Abstract
It is widely accepted that in the brain processes related to learning and memory there are changes at the level of synapses. Synapses have the ability to change their strength depending on the stimuli, which is called activity-dependent synaptic plasticity. To date, many mathematical models describing activity-dependent synaptic plasticity have been introduced. However, the remaining question is whether these rules apply in general to the whole brain or only to individual areas or even just to individual types of cells. Here, we decided to test whether the well-known rule of Spike-Timing Dependent Plasticity (STDP) extended by metaplasticity (meta-STDP) supports long-term stability of major synaptic inputs to hippocampal CA1 pyramidal neurons. For this reason, we have coupled synaptic models equipped with a previously established meta-STDP rule to a biophysically realistic computational model of the hippocampal CA1 pyramidal cell with a simplified dendritic tree. Our simulations show that the meta-STDP rule is able to keep synaptic weights stable during ongoing spontaneous input activity as it happens in the hippocampus in vivo. This is functionally advantageous as neurons should not change their weights during the ongoing activity of neural circuits in vivo. However, they should maintain their ability to display plastic changes in the case of significantly different or “meaningful” inputs. Thus, our study is the first step before we attempt to simulate different stimulation protocols which induce changes in synaptic weights in vivo.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Lee, I., Jerman, T., Kesner, R.: Disruption of delayed memory for a sequence of spatial locations following CA1- or CA3-lesions of the dorsal hippocampus. Neurobiol. Learn. Mem. 84, 138–147 (2005). https://doi.org/10.1016/j.nlm.2005.06.002
Lee, I., Kesner, R.P.: Differential contribution of NMDA receptors in hippocampal subregions to spatial working memory. Nat. Neurosci. 5, 162–168 (2002). https://doi.org/10.1038/nn790
Lee, I., Kesner, R.P.: Differential contributions of dorsal hippocampal subregions to memory acquisition and retrieval in contextual fear-conditioning. Hippocampus. 14, 301–310 (2004). https://doi.org/10.1002/hipo.10177
Hoang, L.T., Kesner, R.P.: Dorsal hippocampus, CA3, and CA1 lesions disrupt temporal sequence completion. Behav. Neurosci. 122, 9–15 (2008). https://doi.org/10.1037/0735-7044.122.1.9
Hughes, J.R.: Post-tetanic potentiation. Physiol. Rev. 38, 91–113 (1958). https://doi.org/10.1152/physrev.1958.38.1.91
Martin, S.J., Grimwood, P.D., Morris, R.G.M.: Synaptic plasticity and memory: an evaluation of the hypothesis. Annu. Rev. Neurosci. 23, 649–711 (2000). https://doi.org/10.1146/annurev.neuro.23.1.649
Mayr, C.G., Partzsch, J.: Rate and pulse based plasticity governed by local synaptic state variables. Front. Synaptic Neurosci. 2, 33 (2010). https://doi.org/10.3389/fnsyn.2010.00033
Benuskova, L., Abraham, W.C.: STDP rule endowed with the BCM sliding threshold accounts for hippocampal heterosynaptic plasticity. J. Comput. Neurosci. 22, 129–133 (2007). https://doi.org/10.1007/s10827-006-0002-x
Markram, H., Lübke, J., Frotscher, M., Sakmann, B.: Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs. Science 275, 213 (1997). https://doi.org/10.1126/science.275.5297.213
Abraham, W.C.: Metaplasticity: tuning synapses and networks for plasticity. Nat. Rev. Neurosci. 9, 387 (2008). https://doi.org/10.1038/nrn2356
Jedlicka, P., Benuskova, L., Abraham, W.C.: A voltage-based STDP rule combined with fast BCM-Like metaplasticity accounts for LTP and concurrent “heterosynaptic” LTD in the dentate gyrus in vivo. PLoS Comput. Biol. 11, e1004588–e1004588 (2015). https://doi.org/10.1371/journal.pcbi.1004588
