Journal of Computational Neuroscience

, Volume 35, Issue 2, pp 187–199 | Cite as

A working memory model for serial order that stores information in the intrinsic excitability properties of neurons

  • Eduardo Conde-Sousa
  • Paulo AguiarEmail author


Models for temporary information storage in neuronal populations are dominated by mechanisms directly dependent on synaptic plasticity. There are nevertheless other mechanisms available that are well suited for creating short-term memories. Here we present a model for working memory which relies on the modulation of the intrinsic excitability properties of neurons, instead of synaptic plasticity, to retain novel information for periods of seconds to minutes. We show that it is possible to effectively use this mechanism to store the serial order in a sequence of patterns of activity. For this we introduce a functional class of neurons, named gate interneurons, which can store information in their membrane dynamics and can literally act as gates routing the flow of activations in the principal neurons population. The presented model exhibits properties which are in close agreement with experimental results in working memory. Namely, the recall process plays an important role in stabilizing and prolonging the memory trace. This means that the stored information is correctly maintained as long as it is being used. Moreover, the working memory model is adequate for storing completely new information, in time windows compatible with the notion of “one-shot” learning (hundreds of milliseconds).


Working memory Serial order Intrinsic excitability modulation Delay period Match enhancement Short-term memory 



Research funded by the European Regional Development Fund through the programme COMPETE and by the Portuguese Government through the FCT - Fundação para a Ciência e a Tecnologia under the project PEst-C/MAT/UI0144/2011. Eduardo Conde-Sousa was supported by the grant SFRH/BD/65633/2009 from FCT, co-financed by European Social Fund under the program POPH of the National Strategic Reference Framework.


