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Short-term depression and transient memory in sensory cortex

Journal of Computational Neuroscience volume 43, pages 273–294 (2017)Cite this article

Abstract

Persistent neuronal activity is usually studied in the context of short-term memory localized in central cortical areas. Recent studies show that early sensory areas also can have persistent representations of stimuli which emerge quickly (over tens of milliseconds) and decay slowly (over seconds). Traditional positive feedback models cannot explain sensory persistence for at least two reasons: (i) They show attractor dynamics, with transient perturbations resulting in a quasi-permanent change of system state, whereas sensory systems return to the original state after a transient. (ii) As we show, those positive feedback models which decay to baseline lose their persistence when their recurrent connections are subject to short-term depression, a common property of excitatory connections in early sensory areas. Dual time constant network behavior has also been implemented by nonlinear afferents producing a large transient input followed by much smaller steady state input. We show that such networks require unphysiologically large onset transients to produce the rise and decay observed in sensory areas. Our study explores how memory and persistence can be implemented in another model class, derivative feedback networks. We show that these networks can operate with two vastly different time courses, changing their state quickly when new information is coming in but retaining it for a long time, and that these capabilities are robust to short-term depression. Specifically, derivative feedback networks with short-term depression that acts differentially on positive and negative feedback projections are capable of dynamically changing their time constant, thus allowing fast onset and slow decay of responses without requiring unrealistically large input transients.

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References

  • Barak, O., & Tsodyks, M. (2007). Persistent activity in neural networks with dynamic synapses. PLoS Computational Biology, 3(2), e35.

    Article  PubMed  PubMed Central  Google Scholar 

  • Beck, O., Chistiakova, M., Obermayer, K., & Volgushev, M. (2005). Adaptation at synaptic connections to layer 2/3 pyramidal cells in rat visual cortex. Journal of Neurophysiology, 94(1), 363–376.

    Article  PubMed  Google Scholar 

  • Castro-Alamancos, M.A., & Connors, B.W. (1997). Distinct forms of short-term plasticity at excitatory synapses of hippocampus and neocortex. Proceedings of the National Academy of Sciences, 94(8), 4161–4166.

    Article  CAS  Google Scholar 

  • Chubykin, A.A., Roach, E.B., Bear, M.F., & Shuler, M.G.H. (2013). A cholinergic mechanism for reward timing within primary visual cortex. Neuron, 77(4), 723–735.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Compte, A., Brunel, N., Goldman-Rakic, P.S., & Wang, X.J. (2000). Synaptic mechanisms and network dynamics underlying spatial working memory in a cortical network model. Cerebral Cortex, 10(9), 910–923.

    Article  CAS  PubMed  Google Scholar 

  • Cormier, R., Greenwood, A., & Connor, J. (2001). Bidirectional synaptic plasticity correlated with the magnitude of dendritic calcium transients above a threshold. Journal of Neurophysiology, 85(1), 399–406.

    CAS  PubMed  Google Scholar 

  • Craft, E., Schütze, H., Niebur, E., & von der Heydt, R. (2007). A neural model of figure-ground organization. Journal of Neurophysiology, 97(6), 4310–26. PMID17442769.

    Article  PubMed  Google Scholar 

  • Curtis, C.E., & D’Esposito, M. (2003). Persistent activity in the prefrontal cortex during working memory. Trends in cognitive sciences, 7(9), 415–423.

    Article  PubMed  Google Scholar 

  • Denève, S., & Machens, C.K. (2016). Efficient codes and balanced networks. Nature Neuroscience, 19(3), 375.

    Article  PubMed  Google Scholar 

  • Ganguli, S., Huh, D., & Sompolinsky, H. (2008). Memory traces in dynamical systems. Proceedings of the National Academy of Sciences, 105(48), 18:970–18:975.

    Article  CAS  Google Scholar 

  • Gavornik, J.P., & Shouval, H.Z. (2011). A network of spiking neurons that can represent interval timing: mean field analysis. Journal of Computational Neuroscience, 30(2), 501–513.

    Article  PubMed  Google Scholar 

  • Gavornik, J.P., Shuler, M.G.H., Loewenstein, Y., Bear, M.F., & Shouval, H.Z. (2009). Learning reward timing in cortex through reward dependent expression of synaptic plasticity. Proceedings of the National Academy of Sciences, 106(16), 6826–6831.

    Article  CAS  Google Scholar 

  • Gillary, G., & Niebur, E. (2016). The edge of stability: Response times and delta oscillations in balanced networks. PLoS Computational Biology, 12(9), e1005,121.

