The switching and learning behavior of an octopus cell implemented on FPGA

Alexej Tschumak; Frank Feldhoff; Frank Klefenz; Alexej Tschumak; Frank Feldhoff; Frank Klefenz

doi:10.3934/mbe.2024254

Mathematical Biosciences and Engineering

2024, Volume 21, Issue 4: 5762-5781. doi: 10.3934/mbe.2024254

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Research article

The switching and learning behavior of an octopus cell implemented on FPGA

1.
Audio Communication Group, Technische Universität Berlin, Berlin, Germany
2.
Advanced Electromagnetics Group, Technische Universität Ilmenau, Ilmenau, Germany
3.
Fraunhofer Institute for Digital Media Technology, Ilmenau, Germany

Academic Editor: Yang Kuang

Received: 16 January 2024 Revised: 08 March 2024 Accepted: 22 March 2024 Published: 25 April 2024

A dendrocentric backpropagation spike timing-dependent plasticity learning rule has been derived based on temporal logic for a single octopus neuron. It receives parallel spike trains and collectively adjusts its synaptic weights in the range [0, 1] during training. After the training phase, it spikes in reaction to event signaling input patterns in sensory streams. The learning and switching behavior of the octopus cell has been implemented in field-programmable gate array (FPGA) hardware. The application in an FPGA is described and the proof of concept for its application in hardware that was obtained by feeding it with spike cochleagrams is given; also, it is verified by performing a comparison with the pre-computed standard software simulation results.
- dendrocentric backpropagation learning rule,
- synaptic subpopulation coactivation,
- collective synaptic weight adjustments,
- temporal logic
Citation: Alexej Tschumak, Frank Feldhoff, Frank Klefenz. The switching and learning behavior of an octopus cell implemented on FPGA[J]. Mathematical Biosciences and Engineering, 2024, 21(4): 5762-5781. doi: 10.3934/mbe.2024254

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Abstract

A dendrocentric backpropagation spike timing-dependent plasticity learning rule has been derived based on temporal logic for a single octopus neuron. It receives parallel spike trains and collectively adjusts its synaptic weights in the range [0, 1] during training. After the training phase, it spikes in reaction to event signaling input patterns in sensory streams. The learning and switching behavior of the octopus cell has been implemented in field-programmable gate array (FPGA) hardware. The application in an FPGA is described and the proof of concept for its application in hardware that was obtained by feeding it with spike cochleagrams is given; also, it is verified by performing a comparison with the pre-computed standard software simulation results.

