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Short-term plasticity-based working memory spiking model is resilient to synaptic heterogeneity

Gianmarco Tiddia1,*,†, Luca Sergi1,2,*, Simone Rubiu2, Antonio Incollu1,2, and Bruno Golosio1,2

  • *These authors contributed equally to this work.
  • †Contact author: gianmarco.tiddia@dsf.unica.it
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Phys. Rev. E 114, 014409 – Published 22 July, 2026

DOI: https://doi.org/10.1103/zcmm-3cgh

Abstract

The presence of activity-silent working memory has been increasingly supported by experimental observation, and short-term synaptic facilitation is suggested to underlie this phenomenon. Originally proposed in the early 2000s, the synaptic theory of working memory was supported by a relatively simple, yet powerful, computational model. Recent advancements in simulation technology eased the introduction of heterogeneity in spiking models, which is a crucial factor in enhancing their dynamical richness and biological plausibility. However, introducing such heterogeneity often disrupts previously observed network dynamics. This work investigates the impact of synaptic heterogeneity in a spiking model of working memory sustained by short-term synaptic facilitation, observing that the model is resilient to additional variability.

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References (55)

  1. G. A. Miller, E. Galanter, and K. H. Pribram, Plans and the Structure of Behavior (Henry Holt and Co., New York, NY, 1960).
  2. A. D. Baddeley and G. Hitch, Working Memory (Academic Press, 1974), pp. 47–89.
  3. N. Cowan, Attention and Memory (Oxford University Press, Oxford, UK, 1998).
  4. B. Golosio, A. Cangelosi, O. Gamotina, and G. L. Masala, A cognitive neural architecture able to learn and communicate through natural language, PLoS One 10, e0140866 (2015).
  5. S. Funahashi, C. J. Bruce, and P. S. Goldman-Rakic, Mnemonic coding of visual space in the monkey's dorsolateral prefrontal cortex, J. Neurophysiol. 61, 331 (1989).
  6. P. Goldman-Rakic, Cellular basis of working memory, Neuron 14, 477 (1995).
  7. M. D'Esposito and B. R. Postle, The cognitive neuroscience of working memory, Annu. Rev. Psychol. 66, 115 (2015).
  8. D. O. Hebb, The Organization of Behavior: A Neuropsychological Theory (Wiley, New York, NY, 1949).
  9. J. J. Hopfield, Neural networks and physical systems with emergent collective computational abilities, Proc. Natl. Acad. Sci. USA 79, 2554 (1982).
  10. N. Brunel, Persistent activity and the single cell frequency-current curve in a cortical network model, Netw. Comput. Neural Syst. 11, 261 (2000).
  11. M. G. Stokes, ‘Activity-silent’ working memory in prefrontal cortex: A dynamic coding framework, Trends Cognit. Sci. 19, 394 (2015).
  12. R. Honkanen, S. Rouhinen, S. H. Wang, J. M. Palva, and S. Palva, Gamma oscillations underlie the maintenance of feature-specific information and the contents of visual working memory, Cereb. Cortex 25, 3788 (2015).
  13. M. Lundqvist, J. Rose, P. Herman, S. L. Brincat, T. J. Buschman, and E. K. Miller, Gamma and beta bursts underlie working memory, Neuron 90, 152 (2016).
  14. O. Barak and M. Tsodyks, Persistent activity in neural networks with dynamic synapses, PLoS Comput. Biol. 3, e35 (2007).
  15. G. Mongillo, O. Barak, and M. Tsodyks, Synaptic theory of working memory, Science 319, 1543 (2008).
  16. E. T. Rolls, L. Dempere-Marco, and G. Deco, Holding multiple items in short term memory: A neural mechanism, PLoS ONE 8, e61078 (2013).
