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The brain has tens of billions of neurons and is one of the most complicated networks that we know. This makes any analysis of brain function hugely time consuming.
Dr Carlo Laing is working with a colleague at Princeton University in the US on the phenomenon of spike timing dependent plasticity. This is where the strengths of connections between neurons are modified as a result of the precise “firing” times of individual neurons.
Many areas of modern science, such as climate science, physics, and chemistry, rely on solving large systems of equations on computers. Usually, these equations cannot be solved exactly and small errors can build up over time. Distinguished Professor Robert McLachlan and PhD student Christian Offen are working on new types of computational methods known as geometric integrators that give more reliable results by preserving certain features of the equations such as mass and energy.
Statistical inverse problems occur when we wish to learn about some phenomenon that is observed only indirectly, or with error. For example in ecology, where we want to count the number of times individual animals are (re)sighted but identification errors may occur.
A $535,000 grant from the 2017 Marsden Fund is allowing Martin Hazelton and a team of statisticians from Massey to look at techniques from algebraic statistics to develop and study new polytope samplers that can do the above effectively and efficiently.
Associate Professor Alona Ben-Tal led a project that developed mathematical equations to describe how the avian respiratory system works. In birds, air flows in one direction during both inspiration and expiration, in an area of the lungs where gas exchange occurs. The project provides a new explanation on the way in which this unidirectional flow is generated.