| Speaker: Alona Ben-Tal Affiliation: Massey Time: 2:00 pm Wednesday, 28 July, 2021 Location: ClockT018/105-018 |
| The respiratory neural network is spectacular in its ability to regulate breathing under a wide range of activities and changing environmental conditions, but its operating mechanisms are not well understood. Mathematical modelling is a vital tool for studying this complex system and models that rely on ordinary differential equations (ODEs) have previously been developed. However, the ODEs-type models have several limitations that we were able to overcome by studying the system using Boolean networks in which the nodes could have only two values: “1” or “0”. Among other things, the new Boolean representation provides a possible explanation of how inspiration and expiration times can be regulated selectively as well as how the neural network can be reconfigured under different control inputs. This talk will present the motivation for and the details of the modelling as well as our efforts to analyse Boolean networks more generally. We will review a previously developed semi-tensor-product-based method to convert Boolean networks into discrete linear dynamical systems and a “shortcut” of this method that we have recently developed. This work has been done in collaboration with Yunjiao Wang and Maria C.A. Leite. |