Investigation of Factors Affecting Motif-Based Short- and Long-Term Memory Behaviour in Brain Neuron Networks

Year 2025, Volume: 12 Issue: 1, 54 - 65, 31.01.2025

Ahmet Turan , Temel Kayıkçıoğlu

Abstract

Learning and memory formation in living things is a subject under investigation. It is thought that the memory formed in the brain's neural network structure is closely related to the connections between neurons. Connections called "motifs" have been identified, usually consisting of three or four neurons and repeating within the neural network. The basic structure of biological memory is thought to be related to such repetitive neural connections. In this study; the effect of the structures of motifs on short- and long-term memory was examined for all triple-neuronal network motifs. We used the Hodgkin-Huxley model of neurons. Using graph theory, we generated all triple-neuron motifs. In the created motifs; the effects of synaptic inputs between neurons, types of synaptic inputs of neurons, and chemical synapse duration on short- and long-term memory were examined. From the data obtained in all triple-neural network motif models; from the structure of the motif and the type of synaptic input, we determined the status of long- and short-term memory. We classified all triple-neural network motifs for situations in which they exhibit short- and long-term memory behaviour. We show that short-term memory varies with synaptic connection duration.

Keywords

Brain neural network motifs, intercellular synaptic type, short-term memory, synaptic conductivity time, long-term memory

Ethical Statement

When our study is a simulation study, it does not require ethical approval since both human and/or animal studies are not done.

