Michael Schmuker

Publications

• Parallel representation of stimulus identity and intensity in a dual pathway model inspired by the olfactory system of the honeybee

  Schmuker M, Yamagata N, Nawrot M, Menzel R

  Frontiers in Neuroengineering. 12/2011; 4:17.

  The honeybee Apis mellifera has a remarkable ability to detect and locate food sources during foraging, and to associate odor cues with food rewards. In the honeybee’s olfactory system, sensory input is first processed in the antennal lobe (AL) network. Uniglomerular projection neurons (PNs) convey ... [more] The honeybee Apis mellifera has a remarkable ability to detect and locate food sources during foraging, and to associate odor cues with food rewards. In the honeybee’s olfactory system, sensory input is first processed in the antennal lobe (AL) network. Uniglomerular projection neurons (PNs) convey the sensory code from the AL to higher brain regions via two parallel but anatomically distinct pathways, the lateral and the medial antenno-cerebral tract (l- and m-ACT). Neurons innervating either tract show characteristic differences in odor selectivity, concentration dependence, and representation of mixtures. It is still unknown how this differential stimulus representation is achieved within the AL network. In this contribution, we use a computational network model to demonstrate that the experimentally observed features of odor coding in PNs can be reproduced by varying lateral inhibition and gain control in an otherwise unchanged AL network. We show that odor coding in the l-ACT supports detection and accurate identification of weak odor traces at the expense of concentration sensitivity, while odor coding in the m-ACT provides the basis for the computation and following of concentration gradients but provides weaker discrimination power. Both coding strategies are mutually exclusive, which creates a tradeoff between detection accuracy and sensitivity. The development of two parallel systems may thus reflect an evolutionary solution to this problem that enables honeybees to achieve both tasks during bee foraging in their natural environment, and which could inspire the development of artificial chemosensory devices for odor-guided navigation in robots.
• 3.03

  Impact points

  The similarity between odors and their binary mixtures in Drosophila.

  Claire Eschbach, Katrin Vogt, Michael Schmuker, Bertram Gerber

  Chemical senses. 04/2011; 36(7):613-21.

  How are odor mixtures perceived? We take a behavioral approach toward this question, using associative odor-recognition experiments in Drosophila. We test how strongly flies avoid a binary mixture after punishment training with one of its constituent elements and how much, in turn, flies avoid an od... [more] How are odor mixtures perceived? We take a behavioral approach toward this question, using associative odor-recognition experiments in Drosophila. We test how strongly flies avoid a binary mixture after punishment training with one of its constituent elements and how much, in turn, flies avoid an odor element if it had been a component of a previously punished binary mixture. A distinguishing feature of our approach is that we first adjust odors for task-relevant behavioral potency, that is, for equal learnability. Doing so, we find that 1) generalization between mixture and elements is symmetrical and partial, 2) elements are equally similar to all mixtures containing it and that 3) mixtures are equally similar to both their constituent elements. As boundary conditions for the applicability of these rules, we note that first, although variations in learnability are small and remain below statistical cut-off, these variations nevertheless correlate with the elements' perceptual "weight" in the mixture; thus, even small differences in learnability between the elements have the potential to feign mixture asymmetries. Second, the more distant the elements of a mixture are to each other in terms of their physicochemical properties, the more distant the flies regard the elements from the mixture. Thus, titrating for task-relevant behavioral potency and taking into account physicochemical relatedness of odors reveals rules of mixture perception that, maybe surprisingly, appear to be fairly simple.
• 3.03

  Impact points

  A behavioral odor similarity "space" in larval Drosophila.

  Yi-chun Chen, Dushyant Mishra, Linda Schmitt, Michael Schmuker, Bertram Gerber

  Chemical senses. 03/2011; 36(3):237-49.

  To provide a behavior-based estimate of odor similarity in larval Drosophila, we use 4 recognition-type experiments: 1) We train larvae to associate an odor with food and then test whether they would regard another odor as the same as the trained one. 2) We train larvae to associate an odor with foo... [more] To provide a behavior-based estimate of odor similarity in larval Drosophila, we use 4 recognition-type experiments: 1) We train larvae to associate an odor with food and then test whether they would regard another odor as the same as the trained one. 2) We train larvae to associate an odor with food and test whether they prefer the trained odor against a novel nontrained one. 3) We train larvae differentially to associate one odor with food, but not the other one, and test whether they prefer the rewarded against the nonrewarded odor. 4) In an experiment like (3), we test the larvae after a 30-min break. This yields a combined task-independent estimate of perceived difference between odor pairs. Comparing these perceived differences to published measures of physicochemical difference reveals a weak correlation. A notable exception are 3-octanol and benzaldehyde, which are distinct in published accounts of chemical similarity and in terms of their published sensory representation but nevertheless are consistently regarded as the most similar of the 10 odor pairs employed. It thus appears as if at least some aspects of olfactory perception are "computed" in postreceptor circuits on the basis of sensory signals rather than being immediately given by them.

