Spiral attractors in a reduced mean-field model of neuron-glial interaction.

This paper investigates various bifurcation scenarios of the appearance of bursting activity in the phenomenological mean-field model of neuron-glial interactions. In particular, we show that the homoclinic spiral attractors in this system can be the source of several types of bursting activity with different properties.

Artificial Neural Network Model with Astrocyte-Driven Short-Term Memory.

In this study, we introduce an innovative hybrid artificial neural network model incorporating astrocyte-driven short-term memory. The model combines a convolutional neural network with dynamic models of short-term synaptic plasticity and astrocytic modulation of synaptic transmission. The model's performance was evaluated using simulated data from visual change detection experiments conducted on mice.

Effects of Maternal Cigarette Smoking on Trace Element Levels and Steroidogenesis in the Maternal-Placental-Fetal Unit.

This study evaluates the interaction of toxic elements cadmium (Cd) and lead (Pb) due to exposure from cigarette smoking, essential elements, and steroidogenesis in the maternal-placental-fetal unit. In a cohort of 155 healthy, postpartum women with vaginal term deliveries in clinical hospitals in Zagreb, Croatia, samples of maternal blood/serum and urine, placental tissue, and umbilical cord blood/serum were collected at childbirth. The biomarkers determined were concentrations of Cd, Pb, iron (Fe), zinc (Zn), copper (Cu), and selenium (Se), and steroid hormones progesterone and estradiol in maternal and umbilical cord blood and the placenta.

Model of Neuromorphic Odorant-Recognition Network.

We propose a new model for a neuromorphic olfactory analyzer based on memristive synapses. The model comprises a layer of receptive neurons that perceive various odors and a layer of "decoder" neurons that recognize these odors. It is demonstrated that connecting these layers with memristive synapses enables the training of the "decoder" layer to recognize two types of odorants of varying concentrations.

Using Machine Learning Algorithms to Determine the Post-COVID State of a Person by Their Rhythmogram.

This study introduces a novel method for detecting the post-COVID state using ECG data. By leveraging a convolutional neural network, we identify "cardiospikes" present in the ECG data of individuals who have experienced a COVID-19 infection. With a test sample, we achieve an 87 percent accuracy in detecting these cardiospikes.