Hiesinger Peter Robin - The Self-Assembling Brain

THERE ARE EASIER THINGS to make than a brain. Driven by the promise and resources of biomedical research, developmental neurobiologists are trying to understand how it is done. Driven by the promise and advances of computer technology, researchers in , scientists study how such abilities are encoded in networks of interconnected components. The components are nerve cells, or neurons, in biological brains. In AI, the term neuron has been readily adopted to describe interconnected signaling components, looking back on some 70 years of ANN research. Yet, to what extent the biological analogy is useful for AI research has been a matter of debate throughout the decades. It is a question of how much biological detail is relevant and needed, a question of the type of information necessary to make a functional network. The information problem underlies both fields. What type of information is necessary to wire a brain? What do biologists mean when they say something is encoded by genes, and how is genetic information transformed into a brain? And finally, to what extent is the same type of information required to wire up biological brains or to create artificial intelligence?

This book is about the information problem and how information unfolds to generate functional neural networks. In the case of biological brains, prior to learning, the information for developmental growth is encoded in the But if this were so, how could, say, a monarch butterfly be born with the ability to follow thousands of miles of a migration route that it has never seen before?

As temperatures drop in the fall in North America, millions of monarch butterflies migrate for up to 3,000 miles to overwinter in Mexico. Remarkably, millions of butterflies distributed over close to 3 million square miles in the north all target only a few overwintering sites that cover less than a single square mile. Many theories have been put forth as to how a butterfly could do this. By the time temperatures drop again in the fall in North America, a monarch butterfly is about to embark on the 3,000-mile trip south to a precise location that was last visited by its great-great-grandfather. Where is this information coming from?

The currently almost exclusive focus of AI on ANNs is a highly successful, but recent development. It followed several decades during which AI and How does information about the role of a gene for a neuronal membrane protein help to program an intelligent system? The history of AI is a history of trying to avoid unnecessary biological detail in trying to create something that so far only exists in biology. The observation begs the question what information can safely be deemed unnecessary. To address this question, we need to look at biological and artificial brain development from the information perspective. An assumption and hope of AI research has long been that there is a shortcut to creating intelligent systems. We may not yet know what shortcuts work best, but it seems a good idea to at least know exactly what it is we are trying to leave out in attempts to create nonbiological brains. My hope is that an understanding of the way information is encoded and transformed during the making of biological brains proves useful in the discussion what can and cannot be shortcut in the making of AI. This is the story of a neurobiologist tracking down that information.