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Neuromorphic Computing

Animals achieve with ease a number of highly complex tasks that are unmatched by today’s artificial systems. Neural  computation  bears several promising  features such as parallel  processing, distributed  memory, and high energy efficiency. Neuromorphic computing refers to the brain-like computing with spiking neural networks. The current challenge of neuromorphic computing lies in the identification and implementation of functional brain algorithms. We argue that the insect nervous system features a moderate complexity and thus it is particularly well suited for translating biological principles of information processing into neuromorphic models for application in intelligent systems.


This project is funded in part by the German Ministry of Education and Research within the Bernstein Focus Neuronal Basis of Learning: Insect inspired robots - The role of memory in decision making.


Prof. Dr. Karlheinz Meier, Thomas Pfeil (Kirchoff Institute for Physics, Heidelberg)

Prof. Dr. Elisabetta Chicca(University of Bielefeld)

Related Publications


  • Schmuker M, Pfeil T, Nawrot MP (2014) A neuromorphic network for generic multivariate data classification. PNAS, published ahead of print Jan 27, 2014. [abstract] [PDF] (Open Access)
  • Rost T, Ramachandran H, Nawrot MP, Chicca E (2013) A neuromorphic approach to auditory pattern recognition in cricket phonotaxis. 21st European Conference on Cricuit Theory and Design, Sep 8-12, 2013 [abstract] [PDF]
  • Landgraf T, Wild B, Ludwig T, Nowak P, Helgadottir L, Daumenlang B, Breinlinger P, Nawrot MP, Rojas R (2013) NeuroCopter: Neuromorphic Computation of 6D Ego-Motion of a Quadcopter. Biomimetic and Biohybrid Systems. Lecture Notes in Computer Science 8064:143-153 [abstract] [PDF]
  • Pfeil T, Grübl A, Jeltsch S, Müller E, Müller P, Petrovici MA, Schmuker M, Brüderle D, Schemmel J, Meier K (2013). Six networks on a universal neuromorphic computing substrate. Frontiers in Neuromorphic Engineering 7:11. doi: 10.3389/fnins.2013.00011 [abstract]  [PDF] [press] [press (in german)]


  • Schmuker M, Häusler C, and Nawrot M P (2010) Insect olfactory microcircuits for better neuromorphic classification devices. ESF-EMBO Conference "Functional Neurobiology in Minibrains: From flies to robots, and back again". October 17-22 2010, Sant Feliù de Guixols, Spain [abstract]
  • Schmuker M, Pfeil T, Nawrot MP (2013) Neuronal variability vs. precise stimulus discriminationin an olfaction-inspired network: A neuromorphic casestudy. Comput. Neurosci. Conference Abstract: Meeting of the German Neuroscience Society Göttingen 2013. [abstract]
  • Schmuker M, Schrader S, Pfeil T and Nawrot MP (2012). A spiking classifier for nonlinear problems implemented on a neuromorphic hardware system. Front. Comput. Neurosci. Conference Abstract: Bernstein Conference 2012. [abstract]
  • Schmuker M, Schrader S, Brüderle D, Nawrot MP (2011) Ten thousand times faster: Classifying multidimensional data on a spiking neuromorphic hardware system. Front. Comput. Neurosci. Conference Abstract: BC11: Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting 2011, October 4-6 2011, Freiburg, Germany. [abstract] [Poster]
  • Schmuker M, Häusler C, Brüderle D, Nawrot MP (2011) Benchmarking the impact of information processing in the insect olfactory system with a neuromorphic classifier. Twentieth Annual Computational Neuroscience Meeting CNS*2011, Stockholm, July 23-28, BMC Neuroscience 2011, 12(Suppl 1):233 [abstract] [PDF]
  • Schmuker M, Häusler Ch, Nawrot MP (2011) The honeybee olfactory system as a template for better neuromorphic classifiers. 9th Göttingen Meeting of the German Neurscience Society, March 23-27
  • Schmuker M, Häusler C and Nawrot MP (2010) Neuromorphic classifier microcircuits. Front. Comput. Neurosci. Conference Abstract: Bernstein Conference on Computational Neuroscience. Berlin, Sep 27 - Oct 1. doi:10.3389/conf.fncom.2010.51.00048 [abstract]
  • Häusler C, Nawrot MP and Schmuker M (2010). A neuromorphic model of dual pathway odour classification. Front. Comput. Neurosci. Conference Abstract: Bernstein Conference on Computational Neuroscience. Berlin, Sep 27 - Oct 1. doi:10.3389/conf.fncom.2010.51.00043 [abstract]