  17. D. Hansel and G. Mato, Short-term plasticity explains irregular persistent activity in working memory tasks, J. Neurosci. 33, 133 (2013).
  18. N. E. Myers, M. G. Stokes, and A. C. Nobre, Prioritizing information during working memory: Beyond sustained internal attention, Trends Cognit. Sci. 21, 449 (2017).
  19. J. Barbosa, H. Stein, R. L. Martinez, A. Galan-Gadea, S. Li, J. Dalmau, K. C. S. Adam, J. Valls-Solé, C. Constantinidis, and A. Compte, Interplay between persistent activity and activity-silent dynamics in the prefrontal cortex underlies serial biases in working memory, Nat. Neurosci. 23, 1016 (2020).
  20. R. Douglas Fields, The enigma of working memory: Changing views, Neuroscientist 28, 420 (2022).
  21. D. Trübutschek, S. Marti, H. Ueberschär, and S. Dehaene, Probing the limits of activity-silent non-conscious working memory, Proc. Natl. Acad. Sci. USA 116, 14358 (2019).
  22. A. Seeholzer, M. Deger, and W. Gerstner, Stability of working memory in continuous attractor networks under the control of short-term plasticity, PLoS Comput. Biol. 15, e1006928 (2019).
  23. Y. Mi, M. Katkov, and M. Tsodyks, Synaptic correlates of working memory capacity, Neuron 93, 323 (2017).
  24. G. Tiddia, B. Golosio, V. Fanti, and P. S. Paolucci, Simulations of working memory spiking networks driven by short-term plasticity, Front. Integr. Neurosci. 16, 972055 (2022).
  25. H. Taher, A. Torcini, and S. Olmi, Exact neural mass model for synaptic-based working memory, PLoS Comput. Biol. 16, e1008533 (2020).
  26. Y. Wang, H. Markram, P. H. Goodman, T. K. Berger, J. Ma, and P. S. Goldman-Rakic, Heterogeneity in the pyramidal network of the medial prefrontal cortex, Nat. Neurosci. 9, 534 (2006).
  27. N. Perez-Nieves, V. Leung, P. Dragotti, and D. Goodman, Neural heterogeneity promotes robust learning, Nat. Commun. 12, 5791 (2021).
  28. R. Gast, S. A. Solla, and A. Kennedy, Neural heterogeneity controls computations in spiking neural networks, Proc. Natl. Acad. Sci. USA 121, e2311885121 (2024),.
  29. J. Hass, S. Ardid, J. Sherfey, and N. Kopell, Constraints on persistent activity in a biologically detailed network model of the prefrontal cortex with heterogeneities, Prog. Neurobiol. 215, 102287 (2022).
  30. A. Renart, P. Song, and X.-J. Wang, Robust spatial working memory through homeostatic synaptic scaling in heterogeneous cortical networks, Neuron 38, 473 (2003).
  31. M.-O. Gewaltig and M. Diesmann, NEST (NEural Simulation Tool), Scholarpedia 2, 1430 (2007).
  32. M. Shafi, Y. Zhou, J. Quintana, C. Chow, J. Fuster, and M. Bodner, Variability in neuronal activity in primate cortex during working memory tasks, Neuroscience 146, 1082 (2007).
  33. S. Raghavachari, M. J. Kahana, D. S. Rizzuto, J. B. Caplan, M. P. Kirschen, B. Bourgeois, J. R. Madsen, and J. E. Lisman, Gating of human theta oscillations by a working memory task, J. Neurosci. 21, 3175 (2001).
  34. H. Lee, G. V. Simpson, N. K. Logothetis, and G. Rainer, Phase locking of single neuron activity to theta oscillations during working memory in monkey extrastriate visual cortex, Neuron 45, 147 (2005).
  35. Y. Xie, Y. Li, H. Duan, X. Xu, W. Zhang, and P. Fang, Theta oscillations and source connectivity during complex audiovisual object encoding in working memory, Front. Hum. Neurosci. 15, 614950 (2021).
  36. J.-P. Thivierge and P. Cisek, Nonperiodic synchronization in heterogeneous networks of spiking neurons, J. Neurosci. 28, 7968 (2008).
  37. J.-N. Teramae and T. Fukai, Computational implications of lognormally distributed synaptic weights, Proc. IEEE 102, 500 (2014).