  1. Abbott, L. F., & Nelson, S. B. (2000). Synaptic plasticity: taming the beast. Nature Neuroscience, 3(Suppl), 1178–1183.PubMedCrossRefGoogle Scholar
  2. Aguiar, P., Sousa, M., & Lima, D. (2010). NMDA channels together with L-type calcium currents and calcium-activated nonspecific cationic currents are sufficient to generate windup in WDR neurons. Journal of Neurophysiology, 104, 1155–1166.PubMedCrossRefGoogle Scholar
  3. Alonso, A., de Curtis, M., & Llinas, R. (1990). Postsynaptic Hebbian and non-Hebbian long-term potentiation of synaptic efficacy in the entorhinal cortex in slices and in the isolated adult guinea pig brain. Proceedings of the National Academy of Sciences of the United States of America, 87, 9280–9284.PubMedCrossRefGoogle Scholar
  4. Baddeley, A. (2003). Working memory: looking back and looking forward. Nature Reviews Neuroscience, 4, 829–839.PubMedCrossRefGoogle Scholar
  5. Bal, T., & McCormick, D. A. (1993). Mechanisms of oscillatory activity in guinea-pig nucleus reticularis thalami in vitro: a mammalian pacemaker. The Journal of Physiology, 468, 669–691.PubMedGoogle Scholar
  6. Bliss, T. V., & Lomo, T. (1973). Long-lasting potentiation of synaptic transmission in the dentate area of the anaesthetized rabbit following stimulation of the perforant path. The Journal of Physiology, 232, 331–356.PubMedGoogle Scholar
  7. Botvinick, M. M., & Plaut, D. C. (2006). Short-term memory for serial order: a recurrent neural network model. Psychology Review, 113, 201–233.CrossRefGoogle Scholar
  8. Burnashev, N., Zhou, Z., Neher, E., & Sakmann, B. (1995). Fractional calcium currents through recombinant GluR channels of the NMDA, AMPA and kainate receptor subtypes. The Journal of Physiology, 485(Pt 2), 403–418.PubMedGoogle Scholar
  9. Buxhoeveden, D. P., & Casanova, M. F. (2002). The minicolumn hypothesis in neuroscience. Brain, 125, 935–951.PubMedCrossRefGoogle Scholar
  10. Crasto, C. J., Marenco, L. N., Liu, N., Morse, T. M., Cheung, K. H., Lai, P. C., et al. (2007). SenseLab: new developments in disseminating neuroscience information. Briefings in Bioinformatics, 8, 150–162.PubMedCrossRefGoogle Scholar
  11. Dehaene, S., Changeux, J. P., & Nadal, J. P. (1987). Neural networks that learn temporal sequences by selection. Proceedings of the National Academy of Sciences of the United States of America, 84, 2727–2731.PubMedCrossRefGoogle Scholar
  12. Destexhe, A., Babloyantz, A., & Sejnowski, T. J. (1993). Ionic mechanisms for intrinsic slow oscillations in thalamic relay neurons. Biophysical Journal, 65, 1538–1552.PubMedCrossRefGoogle Scholar
  13. Destexhe, A., Contreras, D., Sejnowski, T. J., & Steriade, M. (1994). A model of spindle rhythmicity in the isolated thalamic reticular nucleus. Journal of Neurophysiology, 72, 803–818.PubMedGoogle Scholar
  14. Durstewitz, D., Seamans, J. K., & Sejnowski, T. J. (2000). Neurocomputational models of working memory. Nature Neuroscience, 3(Suppl), 1184–1191.PubMedCrossRefGoogle Scholar
  15. Fall, C. P., & Rinzel, J. (2006). An intracellular Ca2+ subsystem as a biologically plausible source of intrinsic conditional bistability in a network model of working memory. Journal of Computational Neuroscience, 20, 97–107.PubMedCrossRefGoogle Scholar
  16. Feldmeyer, D., Egger, V., Lubke, J., & Sakmann, B. (1999). Reliable synaptic connections between pairs of excitatory layer 4 neurones within a single ‘barrel’ of developing rat somatosensory cortex. The Journal of Physiology, 521(Pt 1), 169–190.PubMedCrossRefGoogle Scholar
  17. Fransen, E., Alonso, A. A., & Hasselmo, M. E. (2002). Simulations of the role of the muscarinic-activated calcium-sensitive nonspecific cation current INCM in entorhinal neuronal activity during delayed matching tasks. Journal of Neuroscience, 22, 1081–1097.PubMedGoogle Scholar
  18. Fransen, E., Tahvildari, B., Egorov, A. V., Hasselmo, M. E., & Alonso, A. A. (2006). Mechanism of graded persistent cellular activity of entorhinal cortex layer v neurons. Neuron, 49, 735–746.PubMedCrossRefGoogle Scholar
  19. Gibson, W. G., & Robinson, J. (1992). Statistical analysis of the dynamics of a sparse associative memory. Neural Networks, 5, 645–661.CrossRefGoogle Scholar
  20. Grashow, R., Brookings, T., & Marder, E. (2010). Compensation for variable intrinsic neuronal excitability by circuit-synaptic interactions. Journal of Neuroscience, 30, 9145–9156.PubMedCrossRefGoogle Scholar
  21. Hahn, T. T., McFarland, J. M., Berberich, S., Sakmann, B., & Mehta, M. R. (2012). Spontaneous persistent activity in entorhinal cortex modulates cortico-hippocampal interaction in vivo. Nature Neuroscience, 15, 1531–1538.PubMedCrossRefGoogle Scholar
  22. Haj-Dahmane, S., & Andrade, R. (1999). Muscarinic receptors regulate two different calcium-dependent non-selective cation currents in rat prefrontal cortex. European Journal of Neuroscience, 11, 1973–1980.PubMedCrossRefGoogle Scholar
