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While earlier neuroscience studies on creativity have been criticized due to their heterogeneity of findings, recent studies in this field have converged to some common practices and methodological approaches, which have greatly contributed to enhance both the reliability and reproducibility of findings in this field. Relevant neuroscience findings suggest that creative cognition requires a conglomerate of neurocognitive processes involving executive functions, memory processes, internally-focused attention, or spontaneous modes of thought. Studies investigating creativity in more naturalistic, real-life settings reveal some overlap with conventional creative ideation, but also indicate that creativity and its underlying neural mechanisms are specific to the particular domain. Another trend in the neuroscience of creativity is concerned with approaches to enhance creativity, involving a broad diversity of interventions ranging from cognitively-oriented techniques to interventions using physical activity.

The analysis of large amounts of personal data with artificial neural networks for deep learning is the driving technology behind new artificial intelligence (AI) systems for all areas in science and technology. These AI methods have evolved from applications in computer vision, the automated analysis of images, and now include frameworks and methods for analyzing multimodal datasets that combine data from many different source, including biomedical devices, smartphones and common user behavior in cyberspace. For neuroscience, these widening streams of personal data and machine learning methods provide many opportunities for basic data-driven research as well as for developing new tools for diagnostic, predictive and therapeutic applications for disorders of the nervous system. The increasing automation and autonomy of AI systems, however, also creates substantial ethical challenges for basic research and medical applications. Here, scientific and medical opportunities as well ethical challenges are summarized and discussed.

Technical innovations in the last decade have allowed to sequence transcriptomes of single cells. Single-cell RNA-sequencing (scRNA-seq) has since then opened the window to a deeper understanding of cellular identity and is becoming a widely used method in molecular biology. In neuroscience, scRNA-seq has broad applications, for example in determining cellular diversity in different brain regions and in revealing transcriptomic variations across brain disorders. The method consists of several steps: isolation and lysis of single cells, reverse transcription of RNAs, amplification of cDNAs, and next-generation sequencing. The large datasets can subsequently be analysed using different bioinformatic tools to deduce biological meaning. Current developments aim to integrate scRNA-seq into cellular network analysis through multimodal analysis, spatial localisation and perturbation experiments, in order to understand brain physiology and pathology.

This esssay is a personal account of the evolution of Neurochemistry in the past century. It describes in parallel the authors way from chemistry to biochemistry and finally to Neurochemistry and the progress of a most exciting chapter of the Life Sciences. It covers the successful time period of reductionist research (by no means comprehensively), which lay the ground for the recent and future systems approach. This development promises answers to fundamental questions of our existence as human beings.