DynControl

MECHANISMS FOR ACTIVITY STATE AND NETWORK STRUCTURE DEPENDENT CONTROL OF THE DYNAMICS OF BIOLOGICAL NEURAL NETWORKS


Relevant for Research Area

A - Foundations


Summary

The main goal of DynControl is to find 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. We also examine in detail the interplay between network dynamics and single neuron properties, which could be affected by stimulation. The problem of minimizing the number of stimulation locations sufficient for control of the overall network dynamics is addressed as well. Currently we are further developing our method by incorporating a spatial dimension into our models. This allows us to devise strategies in order to narrow down the sites of stimulation. Almost all experimental and theoretical work in DBS so far has focussed on suprathreshold stimulation. We also plan to address the effects of subthreshold stimulation on neural tissue both theoretically and in experiments. 


Research Status

We have developed a method for closed-loop control of neural activity. The most important result is that we not only manage to suppress undesired activity, but we also recover the ability of the network to perform meaningful computations. That is, we restore both dynamics and function. We have already started to investigate closed-loop interactions and have accumulated experience that is valuable. We have also identified the advantages of true closed-loop control as opposed to other current approaches such as event-triggered control. Finally, we have shown that it is essential for the recovery of the functional properties to stimulate with much lower intensities than it is currently practiced. Thus, we have started to pave the way towards an understanding of how information should be encoded into the brain in a neurobiological meaningful way.

Vlachos I, Deniz T, Aertsen A, Kumar A (2016) Recovery of dynamics and function in spiking neural networks with closed-loop control. PLoS Comput Biol, volume: 12, issue: 2.