Sensory Feedback in a Half-Center Oscillator Model

Abstract

We hypothesize that one role of sensorimotor feedback for rhythmic movements in biological organisms is to synchronize the frequency of movements to the mechanical resonance of the body. Our hypothesis is based on recent studies that have shown the advantage of moving at mechanical resonance and how such synchronization may be possible in biology. We test our hypothesis by developing a physical system that consists of a silicon-neuron central pattern generator (CPG), which controls the motion of a beam, and position sensors that provide feedback information to the CPG. The silicon neurons that we use are integrated circuits that generate neural signals based on the Hodgkin-Huxley dynamics. We use this physical system to develop a model of the interaction between the sensory feedback and the complex dynamics of the neurons to create the closed-loop system behavior. This model is then used to describe the conditions under which our hypothesis is valid and the general effects of sensorimotor feedback on the rhythmic movements of this system.

A Computational Model for Rhythmic and Discrete Movements in Uni- and Bimanual Coordination

“…Although this model is formulated at a phenomenological level, it nevertheless captures the main properties of the CPGs: (1) generation of robust oscillations with nonrhythmic input, (2) organization around two mutually inhibitory half-centers, and (3) the presence of local feedback loops simulating afferent pathways that act as stiffness terms (Dimitrijevic et al, 1998; van de Crommert et al, 1998; Kuo, 2002; Buschges, 2005; Frigon & Rossignol, 2006; Simoni & DeWeerth, 2007). This model is based on the Matsuoka oscillator, which has been used successfully to simulate a large body of rhythmic movements (Matsuoka, 1985, 1987; Taga, 1995a, 1995b; Williamson, 1998; Sternad et al, 2000; de Rugy et al, 2003; de Rugy & Sternad, 2003; Verdaasdonk et al, 2006; Williams & DeWeerth, 2007; White et al, 2008).…”

“…More recently, the Matsuoka oscillator was extended with a term representing the feedback information from the afferent pathways originating from the muscle spindles (Dimitrijevic et al, 1998; van de Crommert, Mulder, & Duysens, 1998; Kuo, 2002; Buschges, 2005; Frigon & Rossignol, 2006; Simoni & DeWeerth, 2007). One way to include this sensory feedback into the firing rate equations (2.1) is in terms of a negative inhibitory influence (Williamson, 1998): $$\begin{array}{c}left{t}_1{\stackrel{.}{\psi }}_i=-{\psi }_i-\beta {\varphi }_i-\eta {[{\psi }_j]}^{+}-\sigma {[\theta -{\theta }^{★}]}^{+}+u_i,\\ t_1{\stackrel{.}{\psi }}_j=-{\psi }_j-\beta {\varphi }_j-\eta {[{\psi }_i]}^{+}-\sigma {[-(\theta -{\theta }^{★})]}^{+}+u_j,\end{array}$$ where σ quantifies the strength of this sensory coupling and θ ★ is the reference position of the output.…”

Closed-loop control of a minimal central pattern generator network

“…see [27,11,28,29,30,31,32,33]). Although some studies have included the role of sensory feedback [34] in a half-center oscillator, the closed-loop control of the irregular activity in this minimal CPG circuit is largely understudied. In this paper we have explored a simple closed-loop protocol to regularize the activity of a minimal CPG circuit by changing one of its inhibitory conductances.…”

Functional Rehabilitation: Coordination of Artificial and Natural Controllers

“…However the need for adaptation of the system to environmental changes, external requirements or proprioceptive information through sensory signals is more rarely addressed. We can nevertheless refer the reader to a few recent papers in the field which give a good idea of the state of the art (Fukuoka et al, 2003;Dong et al, 2006;Simoni and DeWeerth, 2007). Actually, there is a lack of design tools in the framework of oscillators and synchronization.…”