Skip to main content
SpringerLink
Log in

Robust Resting-State Dynamics in a Large-Scale Spiking Neural Network Model of Area CA3 in the Mouse Hippocampus

  • Published:
Cognitive Computation Aims and scope Submit manuscript

Abstract

Hippocampal area CA3 performs the critical auto-associative function underlying pattern completion in episodic memory. Without external inputs, the electrical activity of this neural circuit reflects the spontaneous spiking interplay among glutamatergic Pyramidal neurons and GABAergic interneurons. However, the network mechanisms underlying these resting-state firing patterns are poorly understood. Leveraging the Hippocampome.org knowledge base, we developed a data-driven, large-scale spiking neural network (SNN) model of mouse CA3 with 8 neuron types, 90,000 neurons, 51 neuron-type specific connections, and 250,000,000 synapses. We instantiated the SNN in the CARLsim4 multi-GPU simulation environment using the Izhikevich and Tsodyks-Markram formalisms for neuronal and synaptic dynamics, respectively. We analyzed the resultant population activity upon transient activation. The SNN settled into stable oscillations with a biologically plausible grand-average firing frequency, which was robust relative to a wide range of transient activation. The diverse firing patterns of individual neuron types were consistent with existing knowledge of cell type-specific activity in vivo. Altered network structures that lacked neuron- or connection-type specificity were neither stable nor robust, highlighting the importance of neuron type circuitry. Additionally, external inputs reflecting dentate mossy fibers shifted the observed rhythms to the gamma band. We freely released the CARLsim4-Hippocampome framework on GitHub to test hippocampal hypotheses. Our SNN may be useful to investigate the circuit mechanisms underlying the computational functions of CA3. Moreover, our approach can be scaled to the whole hippocampal formation, which may contribute to elucidating how the unique neuronal architecture of this system subserves its crucial cognitive roles.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Scoville WB, Milner B. Loss of recent memory after bilateral hippocampal lesions. J Neurol Neurosurg Psychiatry. 1957;20(1):11–21.

    Article  Google Scholar 

  2. Nadel L, Moscovitch M. Memory consolidation retrograde amnesia and the hippocampal complex. Curr Opin Neurobiol. 1997;7(2):217–27.

    Article  Google Scholar 

  3. Wang S-H, Morris RGM. Hippocampal-neocortical interactions in memory formation consolidation and reconsolidation. Annu Rev Psychol. 2010;61(1):49–79.

    Article  Google Scholar 

  4. Vanderwolf CH. Hippocampal electrical activity and voluntary movement in the rat. Electroencephalogr Clin Neurophysiol. 1969;26(4):407–18.

    Article  Google Scholar 

  5. Buzsáki G. Hippocampal sharp wave-ripple: a cognitive biomarker for episodic memory and planning. Hippocampus. 2015;25(10):1073–188.

    Article  Google Scholar 

  6. Murakami TC, Mano T, Saikawa S, Horiguchi SA, Shigeta D, Baba K, et al. A three-dimensional single-cell-resolution whole-brain atlas using CUBIC-X expansion microscopy and tissue clearing. Nat Neurosci. 2018;21(4):625–37.

    Article  Google Scholar 

  7. Zhu F, Cizeron M, Qiu Z, Benavides-Piccione R, Kopanitsa MV, Skene NG, et al. Architecture of the mouse brain synaptome. Neuron. 2018;99(4):781–99.

    Article  Google Scholar 

  8. Armañanzas R, Ascoli GA. Towards the automatic classification of neurons. Trends Neurosci. 2015;38(5):307–18.

    Article  Google Scholar 

  9. The Petilla Interneuron Nomenclature Group (PING), Ascoli GA, Alonso-Nanclares L, Anderson SA, Barrionuevo G, Benavides-Piccione R, et al. Petilla terminology: nomenclature of features of GABAergic interneurons of the cerebral cortex. Nat Rev Neurosci. 2008;9(7):557–68.

    Article  Google Scholar 

  10. Wheeler DW, White CM, Rees CL, Komendantov AO, Hamilton DJ, Ascoli GA. Hippocampome.org: a knowledge base of neuron types in the rodent hippocampus. elife. 2015;4:e09960.

  11. Ascoli GA, Wheeler DW. In search of a periodic table of the neurons: axonal-dendritic circuitry as the organizing principle. BioEssays. 2016;38(10):969–76.

    Article  Google Scholar 

  12. Rees CL, Moradi K, Ascoli GA. Weighing the evidence in Peters’ rule: does neuronal morphology predict connectivity? Trends Neurosci. 2017;40(2):63–71.

    Article  Google Scholar 

  13. Moradi K, Ascoli GA. Systematic data mining of hippocampal synaptic properties. In: Cutsuridis V, Graham BP, Cobb S, Vida I, editors. Hippocampal Microcircuits: A Computational Modeler’s Resource Book. Cham: Springer International Publishing; 2018. p. 441–71 (Springer Series in Computational Neuroscience).

    Chapter  Google Scholar 

  14. Rees CL, Wheeler DW, Hamilton DJ, White CM, Komendantov AO, Ascoli GA. Graph theoretic and motif analyses of the hippocampal neuron type potential connectome. eNeuro. 2016;3(6).

  15. White CM, Rees CL, Wheeler DW, Hamilton DJ, Ascoli GA. Molecular expression profiles of morphologically defined hippocampal neuron types: empirical evidence and relational inferences. Hippocampus. 2020;30(5):472–87.

    Article  Google Scholar 

  16. Komendantov AO, Venkadesh S, Rees CL, Wheeler DW, Hamilton DJ, Ascoli GA. Quantitative firing pattern phenotyping of hippocampal neuron types. Sci Rep. 2019;9:17915.

    Article  Google Scholar 

  17. Ascoli GA. The coming of age of the hippocampome. Neuroinformatics. 2010;8(1):1–3.

    Article  Google Scholar 

  18. Attili SM, Silva MFM, Nguyen T, Ascoli GA. Cell numbers distribution shape and regional variation throughout the murine hippocampal formation from the adult brain Allen Reference Atlas. Brain Struct Funct. 2019;224(8):2883–97.

    Article  Google Scholar 

  19. Venkadesh S, Komendantov AO, Listopad S, Scott EO, De Jong K, Krichmar JL, et al. Evolving simple models of diverse intrinsic dynamics in hippocampal neuron types. Front Neuroinform. 2018;12:8.

    Article  Google Scholar 

  20. Tecuatl C, Wheeler DW, Sutton N, Ascoli GA. Comprehensive estimates of potential synaptic connections in local circuits of the rodent hippocampal formation by axonal-dendritic overlap. J Neurosci. 2021;41(8):1665–83.

    Article  Google Scholar 

  21. Moradi K, Ascoli GA. A comprehensive knowledge base of synaptic electrophysiology in the rodent hippocampal formation. Hippocampus. 2020;30(4):314–31.

    Article  Google Scholar 

  22. Dehghani N, Peyrache A, Telenczuk B, Le Van QM, Halgren E, Cash SS, et al. Dynamic balance of excitation and inhibition in human and monkey neocortex. Sci Rep. 2016;6(1):23176.

    Article  Google Scholar 

  23. He H, Cline HT. What is excitation/inhibition and how is it regulated? A case of the elephant and the wisemen. J Exp Neurosci. 2019;13.

  24. Dyhrfjeld-Johnsen J, Santhakumar V, Morgan RJ, Huerta R, Tsimring L, Soltesz I. Topological determinants of epileptogenesis in large-scale structural and functional models of the dentate gyrus derived from experimental data. J Neurophysiol. 2007;97(2):1566–87.

    Article  Google Scholar 

  25. Hendrickson PJ, Yu GJ, Song D, Berger TW. A million-plus neuron model of the hippocampal dentate gyrus: dependency of spatio-temporal network dynamics on topography. Conf Proc IEEE Eng Med Biol Soc. 2015;2015:4713–6.

