Skip to main content
Springer Nature Link
Log in

Effects of Random Inputs and Short-Term Synaptic Plasticity in a LIF Conductance Model for Working Memory Applications

  • Conference paper
  • First Online:
Bioinformatics and Biomedical Engineering (IWBBIO 2022)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 13346))

  • 1151 Accesses

Abstract

Working memory (WM) has been intensively used to enable the temporary storing of information for processing purposes, playing an important role in the execution of various cognitive tasks. Recent studies have shown that information in WM is not only maintained through persistent recurrent activity but also can be stored in activity-silent states such as in short-term synaptic plasticity (STSP). Motivated by important applications of the STSP mechanisms in WM, the main focus of the present work is on the analysis of the effects of random inputs on a leaky integrate-and-fire (LIF) synaptic conductance neuron under STSP. Furthermore, the irregularity of spike trains can carry the information about previous stimulation in a neuron. A LIF conductance neuron with multiple inputs and coefficient of variation (CV) of the inter-spike-interval (ISI) can bring an output decoded neuron. Our numerical results show that an increase in the standard deviations in the random input current and the random refractory period can lead to an increased irregularity of spike trains of the output neuron.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Mongillo, G., Barak, O., Tsodyks, M.: Synaptic theory of working memory. Science 319(5869), 1543–1546 (2008)

    Article  CAS  Google Scholar 

  2. Pals, M., Stewart, T.C., Akyürek, E.G., Borst, J.P.: A functional spiking-neuron model of activity-silent working memory in humans based on calcium-mediated short-term synaptic plasticity. PLoS Comput. Biol. 16(6), e1007936 (2020)

    Google Scholar 

  3. Ghanbari, A., Malyshev, A., Volgushev, M., Stevenson, I.H.: Estimating short-term synaptic plasticity from pre- and postsynaptic spiking. PLoS Comput. Biol. 13(9), e1005738 (2017)

    Google Scholar 

  4. Seeholzer, A., Deger, M., Gerstner, W.: Stability of working memory in continuous attractor networks under the control of short-term plasticity. PLoS Comput. Biol. 15(4), e1006928 (2019)

    Google Scholar 

  5. Tauffer, L., Kumar, A.: Short-term synaptic plasticity makes neurons sensitive to the distribution of presynaptic population firing rates. eNeuro 8(2) (2019). ENEURO.0297-20.2021

    Google Scholar 

  6. Hamaguchi, K., Riehle, A., Brunel, N.: Estimating network parameters from combined dynamics of firing rate and irregularity of single neurons. J. Neurophysiol. 105, 487–500 (2021)

    Google Scholar 

  7. Fitz, H., Uhlmann, M., van den Broek, D., Duarte, R., Hagoort, P., Petersson, K.M.: Neuronal spike-rate adaptation supports working memory in language processing. PNAS 117(34), 20881–20889 (2020)

    Google Scholar 

  8. 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)

    Google Scholar 

  9. 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 

  10. Thieu, T.K.T., Melnik, R.: Effects of noise on leaky integrate-and-fire neuron models for neuromorphic computing applications (2022). https://doi.org/10.48550/arXiv.2202.09482

  11. 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)

    Google Scholar 

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

    Google Scholar 

  13. 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 

  14. Dayan, P., Abbott, L.F.: Theoretical Neuroscience. The MIT Press Cambridge, Massachusetts London, England (2005)

    Google Scholar 

Download references

Acknowledgments

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.

Author information

Authors and Affiliations

  1. MS2Discovery Interdisciplinary Research Institute, Wilfrid Laurier University, 75 University Avenue West, 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
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Roderick Melnik
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Roderick Melnik .

Editor information

Editors and Affiliations

  1. Marcelina Siebold Guest Relations Dept., University of Granada, Granada, Spain

    Ignacio Rojas

  2. Faculty of Sciences, University of Granada, Granada, Spain

    Olga Valenzuela

  3. ETSIIT. CITIC-UGR, University of Granada, Granada, Spain

    Fernando Rojas

  4. ETSIIT, University of Granada, Granada, Spain

    Luis Javier Herrera

  5. University of Granada, Granada, Spain

    Francisco Ortuño

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Thieu, T.K.T., Melnik, R. (2022). Effects of Random Inputs and Short-Term Synaptic Plasticity in a LIF Conductance Model for Working Memory Applications. In: Rojas, I., Valenzuela, O., Rojas, F., Herrera, L.J., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2022. Lecture Notes in Computer Science(), vol 13346. Springer, Cham. https://doi.org/10.1007/978-3-031-07704-3_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-07704-3_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-07703-6

  • Online ISBN: 978-3-031-07704-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics