The behavior of cells and organisms is steered by myriad signals from the external world. While sensing of biochemical signals such as neurotransmitters can be understood from a molecular perspective of receptors and downstream signaling pathways, a different perspective on cellular information is needed to understand how cells sense their physical microenvironment. Here I demonstrate findings from my MURI team that controllable mechano-chemical waves, which have been found in many cell types in the last decade, can act as primary sensors of the physical environment, including local DC electric fields. Applying these findings to neural networks, I show initial evidence that biomechanical waves are also ubiquitous in neural cells and may serve as a mechanical regulator of neuronal activity. I will discuss the implications of our findings for the design of brain inspired AI algorithms.
Schedule
Timezone: PDT
Coupled Excitable Systems – A new perspective on living neural networks
Wolfgang LOSERT, Professor
University of Maryland College Park
Wolfgang LOSERT, Professor
University of Maryland College Park
Wolfgang Losert obtained his PhD from City College of the City University of New York. His research is centered on dynamical properties of Complex Systems at the convergence of physics and biology. A special focus is on applications to cancer biology. Examples of dynamical processes that are often found in complex systems are pattern formation and dynamical phase transitions. The main thrust of his work on living systems is to assess how cell motion and collective behavior are affected by physical cues, in particular the topography of the surface, surface adhesivity, and cell-cell adhesion. We discovered that cell migration can be guided by nanotopography via control of the dynamics of actin waves and that cell-surface adhesion can significantly alter the intracellular and collective cell dynamics. We also developed new tools to integrate measurements of the physical properties of living systems with biomedical phenotypes, via advanced statistical and machine learning analysis of multiple types of information, most at the single cell level.
Schedule subject to change without notice.