    Google Scholar 

  26. Bezaire MJ, Soltesz I. Quantitative assessment of CA1 local circuits: knowledge base for interneuron-pyramidal cell connectivity. Hippocampus. 2013;23(9):751–85.

    Article  Google Scholar 

  27. Yu GJ, Feng Z, Berger TW. Network activity due to topographic organization of Schaffer collaterals in a large-scale model of rat CA1. Conf Proc IEEE Eng Med Biol Soc. 2019;2019:2977–80.

    Google Scholar 

  28. Chou T, Kashyap HJ, Xing J, Listopad S, Rounds EL, Beyeler M, et al. CARLsim 4: an open source library for large scale biologically detailed spiking neural network simulation using heterogeneous clusters. In: 2018 International Joint Conference on Neural Networks (IJCNN). 2018. p. 1–8.

  29. Izhikevich EM. Dynamical Systems in Neuroscience. Cambridge: MIT Press; 2007. p. 522.

    Google Scholar 

  30. Venkadesh S, Komendantov AO, Wheeler DW, Hamilton DJ, Ascoli GA. Simple models of quantitative firing phenotypes in hippocampal neurons: comprehensive coverage of intrinsic diversity. PLOS Comput Biol. 2019;15(10):e1007462.

  31. Attili SM, Mackesey ST, Ascoli GA. Operations research methods for estimating the population size of neuron types. Ann Oper Res. 2020;289(1):33–50.

    Article  MathSciNet  MATH  Google Scholar 

  32. Attili SM, Wheeler DW, Moradi K, Ascoli GA. Quantification of neuron types in the rodent hippocampal formation by data mining and numerical optimization. bioRxiv. 2021. https://doi.org/10.1101/2021.09.20.460986.

  33. Mongillo G, Barak O, Tsodyks M. Synaptic theory of working memory. Science. 2008;319(5869):1543–6.

    Article  Google Scholar 

  34. Senn W, Markram H, Tsodyks M. An algorithm for modifying neurotransmitter release probability based on pre- and postsynaptic spike timing. Neural Comput. 2001;13(1):35–67.

    Article  MATH  Google Scholar 

  35. Tsodyks M, Pawelzik K, Markram H. Neural networks with dynamic synapses. Neural Comput. 1998;10(4):821–35.

    Article  Google Scholar 

  36. Tecuatl C, Wheeler DW, Ascoli GA. A method for estimating the potential synaptic connections between axons and dendrites from 2D neuronal images. Bio-Protoc. 2021;11(13):e4073–e4073.

    Google Scholar 

  37. Soleng AF, Raastad M, Andersen P. Conduction latency along CA3 hippocampal axons from rat. Hippocampus. 2003;13(8):953–61.

    Article  Google Scholar 

  38. Sosa M, Joo HR, Frank LM. Dorsal and ventral hippocampal sharp-wave ripples activate distinct nucleus accumbens networks. Neuron. 2020;105(4):725–41.

    Article  Google Scholar 

  39. Taxidis J, Coombes S, Mason R, Owen MR. Modeling sharp wave-ripple complexes through a CA3-CA1 network model with chemical synapses. Hippocampus. 2012;22(5):995–1017.

    Article  Google Scholar 

  40. Willmore B, Tolhurst DJ. Characterizing the sparseness of neural codes. Network. 2001;12(3):255–70.

    Article  Google Scholar 

  41. Berens P. CircStat: A MATLAB toolbox for circular statistics. J Stat Softw. 2009;31(1):1–21.

    Google Scholar 

  42. Bezaire MJ, Raikov I, Burk K, Vyas D, Soltesz I. Interneuronal mechanisms of hippocampal theta oscillations in a full-scale model of the rodent CA1 circuit. eLife. 2016;5:e18566.

  43. Gerstner W, Kistler WM, Naud R, Paninski L. Neuronal dynamics: from single neurons to networks and models of cognition. Cambridge: Cambridge University Press; 2014. p. 591.

    Book  Google Scholar 

  44. Evstratova A, Tóth K. Information processing and synaptic plasticity at hippocampal mossy fiber terminals. Front Cell Neurosci. 2014;8:28.

