Large language models (LLMs) are a new asset in the machine learning landscape. This article discusses the potential of LLMs in neuroscience and biomedicine. LLMs can enrich neuroscience datasets by adding valuable meta-information, such as text sentiment. They can summarize vast information sources to bridge gaps between siloed neuroscience communities. LLMs can also enable the fusion of disparate information relevant to the brain. They can help deconvolve which cognitive concepts best explain phenomena in the brain. Natural language processing (NLP) has advanced significantly, with models like Word2Vec and GloVe capturing semantic relationships. LLMs are trained on vast amounts of text, allowing them to perform tasks like writing code, planning, and playing games. LLMs can be used to study how the brain processes contextual information and how the human mind generates language. LLMs are based on transformer architectures, which allow for parallel computation and capture interdependencies between words. They use self-attention mechanisms to focus on different parts of the input text. LLMs have shown exceptional transfer learning capabilities, enabling them to adapt to new tasks with minimal data. They can be fine-tuned on smaller datasets, reducing the need for large amounts of data. LLMs can be used to create foundation models that serve as computational "LEGO bricks," enabling the construction of various tasks. They can be used to predict the functional consequences of genetic variants, analyze RNA transcript expression data, and predict protein structures. LLMs can also be used for automated annotation of neuroscience data, improving the efficiency and accuracy of data annotation. LLMs can be used to analyze chemical structures and descriptors, such as SMILES notation, which represents chemical structures as text. They can be used to predict the properties of chemical compounds and their interactions. LLMs can also be used to analyze retinal images for disease detection, improving the efficiency and accuracy of diagnosis. Overall, LLMs have the potential to revolutionize neuroscience and biomedicine by providing new tools for data analysis, annotation, and prediction. They can help researchers to better understand the brain and its functions, leading to new insights and discoveries in the field.Large language models (LLMs) are a new asset in the machine learning landscape. This article discusses the potential of LLMs in neuroscience and biomedicine. LLMs can enrich neuroscience datasets by adding valuable meta-information, such as text sentiment. They can summarize vast information sources to bridge gaps between siloed neuroscience communities. LLMs can also enable the fusion of disparate information relevant to the brain. They can help deconvolve which cognitive concepts best explain phenomena in the brain. Natural language processing (NLP) has advanced significantly, with models like Word2Vec and GloVe capturing semantic relationships. LLMs are trained on vast amounts of text, allowing them to perform tasks like writing code, planning, and playing games. LLMs can be used to study how the brain processes contextual information and how the human mind generates language. LLMs are based on transformer architectures, which allow for parallel computation and capture interdependencies between words. They use self-attention mechanisms to focus on different parts of the input text. LLMs have shown exceptional transfer learning capabilities, enabling them to adapt to new tasks with minimal data. They can be fine-tuned on smaller datasets, reducing the need for large amounts of data. LLMs can be used to create foundation models that serve as computational "LEGO bricks," enabling the construction of various tasks. They can be used to predict the functional consequences of genetic variants, analyze RNA transcript expression data, and predict protein structures. LLMs can also be used for automated annotation of neuroscience data, improving the efficiency and accuracy of data annotation. LLMs can be used to analyze chemical structures and descriptors, such as SMILES notation, which represents chemical structures as text. They can be used to predict the properties of chemical compounds and their interactions. LLMs can also be used to analyze retinal images for disease detection, improving the efficiency and accuracy of diagnosis. Overall, LLMs have the potential to revolutionize neuroscience and biomedicine by providing new tools for data analysis, annotation, and prediction. They can help researchers to better understand the brain and its functions, leading to new insights and discoveries in the field.