  38. Y. Loewenstein, A. Kuras, and S. Rumpel, Multiplicative dynamics underlie the emergence of the log-normal distribution of spine sizes in the neocortex in vivo, J. Neurosci. 31, 9481 (2011).
  39. G. Buzsáki and K. Mizuseki, The log-dynamic brain: How skewed distributions affect network operations, Nat. Rev. Neurosci. 15, 264 (2014).
  40. S. Song, P. J. Sjöström, M. Reigl, S. Nelson, and D. B. Chklovskii, Highly nonrandom features of synaptic connectivity in local cortical circuits, PLoS Biol. 3, e68 (2005).
  41. S. E. Motley, Relationship between neuromodulation and working memory in the prefrontal cortex: It's complicated, Front. Neural Circuits 12, 31 (2018).
  42. S. Raghavachari, J. E. Lisman, M. Tully, J. R. Madsen, E. B. Bromfield, and M. J. Kahana, Theta oscillations in human cortex during a working-memory task: Evidence for local generators, J. Neurophysiol. 95, 1630 (2006).
  43. T. Elbert, W. J. Ray, Z. J. Kowalik, J. E. Skinner, K. E. Graf, and N. Birbaumer, Chaos and physiology: Deterministic chaos in excitable cell assemblies, Physiol. Rev. 74, 1 (1994).
  44. J. F. Mejías and X.-J. Wang, Mechanisms of distributed working memory in a large-scale network of macaque neocortex, eLife 11, e72136 (2022).
  45. L. Ye, J. Feng, and C. Li, Controlling brain dynamics: Landscape and transition path for working memory, PLoS Comput. Biol. 19, e1011446 (2023).
  46. E. Hunsberger, M. Scott, and C. Eliasmith, The competing benefits of noise and heterogeneity in neural coding, Neural Comput. 26, 1600 (2014).
  47. E. Marder and J.-M. Goaillard, Variability, compensation and homeostasis in neuron and network function, Nat. Rev. Neurosci. 7, 563 (2006).
  48. C. M. Hempel, K. H. Hartman, X.-J. Wang, G. G. Turrigiano, and S. B. Nelson, Multiple forms of short-term plasticity at excitatory synapses in rat medial prefrontal cortex, J. Neurophysiol. 83, 3031 (2000).
  49. G. Tiddia, Large-scale neuronal networks: From simulation technology to the study of plasticity-driven cognitive processes, Ph.D. thesis, University of Cagliari, 2024.
  50. J. Senk, B. Kriener, M. Djurfeldt, N. Voges, H.-J. Jiang, L. Schüttler, G. Gramelsberger, M. Diesmann, H. E. Plesser, and S. J. van Albada, Connectivity concepts in neuronal network modeling, PLoS Comput. Biol. 18, e1010086 (2022).
  51. E. Nordlie, M.-O. Gewaltig, and H. E. Plesser, Towards reproducible descriptions of neuronal network models, PLoS Comput. Biol. 5, e1000456 (2009).
  52. C. Linssen, P. N. Babu, J. M. Eppler, L. Koll, B. Rumpe, and A. Morrison, NESTML: A generic modeling language and code generation tool for the simulation of spiking neural networks with advanced plasticity rules, Front. Neuroinform. 19, 1544143 (2025).
  53. C. Linssen, P. N. Babu, Y. Bouhadjar, L. Ewert, W. Wybo, M. Lober, F. Feller, B. Rumpe, and A. Morrison, NESTML 8.0.0 (Version 8.0.0) [Computer software], Zenodo, 2024, https://doi.org/10.5281/zenodo.12191059.
  54. G. Tiddia, L. Sergi, S. Rubiu, A. Incollu, and B. Golosio, Short-term plasticity-based working memory spiking model is resilient to synaptic heterogeneity, Zenodo, 2025, https://doi.org/10.5281/zenodo.17991881.
  55. M. Tsodyks, K. Pawelzik, and H. Markram, Neural networks with dynamic synapses, Neural Comput. 10, 821 (1998).

Outline

Information

Phys. Rev. E 114, 014409– Published 22 July, 2026
  • Received 5 May 2025
  • Accepted 19 December 2025
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

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