    Article  Google Scholar 

  45. Mizuseki K, Buzsáki G. Preconfigured skewed distribution of firing rates in the hippocampus and entorhinal cortex. Cell Rep. 2013;4(5):1010–21.

    Article  Google Scholar 

  46. Marr D. Vision: a computational investigation into the human representation and processing of visual information. Cambridge: MIT Press; 1982. p. 428.

    Google Scholar 

  47. Kriegeskorte N, Douglas PK. Cognitive computational neuroscience. Nat Neurosci. 2018;21(9):1148–60.

    Article  Google Scholar 

  48. Jinno S, Kosaka T. Stereological estimation of numerical densities of glutamatergic principal neurons in the mouse hippocampus. Hippocampus. 2010;20(7):829–40.

    Google Scholar 

  49. Shadlen MN, Newsome WT. The variable discharge of cortical neurons: implications for connectivity computation and information coding. J Neurosci. 1998;18(10):3870–96.

    Article  Google Scholar 

  50. Colgin LL. Rhythms of the hippocampal network. Nat Rev Neurosci. 2016;17(4):239–49.

    Article  Google Scholar 

  51. Dugladze T, Schmitz D, Whittington MA, Vida I, Gloveli T. Segregation of axonal and somatic activity during fast network oscillations. Science. 2012;336(6087):1458–61.

    Article  Google Scholar 

  52. Huang Y-C, Wang C-T, Su T-S, Kao K-W, Lin Y-J, Chuang C-C, et al. A Single-Cell Level and Connectome-Derived Computational Model of the Drosophila Brain. Front Neuroinform. 2019;12:99.

    Article  Google Scholar 

  53. Trimper JB, Galloway CR, Jones AC, Mandi K, Manns JR. Gamma oscillations in rat hippocampal subregions dentate gyrus, CA3, CA1, and subiculum underlie associative memory encoding. Cell Rep. 2017;21(9):2419–32.

    Article  Google Scholar 

  54. Sullivan D, Csicsvari J, Mizuseki K, Montgomery S, Diba K, Buzsáki G. Relationships between hippocampal sharp waves, ripples, and fast gamma oscillation: influence of dentate and entorhinal cortical activity. J Neurosci. 2011;31(23):8605–16.

    Article  Google Scholar 

  55. ter Wal M, Tiesinga P. Hippocampal oscillations mechanisms (PING ING Sparse). In: Jaeger D, Jung R, editors. Encyclopedia of Computational Neuroscience. New York: Springer; 2013. p. 1–14.

    Google Scholar 

  56. Tukker JJ, Lasztóczi B, Katona L, Roberts JDB, Pissadaki EK, Dalezios Y, et al. Distinct dendritic arborization and in vivo firing patterns of parvalbumin-expressing basket cells in the hippocampal area CA3. J Neurosci. 2013;33(16):6809–25.

    Article  Google Scholar 

  57. Viney TJ, Lasztoczi B, Katona L, Crump MG, Tukker JJ, Klausberger T, et al. Network state-dependent inhibition of identified hippocampal CA3 axo-axonic cells in vivo. Nat Neurosci. 2013;16(12):1802–11.

    Article  Google Scholar 

  58. Fuentealba P, Begum R, Capogna M, Jinno S, Márton LF, Csicsvari J, et al. Ivy cells: a population of nitric-oxide-producing, slow-spiking GABAergic neurons and their involvement in hippocampal network activity. Neuron. 2008;57(6):917–29.

    Article  Google Scholar 

  59. Hájos N, Pálhalmi J, Mann EO, Németh B, Paulsen O, Freund TF. Spike timing of distinct types of GABAergic Interneuron during hippocampal gamma oscillations in vitro. J Neurosci. 2004;24(41):9127–37.

    Article  Google Scholar 

  60. Lasztóczi B, Tukker JJ, Somogyi P, Klausberger T. Terminal field and firing selectivity of cholecystokinin-expressing interneurons in the hippocampal CA3 area. J Neurosci. 2011;31(49):18073–93.