• Differential odor processing in two olfactory pathways in the honeybee.

  Nobuhiro Yamagata, Michael Schmuker, Paul Szyszka, Makoto Mizunami, Randolf Menzel

  Frontiers in systems neuroscience. 01/2009; 3:16.

  An important component in understanding central olfactory processing and coding in the insect brain relates to the characterization of the functional divisions between morphologically distinct types of projection neurons (PN). Using calcium imaging, we investigated how the identity, concentration an... [more] An important component in understanding central olfactory processing and coding in the insect brain relates to the characterization of the functional divisions between morphologically distinct types of projection neurons (PN). Using calcium imaging, we investigated how the identity, concentration and mixtures of odors are represented in axon terminals (boutons) of two types of PNs - lPN and mPN. In lPN boutons we found less concentration dependence, narrow tuning profiles at a high concentration, which may be optimized for fine, concentration-invariant odor discrimination. In mPN boutons, however, we found clear rising concentration dependence, broader tuning profiles at a high concentration, which may be optimized for concentration coding. In addition, we found more mixture suppression in lPNs than in mPNs, indicating lPNs better adaptation for synthetic mixture processing. These results suggest a functional division of odor processing in both PN types.
• 9.43

  Impact points

  Processing and classification of chemical data inspired by insect olfaction.

  Michael Schmuker, Gisbert Schneider

  Proceedings of the National Academy of Sciences of the United States of America. 01/2008; 104(51):20285-9.

  The chemical sense of insects has evolved to encode and classify odorants. Thus, the neural circuits in their olfactory system are likely to implement an efficient method for coding, processing, and classifying chemical information. Here, we describe a computational method to process molecular repre... [more] The chemical sense of insects has evolved to encode and classify odorants. Thus, the neural circuits in their olfactory system are likely to implement an efficient method for coding, processing, and classifying chemical information. Here, we describe a computational method to process molecular representations and classify molecules. The three-step approach mimics neurocomputational principles observed in olfactory systems. In the first step, the original stimulus space is sampled by "virtual receptors," which are chemotopically arranged by a self-organizing map. In the second step, the signals from the virtual receptors are decorrelated via correlation-based lateral inhibition. Finally, in the third step, olfactory scent perception is modeled by a machine learning classifier. We found that signal decorrelation during the second stage significantly increases the accuracy of odorant classification. Moreover, our results suggest that the proposed signal transform is capable of dimensionality reduction and is more robust against overdetermined representations than principal component scores. Our olfaction-inspired method was successfully applied to predicting bioactivities of pharmaceutically active compounds with high accuracy. It represents a way to efficiently connect chemical structure with biological activity space.
• 2.34

  Impact points

  SOMMER: self-organising maps for education and research.

  Michael Schmuker, Florian Schwarte, André Brück, Ewgenij Proschak, Yusuf Tanrikulu, Alireza Givehchi, Kai Scheiffele, Gisbert Schneider

  Journal of molecular modeling. 02/2007; 13(1):225-8.

  SOMMER is a publicly available, Java-based toolbox for training and visualizing two- and three-dimensional unsupervised self-organizing maps (SOMs). Various map topologies are implemented for planar rectangular, toroidal, cubic-surface and spherical projections. The software allows for visualization... [more] SOMMER is a publicly available, Java-based toolbox for training and visualizing two- and three-dimensional unsupervised self-organizing maps (SOMs). Various map topologies are implemented for planar rectangular, toroidal, cubic-surface and spherical projections. The software allows for visualization of the training process, which has been shown to be particularly valuable for teaching purposes.
• 1.31

  Impact points

  Predicting olfactory receptor neuron responses from odorant structure.

  Michael Schmuker, Marien de Bruyne, Melanie Hähnel, Gisbert Schneider

  Chemistry Central journal. 02/2007; 1:11.

