We translate neurobiological principles of sensory computing into event-based algorithms for data processing, inference and control. We implement these algorithms on neuromorphic systems, i.e. novel brain-inspired computer architectures that accelerate event-based computation. We evaluate the performance of those systems and algorithms in robotic AI tasks such as neuromorphic olfaction and gas-based navigation.


Olfaction-inspired neuromorphic computing

The architecture of the olfactory system maps nicely on many data classification problems in which observations of a number of variables have to be mapped to target values or class labels. We design spiking networks for data classification inspired by computing principles found in the olfactory system. We implement those networks in neuronal simulators and on the SpiNNaker neuromorphic hardware system with the aim to enable high-performance neurocomputing.

We are a member of the EU flagship Human Brain Project, part of the Neuromorphic computing subproject. 


Schmuker M, Pfeil T, Nawrot MP (2014) A neuromorphic network for generic multivariate data classification. Proceedings of the National Academy of Science 111(6):2081-2086. Abstract | PDF + SI (Open Access)

Lungu I, Riehle A, Nawrot M P, and Schmuker M (2017). Predicting voluntary movements from motor cortical activity with neuromorphic hardware. IBM J Res Dev. 61(2):5:1-5:12. IEEE Xplore | PDF preprint 

Pfeil T, Grübl A, Jeltsch S, Müller E, Müller P, Petrovici MA, Schmuker M, Brüderle D, Schemmel J and Meier K (2013). Six networks on a universal neuromorphic computing substrate. Frontiers in Neuroscience 7:11.  Abstract | Full Text | PDF (Open Access)

Diamond A, Nowotny T, and Schmuker M (2016). Comparing Neuromorphic Solutions in Action: Implementing a Bio-Inspired Solution to a Benchmark Classification Task on Three Parallel-Computing Platforms. Front. Neurosci. 9:491 Full Text | PDF (Open Access).

Diamond A, Schmuker M, Berna AZ, Trowell S, Nowotny T (2016). Classifying continuous, real-time e-nose sensor data using a bio-inspired spiking network modelled on the insect olfactory system. Bioinspir. Biomim. 11, 026002. Full Text | PDF (Open Access).

Kasap B and Schmuker M (2013). Improving odor classification through self-organized lateral inhibition in a spiking olfaction-inspired network. Proceedings of the 6th International Conference on Neural Engineering (NER2013). PDF

Häusler C, Nawrot M P and Schmuker M (2011): A spiking neuron classifier network with a deep architecture inspired by the olfactory system of the honeybee. Proc. of 5th International IEEE EMBS Conference on Neural Engineering, April 27 - May 01 2011, Cancun, Mexico. doi:10.1109/NER.2011.5910522 | Full text

Virtual screening in the chemical space of odorants

The first step of olfactory perception happens when an odor molecule hits the membrane of a receptor neuron, evoking a neuronal response (or not). The relationship between the chemical world of odorants and the responses of odorant receptor neurons is important to understand how the olfactory world is encoded in the brain. We employ machine-learning methods to examine how physico-chemical properties of odorants relate to the responses of primary receptor neurons in the olfactory system of vertebrates and insects (virtual odorant screening). Our aim is to predict from the chemical structure of an odorant which receptor neurons respond to it. Another interesting question is how the chemical similarity of odorants relates to their perceptual similarity.

Contributors: Jan Sölter (cand. PhD), Stephan Gabler (student assistant), Marcus Schroeder (master student).

Funding: DFG SPP 1392 "Integrative Analysis of olfaction", SCHM 2474/1-1 and SCHM 2474/1-2. Collaboration with Hartwig Spors, MPI for Biophysics, Frankfurt/Main, and Silke Sachse, MPI for Chemical Ecology, Jena.


Gabler S, Soelter J, Hussain T, Sachse S and Schmuker M (2013). Physicochemical vs. vibrational descriptors for prediction of odor receptor responses. Molecular Informatics 32(9-10):855-865. Abstract | PDF

Schmuker M, de Bruyne M, Hähnel M and Schneider, G (2007): Predicting olfactory receptor neuron responses from odorant structure, Chemistry Central Journal, 1:11. Free full text (Open Access)

Chen Y-C, Mishra D, Schmitt L, Schmuker M and Gerber B (2011): A Behavioral Odor Similarity "Space" in Larval Drosophila. Chemical Senses, 36:223-235. Free full text (Open Access)

Eschbach C, Vogt K, Schmuker M, Gerber B: The Similarity between Odors and Their Binary Mixtures in Drosophila. Chemical Senses 36:613-621. Abstract | Full Text | PDF (Open Access)

Image analysis for Optophysiology

Optical imaging methods allow to measure the spatial and temporal pattern of activity of a large populations of neurons. We analyze optophysiological data from mice and Drosophila, obtained with intrinsic signal imaging or genetically encoded fluorescent dyes. To this end, we develop novel approaches for image analysis with improved resolution and resistance against noise.

