THE SYNCHRONIZATION BEHAVIOR OF BASAL GANGLIA

Abstract

A network model of basal ganglia (BG) that comprises striatum, internal and external segments of globus pallidus, subthalamic nucleus, substantia nigra pars reticulate neuronal sub populations and thalamus is constructed. The dynamic behavior of network is investigated using Izhikevich neuron model. The influences of dopamine, the synaptic strength and the neuronal interconnection density on the synchronization behavior of the subpopulations are investigated. In the case of dopamine depletion, the increased striatal synchronization is observed. Considerable effect on the synchronization of other basal ganglia sub populations is not observed in the case of dopamine depletion. The highest synchronization values are observed for the lowest synaptic strength and the neuronal interconnection density. The decreased neuronal interconnection density causes a decrease in the fluctuation in synchronization in thalamic neurons depending on dopamine value.

Keywords

• basal ganglia,synchronization

References

1. REFERENCES
2. [1] T. Arakaki, “Collective dynamics of basal ganglia-thalamo-cortical loops and their roles in functions and dysfunctions”, Neurons and Cognition [q-bio.NC]. Université Pierre et Marie Curie - Paris VI, 2016. English.
3. [2] Park , L. L. Rubchinsky, “Potential Mechanisms for Imperfect Synchronization in Parkinsonian Basal Ganglia”, PLOS ONE, Volume 7 , Issue 12, 2012 , pp. 1-12.
4. [3] Galvan, A. Devergnas, T. Wichmann, “Alterations in neuronal activity in basal ganglia-thalamocortical circuits in the parkinsonian state”, Frontiers in Neuroanatomy, Vol. 9, Art. 5, 2015, pp.1-21.
5. [4] Mandali, M. Rengaswam, V.S. Chakravarthy, A. A.Moustafa, “A spiking Basal Ganglia model of synchrony, exploration and decision making”, Frontiers in Neuroscience, Vol. 9, Art. 191, 2015 , pp.1-21.
6. [5] W. D. Hutchison, J. O. Dostrovsky, J. R. Walters, R. Courtemanche, T. Boraud, J. Goldberg, P. Brown, “Neuronal Oscillations in the Basal Ganglia and Movement Disorders: Evidence from Whole Animal and Human Recordings”, The Journal of Neuroscience, 24(42), 2004 , pp. 9240 –9243.
7. [6] T. Niikura, H. Tajima, Y. Kita, “Neuronal Cell Death in Alzheimer’s Disease and a Neuroprotective Factor, Humanin”, Current Neuropharmacology, 4, 2006, pp.139-147.
8. [7] G. Kashyap, D. Bapat, D. Das, R.Gowaikar, R. E.Amritkar, G. Rangarajan, V. Ravindranath, G.Ambika, “Synapse loss and progress of Alzheimer’s disease -A network model”, Scientific Reports, 9: 6555, 2019, pp.1-9.

9. [8] Liu, J. Wanga, H. Yua, B. Denga, X. Weia, H. Lic, K. A. Loparob, C. Fietkiewicz, “Dynamical analysis of Parkinsonian state emulated by hybrid Izhikevich neuron models”, Commun Nonlinear Sci Numer Simulat.,Vol. 28, 2015, pp. 10–26.
10. [9] Y. Cakir, “Modeling influences of dopamine on synchronization behavior of striatum”, Network: Computation in Neural Systems, Vol. 28, No 1, 2017, pp. 28-52.
11. [10] C.R. Gerfen, D.J. Surmeier, “Modulation of striatal projection systems by dopamine”, Annu Rev Neurosci., 34, 2011, pp. 441–466.
12. [11] M.D. Humphries, J. Obeso, J.K. Dreyer, “Insights into Parkinson’s disease from computational models of the basal ganglia”, Neurol Neurosurg Psychiatry, 89, 2018, pp.1181–1188.
13. [12] S. J. van Albada, P. A. Robinson, “Mean-field modeling of the basal ganglia-thalamocortical system. I Firing rates in healthy and parkinsonian states”, Journal of Theoretical Biology, Vol. 257, 2009, pp. 642–663.
14. [13] E.M. Izhikevich, “Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting”, The MIT Press Cambridge, Massachusetts, London, England. 2007.
15. [14] Y. Cakir, “Modeling of synchronization behavior of bursting neurons at nonlinearly coupled dynamical networks” Network: Computation in Neural Systems, Vol.27 No. 4, 2016, pp. 289-305.
16. [15] D. Humphries, R. Wood, K. Gurney, “Dopamine-modulated dynamic cell assemblies generated by the GABAergic striatal microcircuit”, Neural Networks, Vol. 22, No. 8, 2009, pp. 1174-1188.
17. [16] Guo, Q. Wang, M. Perc, “Complex synchronous behavior in interneuronal networks with delayed inhibitory and fast electrical synapses”, Phys Rev E., Vol. 85, 061905, 2012, pp. 1-8.
18. [17] Hansel, H. Sompolinsky , “Synchronization and Computation in a Chaotic Neural Network”, Phys Rev Lett., Vol. 68, No. 5, 1992, pp.718–721.