    Article  Google Scholar 

  61. Hefft S, Jonas P. Asynchronous GABA release generates long-lasting inhibition at a hippocampal interneuron–principal neuron synapse. Nat Neurosci. 2005;8(10):1319–28.

    Article  Google Scholar 

  62. Lazarewicz MT, Migliore M, Ascoli GA. A new bursting model of CA3 pyramidal cell physiology suggests multiple locations for spike initiation. Biosystems. 2002;67(1):129–37.

    Article  Google Scholar 

  63. Hemond P, Epstein D, Boley A, Migliore M, Ascoli GA, Jaffe DB. Distinct classes of pyramidal cells exhibit mutually exclusive firing patterns in hippocampal area CA3b. Hippocampus. 2008;18(4):411–24.

    Article  Google Scholar 

  64. Neymotin SA, Lazarewicz MT, Sherif M, Contreras D, Finkel LH, Lytton WW. Ketamine disrupts theta modulation of gamma in a computer model of hippocampus. J Neurosci. 2011;31(32):11733–43.

    Article  Google Scholar 

  65. Kumbhar P, Hines M, Fouriaux J, Ovcharenko A, King J, Delalondre F, et al. CoreNEURON: an optimized compute engine for the NEURON simulator. Front Neuroinform. 2019;13:63.

    Article  Google Scholar 

  66. Yu GJ, Bouteiller J-MC, Berger TW. Topographic organization of correlation along the longitudinal and transverse axes in rat hippocampal CA3 due to excitatory afferents. Front Comput Neurosci. 2020;14:588881.

  67. Zheng P, Dimitrakakis C, Triesch J. Network self-organization explains the statistics and dynamics of synaptic connection strengths in cortex. PLOS Comput Biol. 2013;9(1):e1002848.

  68. Sanchez-Aguilera A, Wheeler DW, Jurado-Parras T, Valero M, Nokia MS, Cid E, et al. An update to Hippocampome.org by integrating single-cell phenotypes with circuit function in vivo. PLOS Biol. 2021;19(5):e3001213.

  69. Oren I, Mann EO, Paulsen O, Hájos N. Synaptic currents in anatomically identified CA3 neurons during hippocampal gamma oscillations in vitro. J Neurosci. 2006;26(39):9923–34.

    Article  Google Scholar 

  70. Kay K, Sosa M, Chung JE, Karlsson MP, Larkin MC, Frank LM. A hippocampal network for spatial coding during immobility and sleep. Nature. 2016;531(7593):185–90.

    Article  Google Scholar 

  71. Oliva A, Fernández-Ruiz A, Buzsáki G, Berényi A. Spatial coding and physiological properties of hippocampal neurons in the Cornu Ammonis subregions. Hippocampus. 2016;26(12):1593–607.

    Article  Google Scholar 

  72. Ding L, Chen H, Diamantaki M, Coletta S, Preston-Ferrer P, Burgalossi A. Structural correlates of CA2 and CA3 pyramidal cell activity in freely-moving mice. J Neurosci. 2020;40(30):5797–806.

    Article  Google Scholar 

  73. Lapray D, Lasztoczi B, Lagler M, Viney TJ, Katona L, Valenti O, et al. Behavior-dependent specialization of identified hippocampal interneurons. Nat Neurosci. 2012;15(9):1265–71.

    Article  Google Scholar 

  74. Varga C, Golshani P, Soltesz I. Frequency-invariant temporal ordering of interneuronal discharges during hippocampal oscillations in awake mice. PNAS. 2012;109(40):E2726–34.

    Article  Google Scholar 

  75. Klausberger T, Márton LF, Baude A, Roberts JDB, Magill PJ, Somogyi P. Spike timing of dendrite-targeting bistratified cells during hippocampal network oscillations in vivo. Nat Neurosci. 2004;7(1):41–7.

    Article  Google Scholar 

  76. Katona L, Lapray D, Viney TJ, Oulhaj A, Borhegyi Z, Micklem BR, et al. Sleep and movement differentiates actions of two types of somatostatin-expressing GABAergic interneuron in rat hippocampus. Neuron. 2014;82(4):872–86.

