Abstract
To our understanding, modelling the dynamics of brain functions on cell level is essential to develop both a deeper understanding and classification of the experimental data as well as a guideline for further research. This paper now presents the implementation and training of a direction sensitive network on the basis of a biophisical neurone model including synaptic excitation, dendritic propagation and action-potential generation. The underlying model not only describes the functional aspects of neural signal processing, but also provides insight into their underlying energy consumption. Moreover, the training data set has been recorded by means of a real robotics system, thus bridging the gap to technical applications.
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References
A. Löffler, J. Klahold, U. Rückert, The Mini-Robot Khepera as a foraging Animate: Synthesis and Analysis of Behaviour, Autonomous Minirobots for Research and Edutainment AMiRE 2001, Proceedings of the 5th International Heinz Nixdorf Symposium, pp. 93–130, 2001
J. J. Hopfield, Pattern recognition computation using action potential timing for stimulus representation, Nature, 376, pp. 33 to 36, 6 July 1995
A. Löffler, B. Iske, U. Rückert, A New Neurone Model Describing Biophysical Signal Processing and Energy Consumption, submitted to ICANN 2002, Madrid, Spain
A. Löffler, Energetische Modellierung Neuronaler Signalverarbeitung, PhD thesis (in German), HNI-Verlagsschriftenreihe 72, Paderborn, 2000
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Iske, B., Löffler, A., Rückert, U. (2002). A Direction Sensitive Network Based on a Biophysical Neurone Model. In: Dorronsoro, J.R. (eds) Artificial Neural Networks — ICANN 2002. ICANN 2002. Lecture Notes in Computer Science, vol 2415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46084-5_26
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DOI: https://doi.org/10.1007/3-540-46084-5_26
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