Contributors: Jan Sölter (cand. PhD), Christine Winter (Bachelor student), Stephan Gabler (student assistant). Collaborators: Hartwig Spors, MPI for Biophysics, Frankfurt; Silke Sachse, MPI for Chemical Ecology, Jena.

Funding: DFG SPP 1392 "Integrative Analysis of olfaction", SCHM 2474/1-1 and SCHM 2474/1-2.


Soelter J, Schumacher J, Spors H, Schmuker M (2014). Automatic Segmentation of Odour Maps in the Mouse Olfactory Bulb using regularized Non-negative Matrix Factorization. Neuroimage 98:279-288. [abstract] [PDF] (Open Access)

Transformation of information in the insect olfactory system

In insects, olfactory receptor neurons relay their signals to the antennal lobe (AL), where the representation of olfactory information is enhanced before it is passed on to higher brain areas. Based on physiological data from the honeybee and Drosophila, we use rate-based and spiking network models to analyze how the AL network achieves decorrelation between channels of olfactory information and improves the separability of odor representations. In another project, we compare the representation of hedonic odor quality (i.e. whether it is attractive or unattractive for an animal) in higher brain areas like the lateral horn.

Contributors: Jan Sölter (PhD), Benjamin Auffarth (Alumnus postdoc), Bahadir Kasap (Alumnus master student). Collaboration with Silke Sachse (MPI for Chemical Ecology, Jena).


A. Strutz, J. Soelter, A. Baschwitz, A. Farhan, V. Grabe, J. Rybak, M. Knaden, M. Schmuker, B. S. Hansson, and S. Sachse (2014). Decoding odor quality and intensity in the Drosophila brain. Elife 3:e04147 Full text | PDF (Open Access).

Schmuker M, Yamagata N, Nawrot M and Menzel R (2011). Parallel representation of stimulus identity and intensity in a dual pathway model inspired by the olfactory system of the honeybee. Frontiers in Neuroengineering 4:17 AbstractPDF (Open Access).

Schmuker M, Weidert M and Menzel R (2008): A network model for learning-induced changes in odor representation in the antennal lobe, in Proceedings of the second french conference on Computational Neuroscience (Neurocomp08), Marseille, Laurent U. Perrinet and Emmanuel Daucé (ed.). PDF | HAL

Schmuker M, Schneider G (2007): Processing and classification of chemical data inspired by insect olfaction. Proceedings of the National Academy of Science, 104:20285-20289. PubMed | PDF

Somatosensory coding of noxious stimuli (concluded)

Noxious stimuli are encoded in the skin by C-fiber afferents. These fibers are reported to exhibit polymodal responses, for example, responding to noxious heat and mechanical stimulation. This raises the question how downstream neurons discriminate between the various stimulus qualities. Based on extracellular recordings of C-fiber nocicepotors, we examine how heat and mechanical pain are encoded into spike trains.

Contributors: Tara Dezdhar (cand. PhD). Collaboration with Prof. Gary Lewin (Max-Delbrück Center for Molecular Medicine, Berlin).

Funding: German ministry of education and research via a grant to Bernstein Center for Computational Neuroscience Berlin (grant no. 01GQ1001D).


Dezhdar T, Moshourab RA, Fründ I, Lewin GR, and Schmuker M (2015) A Probabilistic Model for Estimating the Depth and Threshold Temperature of C-fiber Nociceptors. Scientific Reports 5:17670. Full Text | PDF (Open Access).

About BioMachineLearning

Unit Leader:

Dr. Michael Schmuker
Reader in Data Science

email: This email address is being protected from spambots. You need JavaScript enabled to view it.

Research interests and skills

  • Event-based artificial intelligence
  • Neuromorphic computing
  • Olfaction, chemical sensing, electronic nose technology
  • Data analytics in life science and biotechnology

I do some coding (github) and publish my research (google scholar).


University of Hertfordshire
Department of Computer Science
College Lane
Hatfield, Herts AL10 9AB
United Kingdom