Abraham, W.C., Logan, B., Wolff, A., Benuskova, L.: “Heterosynaptic” LTD in the dentate gyrus of anesthetized rat requires homosynaptic activity. J. Neurophysiol. 98, 1048–1051 (2007). https://doi.org/10.1152/jn.00250.2007
Frank, L.M., Brown, E.N., Wilson, M.A.: A comparison of the firing properties of putative excitatory and inhibitory neurons from CA1 and the entorhinal cortex. J. Neurophysiol. 86, 2029–2040 (2001). https://doi.org/10.1152/jn.2001.86.4.2029
Deshmukh, S.S., Yoganarasimha, D., Voicu, H., Knierim, J.J.: Theta modulation in the medial and the lateral entorhinal cortices. J. Neurophysiol. 104, 994–1006 (2010). https://doi.org/10.1152/jn.01141.2009
Cutsuridis, V., Cobb, S., Graham, B.P.: Encoding and retrieval in a model of the hippocampal CA1 microcircuit. Hippocampus 20, 423–446 (2010). https://doi.org/10.1002/hipo.20661
Tomko, M., Jedlička, P., Beňušková, Ľ.: Computational model of CA1 pyramidal cell with meta-STDP stabilizes under ongoing spontaneous activity as in vivo. In: Kognícia a umelý život 2019. Vydavateľstvo Univerzity Komenského, Bratislava (2019)
Megı́as, M., Emri, Z., Freund, T.F., Gulyás, A.I.: Total number and distribution of inhibitory and excitatory synapses on hippocampal CA1 pyramidal cells. Neuroscience 102, 527–540 (2001). https://doi.org/10.1016/S0306-4522(00)00496-6
Migliore, R., et al.: The physiological variability of channel density in hippocampal CA1 pyramidal cells and interneurons explored using a unified data-driven modeling workflow. PLoS Comput. Biol. 14, e1006423–e1006423 (2018). https://doi.org/10.1371/journal.pcbi.1006423
Hines, M.L., Carnevale, N.T.: The NEURON Simulation Environment. Neural Comput. 9, 1179–1209 (1997). https://doi.org/10.1162/neco.1997.9.6.1179
Buzsáki, G.: Theta oscillations in the hippocampus. Neuron 33, 325–340 (2002). https://doi.org/10.1016/S0896-6273(02)00586-X
Bienenstock, E.L., Cooper, L.N., Munro, P.W.: Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex. J. Neurosci. Off. J. Soc. Neurosci. 2, 32–48 (1982). https://doi.org/10.1523/JNEUROSCI.02-01-00032.1982
Izhikevich, E.M., Desai, N.S.: Relating STDP to BCM. Neural Comput. 15, 1511–1523 (2003). https://doi.org/10.1162/089976603321891783
Mizuseki, K., Buzsáki, G.: Preconfigured, skewed distribution of firing rates in the hippocampus and entorhinal cortex. Cell Rep. 4, 1010–1021 (2013). https://doi.org/10.1016/j.celrep.2013.07.039
Buzsáki, G., Mizuseki, K.: The log-dynamic brain: how skewed distributions affect network operations. Nat. Rev. Neurosci. 15, 264–278 (2014). https://doi.org/10.1038/nrn3687
Benusková, L., Diamond, M.E., Ebner, F.F.: Dynamic synaptic modification threshold: computational model of experience-dependent plasticity in adult rat barrel cortex. Proc. Natl. Acad. Sci. U. S. A. 91, 4791–4795 (1994). https://doi.org/10.1073/pnas.91.11.4791
Sáray, S., et al.: Systematic comparison and automated validation of detailed models of hippocampal neurons. bioRxiv. 2020.07.02.184333 (2020). https://doi.org/10.1101/2020.07.02.184333
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Tomko, M., Jedlička, P., Beňušková, L. (2020). Meta-STDP Rule Stabilizes Synaptic Weights Under in Vivo-like Ongoing Spontaneous Activity in a Computational Model of CA1 Pyramidal Cell. In: Farkaš, I., Masulli, P., Wermter, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2020. ICANN 2020. Lecture Notes in Computer Science(), vol 12397. Springer, Cham. https://doi.org/10.1007/978-3-030-61616-8_54
Download citation
DOI: https://doi.org/10.1007/978-3-030-61616-8_54
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-61615-1
Online ISBN: 978-3-030-61616-8
eBook Packages: Computer ScienceComputer Science (R0)