Recovery of Dynamics and Function in Spiking Neural Networks with Closed-Loop Control

Abstract

There is a growing interest in developing novel brain stimulation methods to control disease-related aberrant neural activity and to address basic neuroscience questions. Conventional methods for manipulating brain activity rely on open-loop approaches that usually lead to excessive stimulation and, crucially, do not restore the original computations performed by the network. Thus, they are often accompanied by undesired side-effects. Here, we introduce delayed feedback control (DFC), a conceptually simple but effective method, to control pathological oscillations in spiking neural networks (SNNs). Using mathematical analysis and numerical simulations we show that DFC can restore a wide range of aberrant network dynamics either by suppressing or enhancing synchronous irregular activity. Importantly, DFC, besides steering the system back to a healthy state, also recovers the computations performed by the underlying network. Finally, using our theory we identify the role of single neuron and synapse properties in determining the stability of the closed-loop system.

A theory of how active behavior stabilises neural activity: Neural gain modulation by closed-loop environmental feedback

“…We supported this with modelling and data analysis in the whisker system and in a behaving zebrafish, see summary Fig 7 . The formal component of our theory, i.e., that closed-loop sensory feedback can modulate a system's gain, is well documented in dynamical systems theory and control theory [ 32 , 33 ]. This gain control occurs even though the pathways mediating feedback are purely additive (c.f.…”

A Review of Control Strategies in Closed-Loop Neuroprosthetic Systems

“…Although closed-loop systems have been demonstrated experimentally there remain significant limits on our ability to describe the activity in the brain, and consequently develop control policies to respond to that activity. Simulation of cortical activity (Ehrens et al, 2015 ; Sandler et al, 2015 ; Vlachos et al, 2016 ), the use of experimental platforms (Keren and Marom, 2014 ), and the use of animal models has enabled the development of a wide range of neuroprosthetic systems. However, the appropriate method to transition these systems in human subjects is not clear.…”

“…By taking advantage of the chaotic system sensitivity to perturbation, system state can be changed with minimal cost. In Vlachos et al ( 2016 ) the use of delayed feedback control enables closed loop control of a seizure model (a spiking neural network) and the recovery of the non-seizure dynamics, while in Slutzky et al ( 2003 ) seizure activity induced in rat hippocampal slice preparations was moderately controlled.…”

Controlling Synchronization of Spiking Neuronal Networks by Harnessing Synaptic Plasticity

“…Recently, control-theoretic approaches have been used to design more effective and energy efficient desynchronization strategies (Popovych and Tass, 2014). These optimal stimulation protocols include a single pulse minimum energy desynchronizing control input (Deuschl et al, 2006; Nabi et al, 2013a,b; Mauroy et al, 2014; Wilson and Moehlis, 2014; Monga et al, 2018) and closed-loop delayed feedback (Hauptmann et al, 2005; Popovych et al, 2005, 2017; Kiss et al, 2007; Vlachos et al, 2016) approaches. The minimum energy control input approach designs a pulse that pushes the state of the network to a phaseless set-point, which is the point where all the isochrons of the system converge (Nabi et al, 2013a,b).…”

“…Another approach is closed-loop delayed feedback control. In the closed-loop delayed feedback control approach (Vlachos et al, 2016), the time-delayed average population activity is used as a feedback to desynchronize the network. Since this approach only feeds the past population activity, the applied desynchronizing input is only active whenever the network becomes synchronous.…”