In the face of rapidly increasing volumes of neural data, identifying the key elements within extensive brain recordings presents a growing challenge. We have developed…
On the question of how close is the machinery of modern artificial intelligence to biological processes of reasoning we went through the whole spectrum during…
By now we can say that there is a long tradition of comparing the activity in the human brain, especially along the ventral stream, with…
The deep learning architecture that is behind the success of large language models is called a Transformer [1], and the approach can be, in fact,…
In the early stages of neurotechnology development, one critical question often arises: Can current scientific understanding and technology effectively support the envisioned idea? Addressing this…
Action-based brain-computer interfaces operate on the premise that when a participant thinks a specific thought, it creates a unique pattern of brain activity. This pattern…
In our observations of the discussions within the computational neuroscience community, there has been a recurring debate: are machine learning models merely tools, or do…
In this short note we want to make the case for classical (by this we mean non-neural networks) machine learning algorithms can still play an…