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Category: neuroscience – Page 520

Novel framework allows for automated tuning of large-scale neuronal models

Developing large-scale neural network models that mimic the brain’s activity is a major goal in the field of computational neuroscience. Existing models that accurately reproduce aspects of brain activity are notoriously complex, and fine-tuning model parameters often requires significant time, intuition, and expertise.

New published research from an interdisciplinary group of researchers primarily based at Carnegie Mellon University and the University of Pittsburgh presents a novel solution to mitigate some of these challenges. The machine learning-driven framework, Spiking Network Optimization using Population Statistics (SNOPS), can quickly and accurately customize models that reproduce activity to mimic what’s observed in the .

The work is published in the journal Nature Computational Science.

Spike Mutations that Help SARS-CoV-2 Infect the Brain Discovered

Scientists have discovered a mutation in SARS-CoV-2, the virus that causes COVID-19, that plays a key role in its ability to infect the central nervous system. The findings may help scientists understand its neurological symptoms and the mystery of “long COVID,” and they could one day even lead to specific treatments to protect and clear the virus from the brain.

The new collaborative study between scientists at Northwestern University and the University of Illinois-Chicago uncovered a series of mutations in the SARS-CoV-2 spike protein (the outer part of the virus that helps it penetrate cells) that enhanced the virus’s ability to infect the brains of mice.

“Looking at the genomes of viruses found in the brain compared to the lung, we found that viruses with a specific deletion in spike were much better at infecting the brains of these animals,” said co-corresponding author Judd Hultquist, assistant professor of medicine (infectious diseases) and microbiology-immunology at Northwestern University Feinberg School of Medicine. “This was completely unexpected, but very exciting.”

Using machine learning to uncover predictors of well-being

Irrespective of their personal, professional and social circumstances, different individuals can experience varying levels of life satisfaction, fulfillment and happiness. This general measure of life satisfaction, broadly referred to as “well-being,” has been the key focus of numerous psychological studies.

Better understanding the many factors contributing to well-being could help to devise personalized and targeted interventions aimed at improving people’s levels of fulfillment. While many past studies have tried to delineate these factors, few have done so leveraging the advanced machine learning models available today.

Machine learning models are designed to analyze large amounts of data, unveiling hidden patterns and making . Using these tools to analyze data collected in previous studies in neuroscience and psychology could help to shed light on the environmental and influencing well-being.

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