At the University of Groningen, The Netherlands, I finished my Bachelor in Artificial Intelligence and a Research Master in Behavioral and Cognitive Neurosciences. During the course of my education I became more and more interested in the study of dynamical biological systems. In subjects ranging from the 'human memory system during sleep' to 'cumulative cultural evolution of groups' I used methods of study that model from the bottom up. This entails building a computer model by starting to model the smallest discrete unit of interest, e.g. an artificial neuron or an individual, and from there observe the interactions of these units in a larger integrated system. This methodology contrasts with more traditional analytic approaches used in biology.
During my PhD it is my intent to use this methodology to find common dynamical properties in different biological systems, as well as directly contribute to the understanding of those systems under study. The two topics chosen for this attempt are within the fields of evolution and neuroscience, and more specifically:
- Mechanisms of sympatric speciation: Within evolution, the process of speciation is of vital interest, since it explains how biodiversity came about. It is relatively well understood how a single species can give rise to many in case a population gets separated. This process, called allopatric speciation, can for instance be caused by a newly formed mountain range splitting the habitat of a species in two. An alternative form of speciation is that of sympatric speciation, whereby a single cohesive population can give rise to new species, without initial isolation of any kind during the process. This theory of sympatric speciation has gained much attention over the last few decades, but it has not been studied yet using individual based computational modeling. Using such a model, initially built by my supervisor Kirsten ten Tusscher, we hope to better understand how genome re-organisation during evolution (through genetic mutation and recombination) can allow for species-variation without reproductive isolation between the individuals.
- Dynamics of low-level adaptivity in a neural network: The brain consists of a large quantity of inter-connected neurons, with a high degree of adaptivity on a small scale. This adaptivity is often on the level of the highly plastic synapse, which is the main physiological unit that allows for communication between neurons. Although the differences greatly outnumber the similarities, natural evolution and synaptic plasticity in the brain share some interesting properties. Both systems contain large numbers of sub-units that are relatively independent and simple; yet are capable of amazing adaptivity when observing the system as an interactive whole over time. To better understand possible similarities, I will be studying the dynamics of such adaptivity in neural networks. Specifically, I will look at a model of epilepsy and how adaptivity effects wave dynamics. Eventually, I wish to focus on low-level adaptivity in the visual pathway of the brain, in an attempt to better understand pre-attentive attribute detection. All the computational neuro-scientific work is done under supervision of Hans Ekkehard Plesser, at the computational neuroscience group of the Norwegian University of Life Sciences (UMB in Ås).