    Article  Google Scholar 

  • Greenlee, M.W., Georgeson, M.A., Magnussen, S., & Harris, J.P. (1991). The time course of adaptation to spatial contrast. Vision Research, 31(2), 223–236.

    Article  CAS  PubMed  Google Scholar 

  • Guo, K., Mahmoodi, S., Robertson, R.G., & Young, M.P. (2006). Longer fixation duration while viewing face images. Experimental Brain Research, 171(1), 91–98.

    Article  PubMed  Google Scholar 

  • Gupta, A., Wang, Y., & Markram, H. (2000). Organizing principles for a diversity of gabaergic interneurons and synapses in the neocortex. Science, 287(5451), 273–278.

    Article  CAS  PubMed  Google Scholar 

  • Hansel C, Artola A, & Singer W (1997). Relation between dendritic Ca2+ levels and the polarity of synaptic long-term modifications in rat visual cortex neurons. European Journal of Neuroscience, 9(11), 2309–2322.

    Article  PubMed  Google Scholar 

  • Hardy, N.F., & Buonomano, D.V. (2016). Neurocomputational models of interval and pattern timing. Current Opinion in Behavioral Sciences, 8, 250–257.

    Article  PubMed  PubMed Central  Google Scholar 

  • Hempel, C.M., Hartman, K.H., Wang, X.J., Turrigiano, G.G., & Nelson, S.B. (2000). Multiple forms of short-term plasticity at excitatory synapses in rat medial prefrontal cortex. Journal of Neurophysiology, 83(5), 3031–3041.

    CAS  PubMed  Google Scholar 

  • Johnson, H.A., Goel, A., & Buonomano, D.V. (2010). Neural dynamics of in vitro cortical networks reflects experienced temporal patterns. Nature Neuroscience, 13(8), 917–919.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Leopold, D.A., Rhodes, G., Müller, K. M., & Jeffery, L. (2005). The dynamics of visual adaptation to faces. Proceedings of the Royal Society of London B: Biological Sciences, 272(1566), 897–904.

    Article  Google Scholar 

  • Lim, S., & Goldman, M.S. (2013). Balanced cortical microcircuitry for maintaining information in working memory. Nature Neuroscience, 16(9), 1306–1314.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Lim, S., & Goldman, M.S. (2014). Balanced cortical microcircuitry for spatial working memory based on corrective feedback control. The Journal of Neuroscience, 34(20), 6790–6806.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Major, G., & Tank, D. (2004). Persistent neural activity: prevalence and mechanisms. Current Opinion in Neurobiology, 14(6), 675–684.

    Article  CAS  PubMed  Google Scholar 

  • Mi, Y., Li, L., Wang, D., & Wu, S. (2014). A synaptical story of persistent activity with graded lifetime in a neural system. In Advances in Neural Information Processing Systems (pp. 352–360).

  • Mihalas, S., Dong, Y., von der Heydt, R., & Niebur, E. (2011). Mechanisms of perceptual organization provide auto-zoom and auto-localization for attention to objects. Proceedings of the National Academy of Sciences, 108(18), 7583–8. PMC3088583.

    Article  CAS  Google Scholar 

  • Murphy, B., & Miller, K. (2009). Balanced amplification: A new mechanism of selective amplification of neural activity patterns. Neuron, 61(4), 635–648.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Myme, C.I., Sugino, K., Turrigiano, G.G., & Nelson, S.B. (2003). The NMDA-to-AMPA ratio at synapses onto layer 2/3 pyramidal neurons is conserved across prefrontal and visual cortices. Journal of Neurophysiology, 90 (2), 771–779.

    Article  CAS  PubMed  Google Scholar 

  • Nikolić, D, Häusler, S, Singer, W., & Maass, W. (2009). Distributed fading memory for stimulus properties in the primary visual cortex. PLoS Biology, 7(12), e1000, 260.

    Article  Google Scholar 

  • O’Herron, P., & von der Heydt, R (2009). Short-term memory for figure-ground organization in the visual cortex. Neuron, 61(5), 801–809. PMC2707495.

    Article  PubMed  PubMed Central  Google Scholar 

  • O’Herron, P., & von der Heydt, R. (2011). Representation of object continuity in the visual cortex. Journal of Vision, 11(2). PMC3160770.

  • Pasternak, T., & Greenlee, M.W. (2005). Working memory in primate sensory systems. Nature Reviews Neuroscience, 6(2), 97–107.

