@incollection{SchmukerMichaelandNawrot2022,
address = {New York, NY},
author = {Schmuker, Michael and Nawrot, Martin and Chicca, Elisabetta},
booktitle = {Encyclopedia of Computational Neuroscience},
doi = {10.1007/978-1-0716-1006-0_119},
edition = {2},
editor = {Jaeger, Dieter and Jung, Ranu},
isbn = {978-1-0716-1006-0},
month = {jan},
pages = {2334--2340},
publisher = {Springer New York},
title = {{Neuromorphic Sensors, Olfaction}},
url = {https://doi.org/10.1007/978-1-0716-1006-0{\_}119},
year = {2022}
}

@article{Anna-MariaJurgensen*aAfshinKhalili*abElisabettaChiccacGiacomoIndiverid,
author = {J{\"{u}}rgensen, Anna-Maria and Khalili, Afshin and Chicca, Elisabetta and Indiveri, Giacomo and Nawrot, Martin Paul},
doi = {10.1088/2634-4386/ac3ba6},
file = {:Users/springer/Downloads/J{\"{u}}rgensen+et+al{\_}2021{\_}Neuromorph.{\_}Comput.{\_}Eng.{\_}10.1088{\_}2634-4386{\_}ac3ba6.pdf:pdf},
isbn = {9781509008964},
issn = {13616579},
journal = {Neuromorphic Computing and Engineering},
month = {nov},
title = {{A neuromorphic model of olfactory processing and sparse coding in the Drosophila larva brain}},
url = {https://doi.org/10.1088/2634-4386/ac3ba6},
year = {2021}
}

@article{doi:10.1146/annurev-control-061920-103228,
abstract = {Biomimetic robots that replace living social interaction partners can help elucidate the underlying interaction rules in animal groups. Our review focuses on the use of interactive robots that respond dynamically to animal behavior as part of a closed control loop. We discuss the most influential works to date and how they have contributed to our understanding of animal sociality. Technological advances permit the use of robots that can adapt to the situations they face and the conspecifics they encounter, or robots that learn to optimize their social performance from a set of experiences. We discuss how adaptation and learning may provide novel insights into group sociobiology and describe the technical challenges associatedwith these types of interactive robots. This interdisciplinary field provides a rich set of problems to be tackled by roboticists, machine learning engineers, and control theorists. By cultivating smarter robots, we can usher in an era of more nuanced exploration of animal behavior.},
author = {Landgraf, Tim and Gebhardt, Gregor H W and Bierbach, David and Romanczuk, Pawel and Musiolek, Lea and Hafner, Verena V and Krause, Jens},
doi = {10.1146/annurev-control-061920-103228},
journal = {Annual Review of Control, Robotics, and Autonomous Systems},
month = {may},
number = {1},
pages = {487--507},
title = {{Animal-in-the-Loop: Using Interactive Robotic Conspecifics to Study Social Behavior in Animal Groups}},
url = {https://doi.org/10.1146/annurev-control-061920-103228},
volume = {4},
year = {2021}
}

@article{Lungu2017,
abstract = {Neurons in the mammalian motor cortices encode physical parameters of voluntary movements during planning and execution of a motor task. Brain-machine interfaces can decode limb movements from the activity of these neurons in real time. The future goal is to control prosthetic devices in severely paralyzed patients or to restore communication if the ability to speak or make gestures is lost. Here, we implemented a spiking neural network that decodes movement intentions from individual neuronal activity recorded in the motor cortex of a monkey. The network runs on neuromorphic hardware and performs its computations in a purely spike-based fashion. It incorporates an insect-brain-inspired, three-layer architecture with 176 neurons. Cortical signals are filtered using lateral inhibition, and the network is trained in a supervised fashion to predict two opposing directions of the monkey?s arm reaching movement before the movement is carried out. Our network operates on the actual spikes that have been emitted by motor cortical neurons, without the need to construct intermediate non-spiking representations. Using a pseudo-population of 12 manually-selected neurons, it reliably predicts the movement direction with an accuracy of 89.32 {\%} on unseen data after only 100 training trials. Our results provide a proof of concept for the first-time use of a neuromorphic device for decoding movement intentions.},
author = {Lungu, I. A. and Riehle, A. and Nawrot, M. P. and Schmuker, M.},
doi = {10.1147/JRD.2017.2656063},
file = {::},
issn = {21518556},
journal = {IBM Journal of Research and Development},
keywords = {brain machine interface,insect inspired,monkey,motor cortex,neuromorphic computing,spiking neural network},
mendeley-tags = {brain machine interface,insect inspired,monkey,motor cortex,neuromorphic computing,spiking neural network},
number = {2},
title = {{Predicting voluntary movements from motor cortical activity with neuromorphic hardware}},
volume = {61},
year = {2017}
}