References

• [1] R. Milo, S. Shen-Orr, S. Itzkovitz, N. Kashtan, D. Chklovskii, and U. Alon, ‘‘Network motifs: Simple building blocks of complex networks,’’ Science, vol. 298, no. 5594, pp. 824–827, 2002.
• [2] S. Song, P. J. Sjöström, M. Reigl, S. Nelson, and D. B. Chklovskii, ‘‘Correction: Highly nonrandom features of synaptic connectivity in local cortical circuits,’’ PLOS Biology, vol. 3, no. 10, 2005.
• [3] H. Kugler, S.-J. Dunn, and B. Yordanov, ‘‘Formal analysis of network motifs,’’ bioRxiv.
• [4] O. Sporns and R. Kötter, ‘‘Motifs in brain networks,’’ PLOS Biology, vol. 2, 2004.
• [5] R. J. Prill, P. A. Iglesias, and A. Levchenko, ‘‘Dynamic properties of network motifs contribute to biological network organization,’’ PLOS Biology, vol. 3, no. 11, 2005.
• [6] S. Song, P. J. Sjöström, M. Reigl, S. Nelson, and D. B. Chklovskii, ‘‘Highly nonrandom features of synaptic connectivity in local cortical circuits,’’ PLOS Biology, vol. 3, no. 3, 2005.
• [7] C. Li, ‘‘Functions of neuronal network motifs,’’ Phys. Rev. E, vol. 78, no. 3, p. 037101, 2008.
• [8] C. Bargmann and E. Marder, ‘‘From the connectome to brain function,’’ Nature methods, vol. 10, pp. 483–90, 2013.
• [9] M. D. Humphries, ‘‘Dynamical networks: Finding, measuring, and tracking neural population activity using network science,’’ Network Neuroscience, vol. 1, no. 4, pp. 324–338, 2017.
• [10] S. Fornari, A. Schäfer, M. Jucker, A. Goriely, and E. Kuhl, ‘‘Prion-like spreading of alzheimer’s disease within the brain’s connectome,’’ Journal of The Royal Society Interface, vol. 16, p. 20190356, 2019.
• [11] M. De Roo, P. Klauser, P. Méndez, L. Poglia, and D. Muller, ‘‘Chapter 11 spine dynamics and synapse remodeling during ltp and memory processes,’’ Progress in brain research, vol. 169, pp. 199–207, 2008.
• [12] E. Bruel-Jungerman, S. Davis, and S. Laroche, ‘‘Brain plasticity mechanisms and memory: A party of four,’’ The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry, vol. 13, pp. 492–505, 2007.
• [13] D. Chao-Yi, J. Lim, Y. Nam, and C. Kwang-Hyun, ‘‘Systematic analysis of synchronized oscillatory neuronal networks reveals an enrichment for coupled direct and indirect feedback motifs,’’ Bioinformatics, vol. 25, no. 13, pp. 1680–1685, 2009.
• [14] B. Albensi, ISynaptic Plasticity: New Research, 2009.
• [15] M. Mayford, S. Siegelbaum, and E. Kandel, ‘‘Synapses and memory storage,’’ Cold Spring Harbor perspectives in biology, vol. 4, no. 13, 2012.
• [16] N. Chenkov, H. Sprekeler, and R. Kempter, ‘‘Memory replay in balanced recurrent networks,’’ PLOS Computational Biology, vol. 13, no. 1, pp. 1–36, 2017.
• [17] M. T. Owens and K. D. Tanner, ‘‘Teaching as brain changing: Exploring connections between neuroscience and innovative teaching,’’ CBE—Life Sciences Education, vol. 16, no. 2, p. fe2, 2017.
• [18] J. Hennig, E. Oby, D. Losey, A. Batista, B. Yu, and S. Chase, ‘‘How learning unfolds in the brain: toward an optimization view,’’ Neuron, vol. 109, 2021.
• [19] O. Atay, A. Doncic, and J. M. Skotheim, ‘‘Switch-like transitions insulate network motifs to modularize biological networks.’’ Cell systems, vol. 32, pp. 121–132, 2016.
• [20] J.-R. Kim, Y. Yoon, and K.-H. Cho, ‘‘Coupled feedback loops form dynamic motifs of cellular networks,’’ Biophysical journal, vol. 94, pp. 359–65, 2008.
• [21] H. Koeppl and S. Haeusler, ‘‘Motifs, algebraic connectivity and computational performance of two data-based cortical circuit templates,’’ in Proceedings of the sixth International Workshop on Computational Systems Biology, 2009, p. 4.
• [22] L. Peris, M. Bisbal, J. Martínez-Hernández, Y. Saoudi, J. Jonckheere, M. Rolland, M. Sebastien, J. Brocard, E. Denarier, C. Bosc, C. Guerin, S. Gory-Fauré, J. C. Deloulme, F. Lanté, I. Arnal, A. Buisson, Y. Goldberg, L. Blanchoin, C. Delphin, and A. Andrieux, ‘‘A key function for microtubule-associated-protein 6 in activity-dependent stabilisation of actin filaments in dendritic spines,’’ Nature Communications.
• [23] D. Braha, ‘‘Patterns of ties in problem-solving networks and their dynamic properties,’’ Scientific Reports, vol. 10, p. 18137, 2020.
• [24] S. Patra and A. Mohapatra, ‘‘Application of dynamic expansion tree for finding large network motifs in biological networks,’’ PeerJ, vol. 7, p. e6917, 2019.
• [25] E. Martorana, G. Micale, A. Ferro, and A. Pulvirenti, ‘‘Establish the expected number of induced motifs on unlabeled graphs through analytical models,’’ Applied Network Science, vol. 5, p. 58, 2020.
• [26] M. Arbib, The Handbook of Brain Theory and Neural Network, 2003.