References

[1]	B. A. Bicknell, M. Häusser, A synaptic learning rule for exploiting nonlinear dendritic computation, Neuron, 109 (2021), 4001–4017. https://doi.org/10.1016/j.neuron.2021.09.044 doi: 10.1016/j.neuron.2021.09.044
[2]	K. Boahen, Dendrocentric learning for synthetic intelligence, Nature, 612 (2022), 43–50. https://doi.org/10.1038/s41586-022-05340-6 doi: 10.1038/s41586-022-05340-6
[3]	D. J. Hermes, Pitch Perception, Springer International Publishing, Cham, (2023), 381–448. https://doi.org/10.1007/978-3-031-25566-3_8
[4]	T. Harczos, A. Chilian, P. Husar, Making use of auditory models for better mimicking of normal hearing processes with cochlear implants: The sam coding strategy, IEEE Trans. Biomed. Circuits Syst., 7 (2013), 414–425. https://doi.org/10.1109/TBCAS.2012.2219530 doi: 10.1109/TBCAS.2012.2219530
[5]	T. Harczos, Cochlear Implant Electrode Stimulation Strategy Based on a Human Auditory Model, PhD thesis, Ilmenau University of Technology, 2015.
[6]	F. Feldhoff, H. Toepfer, T. Harczos, F. Klefenz, Periodicity pitch perception part Ⅲ: sensibility and pachinko volatility, Front. Neurosci., 16 (2022), 736642. https://doi.org/10.3389/fnins.2022.736642 doi: 10.3389/fnins.2022.736642
[7]	M. A. Rutherford, H. von Gersdorff, J. D. Goutman, Encoding sound in the cochlea: from receptor potential to afferent discharge, J. Physiol., 599 (2021), 2527–2557. https://doi.org/10.1113/JP279189 doi: 10.1113/JP279189
[8]	M. Cartiglia, A. Rubino, S. Narayanan, C. Frenkel, G. Haessig, G. Indiveri, et al., Stochastic dendrites enable online learning in mixed-signal neuromorphic processing systems, in 2022 IEEE International Symposium on Circuits and Systems (ISCAS), IEEE, (2022), 476–480. https://doi.org/10.1109/iscas48785.2022.9937833
[9]	M. Saponati, M. Vinck, Sequence anticipation and spike-timing-dependent plasticity emerge from a predictive learning rule, Nat. Commun., 14 (2023), 4985. https://doi.org/10.1038/s41467-023-40651-w doi: 10.1038/s41467-023-40651-w
[10]	H. Zheng, Z. Zheng, R. Hu, B. Xiao, Y. Wu, F. Yu, et al., Temporal dendritic heterogeneity incorporated with spiking neural networks for learning multi-timescale dynamics, Nat. Commun., 15 (2024), 277. https://doi.org/10.1038/s41467-023-44614-z doi: 10.1038/s41467-023-44614-z
[11]	V. Francioni, M. T. Harnett, Rethinking single neuron electrical compartmentalization: dendritic contributions to network computation in vivo, Neuroscience, 489 (2022), 185–199. https://doi.org/10.1016/j.neuroscience.2021.05.038 doi: 10.1016/j.neuroscience.2021.05.038
[12]	M. Payvand, F. Moro, K. Nomura, T. Dalgaty, E. Vianello, Y. Nishi, et al., Self-organization of an inhomogeneous memristive hardware for sequence learning, Nat. Commun., 13 (2022), 5793. https://doi.org/10.1038/s41467-022-33476-6 doi: 10.1038/s41467-022-33476-6
[13]	M. Payvand, S. D'Agostino, F. Moro, Y. Demirag, G. Indiveri, E. Vianello, Dendritic computation through exploiting resistive memory as both delays and weights, in Proceedings of the 2023 International Conference on Neuromorphic Systems, ICONS '23, Association for Computing Machinery, New York, NY, USA, (2023), 1–4. https://doi.org/10.1145/3589737.3605977
[14]	A. J. M. Houtsma, J. L. Goldstein, The central origin of the pitch of complex tones: Evidence from musical interval recognition, J. Acoust. Soc. Am., 51 (2005), 520–529. https://doi.org/10.1121/1.1912873 doi: 10.1121/1.1912873
[15]	Y. H. Li, P. X. Joris, Case reopened: A temporal basis for harmonic pitch templates in the early auditory system, J. Acoust. Soc. Am., 154 (2023), 3986–4003. https://doi.org/10.1121/10.0023969 doi: 10.1121/10.0023969
[16]	L. Faye, S. Kuhn, A. Venkatesh, Relative periodicity of empirical audio samples with application to dissonance perception, LASER J., 1 (2023), 6.
[17]	F. Klefenz, T. Harczos, Periodicity pitch perception, Front. Neurosci., 14 (2020), 486, https://doi.org/10.3389/fnins.2020.00486 doi: 10.3389/fnins.2020.00486
[18]	G. D. Langner, The Neural Code of Pitch and Harmony, Cambridge University Press, 2015.
[19]	R. Meddis, L. O'Mard, A unitary model of pitch perception, J. Acoust. Soc. Am., 102 (1997), 1811–1820. https://doi.org/10.1121/1.420088 doi: 10.1121/1.420088
[20]	Y. Yang, X. Li, H. Li, C. Zhang, Y. Todo, H. Yang, Yet another effective dendritic neuron model based on the activity of excitation and inhibition, Mathematics, 11 (2023), 1701. https://doi.org/10.3390/math11071701 doi: 10.3390/math11071701
[21]	M. Sinha, R. Narayanan, Active dendrites and local field potentials: Biophysical mechanisms and computational explorations, Neuroscience, 489 (2022), 111–142. https://doi.org/10.1016/j.neuroscience.2021.08.035 doi: 10.1016/j.neuroscience.2021.08.035
