MECHANISMS FOR ACTIVITY STATE AND NETWORK STRUCTURE DEPENDENT CONTROL OF THE DYNAMICS OF BIOLOGICAL NEURAL NETWORKS
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The main goal of the initial proposal was to identify novel ways of applying external brain stimulation to control or even correct pathological dynamics in biological neural networks and specifically in Parkinson's disease (PD).
From the beginning our aim was twofold: (1) To suppress pathological activity and (2) to recover the healthy physiological state of the neuronal network. To this end, we have devised a method for closed-loop control (delayed feedback control, DFC) of pathological oscillations, which achieves precisely these two goals. In models of large networks of spiking neurons (SNN), DFC proves to be highly effective, while at the same time it allows for a recovery of the original operating point of the system. Importantly, our theory allows us to calculate the space of control parameters for stimulation, thus overcoming the need for tedious manual tuning of parameters (Vlachos et al. 2016).
We also examined in detail the interplay between network dynamics and single neuron properties, which could be affected by stimulation (Sahasranamam et al. 2016). The problem of minimizing the number of stimulation locations sufficient for control of the overall network dynamics was addressed as well (Mengiste et al. 2015). Currently we are further developing our method by incorporating a spatial dimension into our models. This allows us to come up with strategies to narrow down on the site of stimulation.
The work has sparked interest in the DBS community, which is reflected in a number of invitations to scientific meetings. We have established a collaboration with an experimental group (Dr. Nico Mallet, Bordeaux) working on a PD animal model to test the method experimentally. First results are described below. We are also in ongoing discussions with the groups of Prof. Peter Brown (Oxford) and Prof. Peter Tass (Jülich) to discuss feasible approaches for the application in human PD patients.
Almost all experimental and theoretical work in DBS so far has focused on supra-threshold stimulation. Therefore, the effects of subthreshold stimulation on neural tissue are not known. We addressed this question both theoretically and in experiments in collaboration with the group of Prof. Ulrich Hofmann. Now that a strong theoretical foundation has been laid, the final project period was devoted to develop the closed-loop control paradigm further in conjunction with ongoing experiments, and to perform new model-driven experiments.
1. *Bujan AF, Aertsen A, Kumar A. Role of input correlations in shaping the variability and noise correlations of evoked activity in the neocortex. Journal of Neuroscience 35(22): 8611-8625, 2015
2. *Gallinaro JV, Rotter S. Associative properties of structural plasticity based on firing rate homeostasis in recurrent neuronal networks. Scientific Reports 8: 3754, 2018
3. *Hahn G, Bujan AF, Frégnac Y, Aertsen A, Kumar A. Communication through resonance in spiking neuronal networks. PLOS Computational Biology 10(8): e1003811, 2014
4. *Kumar A, Vlachos I, Aertsen A, Boucsein C. Challenges of understanding brain function by selective modulation of neuronal subpopulations. Trends in Neurosciences 36(10): 579-586, 2013
5. *Mengiste SA, Aertsen A, Kumar A. Effect of edge pruning on structural controllability and observability of complex networks. Scientific Reports 5: 18145, 2015
6. *Sahasranamam A, Vlachos I, Aertsen A, Kumar A (2016). Dynamical state of the network determines the efficacy of single neuron properties in shaping the network activity. Scientific Reports 6: 26029, 2016
7. *Schnepel P, Kumar A, Zohar M, Aertsen A, Boucsein C. Physiology and impact of horizontal connections in rat neocortex. Cerebral Cortex 25(10): 3818-3835, 2015
8. *Vlachos I, Deniz T, Aertsen A, Kumar A. Recovery of dynamics and function in spiking neural networks with closed-loop control. PLOS Computational Biology 12(2): e1004720, 2016
1. Gallinaro J, Rotter S. Non-random connectivity of networks induced by homeostatic structural plasticity. Berlin, Bernstein Conference 2018, www.bernstein- conference.de/2018
2. Gallinaro J, Rotter S. Associative properties of structural plasticity based on firing rate homeostasis. Berlin, Bernstein Conference 2018, http://www.bernstein- conference.de/2018
3. Roohisefat L, Mottaghi S, Li M, Hofmann U, Coenen V. Simulation of DBS Selective Field Deformation in the Proximity of White Matter Pathways, Comsol Conference, Munich, Germany, 2016
4. Vlachos I, Bernstein Conference 2014, Göttingen
5. Vlachos I, Neurotechnix, Rome, Italy 2014, Neuromodulation and neural prostheses
6. Vlachos I, GSO Workshop, Freiburg, 2014
7. Vlachos I, ELSC Annual Meeting, Ein-Gedi, Israel 2015
8. Vlachos I, Biomedical Translation Meeting, Oxford 2015, Application of invasive and non-invasive closed-loop methods to PD in human patients
9. Vlachos I, Computational Neuroscience and the Hybrid Brain, Freiburg 2015
10. Vlachos I, Parkinson's Disease Meeting, CNRS, Bordeaux 2016