• [27] D. Basset and E. Bullmor, ‘‘Small-world brain networks,’’ The Neuroscientist : a review journal bringing neurobiolog, neurology and psychiatry, vol. 12, pp. 512–23, 2007.
• [28] B. Danielle and B. Edward, ‘‘Small-world brain networks revisited,’’ The Neuroscientist, vol. 23, 2016.
• [29] H. Schwöbbermeyer, Network Motifs. John Wiley and Sons, Ltd, 2008.
• [30] N. Borovok, E. Nesher, Y. Levin, M. Reichenstein, A. Pinhasov, and I. Michaelevski, ‘‘Dynamics of hippocampal protein expression during long-term spatial memory formation,’’ Molecular and Cellular Proteomics, vol. 15, p. mcp.M115.051318, 2015.
• [31] A. Spiegler, E. C. A. Hansen, C. Bernard, A. R. McIntosh, and V. K. Jirsa, ‘‘Selective activation of resting-state networks following focal stimulation in a connectome-based network model of the human brain,’’ eNeuro.
• [32] A. Mirisis, A. Alexandrescu, T. Carew, and A. Kopec, ‘‘The contribution of spatial and temporal molecular networks in the induction of long-term memory and its underlying synaptic plasticity,’’ AIMS Neuroscience, vol. 3, pp. 356–384, 2016.
• [33] R. Staudemeyer and E. Morris, ‘‘Understanding lstm – a tutorial into long short-term memory recurrent neural networks,’’ 2019.
• [34] J. Keener and J. Sneyd, Cellular Homeostasis. Springer New York, 2009, pp. 49–119.
• [35] B. F. Grewe, J. Gründemann, L. J. Kitch, J. A. Lecoq, J. G. Parker, J. D. Marshall, M. C. Larkin, P. E. Jercog, F. Grenier, J. Z. Li, A. Lüthi, and M. J. Schnitzer, ‘‘Neural ensemble dynamics underlying a long-term associative memory,’’ Nature, vol. 543, pp. 670 – 675, 2017.
• [36] N. S.R., I. Boybat, M. Gallo, E. Eleftheriou, A. Sebastian, and B. Rajendran, ‘‘Experimental demonstration of supervised learning in spiking neural networks with phase-change memory synapses,’’ Scientific Reports, vol. 10, 2020.
• [37] Z. Harper and C. Welzig, ‘‘Exploring spatiotemporal functional connectivity dynamics of the human brain using convolutional and recursive neural networks,’’ 2019, pp. 1–6.
• [38] T. Gorochowski, C. Grierson, and M. Di Bernardo, ‘‘Organization of feed-forward loop motifs reveals architectural principles in natural and engineered networks,’’ Science Advances, vol. 4, p. eaap9751, 2018.
• [39] D. Schachinger, ‘‘Simulation of extracellularly recorded activities from small nerve formations in the brain,’’ 2003.
• [40] P. Dayan and L. F. Abbott, Theoretical neuroscience: computational and mathematical modeling of neural systems. MIT press, 2005.
• [41] E. M. Izhikevich, Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting. The MIT Press, 2006.
• [42] Z. Tian and D. Zhou, ‘‘Library-based fast algorithm for simulating the hodgkin-huxley neuronal networks,’’ 2021.
• [43] T. Zhongqi and Z. Douglas, ‘‘Design fast algorithms for hodgkin-huxley neuronal networks,’’ 2021.
• [44] S. Navlakha, Z. Bar-Joseph, and A. Barth, ‘‘Network design and the brain,’’ Trends in Cognitive Sciences, vol. 22, 2017.
• [45] J. M. Bower, D. Beeman, J. M. Bower, and D. Beeman, ‘‘Compartmental modeling,’’ The Book of GENESIS: Exploring Realistic Neural Models with the GEneral NEural SImulation System, pp. 7–15, 1998.
• [46] W. Gerstner and W. M. Kistler, 2002.
• [47] J. Wang, J. Geng, and X. Fei, ‘‘Two-parameters hopf bifurcation in the hodgkin–huxley model,’’ Chaos, Solitons and Fractals, vol. 23, no. 3, pp. 973–980, 2005.
• [48] L. Meng and J. Xiang, ‘‘Brain network analysis and classification based on convolutional neural network,’’ Frontiers in Computational Neuroscience, vol. 12, 2018.
• [49] L. J. Boaz, R. R. A., K. Xiangnan, K. Sungchul, K. Eunyee, and R. Anup, ‘‘Graph convolutional networks with motif-based attention,’’ in Proceedings of the 28th ACM International Conference on Information and Knowledge Management, New York, NY, USA, 2019, p. 499–508.
• [50] K. Patrick, P. Karin, S. Achim, and M. Claus, ‘‘Recurrence resonance in three-neuron motifs,’’ Frontiers in Computational Neuroscience, vol. 13, p. 64, 2019.
• [51] P. Sabyasachi and M. Anjali, ‘‘Application of dynamic expansion tree for finding large network motifs in biological networks,’’ PeerJ, vol. 7, p. e6917, 2019.
• [52] ——, ‘‘Disjoint motif discovery in biological network using pattern join method,’’ IET Systems Biology, vol. 13, no. 5, pp. 213–224, 2019.
• [53] H. Li, C. Liu, and J. Wang, ‘‘Memory and computing function of four-node neuronal network motifs,’’ in Proceeding of the 11th World Congress on Intelligent Control and Automation, 2014, pp. 5818–5823.
• [54] L. Peris, M. Bisbal, and e. Martínez-Hernández, ‘‘A key function for microtubule-associated-protein 6 in activity-dependent stabilisation of actin filaments in dendritic spines,’’ Nature Communications, vol. 9, 2018.