    Article  CAS  PubMed  Google Scholar 

  • Patterson, M.A., Lagier, S., & Carleton, A. (2013). Odor representations in the olfactory bulb evolve after the first breath and persist as an odor afterimage. Proceedings of the National Academy of Sciences, 110(35), E3340–E3349.

    Article  CAS  Google Scholar 

  • Petersen, C.C. (2002). Short-term dynamics of synaptic transmission within the excitatory neuronal network of rat layer 4 barrel cortex. Journal of neurophysiology, 87(6), 2904–2914.

    PubMed  Google Scholar 

  • Petreanu, L., Gutnisky, D.A., Huber, D., Xu, N.L., O’Connor, D.H., Tian, L., Looger, L., & Svoboda, K. (2012). Activity in motor-sensory projections reveals distributed coding in somatosensation. Nature, 489(7415), 299–303.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Reinhold, K., Lien, A.D., & Scanziani, M. (2015). Distinct recurrent versus afferent dynamics in cortical visual processing. Nature Neuroscience, 18(12), 1789–1797.

    Article  CAS  PubMed  Google Scholar 

  • Reyes, A.D. (2011). Synaptic short-term plasticity in auditory cortical circuits. Hearing research, 279(1), 60–66.

    Article  PubMed  PubMed Central  Google Scholar 

  • Rubin, D.B., Van Hooser, S.D., & Miller, K.D. (2015). The stabilized supralinear network: a unifying circuit motif underlying multi-input integration in sensory cortex. Neuron, 85(2), 402–417.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Russell, A.F., Mihalas, S., von der Heydt, R., Niebur, E., & Etienne-Cummings, R. (2014). A model of proto-object based saliency. Vision Research, 94, 1–15.

    Article  PubMed  Google Scholar 

  • Shuler, M.G., & Bear, M.F. (2006). Reward timing in the primary visual cortex. Science, 311(5767), 1606–1609.

    Article  CAS  PubMed  Google Scholar 

  • Sugihara, T., Qiu, F.T., & von der Heydt, R. (2011). The speed of context integration in the visual cortex. Journal of neurophysiology, 106(1), 374–385. PMC3129740.

    Article  PubMed  PubMed Central  Google Scholar 

  • Super, H., Spekreijse, H., & Lamme, V. (2001). A neural correlate of working memory in the monkey primary visual cortex. Science, 293, 120–124.

    Article  CAS  PubMed  Google Scholar 

  • Tsodyks, M., Pawelzik, K., & Markram, H. (1998). Neural networks with dynamic synapses. Neural Computation, 10, 821– 835.

    Article  CAS  PubMed  Google Scholar 

  • Tsodyks, M.V., & Markram, H. (1997). The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability. Proceedings of the National Academy of Sciences, 94, 719–23.

    Article  CAS  Google Scholar 

  • Tsumoto, T., & Yasuda, H. (1996). A switching role of postsynaptic calcium in the induction of long-term potentiation or long-term depression in visual cortex. In Seminars in Neuroscience, (Vol. 8 pp. 311–319): Elsevier.

  • Wang, X.J. (1999). Synaptic basis of cortical persistent activity: the importance of NMDA receptors to working memory. The Journal of Neuroscience, 19(21), 9587–9603. PMID10531461.

    CAS  PubMed  Google Scholar 

  • Xue, M., Atallah, B.V., & Scanziani, M. (2014). Equalizing excitation-inhibition ratios across visual cortical neurons. Nature, 511(7511), 596.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank Daniel Jeck and Brian Hu for many useful discussions. We would also like to thank Philip O’Herron for help in accessing the physiological data.

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Authors and Affiliations

  1. Zanvyl Krieger Mind/Brain Institute and Solomon Snyder Department of Neuroscience, Johns Hopkins University, Baltimore, MD, 21218, USA

    Grant Gillary, Rüdiger von der Heydt & Ernst Niebur

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  1. Grant Gillary
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  2. Rüdiger von der Heydt
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  3. Ernst Niebur
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Correspondence to Ernst Niebur.

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Action Editor: Mark Goldman

This work is supported by the Hertz Foundation George Lerman Fellowship and the National Institutes of Health under grant R01DA040990.

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Gillary, G., Heydt, R. & Niebur, E. Short-term depression and transient memory in sensory cortex. J Comput Neurosci 43, 273–294 (2017). https://doi.org/10.1007/s10827-017-0662-8

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  • DOI: https://doi.org/10.1007/s10827-017-0662-8

Keywords

  • Positive feedback network
  • Derivative feedback network
  • Balanced network
  • Sensory memory
  • Persistent neuronal activity
  • Short-term depression