from non-rapid eye movement (NREM) to REM sleep. However, the functional significance of these transitions is unknown. We use a simplified biophysical network model to show that state-specific changes in cholinergic signaling during NREM and REM sleep can mediate dramatic changes in network dynamics and subsequently can...
To study learning processes on behavioral level various experiments are performed on animals. In the last two decades several intelligent cases have been designed to increase throughput of such experiments. In my talk I will focus on Intellicage system where up to 14 mice can be housed and various learning protocols can be studied. I will propose a conceptual and computational framework...
In this contribution, we present A) a detrended fluctuation analysis of functional magnetic resonance imaging (fMRI) data from a working memory experiment and B) a multifractal analysis of the electroencephalography (EEG) data obtained from patients with multiple sclerosis (MS). fMRI and EEG signals are notoriously challenging to analyse due to their very low temporal and spatial resolution,...
In the traditional view, we often see a single neuron as less computationally efficient
than a multilayer artificial neural network. But is this truly the case? Our investigation delves
deep into the computational efficiency of morphologically complex neurons, especially their
ability to distinguish between different synaptic patterns. We posed a question: What's the
simplest dendritic...
Understanding connections in the brain at one major neurotransmitter level provides us with detailed information from the molecular scale to behaviour and functionality.
The Relaxin 3/RXFP3 system plays an important role in the modulation of emotional and behavioural actions, such as arousal, regulation of appetite, sociability, stress, anxiety, memory, sleep, and circadian rhythms. The...
While machine learning is usually focused on prediction of values, on various applications I will introduce to simple family of methods to work with learned probability distributions - e.g. model joint, predict conditional, their time evolution. One proposed application direction will be multi-feature Granger causality, enhancing the standard method with evaluation of propagation speed, and...