[22]	W. A. Wybo, J. Jordan, B. Ellenberger, U. Marti Mengual, T. Nevian, W. Senn, Data-driven reduction of dendritic morphologies with preserved dendro-somatic responses, Elife, 10 (2021), e60936. https://doi.org/10.7554/eLife.60936 doi: 10.7554/eLife.60936
[23]	M. Pagkalos, S. Chavlis, P. Poirazi, Introducing the dendrify framework for incorporating dendrites to spiking neural networks, Nat. Commun., 14 (2023), 131. https://doi.org/10.1038/s41467-022-35747-8 doi: 10.1038/s41467-022-35747-8
[24]	E. Baek, S. Song, Z. Rong, L. Shi, C. V. Cannistraci, Neuromorphic dendritic computation with silent synapses for visual motion perception, Preprint, 2023. https://doi.org/10.20944/preprints202306.0438.v1
[25]	H. W. Lu, P. H. Smith, P. X. Joris, Mammalian octopus cells are direction selective to frequency sweeps by excitatory synaptic sequence detection, Proc. Natl. Acad. Sci., 119 (2022), e2203748119. https://doi.org/10.1073/pnas.2203748119 doi: 10.1073/pnas.2203748119
[26]	R. Makarov, M. Pagkalos, P. Poirazi, Dendrites and efficiency: Optimizing performance and resource utilization, Curr. Opin. Neurobiol., 83 (2023), 102812. https://doi.org/10.1016/j.conb.2023.102812 doi: 10.1016/j.conb.2023.102812
[27]	J. Kaiser, S. Billaudelle, E. Müller, C. Tetzlaff, J. Schemmel, S. Schmitt, Emulating dendritic computing paradigms on analog neuromorphic hardware, Neuroscience, 489 (2022), 290–300. https://doi.org/10.1016/j.neuroscience.2021.08.013 doi: 10.1016/j.neuroscience.2021.08.013
[28]	M. E. Larkum, J. Wu, S. A. Duverdin, A. Gidon, The guide to dendritic spikes of the mammalian cortex in vitro and in vivo, Neuroscience, 489 (2022), 15–33. https://doi.org/10.1016/j.neuroscience.2022.02.009 doi: 10.1016/j.neuroscience.2022.02.009
[29]	L. Benatti, T. Zanotti, D. Gandolfi, J. Mapelli, F. M. Puglisi, Biologically plausible information propagation in a complementary metal-oxide semiconductor integrate-and-fire artificial neuron circuit with memristive synapses, Nano Futures, 7 (2023), 025003. https://dx.doi.org/10.1088/2399-1984/accf53 doi: 10.1088/2399-1984/accf53
[30]	A. Madhavan, M. Stiles, Storing and retrieving wavefronts with resistive temporal memory, in Proceedings of the IEEE International Symposium on Circuits and Systems, Seville, (2020), 1–5. https://doi.org/10.1109/ISCAS45731.2020.9180662
[31]	A. Madhavan, M. W. Daniels, M. D. Stiles, Temporal state machines: Using temporal memory to stitch time-based graph computations, J. Emerg. Technol. Comput. Syst., 17 (2021), 1–27. https://doi.org/10.1145/3451214 doi: 10.1145/3451214
[32]	G. Tzimpragos, D. Vasudevan, N. Tsiskaridze, G. Michelogiannakis, A. Madhavan, J. Volk, et al., A computational temporal logic for superconducting accelerators, in Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systems, (2020), 435–448. https://doi.org/10.1145/3373376.3378517
[33]	J. E. Smith, Space-time algebra: A model for neocortical computation, in Proceedings of the 45th Annual International Symposium on Computer Architecture, ISCA '18, IEEE Press, (2018), 289–300. https://doi.org/10.1109/ISCA.2018.00033
[34]	D. D. Greenwood, A cochlear frequency‐position function for several species—29 years later, J. Acoust. Soc. Am., 87 (1990), 2592–2605. https://doi.org/10.1121/1.399052 doi: 10.1121/1.399052
[35]	T. Li, J. Tang, J. Chen, X. Li, H. Zhao, Y. Xi, et al., Monolithic 3d integration of dendritic neural network with memristive synapse, dendrite and soma on Si CMOS, in 2023 China Semiconductor Technology International Conference (CSTIC), (2023), 1–3. https://doi.org/10.1109/CSTIC58779.2023.10219334
[36]	D. Gutierrez-Galan, A. Rios-Navarro, J. P. Dominguez-Morales, L. Duran-Lopez, G. Jimenez-Moreno, A. Jimenez-Fernandez, Interfacing PDM MEMS microphones with PFM spiking systems: Application for neuromorphic auditory sensors, Neural Process. Lett., 55 (2023), 1281–1292. https://doi.org/10.1007/s11063-022-10936-0 doi: 10.1007/s11063-022-10936-0
[37]	P. Cai, Y. Zhang, T. Jin, Y. Todo, S. Gao, Self-adaptive forensic-based investigation algorithm with dynamic population for solving constraint optimization problems, Int. J. Comput. Intell. Syst., 17 (2024), 1–17. https://doi.org/10.1007/s44196-023-00396-2 doi: 10.1007/s44196-023-00396-2
[38]	Z. Yao, Z. Wang, D. Wang, J. Wu, L. Chen, An ensemble cnn-lstm and gru adaptive weighting model based improved sparrow search algorithm for predicting runoff using historical meteorological and runoff data as input, J. Hydrol., 625 (2023), 129977. https://doi.org/10.1016/j.jhydrol.2023.129977 doi: 10.1016/j.jhydrol.2023.129977
[39]	T. Tsuchiya, T. Nakayama, K. Ariga, Nanoarchitectonics intelligence with atomic switch and neuromorphic network system, Appl. Phys. Express, 15 (2022), 100101. https://doi.org/10.35848/1882-0786/ac926b doi: 10.35848/1882-0786/ac926b

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