Postdoctoral Research Fellow
Biocomputation Research Group (http://biocomputation.herts.ac.uk/)
University of Hertfordshire
Over the last decades a large amount of cellular/molecular level abnormalities have been identified in the brain of patients suffering from neurological and psychiatric disorders (e.g. schizophrenia). However, this neurobiological knowledge has not translated into effective medical treatments. This is mainly due to the fact that we cannot convincingly link these abnormalities to behavioural/sensory deficits or symptoms. Computational modelling is a powerful tool to bridge this gap.
I build computational models of sensory information processing in the human brain and its impairments in disorders. The detailed nature of these allows for an incorporation of cellular/molecular level abnormalities as found in patients and, therefore, enables us to investigate the underlying neuronal mechanisms. Furthermore, these computational models can be used to develop and test possible medical treatments.
I focus on two sensory phenomena: centre-surround suppression (i.e. the mutual inhibition of a central stimulus and its surround) in the visual system and oscillatory activity (i.e. organised rhythmic behaviour of populations of neurons) in the auditory system both with respect to alterations in patients suffering from schizophrenia. The figure below shows an example of an oscillatory impairment in schizophrenia. On the left hand side the network the time-frequency plot shows a normal 40Hz oscillations in a 'healthy' simulated network. The right hand side shows a network simulation which resembles deficits seen in schizophrenic patients (i.e. less power in the 40Hz range and an increase in power in the 20Hz range).
Classically the olfactory system is viewed as an associative memory (like a Hopfield network), which learns to classify odours by adapting its connections weights. This view is supported by the fact that the projections from the olfactory bulb to the olfactory (or piriform) cortex as well as the connections within olfactory cortex don't seem to have a spatial organization (let's call it the 'associative network hypothesis').
However, modelling work suggests that there might exist several sub-networks within the overall circuit (let's call it the 'sub-network hypothesis'). These sub-networks are characterized by a very high likelihood of making connections within the sub-network and a very low likelihood of forming inter-network connections.
Based on a fusion of existing models of the olfactory bulb and piriform cortex, I study the experimentally well-documented oscillatory activity in the olfactory assuming that one of the above mentioned hypotheses holds. Furthermore, from a machine learning/information theoretic perspective, I investigate whether one of the hypotheses is advantageous in terms of classification of odours (i.e. is it easier/faster to classify odours assuming one hypothesis compared to the other?) or in terms of odour capacity (i.e. how many odours can I store?)
Despite the tremendous advances over the last years, we still don't understand how memories are formed and maintained within the brain. It is clear that plasticity at synapses between neurons is one major contributor, however, how different plasticity mechanisms at synapses from different connection types interact in order to efficiently store memories is far from clear. Especially the role of plasticity at inhibitory synapses remains largely unexplored.
Here, I explore how memory capacity is affected, when adding different (biologically plausible) plasticity mechanisms to a spiking neural network (which achieves attractorless memory storage using a homeostatic, inhibitory plasticity mechanism).
If you are interested in my work and would like to do a project/internetship or a Bachelor/Master/PhD thesis, feel free to contact me via: email@example.com
I achieved a Diplom (equivalent to a Master's degree) in mathematics and a Bachelor's degree in computer science from Saarland University, Saarbruecken, Germany, in 2009 and 2008, respectively. In 2009, I joined the Institute of Robotics and Cognitive Systems and the Graduate School for Computing in Medicine and Life Sciences at the University of Luebeck, Luebeck, Germany, to pursue a PhD. I was awarded a PhD in 2014 and continued as a postdoctoral researcher until 06/2015. In 07/2015 I was awarded a postdoctoral fellowship by the German Research Foundation (DFG) and joined the Biocomputation Research Group led by Dr. Volker Steuber at the University of Hertfordshire.
Tuomo Maeki-Marttunen, Gaute Einevoll, Oslo/As, Norway
Hagenah, J., Scharfschwerdt, M., Schlaefer, A., & Metzner, C. (2015). A machine learning approach for planning valve-sparing aortic root reconstruction. Current Directions in Biomedical Engineering, 1(1), 361-365.
J Hagenah, M Scharfschwerdt, C Metzner, A Schlaefer, HH Sievers and A Schweikard, An approach for patient specific modeling of the aortic valve leaflets, in: BioMedTec Studierendentagung, 2014
J Hagenah, MScharfschwerdt, C Metzner, A Schlaefer, HH Sievers and A Schweikard, An approach for patient specific modeling of the aortic valve leaflets, GRIN Verlag, 2014
C Metzner, Coding in the olfactory system: linking realistic and abstract models, in: Flavour, pages P10, 2014
C Metzner, A Schweikard and B Zurowski, Computational Multifactoriality in a Detailed Neural Network Model Resembling Centre-Surround Suppression Deficits in Schizophrenia, in: BMC Neuroscience, 2014
C Metzner, A Schweikard and B Zurowski, Center-Surround Interactions in a Network Model of Layer 4Calpha of Primary Visual Cortex, in: BMC Neuroscience, 2013
B Zurowski, F Hamm, C Metzner, H Scholand-Engler, A Wells and F Hohagen, Cortical levels of GABA in patients with panic disorder are associated with the strength of metacognitive beliefs, in: Proceedings of 2nd International Conference of Metacognitive Therapy, 2013
C Metzner, F Guth, A Schweikard and B Zurowski, Spike-timing Dependent Plasticity Facilitates Excitatory/Inhibitory Disbalances in Early Phases of Tinnitus Manifestation, in: BMC Neuroscience, 2012
C Metzner, M Menzinger, A Schweikard and B Zurowski, Early Signs of Tinnitus in a Simulation of the Mammalian Primary Auditory Cortex, in: BMC Neuroscience, pages P383, 2011
C Metzner, A Schweikard and B Zurowski, Neurochemical Mechanisms of Perceptual Deficits in Schizophrenic Patients – A Spiking Neural Network Approach, in: Front. Comput. Neurosci. Conference Abstract: BC11 : Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting, 2011
C Metzner, ASchweikard and B Zurowski, Towards Realistic Receptive Field Properties in a Biologically Inspired Spiking Network Model of the Mammalian Primary Visual Cortex, in: Front. Comput. Neurosci. Conference Abstract: BC11 : Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting, 2011
C Metzner, A Schweikard and B Zurowski, Context Integration in Visual Processing: A Computational Model of Center-Surround Suppression in the Visual System , in: BMC Neuroscience, pages P100, 2010
Artificial Intelligence Module (5COM1056), Semester A, University of Hertfordshire
Neural Networks and Machine Learning Module (7Com1033), Semester B, University of Hertfordshire
Neural Computation and Intelligent Systems (6Com0275), Semester B, University of Hertfordshire