    Article  Google Scholar 

  77. Izhikevich EM. Polychronization: computation with spikes. Neural Comput. 2006;18(2):245–82.

    Article  MathSciNet  MATH  Google Scholar 

  78. Lisman JE, Jensen O. The Theta-gamma neural code. Neuron. 2013;77(6):1002–16.

    Article  Google Scholar 

  79. Josh Lawrence J, Cobb S. Neuromodulation of hippocampal cells and circuits. In: Cutsuridis V, Graham BP, Cobb S, Vida I, editors. Hippocampal Microcircuits: A Computational Modeler’s Resource Book. New York: Springer International Publishing; 2018. p. 227–325 (Springer Series in Computational Neuroscience).

    Chapter  Google Scholar 

  80. Nadim F, Bucher D. Neuromodulation of neurons and synapses. Curr Opin Neurobiol. 2014;29:48–56.

    Article  Google Scholar 

  81. Sporns O, Kötter R. Motifs in Brain Networks. PLOS Biol. 2004;2(11):e369.

  82. Guzman SJ, Schlögl A, Frotscher M, Jonas P. Synaptic mechanisms of pattern completion in the hippocampal CA3 network. Science. 2016;353(6304):1117–23.

    Article  Google Scholar 

  83. Patel J, Fujisawa S, Berényi A, Royer S, Buzsáki G. Traveling theta waves along the entire septotemporal axis of the hippocampus. Neuron. 2012;75(3):410–7.

    Article  Google Scholar 

Download references

Acknowledgements

The authors are grateful to Drs. Diek Wheeler, David Hamilton, and Siva Venkadesh for helpful discussions.

Funding

This research was supported in part by the National Institutes of Health through grants U01MH114829 (BICCN) and R01NS39600.

Author information

Authors and Affiliations

  1. Interdepartmental Program in Neuroscience, George Mason University, Fairfax, VA, USA

    Jeffrey D. Kopsick, Keivan Moradi, Sarojini M. Attili & Giorgio A. Ascoli

  2. Bioengineering Department, Volgenau School of Engineering, George Mason University, Fairfax, VA, USA

    Carolina Tecuatl & Giorgio A. Ascoli

  3. Department of Cognitive Sciences, University of California, Irvine, Irvine, CA, USA

    Jinwei Xing, Kexin Chen & Jeffrey L. Krichmar

  4. Department of Computer Science, University of California, Irvine, Irvine, CA, USA

    Hirak J. Kashyap & Jeffrey L. Krichmar

Authors
  1. Jeffrey D. Kopsick
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Carolina Tecuatl
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Keivan Moradi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sarojini M. Attili
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hirak J. Kashyap
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jinwei Xing
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Kexin Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Jeffrey L. Krichmar
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Giorgio A. Ascoli
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

JDK and GAA designed and conceptualized the study. CT and KM provided parameter estimates related to connectivity and short-term plasticity of the connection types, respectively. SMA provided the parameter estimates for the population sizes of the neuron types. HJK, JX, and KC updated CARLsim to allow connection-type specificity between neuron types and the recording of the instantaneous membrane potential and input current for all neurons in our network model. JLK oversaw the development of the CARLsim updates. JDK wrote the software to create, simulate, and analyze the network. JDK and GAA analyzed the data and wrote the manuscript with feedback from CT, KM, SMA, and JLK.

Corresponding author

Correspondence to Giorgio A. Ascoli.

Ethics declarations

Ethics Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed Consent

Informed consent was not required as no humans or animals were involved.

Conflict of Interest

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kopsick, J.D., Tecuatl, C., Moradi, K. et al. Robust Resting-State Dynamics in a Large-Scale Spiking Neural Network Model of Area CA3 in the Mouse Hippocampus. Cogn Comput 15, 1190–1210 (2023). https://doi.org/10.1007/s12559-021-09954-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12559-021-09954-2

Keywords

Search

Navigation