The modelling of place cells within the framework of statistical physics is here studied. Neurons are represented as binary units that can be organized in different orders, each corresponding to a specific cognitive map. Clusters of closely situated active neurons form "activity bumps" within the map that is then indicated as the one recalled by the network. During transition phases, these bumps may shift from one map to another. The nature of these transitions allows for the classification into distinct phases: one-bump, two-bump, spin glass, and paramagnetic. The instance of an earlier attractor neural network is further developed by incorporating bioinspired components, and is used for a comparative analysis of the results. Specifically, a Poisson process, theta waves and an evolving external stimulus are integrated into the model, yielding novel interesting outcomes.

Biosensors are becoming an increasingly popular method of detecting specific chemical substances via the combination of a biological component and modern technologies. Protein react in a unique way to aptamers, so certain aptamers can be used on a biosensor to capture specific proteins. The crucial point is the binding affinity between a protein-aptamer pair, which can be measured with Kd values, where lower figures indicate better affinity. In a traditional lab setting, testing the binding affinity between one protein and aptamer pair proves too costly in terms of both time and money. This is where machine learning comes in. Given protein-aptamer complexes as input, machine learning models are able to rank them in order of binding affinity. This implicitly helps identify which aptamers bind best to which proteins and may find applications in the search for a simpler and time-saving way to build biosensors able to detect proteins.

This research aims to enhance the understanding and visualization of brain activity in conditions like Amyotrophic Lateral Sclerosis (ALS) and severe neurological damage, which impair cognitive and motor functions. By developing methods to decode fMRI signals, we plan to visualize thoughts and intended communications, aiding clinicians in ”reading” the thoughts of those unable to express themselves. This work also optimizes the ”Brain Diffuser” framework for use on standard computers, broadening accessibility and fostering innovation in neuroimaging.

If it is true that there are still several major milestones AI needs to overcome to reach human-level, on the other hand it has nowadays made its way into the biological field, demonstrating its worth through innovative procedures and playing a critical role in the context of life sciences and health. Not by chance, the cost of a human genome sequence dropped from an estimated 1 million dollars in 2007, to 1000 dollars in 2014, and today it is approximately $600, while the time needed for vaccine development decreased from nearly a hundred years at the beginning of the 20th century, to the few months that provided us with the COVID vaccine. In this research, we will employ large datasets about gene expression of cancerous cells maintained in two different conditions, hypoxia or normoxia. The overall aim is being able to understand and analyse the data through the implementation of machine learning algorithm and visualization methods, extracting from them the most important notions. Finally, this information will be used to build a classifier, with the scope of answering a precise final question: can we identify hypoxic cells?