@inbook{Landgraf2017,
abstract = {Auch Tiere mit relativ kleinen Gehirnen zeigen erstaunlich komplexe und robuste Wahrnehmungs‐ und Verhaltensleistungen. Dies gilt insbesondere f{\"{u}}r Insekten. Deren Mini‐Gehirne sind anpassungs‐ und lernf{\"{a}}hig, sie erm{\"{o}}glichen ihnen, langfristige Ged{\"{a}}chtnisse herauszubilden und sich kurzfristig an neue Gegebenheiten anzupassen.},
address = {Wiesbaden},
author = {Landgraf, Tim and Nawrot, Martin Paul},
booktitle = {Planen und Handeln: Neurowissenschaftliche, psychologische, medizinische und gesellschaftsrelevante Aspekte},
doi = {10.1007/978-3-658-16891-9_9},
editor = {Walkowiak, Wolfgang and Erber-Schropp, Julia Maria},
isbn = {978-3-658-16891-9},
pages = {135--150},
publisher = {Springer Fachmedien Wiesbaden},
title = {{K{\"{u}}nstliche Mini‐Gehirne f{\"{u}}r Roboter}},
url = {https://doi.org/10.1007/978-3-658-16891-9{\_}9},
year = {2017}
}

@inbook{Schmuker2015,
address = {New York, NY},
author = {Chicca, Elisabetta and Schmuker, Michael and Nawrot, Martin Paul},
booktitle = {Encyclopedia of Computational Neuroscience},
doi = {10.1007/978-1-4614-6675-8_119},
editor = {Jaeger, Dieter and Jung, Ranu},
isbn = {978-1-4614-6675-8},
pages = {1991--1997},
publisher = {Springer New York},
title = {{Neuromorphic Sensors, Olfaction}},
url = {https://doi.org/10.1007/978-1-4614-6675-8{\_}119},
year = {2015}
}

@article{Schmuker2014,
abstract = {Computational neuroscience has uncovered a number of computational principles used by nervous systems. At the same time, neuromorphic hardware has matured to a state where fast silicon implementations of complex neural networks have become feasible. En route to future technical applications of neuromorphic computing the current challenge lies in the identification and implementation of functional brain algorithms. Taking inspiration from the olfactory system of insects, we constructed a spiking neural network for the classification of multivariate data, a common problem in signal and data analysis. In this model, real-valued multivariate data are converted into spike trains using virtual receptors (VRs). Their output is processed by lateral inhibition and drives a winner-take-all circuit that supports supervised learning. VRs are conveniently implemented in software, whereas the lateral inhibition and classification stages run on accelerated neuromorphic hardware. When trained and tested on real-world datasets, we find that the classification performance is on par with a na{\"{i}}ve Bayes classifier. An analysis of the network dynamics shows that stable decisions in output neuron populations are reached within less than 100 ms of biological time, matching the time-to-decision reported for the insect nervous system. Through leveraging a population code, the network tolerates the variability of neuronal transfer functions and trial-totrial variation that is inevitably present on the hardware system. Our work provides a proof of principle for the successful implementation of a functional spiking neural network on a configurable neuromorphic hardware system that can readily be applied to realworld computing problems.},
author = {Schmuker, Michael and Pfeil, Thomas and Nawrot, Martin Paul},
doi = {10.1073/pnas.1303053111},
file = {::},
issn = {00278424},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
keywords = {Bioinspired computing,Machine learning,Multivariate classification,Spiking networks,insect inspired,machine learning,neuromorphic computing,spiking neural network},
mendeley-tags = {insect inspired,machine learning,neuromorphic computing,spiking neural network},
number = {6},
pages = {2081--2086},
title = {{A neuromorphic network for generic multivariate data classification}},
volume = {111},
year = {2014}
}