  ABSTRACT: BACKGROUND: Olfactory receptors work at the interface between the chemical world of volatile molecules and the perception of scent in the brain. Their main purpose is to translate chemical space into information that can be processed by neural circuits. Assuming that these receptors have e... [more] ABSTRACT: BACKGROUND: Olfactory receptors work at the interface between the chemical world of volatile molecules and the perception of scent in the brain. Their main purpose is to translate chemical space into information that can be processed by neural circuits. Assuming that these receptors have evolved to cope with this task, the analysis of their coding strategy promises to yield valuable insight in how to encode chemical information in an efficient way. RESULTS: We mimicked olfactory coding by modeling responses of primary olfactory neurons to small molecules using a large set of physicochemical molecular descriptors and artificial neural networks. We then tested these models by recording in vivo receptor neuron responses to a new set of odorants and successfully predicted the responses of five out of seven receptor neurons. Correlation coefficients ranged from 0.66 to 0.85, demonstrating the applicability of our approach for the analysis of olfactory receptor activation data. The molecular descriptors that are best-suited for response prediction vary for different receptor neurons, implying that each receptor neuron detects a different aspect of chemical space. Finally, we demonstrate that receptor responses themselves can be used as descriptors in a predictive model of neuron activation. CONCLUSION: The chemical meaning of molecular descriptors helps understand structure-response relationships for olfactory receptors and their "receptive fields". Moreover, it is possible to predict receptor neuron activation from chemical structure using machine-learning techniques, although this is still complicated by a lack of training data.
• 11.83

  Impact points

  Searching for drug scaffolds with 3D pharmacophores and neural network ensembles.

  Steffen Renner, Mirko Hechenberger, Tobias Noeske, Alexander Böcker, Claudia Jatzke, Michael Schmuker, Christopher G Parsons, Tanja Weil, Gisbert Schneider

  Angewandte Chemie (International ed. in English). 02/2007; 46(28):5336-9.
• 3.43

  Impact points

  Optimized Particle Swarm Optimization (OPSO) and its application to artificial neural network training.

  Michael Meissner, Michael Schmuker, Gisbert Schneider

  BMC bioinformatics. 02/2006; 7:125.

  BACKGROUND: Particle Swarm Optimization (PSO) is an established method for parameter optimization. It represents a population-based adaptive optimization technique that is influenced by several "strategy parameters". Choosing reasonable parameter values for the PSO is crucial for its conve... [more] BACKGROUND: Particle Swarm Optimization (PSO) is an established method for parameter optimization. It represents a population-based adaptive optimization technique that is influenced by several "strategy parameters". Choosing reasonable parameter values for the PSO is crucial for its convergence behavior, and depends on the optimization task. We present a method for parameter meta-optimization based on PSO and its application to neural network training. The concept of the Optimized Particle Swarm Optimization (OPSO) is to optimize the free parameters of the PSO by having swarms within a swarm. We assessed the performance of the OPSO method on a set of five artificial fitness functions and compared it to the performance of two popular PSO implementations. RESULTS: Our results indicate that PSO performance can be improved if meta-optimized parameter sets are applied. In addition, we could improve optimization speed and quality on the other PSO methods in the majority of our experiments. We applied the OPSO method to neural network training with the aim to build a quantitative model for predicting blood-brain barrier permeation of small organic molecules. On average, training time decreased by a factor of four and two in comparison to the other PSO methods, respectively. By applying the OPSO method, a prediction model showing good correlation with training-, test- and validation data was obtained. CONCLUSION: Optimizing the free parameters of the PSO method can result in performance gain. The OPSO approach yields parameter combinations improving overall optimization performance. Its conceptual simplicity makes implementing the method a straightforward task.
• 2.07

  Impact points

  Impact of different software implementations on the performance of the Maxmin method for diverse subset selection.

  Michael Schmuker, Alireza Givehchi, Gisbert Schneider

  Molecular diversity. 02/2004; 8(4):421-5.

  Besides the choice of an automated software method for selecting 'maximally diverse' compounds from a large pool of molecules, it is the implementation of the algorithm that critically determines the usefulness of the approach. The speed of execution of two implementations of the Maxmin algo... [more] Besides the choice of an automated software method for selecting 'maximally diverse' compounds from a large pool of molecules, it is the implementation of the algorithm that critically determines the usefulness of the approach. The speed of execution of two implementations of the Maxmin algorithm is compared for the selection of maximally diverse subsets of large compound collections. Different versions of the software are compared using various C compiler options and Java virtual machines. The analysis shows that the Maxmin algorithm can be implemented in both languages yielding sufficient speed of execution. For large compound libraries the Java version outperformes the C version. While the Java version selects the same compounds independent of the virtual machine used, the C version produces slightly different subsets depending on the compiler and on the optimization settings.
• 2.42

  Impact points

  Deciphering apicoplast targeting signals--feature extraction from nuclear-encoded precursors of Plasmodium falciparum apicoplast proteins.

  J Zuegge, S Ralph, M Schmuker, G I McFadden, G Schneider

  Gene. 01/2002; 280(1-2):19-26.

