Marcus Kaiser

Marcus Kaiser (b. Essen, Germany) is RCUK Academic Fellow for Complex Neural Systems and Deputy Director of the Wellcome Trust 4-year PhD programme in Systems Neuroscience at Newcastle University (UK).
Dr. Kaiser received his master degree from Bochum University, Germany before studying for a PhD with Prof. Claus C. Hilgetag. After receiving his PhD in 2005, he took up a tenure-track position at Newcastle University. He is member of the editorial boards of Frontiers in Neuroinformatics and PLoS ONE.
His research links structural brain connectivity with neural dynamics (Website). He applies large-scale simulations of neural activity based on anatomical connectivity to simulate strategies for stopping epilepsy spreading in silico. He also works on the spatial organization and development of neuronal networks.
Spatial and modular organization of neural systems
What constraints shape the spatial layout of neural networks? One influential idea in theoretical neuroscience has been that the overall wiring of neural networks should be as short as possible. Wire-saving could be achieved, for instance, through an optimal spatial arrangement of the connected network components. We evaluated this concept of component placement optimization in two representative systems, the neuronal network of the Caenorhabditis elegans worm and the long-range cortical connections of the primate brain. Contrary to previous results, we found many network layouts with substantially shorter total wiring than that of the original biological networks. This nonoptimal component placement arose from the existence of long-distance connections in the networks. Such connections may come at a developmental and metabolic cost; however, they also help to reduce the number of signal processing steps across the networks (Kaiser & Hilgetag, PLoS Computational Biology, 2006). Topological principles for neural systems include a small-world and scale-free architecture and a modular and hierarchical organisation in the form of multiple clusters at different levels (Sporns et al. Trends in Cognitive Sciences, 2004).
Robustness against lesions and functional recovery
How does the effect of lesions and the probability of recovery relate to the structure of cortical networks? Why do some lesions cause more severe deficits than others? Among several measures to predict the effect of removing individual connections, edge betweenness was found to be the best measure for estimating the subsequent increase in characteristic path length after the removal. It turned out that the most important connections (connections whose removal led to the highest deficit) were found between network clusters (Kaiser & Hilgetag, Biological Cybernetics, 2004). In addition, cortical networks behave similar to scale-free networks after the removal of regions or connections. Whereas the characteristic path length slowly increases for a random removal of nodes, it raises steeply for removing the most highly connected nodes first followed by a disintegration of the network into several disconnected components (Kaiser et al., European Journal of Neuroscience, 2007).
Development of neural networks
Neural systems as well as other biological networks show several organisational properties: they have a high clustering coefficient and a low characteristic path length thus resembling properties of small-world networks, they consists of multiple clusters (Hilgetag & Kaiser, Neuroinformatics, 2004) and they have a distinct spatial organisation (see previous project). How can networks with such properties arise? Which factors are necessary for getting each of these network properties? I could show that a simple model for the development of networks in space, spatial growth, can generate networks with small-world properties (Kaiser & Hilgetag, Physical Review E, 2004). The algorithm can generate networks with similar properties than cortical networks (Kaiser & Hilgetag, Neurocomputing, 2004). However, multiple clusters only arise in few cases. The existence of multiple clusters can be secured if there are time windows for connection establishment so that some parts of the network develops earlier than other parts and there is a higher probability to form connections if both regions have similar time windows for synaptogenesis (Kaiser & Hilgetag, Neurocomputing, 2007).
Seizure spreading in cortical networks
Can patterns of activity spreading be related to brain connectivity? An essential requirement for the representation of functional patterns in complex neural networks, such as the mammalian cerebral cortex, is the existence of stable network activations within a limited critical range. In this range, the activity of neural populations in the network persists between the extremes of quickly dying out, or activating the whole network. Whereas standard explanations for balanced activity involve populations of inhibitory neurons for limiting activity, we observe the effect of network topology on limiting activity spreading. A cluster hierarchy at different levels, from cortical clusters such as the visual cortex at the highest level to individual columns at the lowest level, enables sustained activity in neural systems and prevents large-scale activation as observed during epileptic seizures. Such topological inhibition, in addition to neuronal inhibition, might help to maintain healthy levels of neural activity (Kaiser et al., New Journal of Physics, 2007).

Psychophysics - updating visual information during saccades
It was known before that objects that are briefly presented during saccadic eye movements are perceived at the wrong position - shifted along the direction of the eye movement. This also occurs orthogonal to the saccade direction for objects that are in the periphery of the fixation point before the eye movement (Kaiser & Lappe, Neuron, 2004). There are several possible explanations for orthogonal misplacement, one of them being a reafferent connection from the frontal lobe (e.g. the frontal eye field) to the dorsal pathway (e.g. LIP or MT/MST). Interestingly, we found that mislocalization only affects the dorsal pathway. The ventral pathway received the correct visual information in that objects are processed at the presented rather than the (wrongly) perceived position (Lappe et al., Journal of Vision, 2006). There are numerous theories how this kind of mislocalization, or perisaccadic compression, could be explained. However, testing these theories goes beyond the limits of psychophysics and would involve testing the pathways and mapping between brain regions.

Main Menu

See Also

Main Menu