Topic: bottom-up neuroscience

Artificial intelligence (AI) has proven to be effective in solving complex problems in fields such as computer vision, natural language processing and medicine. However, in terms of energy efficiency, deep learning approaches are still outmatched by their biological counterparts. Yet, the underlying fundamental mechanisms of information storage, processing and computation of biological neuronal networks (BNNs) remain poorly understood.In bottom-up neuroscience, we tackle this challenge by analyzing low-density, engineered BNNs in vitro. Primary hippocampal rat neurons or human induced pluripot

How the brain processes information to learn and form memories is still a widely unanswered question. To gain insights, neurons are studied under great effort in vivo. However, the applied methods often only enable monitoring subsets of neurons embedded in a complex network or lack spatiotemporal resolution for analysis on a single-cell level. In bottom-up neuroscience, small neural networks of reduced complexity are engineered by seeding primary rat neurons or human induced pluripotent stem cell derived neurons into polydimethylsiloxane microstructures on top of microelectrode arrays (MEAs) t