@inproceedings{Helgadottir2013,
abstract = {Insects show a rich repertoire of goal-directed and adaptive behaviors that are still beyond the capabilities of today's artificial systems. Fast progress in our comprehension of the underlying neural computations make the insect a favorable model system for neurally inspired computing paradigms in autonomous robots. Here, we present a robotic platform designed for implementing and testing spiking neural network control architectures. We demonstrate a neuromorphic realtime approach to sensory processing, reward-based associative plasticity and behavioral control. This is inspired by the biological mechanisms underlying rapid associative learning and the formation of distributed memories in the insect. {\textcopyright} 2013 IEEE.},
author = {Helgadottir, L.I. and Haenicke, J. and Landgraf, T. and Rojas, R. and Nawrot, M.P.},
booktitle = {International IEEE/EMBS Conference on Neural Engineering, NER},
doi = {10.1109/NER.2013.6696078},
file = {::},
isbn = {9781467319690},
issn = {19483546},
keywords = {insect olfaction,plasticity,robotics,spiking neural network},
mendeley-tags = {insect olfaction,plasticity,robotics,spiking neural network},
title = {{Conditioned behavior in a robot controlled by a spiking neural network}},
year = {2013}
}

@incollection{Landgraf2013,
abstract = {The navigation capabilities of honeybees are surprisingly complex. Experimental evidence suggests that honeybees rely on a map-like neuronal representation of the environment. Intriguingly, a honeybee brain exhibits approximately one million neurons only. In an interdisciplinary enterprise, we are investigating models of high-level processing in the nervous system of insects such as spatial mapping and decision making. We use a robotic platform termed NeuroCopter that is controlled by a set of functional modules. Each of these modules initially represents a conventional control method and, in an iterative process, will be replaced by a neural control architecture. This paper describes the neuromorphic extraction of the copter's ego motion from sparse optical flow fields. We will first introduce the reader to the system's architecture and then present a detailed description of the structure of the neural model followed by simulated and real-world results. {\textcopyright} 2013 Springer-Verlag Berlin Heidelberg.},
author = {Landgraf, Tim and Wild, Benjamin and Ludwig, Tobias and Nowak, Philipp and Helgadottir, Lovisa and Daumenlang, Benjamin and Breinlinger, Philipp and Nawrot, Martin Paul and Rojas, Ra{\'{u}}l},
booktitle = {Biomimetic and Biohybrid Systems. Living Machines 2013. Lecture Notes in Computer Science},
doi = {10.1007/978-3-642-39802-5_13},
editor = {Lepora, N.F. and Mura, A. and Krapp, H.G. and Verschure, P.F.M.J. and Prescott, T.J.},
file = {::},
isbn = {9783642398018},
issn = {03029743},
keywords = {biomimetics,neural networks,neuromorphic computation,robotics,self-localization},
mendeley-tags = {robotics},
publisher = {Springer Berlin Heidelberg},
title = {{NeuroCopter: Neuromorphic computation of 6D ego-motion of a quadcopter}},
url = {https://link.springer.com/chapter/10.1007/978-3-642-39802-5{\_}13{\#}citeas},
volume = {8064},
year = {2013}
}

@inproceedings{Schmuker2011a,
author = {Schmuker, Michael and H{\"{a}}usler, Chris and Br{\"{u}}derle, Daniel and Nawrot, Martin P},
booktitle = {BMC Neuroscience},
doi = {10.1186/1471-2202-12-s1-p233},
file = {:Users/springer/Downloads/1471-2202-12-S1-P233.pdf:pdf},
issn = {1471-2202},
number = {S1},
pages = {P233},
publisher = {BioMed Central Ltd},
title = {{Benchmarking the impact of information processing in the insect olfactory system with a spiking neuromorphic classifier}},
url = {http://www.biomedcentral.com/1471-2202/12/S1/P233},
volume = {12},
year = {2011}
}