Research
(This site is not currently up to date)
Data Science
Data science has become this buzzword many a people use when they actually mean applying statistics. I am a huge fan of applying statistics, particularly statistical models to gain insights from datasets or to examine how different variables covary (e.g. neural activity during behaviour). I hope to clean up some code and upload it to github soon, and publish some of my kernels on kaggle.
python (pandas, scikit, numpy, etc.)
CI/CD (cometML, Jenkins, GitLab...)
Publications
Dhole, K. D., Gangal, V., Gehrmann, S., Gupta, A., Li, Z., Mahamood, S., ..., Tolkiehn, M., ... & Zhang, Y. (2021). NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation. arXiv preprint arXiv:2112.02721.
Srivastava, A., Rastogi, A., Rao, A., Shoeb, A. A. M., Abid, A., Fisch, A., ..., Tolkiehn, M., ..., & Kim, H. (2022). Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615.
Talks
Tolkiehn, M., (Jan 2018). What can we learn from Telecommunication Data? Talk at Valantic GmbH Hamburg, Germany
Neuroscience - Cerebellar Circuitry
Investigation of learning mechanisms in the intact brain in Dr Paul Chadderton's Laboratory.
The cerebellum hosts about 5 times as many neurons than the cerebral cortex (21-26 billion neurons in cerebral cortex, Pelvig et al., 2008, in contrast to 101 billion neurons in the cerebellum, Andersen et al., 1992), yet it occupies only about a fifth of the space. It is implied in many aspects of motor control such as smooth movements, yet a complete lack of cerebellum can be in parts compensated for by the rest of the brain.
I want to understand the cellular and circuit mechanisms of cerebellar activity between its processing stage (cerebral cortex) and its output stage (deep cerebellar nuclei). Then, how does the modulation of network connectivity through various means affect the processing, and how does learning a motor command change the network structure and outputs?
Neuroscience - Primary Visual Cortex
Data analysis of silicon multielectrodearray electrophysiology data of wild-type mice
Early sensory processing in mouse primary visual cortex
Neural processing in mouse V1 is part and parcel of modern neuroscience. The majority of research focussed on characterising single cell properties, whilst the role of population activity on processing has only recently been moving into the focus of investigations (Mont.n et al., 2014; Gutnisky et al., 2016; Gardella et al., 2016; O’Donnell et al., 2017; Okun et al., 2012).
In my work, I aim to analyse and compare individual and population responses (e.g. patterns of co-occurring activity/active variables) with computation models such as maximum entropy models (e.g. Ising model, sRBM) or other statistical or information-theoretic models. Including higher-order interactions or population responses improved the predicted pattern distributions, suggesting the significant role of population dynamics particularly for larger ensemble sizes.
Publications
Tolkiehn, M., Schultz, S. R. (2015). Multi-Unit Activity contains information about spatial stimulus structure in mouse primary visual cortex. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Tolkiehn, M., Schultz, S. R. (2019) Temporo-nasally biased moving grating selectivity in mouse primary visual cortex. Preprint bioRxiv [19 Jul 2019] 10.1101/708644
Tolkiehn, M., Schultz, S. R. (2019). Neural ensemble activity depends on stimulus type in mouse primary visual cortex. Preprint bioRxiv [19 Jul 2019] 10.1101/708636
(more under construction)
Posters (P) and Talks (T)
(T) Tolkiehn, M., (September 2019). Information theory on neural ensemble activity in mouse primary visual cortex. Invited talk at The 3rd International Neural Dynamics Summer School 2019 (Training and research experience in neural dynamics (TRENDs)), University of Bristol
(T) Tolkiehn, M., (Feb 2019). Neural ensemble activity depends on stimulus type in mouse primary visual cortex. Talk at the Computational Neuroscience Unit, University of Bristol
(T) Tolkiehn, M., (June 2018). Mouse V1 contains information about behavioural outcome. Invited talk at the School of Physiology, Pharmacology and Neuroscience, University of Bristol
(T) Tolkiehn, M., (Feb 2018). Structured spatial patterns in mouse primary visual cortex. Invited talk at the Engineering Department, University of Bristol
(T) Tolkiehn, M., (June 2017). Multi-Unit Activity contains information about spatial stimulus structure in mouse primary visual cortex. Invited talk at the NST Group, TU Munich
(P) Tolkiehn, M., Berditchevskaia, A., Schultz, S. R., (2016) The entropy of neural ensemble firing patterns in mouse primary visual cortex correlates with behavioural performance, Society for Neuroscience 2016, San Diego, CA, USA
(P) Tolkiehn, M., Schultz, S. R., (2015) Decoding grating spatial frequency and direction from Multi-Unit-Activity in mouse visual cortex, Society for Neuroscience 2015, Chicago, IL, USA
(T) Tolkiehn, M., (2015). Multi-Unit Activity contains information about spatial stimulus structure in mouse primary visual cortex. Presentation at the Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Ethomics
Ethomics is the neologism of the "-omics" suffix (which describes a ) and ethology (the study of human/animal behaviour from a biological perspective)
Ethomics looks at the quantification of behaviour. Similar to techniques in body sensor networks, I was interested in observing and quantifying the normal behavioural movement types in healthy persons using a full-body motion capturing system (with 48 sensors streaming 3d accelerometer data) in activities of daily living scenarios (e.g. sleeping, eating breakfast, working at a computer). The constitution of a database of unconstained natural movements then allows us to objectively quantify and contrast healthy movements with those of Parkinsons patients. In our studies, we found differences in the complexity of movements across genders and health state.
Poster and presentations
Thomik, A., Tolkiehn, M., Vella, I. and Faisal, A. A. (2012), Human ethomics as a window into motor control, Champalimaud Neuroscience Symposium, Lisbon, Portugal
Tolkiehn*, M., Vella*, I., Thomik, A. and Faisal, A. A. (2012), Towards the Human Ethome: Collection and Analysis of Unconstrained Natural Human Movements, Bioengineering12, Oxford
Ubiquitous sensing/Body Sensor Networks
Ubiquitous sensing deals with highly portable and without a problem wearable sensory devices.
A personal experience of my grandmother falling over in the garden without anyone noticing inspired me to delve into researching fall detection for the elderly. I devised and tested an algorithm that reliably detects and classifies falls from non-falls, simple enough to be ported onto a microcontroller of a triaxial accelerometer and barometric pressure sensor of a small waist-worn device.
Publications
Tolkiehn, M., Atallah, L., Lo, B. and Yang, G.-Z. (2011) Direction-sensitive fall detection using a triaxial accelerometer and a barometric pressure sensor. Proceedings of the 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS