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Effects of Noise on Leaky Integrate-and-Fire Neuron Models for Neuromorphic Computing Applications

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Computational Science and Its Applications – ICCSA 2022 (ICCSA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13375))

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Abstract

Artificial neural networks (ANNs) have been extensively used for the description of problems arising from biological systems and for constructing neuromorphic computing models. The third generation of ANNs, namely, spiking neural networks (SNNs), inspired by biological neurons enable a more realistic mimicry of the human brain. A large class of the problems from these domains is characterized by the necessity to deal with the combination of neurons, spikes and synapses via integrate-and-fire neuron models. Motivated by important applications of the integrate-and-fire of neurons in neuromorphic computing for bio-medical studies, the main focus of the present work is on the analysis of the effects of additive and multiplicative types of random input currents together with a random refractory period on a leaky integrate-and-fire (LIF) synaptic conductance neuron model. Our analysis is carried out via Langevin stochastic dynamics in a numerical setting describing a cell membrane potential. We provide the details of the model, as well as representative numerical examples, and discuss the effects of noise on the time evolution of the membrane potential as well as the spiking activities of neurons in the LIF synaptic conductance model scrutinized here. Furthermore, our numerical results demonstrate that the presence of a random refractory period in the LIF synaptic conductance system may substantially influence an increased irregularity of spike trains of the output neuron.

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References

  1. Bauermann, J., Lindner, B.: Multiplicative noise is beneficial for the transmission of sensory signals in simple neuron models. Biosystems 178, 25–31 (2019)

    Article  Google Scholar 

  2. Brigner, W.H., et al.: Three artificial spintronic leaky integrate-and-fire neurons. SPIN 10(2), 2040003 (2020)

    Article  Google Scholar 

  3. Burkitt, A.: A review of the integrate-and-fire neuron model: I. homogeneous synaptic input. Biological Cybern. 95(2), 97–112 (2006)

    Google Scholar 

  4. Burkitt, A.: A review of the integrate-and-fire neuron model: II. inhomogeneous synaptic input and network properties. Biol. Cybern. 95(1), 1–19 (2006)

    Google Scholar 

  5. Cavallari, S., Panzeri, S., Mazzoni, A.: Comparison of the dynamics of neural interactions between current-based and conductance-based integrate-and-fire recurrent networks. Front. Neural Circuits 8, 12 (2014)

    Google Scholar 

  6. Chen, X., Yajima, T., Inoue, I.H., Iizuka, T.: An ultra-compact leaky integrate-and-fire neuron with long and tunable time constant utilizing pseudo resistors for spiking neural networks. J. Appl. Phys. 61, SC1051 (2021). Accepted for publication in Japanese

    Google Scholar 

  7. Chowdhury, S.S., Lee, C., Roy, K.: Towards understanding the effect of leak in spiking neural networks. Neurocomputing 464, 83–94 (2021)

    Article  Google Scholar 

  8. Christodoulou, C., Bugmann, G.: Coefficient of variation vs. mean interspike interval curves: What do they tell us about the brain? Neurocomputing 38–40, 1141–1149 (2001)

    Google Scholar 

  9. Dayan, P., Abbott, L.F.: Theoretical Neuroscience. The MIT Press, Massachusetts (2005)

    MATH  Google Scholar 

  10. Dutta, S., Kumar, V., Shukla, A., Mohapatra, N.R., Ganguly, U.: Leaky integrate and fire neuron by charge-discharge dynamics in floating-body mosfet. Sci. Rep. 7(8257), 8257 (2017)

    Google Scholar 

  11. Faisal, A.D., Selen, L.P.J., Wolpert, D.M.: Noise in the nervous system. Nat. Rev. Neurosci. 9, 292–303 (2008)

    Article  Google Scholar 

  12. Fardet, T., Levina, A.: Simple models including energy and spike constraints reproduce complex activity patterns and metabolic disruptions. PLoS Comput. Biol. 16(12), e1008503 (2020)

    Article  Google Scholar 

  13. Gallinaro, J.V., Clopath, C.: Memories in a network with excitatory and inhibitory plasticity are encoded in the spiking irregularity. PLoS Comput. Biol. 17(11), e1009593 (2021)

    Article  Google Scholar 

  14. Gerstner, W., Kistler, W.M., Naud, R., Paninski, L.: Neuronal Dynamics: from single neurons to networks and models of cognition. Cambridge University Press (2014)

    Google Scholar 

  15. Gerum, R.C., Schilling, A.: Integration of leaky-integrate-and-fire neurons in standard machine learning architectures to generate hybrid networks: A surrogate gradient approach. Neural Comput. 33, 2827–2852 (2021)

    Article  MathSciNet  Google Scholar 

  16. Guo, T., Pan, K., Sun, B., Wei, L., Y., Zhou, Y.N., W, Y.A.: Adjustable leaky-integrate-and-fire neurons based on memristor coupled capacitors. Materials Today Advances 12, 100192 (2021)

    Google Scholar 

  17. Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. In: International Conference on Learning (2019)

    Google Scholar 

  18. Jaras, I., Harada, T., Orchard, M.E., Maldonado, P.E., Vergara, R.C.: Extending the integrate-and-fire model to account for metabolic dependencies. Eur. J. Neurosci. 54(3), 5249–5260 (2021)

    Article  Google Scholar 

  19. Kepecs, A., Lisman, J.: Information encoding and computation with spikes and bursts. Network: Comput. Neural Syst. 14, 103–118 (2003)

    Google Scholar 

  20. Latimer, K.W., Rieke, F., Pillow, J.W.: Inferring synaptic inputs from spikes with a conductance-based neural encoding model. eLife 8(e47012) (2019)

    Google Scholar 

  21. Li, S., Liu, N., Yao, L., Zhang, X., Zhou, D., Cai, D.: Determination of effective synaptic conductances using somatic voltage clamp. PLoS Comput. Biol. 15(3), e1006871 (2019)

    Article  Google Scholar 

  22. Mahdi, A., Sturdy, J., Ottesen, J.T., Olufsen, M.S.: Modeling the afferent dynamics of the baroreflex control system. PLoS Comput. Biol. 9(12), e1003384 (2013)

    Article  Google Scholar 

  23. Maimon, G., Assad, J.A.: Beyond Poisson: Increased spike-time regularity across primate parietal cortex. Neuron 62(3), 426–440 (2009)

    Article  Google Scholar 

  24. Nandakumar, S.R., Boybat, I., Gallo, M.L., Eleftheriou, E., Sebastian, A., Rajendran, B.: Experimental demonstration of supervised learning in spiking neural networks with phase change memory synapses. Sci. Rep. 10(8080), 1–11 (2020)

    Google Scholar 

  25. Roberts, J.A., Friston, K.J., Breakspear, M.: Clinical applications of stochastic dynamic models of the brain, part i: a primer. Biol. Psychiatry: Cogn. Neuroscience Neuroimaging 2, 216–224 (2017)

    Google Scholar 

  26. So, R.Q., Kent, A.R., Grill, W.M.: Relative contributions of local cell and passing fiber activation and silencing to changes in thalamic fidelity during deep brain stimulation and lesioning: a computational modeling study. J. Comput. Neurosci. 32, 499–519 (2012)

    Article  Google Scholar 

  27. Stiefel, K.M., Englitz, B., Sejnowski, T.J.: Origin of intrinsic irregular firing in cortical interneurons. PNAS 110(19), 7886–7891 (2013)

    Article  Google Scholar 

  28. Teeter, C., et al.: Generalized leaky integrate-and-fire models classify multiple neuron types. Nature Commun. 9(709), 1–15 (2018)

    Google Scholar 

  29. Teka, W., Marinov, T.M., Santamaria, F.: Neuronal integration of synaptic input in the fluctuation-driven regime. J. Neurosci. 24(10), 2345–2356 (2004)

    Article  Google Scholar 

  30. Teka, W., Marinov, T.M., Santamaria, F.: Neuronal spike timing adaptation described with a fractional leaky integrate-and-fire model. PLoS Comput. Biol. 10(3), e1003526 (2014)

    Article  Google Scholar 

  31. Van Pottelbergh, T., Drion, G., Sepulchre, R.: From biophysical to integrate-and-fire modeling. Neural Comput. 33(3), 563–589 (2021)

    Article  MathSciNet  Google Scholar 

  32. Woo, J., Kim, S.H., Han, K., Choi, M.: Characterization of dynamics and information processing of integrate-and-fire neuron models. J. Phys. A Math. Theor. 54, 445601 (2021)

    Google Scholar 

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Acknowledgment

Authors are grateful to the NSERC and the CRC Program for their support. RM is also acknowledging support of the BERC 2022–2025 program and Spanish Ministry of Science, Innovation and Universities through the Agencia Estatal de Investigacion (AEI) BCAM Severo Ochoa excellence accreditation SEV-2017-0718 and the Basque Government fund AI in BCAM EXP. 2019/00432.

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Authors and Affiliations

  1. MS2Discovery Interdisciplinary Research Institute, Wilfrid Laurier University, 75 University Ave W, Waterloo, ON, N2L 3C5, Canada

    Thi Kim Thoa Thieu & Roderick Melnik

  2. BCAM - Basque Center for Applied Mathematics, Bilbao, Spain

    Roderick Melnik

Authors
  1. Thi Kim Thoa Thieu
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  2. Roderick Melnik
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Correspondence to Thi Kim Thoa Thieu .

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Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Universidad de Málaga, Malaga, Spain

    Eligius M. T. Hendrix

  4. Monash University, Clayton, VIC, Australia

    David Taniar

  5. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

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Thieu, T.K.T., Melnik, R. (2022). Effects of Noise on Leaky Integrate-and-Fire Neuron Models for Neuromorphic Computing Applications. In: Gervasi, O., Murgante, B., Hendrix, E.M.T., Taniar, D., Apduhan, B.O. (eds) Computational Science and Its Applications – ICCSA 2022. ICCSA 2022. Lecture Notes in Computer Science, vol 13375. Springer, Cham. https://doi.org/10.1007/978-3-031-10522-7_1

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  • DOI: https://doi.org/10.1007/978-3-031-10522-7_1

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