System identification of the nonlinear dynamics in the thalamocortical circuit in response to patterned thalamic microstimulationin vivo

Abstract

Objective Nonlinear system identification approaches were used to develop a dynamical model of the network level response to patterns of microstimulation in-vivo. Approach The thalamocortical circuit of the rodent vibrissa pathway was the model system, with voltage sensitive dye imaging capturing the cortical response to patterns of stimulation delivered from a single electrode in the ventral posteromedial thalamus. The results of simple paired stimulus experiments formed the basis for the development of a phenomenological model explicitly containing nonlinear elements observed experimentally. The phenomenological model was fit using datasets obtained with impulse train inputs, Poisson-distributed in time and uniformly varying in amplitude. Main Results The phenomenological model explained 58% of the variance in the cortical response to out of sample patterns of thalamic microstimulation. Furthermore, while fit on trial averaged data, the phenomenological model reproduced single trial response properties when simulated with noise added into the system during stimulus presentation. The simulations indicate that the single trial response properties were dependent on the relative sensitivity of the static nonlinearities in the two stages of the model, and ultimately suggest that electrical stimulation activates local circuitry through linear recruitment, but that this activity propagates in a highly nonlinear fashion to downstream targets. Significance The development of nonlinear dynamical models of neural circuitry will guide information delivery for sensory prosthesis applications, and more generally reveal properties of population coding within neural circuits.

Rapid Effects of Vagus Nerve Stimulation on Sensory Processing Through Activation of Neuromodulatory Systems

“…Our laboratory has recently demonstrated that VNS can be used to induce a rapid, general improvement of thalamic sensory processing ( Figure 1 ). This is a continuation of our team’s studies investigating the effects of the LC-NE system on thalamocortical circuitry ( Rodenkirch et al, 2019 ), a critical stage for sensory processing and perception ( Saalmann and Kastner, 2011 ; Stanley et al, 2012 ; Wang et al, 2012 ; Millard et al, 2013 ; Kelly et al, 2014 ; Ollerenshaw et al, 2014 ; Wimmer et al, 2015 ; Rikhye et al, 2018 ). These studies found that direct activation of the LC-NE system (electrical or optogenetic), in a continuous tonic fashion, optimized intrathalamic dynamics for sensory processing.…”

Pupil-linked arousal modulates behavior in rats performing a whisker deflection direction discrimination task

“…This decrease in discrimination performance may be due to the reduction in time for the animal to accumulate evidence concerning the direction of the whisker deflection to make Go or No-Go decisions (Gold and Shadlen 2007). Neurons in the thalamus and cortex of the whisker pathway display strong sensitivity to the velocity of whisker movement; thus another explanation is that the velocity of the shortest duration stimulus was too strong and saturated the neural responses within the pathway, leading to deterioration in performance (Lee and Simons 2004;Millard et al 2013;Pinto et al 2000;Stüttgen and Schwarz 2008;Zheng et al 2015). However, the peak velocity of whisker movement associated with stick-slip events (repeated sticking then high-acceleration slipping over surfaces when rats whisk across objects) is comparable to the highest velocity used in this study (i.e.,~1,200°/s) (Jadhav et al 2009;Ritt et al 2008).…”

Optogenetic feedback control of neural activity

“…Alternatively, if an accurate model of feedforward network dynamics could be incorporated into the controller, reliable control over fast events might be possible with a modestly sized population of cells, a slower feedback loop, and standard opsin variants. This is a viable approach in circuits for which predictive models of feedforward network dynamics are available, such as early visual, auditory, and vibrissal pathways ( Wu et al, 2006 ; Millard et al, 2013 ), or, for which accurate input/output relationships can be deduced in situ using real-time system identification ( Grosenick et al, 2015 ). Additionally, control algorithms that incorporate models of feed-forward neural dynamics will be more capable of stabilizing firing in unstable circuits, such as epileptic networks, without total cessation of ongoing activity.…”