  The malaria causing protozoan Plasmodium falciparum contains a vestigal, non-photosynthetic plastid, the apicoplast. Numerous proteins encoded by nuclear genes are targeted to the apicoplast courtesy of N-terminal extensions. With the impending sequence completion of an entire genome of the malaria ... [more] The malaria causing protozoan Plasmodium falciparum contains a vestigal, non-photosynthetic plastid, the apicoplast. Numerous proteins encoded by nuclear genes are targeted to the apicoplast courtesy of N-terminal extensions. With the impending sequence completion of an entire genome of the malaria parasite, it is important to have software tools in place for prediction of subcellular locations for all proteins. Apicoplast targeting signals are bipartite; containing a signal peptide and a transit peptide. Nuclear-encoded apicoplast protein precursors were analyzed for characteristic features by statistical methods, principal component analysis, self-organizing maps, and supervised neural networks. The transit peptide contains a net positive charge and is rich in asparagine, lysine, and isoleucine residues. A novel prediction system (PATS, predict apicoplast-targeted sequences) was developed based on various sequence features, yielding a Matthews correlation coefficient of 0.91 (97% correct predictions) in a 40-fold cross-validation study. This system predicted 22% apicoplast proteins of the 205 potential proteins on P. falciparum chromosome 2, and 21% of 243 chromosome 3 proteins. A combination of the PATS results with a signal peptide prediction yields 15% potentially nuclear-encoded apicoplast proteins on chromosomes 2 and 3. The prediction tool will advance P. falciparum genome analysis, and it might help to identify apicoplast proteins as drug targets for the development of novel anti-malaria agents.

• Optimized Particle Swarm Optimization (OPSO) and its application to artificial neural network training

  Michael Meissner, Michael Schmuker, Gisbert Schneider

  BMC Bioinformatics. 7:125.

• A network model for learning-induced changes in odor representation in the antennal lobe

  Michael Schmuker, Marcel Weidert, Randolf Menzel

  The antennal lobe (AL) is the insect homolog of the olfactory bulb in mammals. As such, it is the first processing station in the insect olfactory system. It has been shown previously that odorant representations change during associative odor learning, but contradictory findings have also been publ... [more] The antennal lobe (AL) is the insect homolog of the olfactory bulb in mammals. As such, it is the first processing station in the insect olfactory system. It has been shown previously that odorant representations change during associative odor learning, but contradictory findings have also been published. We recorded Ca2+-activity of uniglomerular projection neurons (PNs) in the AL of the honeybee Apis mellifera during differential olfactory conditioning. Our results indicate that the activity pattern of PNs in response to odorants can change for the conditioned odor, for the unconditioned odor and for control odors which were not presented during conditioning. We designed a computational model of the glomerular network that can explain the apparent contradiction between the findings we present here and previously reported results.

• Deciphering apicoplast targeting signals – feature extraction from nuclear-encoded precursors of Plasmodium falciparum apicoplast proteins

  Jochen Zuegge, Stuart Ralph, Michael Schmuker, Geoffrey I McFadden, Gisbert Schneider

  Gene.

  The malaria causing protozoan Plasmodium falciparum contains a vestigal, non-photosynthetic plastid, the apicoplast. Numerous proteins encoded by nuclear genes are targeted to the apicoplast courtesy of N-terminal extensions. With the impending sequence completion of an entire genome of the malaria ... [more] The malaria causing protozoan Plasmodium falciparum contains a vestigal, non-photosynthetic plastid, the apicoplast. Numerous proteins encoded by nuclear genes are targeted to the apicoplast courtesy of N-terminal extensions. With the impending sequence completion of an entire genome of the malaria parasite, it is important to have software tools in place for prediction of subcellular locations for all proteins. Apicoplast targeting signals are bipartite; containing a signal peptide and a transit peptide. Nuclear-encoded apicoplast protein precursors were analyzed for characteristic features by statistical methods, principal component analysis, self-organizing maps, and supervised neural networks. The transit peptide contains a net positive charge and is rich in asparagine, lysine, and isoleucine residues. A novel prediction system (PATS, predict apicoplast-targeted sequences) was developed based on various sequence features, yielding a Matthews correlation coefficient of 0.91 (97% correct predictions) in a 40-fold cross-validation study. This system predicted 22% apicoplast proteins of the 205 potential proteins on P. falciparum chromosome 2, and 21% of 243 chromosome 3 proteins. A combination of the PATS results with a signal peptide prediction yields 15% potentially nuclear-encoded apicoplast proteins on chromosomes 2 and 3. The prediction tool will advance P. falciparum genome analysis, and it might help to identify apicoplast proteins as drug targets for the development of novel anti-malaria agents.