Zusammenfassung
Auch Tiere mit relativ kleinen Gehirnen zeigen erstaunlich komplexe und robuste Wahrnehmungs‐ und Verhaltensleistungen. Dies gilt insbesondere für Insekten. Deren Mini‐Gehirne sind anpassungs‐ und lernfähig, sie ermöglichen ihnen, langfristige Gedächtnisse herauszubilden und sich kurzfristig an neue Gegebenheiten anzupassen.
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Literatur
Ardin P, Peng F, Mangan M, Lagogiannis K, Webb B (2016) Using an insect mushroom body circuit to encode route memory in complex natural environments. PLoS Comput Biol 12(2):e1004683
Avarguès‐Weber A, Giurfa M (2013) Conceptual learning by miniature brains. Proc R Soc B 280:20131907. doi:10.1098/rspb.2013.1907
Avarguès‐Weber A, Deisig N, Giurfa M (2011) Visual cognition in social insects. Annu Rev Entomol 56:423–443
Baddeley B, Graham P, Philippides A, Husbands P (2011) Holistic visual encoding of antlike routes: Navigation without waypoints. Adapt Behav 19(1):3–15
Benjamin BV, Gao P, McQuinn E, Choudhary S, Chandrasekaran AR, Bussat JM, Alvarez‐Icaza R, Arthur JV, Merolla PA, Boahen K (2014) Neurogrid: A Mixed‐Analog‐Digital Multichip System for Large‐Scale Neural Simulations. Proc. IEEE 102:699–716
Boahen K (2005) Neuromorphic microchips. Sci Am 292(5):56–63
Chittka L, Niven J (2009) Are bigger brains better? Curr Biol 19(21):R995–R1008
Furber SB, Lester DR, Plana LA, Garside JD, Painkras E, Temple S, Brown AD (2013) Overview of the SpiNNaker System Architecture. IEEE Trans. Comput 62:2454–2467
Hammer M (1997) The neural basis of associative reward learning in honeybees. Trends Neurosci 20(6):245–252
Hammer M, Menzel R (1995) Learning and memory in the honeybee. J Neurosci 15:1617–1630
Hammer M, Menzel R (1998) Multiple sites of associative odor learning as revealed by local brain microinjections of octopamine in honeybees. Learn Memory 5:146–156
Helgadottir LI, Haenicke J, Landgraf T, Rojas R, Nawrot MP (2013) Conditioned behavior in a robot controlled by a spiking neural network. In: IEEE (Hrsg) 6th International IEEE/EMBS Conference on Neural Engineering (NER). doi:10.1109/NER.2013.6696078, S 891–894
Helias M, Kunkel S, Masumoto G, Igarashi J, Eppler JM, Ishii S, Fukai T, Morrison A, Diesmann M (2012) Supercomputers ready for use as discovery machines for neuroscience. Front Neuroinform 6:26. doi:10.3389/fninf.2012.00026
Indiveri G, Horiuchi TK (2011). Frontiers in neuromorphic engineering. Front Neurosci 5:118
Landgraf T (2013) RoboBee: A Biomimetic Honeybee Robot for the Analysis of the Dance Communication System (Doctoral dissertation, Freie Universität Berlin)
Liu SC, Delbruck T (2010) Neuromorphic sensory systems. Curr Opin Neurobiol 20(3):288–295
Mead C (1989) Analog VLSI and Neural Systems. Addison‐Wesley, Reading, MA
Menzel R, Kirbach A, Haass WD, Fischer B, Fuchs J, Koblofsky M, Lehmann K, Reiter L, Meyer H, Nguyen H, Jones S (2011) A common frame of reference for learned and communicated vectors in honeybee navigation. Curr Biol 21(8):645–650
Menzel R (2012) The honeybee as a model for understanding the basis of cognition. Nat Rev Neurosci 13(11):758–768
Menzel R, Eckholdt M (2016) Die Intelligenz der Bienen: Wie sie denken, planen, fühlen und was wir daraus lernen können. Knaus, München
Merolla PA, Arthur JV, Alvarez‐Icaza R, Cassidy AS, Sawada J, Akopyan F et al. (2014) A million spiking‐neuron integrated circuit with a scalable communication network and interface. Science 345:668–673
Neftci E, Binas J, Rutishauser U, Chicca E, Indiveri G, Douglas RJ (2013) Synthesizing cognition in neuromorphic electronic systems. P Natl A Sci USA 110:E3468–E3476
Pamir E, Szyszka P, Scheiner R, Nawrot MP (2014) Rapid learning dynamics in individual honeybees during classical conditioning. Front Behav Neurosci 8
Passino KM, Seeley TD (2006) Modeling and analysis of nest‐site selection by honeybee swarms: the speed and accuracy trade‐off. Behav Ecol Sociobiol 59(3):427–442
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. Front Neurosci 7:11
Schemmel J, Brüderle D, Grübl A, Hock M, Meier K, Millner S (2010) A Wafer‐Scale Neuromorphic Hardware System for Large‐Scale Neural Modeling. In: IEEE (Hrsg) Proceedings of the 2010 International Symposium on Circuits and Systems (ISCAS), S 1947–1950
Schmuker M, Pfeil T, Nawrot MP (2014) A neuromorphic network for generic multivariate data classification. P Natl A Sci USA 111:2081–2086
Walter F, Röhrbein F, Knoll A (2015) Neuromorphic implementations of neurobiological learning algorithms for spiking neural networks. Neural Networks 72:152–167
Seeley TD, Visscher PK, Schlegel T, Hogan PM, Franks NR, Marshall JA (2012) Stop signals provide cross inhibition in collective decision‐making by honeybee swarms. Science 6;335(6064):108–11
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Landgraf, T., Nawrot, M. (2017). Künstliche Mini‐Gehirne für Roboter. In: Walkowiak, W., Erber-Schropp, J. (eds) Planen und Handeln. Springer Spektrum, Wiesbaden. https://doi.org/10.1007/978-3-658-16891-9_9
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