Revolution through AI: New model reveals secrets of the mouse brain!

Revolution through AI: New model reveals secrets of the mouse brain!

An international research team known as MICrONS has developed innovative AI models aimed at analyzing the neural processing of visual stimuli in the brain. These groundbreaking results were published in renowned scientific journals Nature and Nature Communications published. The University of Göttingen is significantly involved in this study, which is entitled “Foundation Model of Neural Activity Predicts Response to New Stimulus Types and Anatomy” and demonstrates the ability of models to learn from large data sets.

As part of this study, over 135,000 nerve cells in the mouse brain were analyzed. The developed AI model can reliably predict how neurons respond to new stimuli, even if these stimuli were previously unknown. Prof. Dr. Fabian Sinz, one of the leading scientists, highlights that the model can provide more accurate responses to various visual stimuli. Another research study examines in detail the shape and structure of nerve cells in the visual cortex. This study, titled “An unsupervised map of excitatory neurons’ dendritic morphology in the mouse visual cortex,” shows that pyramidal cells exhibit fluid transitions between cell types and do not have clearly defined types.

Advanced research with machine learning

For a more in-depth analysis of the nerve cells, the researchers have developed a machine learning method that encodes the 3D shape of these cells. The MICrONS project involves numerous respected research institutions such as Baylor College of Medicine, the Allen Institute for Brain Science and Princeton University. In the course of this, the “MICrONS Multi-Area Data Set” was created, which is the largest data set of its kind collected in a mammalian brain in terms of structure, networking and reaction properties of the nerve cells.

These digital twins of nerve cells were able to successfully predict the shape and structure. The insights gained not only provide deeper insights into the organization of the brain, but could also help make neuroscientific experiments more efficient. It is possible to carry out in silico experiments before actually carrying out in vivo studies.

The role of artificial intelligence

Artificial intelligence (AI) is a branch of computer science that deals with the development of algorithms that imitate human cognitive abilities. As in the report by bpb.de As described, AI can analyze large amounts of data, recognize patterns and gain insights from them. The term AI, coined by American scientists, describes systems that take on increasingly complex tasks that were once reserved for humans.

In particular, entire classes of learning algorithms, such as machine learning, have evolved since the 1950s. These algorithms are trained with large data sets in order to recognize patterns and calculate probabilities. A special form is deep learning, which is based on artificial neural networks and is becoming increasingly important due to its ability to process extremely complex data patterns.

Neural networks are inspired by the connections between nerve cells in the human brain. These networks consist of layers of data nodes linked together by weighted connections and are capable of recognizing patterns in data. Training these networks occurs through repeated presentation of data, and through these processes they improve their ability to classify and process this data.

However, the advancing development and application of AI also brings challenges such as the possibility of bias due to faulty training data, privacy concerns and increasing energy consumption, such as Fraunhofer IKS notes. The efficiency and ethical implications of AI technologies therefore represent an important research topic.