  23. Herz, A. V., Li, Z., & van Hemmen, J. L. (1991). Statistical mechanics of temporal association in neural networks with transmission delays. Physical Review Letters, 66, 1370–1373.PubMedCrossRefGoogle Scholar
  24. Hines, M. L., & Carnevale, N. T. (1997). The NEURON simulation environment. Neural Computation, 9, 1179–1209.PubMedCrossRefGoogle Scholar
  25. Hodgkin, A. L., & Huxley, A. F. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. The Journal of Physiology, 117, 500–544.PubMedGoogle Scholar
  26. Hyde, R. A., & Strowbridge, B. W. (2012). Mnemonic representations of transient stimuli and temporal sequences in the rodent hippocampus in vitro. Nature Neuroscience, 15, 1430–1438.PubMedCrossRefGoogle Scholar
  27. Jackson, M. B., & Scharfman, H. E. (1996). Positive feedback from hilar mossy cells to granule cells in the dentate gyrus revealed by voltage-sensitive dye and microelectrode recording. Journal of Neurophysiology, 76, 601–616.PubMedGoogle Scholar
  28. Jahr, C. E., & Stevens, C. F. (1990). Voltage dependence of NMDA-activated macroscopic conductances predicted by single-channel kinetics. Journal of Neuroscience, 10, 3178–3182.PubMedGoogle Scholar
  29. Jensen, O., Idiart, M. A., & Lisman, J. E. (1996). Physiologically realistic formation of autoassociative memory in networks with theta/gamma oscillations: role of fast NMDA channels. Learning and Memory, 3, 243–256.PubMedCrossRefGoogle Scholar
  30. Kandel, E. R. (2001). The molecular biology of memory storage: a dialogue between genes and synapses. Science, 294, 1030–1038.PubMedCrossRefGoogle Scholar
  31. Klink, R., & Alonso, A. (1997). Morphological characteristics of layer II projection neurons in the rat medial entorhinal cortex. Hippocampus, 7, 571–583.PubMedCrossRefGoogle Scholar
  32. Koene, R. A., & Hasselmo, M. E. (2007). First-in-first-out item replacement in a model of short-term memory based on persistent spiking. Cerebral Cortex, 17, 1766–1781.PubMedCrossRefGoogle Scholar
  33. Koene, R. A., & Hasselmo, M. E. (2008). Consequences of parameter differences in a model of short-term persistent spiking buffers provided by pyramidal cells in entorhinal cortex. Brain Research, 1202, 54–67.PubMedCrossRefGoogle Scholar
  34. Lisman, J. E., & Idiart, M. A. (1995). Storage of 7 +/− 2 short-term memories in oscillatory subcycles. Science, 267, 1512–1515.PubMedCrossRefGoogle Scholar
  35. Marder, E., Abbott, L. F., Turrigiano, G. G., Liu, Z., & Golowasch, J. (1996). Memory from the dynamics of intrinsic membrane currents. Proceedings of the National Academy of Sciences of the United States of America, 93, 13481–13486.PubMedCrossRefGoogle Scholar
  36. McCormick, D. A., & Huguenard, J. R. (1992). A model of the electrophysiological properties of thalamocortical relay neurons. Journal of Neurophysiology, 68, 1384–1400.PubMedGoogle Scholar
  37. Mozzachiodi, R., & Byrne, J. H. (2010). More than synaptic plasticity: role of nonsynaptic plasticity in learning and memory. Trends in Neurosciences, 33, 17–26.PubMedCrossRefGoogle Scholar
  38. Nagy, G. A., Botond, G., Borhegyi, Z., Plummer, N. W., Freund, T. F., & Hajos, N. (2012). DAG-sensitive and Ca(2+) permeable TRPC6 channels are expressed in dentate granule cells and interneurons in the hippocampal formation. Hippocampus.Google Scholar
  39. Otis, T. S., De Koninck, Y., & Mody, I. (1993). Characterization of synaptically elicited GABAB responses using patch-clamp recordings in rat hippocampal slices. The Journal of Physiology, 463, 391–407.PubMedGoogle Scholar
  40. Prinz, A. A., Bucher, D., & Marder, E. (2004). Similar network activity from disparate circuit parameters. Nature Neuroscience, 7, 1345–1352.PubMedCrossRefGoogle Scholar
  41. Suh, J., Rivest, A. J., Nakashiba, T., Tominaga, T., & Tonegawa, S. (2011). Entorhinal cortex layer III input to the hippocampus is crucial for temporal association memory. Science, 334, 1415–1420.PubMedCrossRefGoogle Scholar
  42. Thomson, A. M., West, D. C., Wang, Y., & Bannister, A. P. (2002). Synaptic connections and small circuits involving excitatory and inhibitory neurons in layers 2–5 of adult rat and cat neocortex: triple intracellular recordings and biocytin labelling in vitro. Cerebral Cortex, 12, 936–953.PubMedCrossRefGoogle Scholar
  43. Traub, R. D., & Miles, R. (1991). Neuronal networks of the hippocampus. Cambridge: CUP.CrossRefGoogle Scholar
  44. White, O. L., Lee, D. D., & Sompolinsky, H. (2004). Short-term memory in orthogonal neural networks. Physical Review Letters, 92, 148102.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Faculdade de CiênciasUniversidade do PortoPortoPortugal
  2. 2.Centro de Matemática da Universidade do PortoPortoPortugal

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