Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/26717
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Peer Review Status: Unrefereed
Author(s): Shiva, Ashraya Samba
Gogate, Mandar
Howard, Newton
Graham, Bruce
Hussain, Amir
Contact Email: ahu@cs.stir.ac.uk
Title: Complex-valued computational model of hippocampal CA3 recurrent collaterals
Editor(s): Howard, N
Wang, Y
Hussain, A
Widrow, B
Zadeh, LA
Citation: Shiva AS, Gogate M, Howard N, Graham B & Hussain A (2017) Complex-valued computational model of hippocampal CA3 recurrent collaterals. In: Howard N, Wang Y, Hussain A, Widrow B & Zadeh L (eds.) 2017 IEEE 16th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC). 16th IEEE International Conference on Cognitive Informatics & Cognitive Computing - ICCI*CC 2017, Oxford, 26.07.2017-28.07.2017. Piscataway, NJ, USA: Institute of Electrical and Electronics Engineers Inc, pp. 161-166. https://doi.org/10.1109/ICCI-CC.2017.8109745
Issue Date: 16-Nov-2017
Date Deposited: 15-Feb-2018
Conference Name: 16th IEEE International Conference on Cognitive Informatics & Cognitive Computing - ICCI*CC 2017
Conference Dates: 2017-07-26 - 2017-07-28
Conference Location: Oxford
Abstract: Complex planes are known to simplify the complexity of real world problems, providing a better comprehension of their functionality and design. The need for complex numbers in both artificial and biological neural networks is equally well established. In the latter, complex numbers allows neuroscientists to consider and analyze the phase component of brain oscillations occurring during chains of action potentials. This paper implements complex-valued weights and inputs in the real valued recurrent collaterals model introduced by Káli & Dayan for the CA3 region of the hippocampus, with equations appropriately modified to include the phase component. Complex models can generally be implemented by solving the real and complex parts separately resulting from solving the model equations twice. This implementation is simulated here and the results demonstrate the model's potential utility for further mathematical and neurobiological analysis to define a proper phase function which oscillates in the theta frequency range.
Status: VoR - Version of Record
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