Brains are not unique in their computational abilities. Bacteria, plants, and unicellular organisms exhibit learning and plasticity; nervous systems speed-optimized information-processing that is ubiquitous across the tree of life and was already occurring at multiple scales before neurons evolved. Non-neural computation is especially critical for enabling individual cells to coordinate their activity toward the creation and repair of complex large-scale anatomies. We have found that bioelectric signaling enables all types of cells to form networks that store pattern memories that guide large-scale growth and form. In this talk, I will introduce the basics of developmental bioelectricity, and show how novel conceptual and methodological advances have enabled rewriting pattern memories that guide morphogenesis without genomic editing. In effect, these strategies allow reprogramming the bioelectric software that implements multicellular patterning goal states. I will show examples of applications in regenerative medicine and cognitive neuroplasticity, and illustrate future impacts on synthetic bioengineering, robotics, and machine learning.
Michael Levin (Allen Discovery Center at Tufts University)
Michael Levin is a professor at Tufts University, and director of the Allen Discovery Center at Tufts (allencenter.tufts.edu), working on computation in the medium of living systems. His original training was in computer science; his interest in AI and philosophy of mind led to a life-long focus on embryogenesis and regeneration as quintessential systems in which to understand how biophysical processes underlie complex adaptive decision-making. He received a Ph.D. in genetics from Harvard Medical School in 1996. Now, his group (www.drmichaellevin.org) works at the interface between developmental biology, basal cognition, and computational neuroscience. Projects include the dynamics of memories during complete brain regeneration (how can a malleable living medium store cognitive content?), behavioral studies of artificial living machines and radically-altered anatomies (how can brains learn to operate bodies with novel sensory/motor structures), induction of complex organ regeneration in non-regenerative species, editing body pattern (e.g., inducing complete eyes to form out of gut tissue, repairing birth defects, and creating permanently-propagating 2-headed worms without genomic editing), tumor reprogramming. All of these projects are being pushed toward applications in regenerative medicine, as well as inspiring novel machine learning architectures and robotics approaches. The computational side of the group works on extending connectionist paradigms beyond Neural Networks, and creating software platforms for automating the inference of insights into pattern control (a bioinformatics of shape).