Neuroscience Without Neurons
CATEGORY
Biophysics Theory
ABSTRACT
Over the last decade - our lab has led the search for porto-nervous systems and evolution of the neuron in the animal tree of life. We identified aneural biological substrates with complex behavior at both single cell and multi-cellular scales. In a decade long quest, we directly identified bottom up implementation of a porto-nervous systems in the mechanical domain (aka mechanical brain) and verified them experimentally in biological substrates. Based on the success of this combined theory and experimental endeavor - we hypothesize that these “mechanical brains” are ubiquitous in our living world of adaptive active matter and also present a technological opportunity of the first ever implementation of computation, learning and cognition purely in mechanics.
Here, we aim to study the emergent collective behavior of non- neuronal systems which are complicated enough to teach us something new but simple enough to understand completely. This is a ’goldilocks condition’; simple non-neuronal animals capable of coordinating millions of cells in collective effort. This approach explores how organismic behavior can be directly encoded in the geometry, topology, mechanics and dynamics of a biological materials with observable degrees of freedom.
Cognitive abilities, especially in processing environmental information, are crucial for survival and adaptation. However, our understanding of what cognition means and how it evolved are largely incomplete. Current large scale efforts focus primarily on neuronal systems, but alternate unicellular and multicellular models lacking classical components will greatly expand our understanding of the phylogenetic continuity (or discontinuities) of biological computation. Combining a theoretical framework such as a design language for mechanical circuits, evolutionary approaches from detailed biological study of aneural systems and engineering of the first self-learning mechanical substrate - we intend to usher a new era of engineering of an adaptive “mechanical brain” as a self-learning meta-material.
For the last 12 years, my laboratory has been developing the marine invertebrate in the phyla Placozoan (Trichoplax adherance) as a model system to explore evolutionary and biophysical origins of behavior in a simple animal without muscles or neurons. We focus on Trichoplax adherance because of it’s nearly two-dimensional in shape and hence provides us optical and chemical access to all cells in a living organism (in-toto imaging). This is a unique animal with smallest known metazoan genome (11,000 genes). Only six to ten cell types have been convincingly shown to occur in Placazoans. In this animal, our lab first discovered the world’s fastest acto-myosin driven epithelial contractile system [23], motility induced fracture dynamics and rupture resistance due to contractile activity. Most recently, we have discovered presence of a porto-nervous system - underlying the complex behavior in placazoans via “ciliary flocking” described in a long form three-part story.
The lessons that we learn from this research program may inform the next generation of adaptive technologies which perform computation using the same substrate at the work they produce making truly smart machines without microchips. Whether we discover that hierarchy is essential (exponential explosions with layer number) or we find ways to encode hierarchy within a mechanical brain (through recursion and stable-yet-sensitive dynamics), these lessons will be valuable for shaping technologies of tomorrow.
BIG QUESTION
“Why and how did the first nervous systems evolve in metazoan tree of life?”
Project status
Active
