In the Chapter 2, the results from a novel in vitro brain-machine interface experiment
are presented. By using a virtual model of the biomechanics of a crayfish leg, experiments could be in two conditions. The first was when movements of the leg were coupled to motor neuron activity and the second was when movements of the leg were decoupled from motor neuron activity. Experiments showed that the motor network was capable of reproducing reflex
responses as well as rhythmic bursting corresponding to fictive locomotion while using the in
vitro brain-machine interface. In addition, these results indicated that closing the sensory feedback loop by coupling biomechanical feedback to motor network activity increased the frequency of bursting in the network.
In Chapter 3, a neuromechanical model of the in vitro brain-machine interface
experiments was constructed in order to test the hypothesis that the increased burst frequency of the motor network was due to positive feedback. The model was a simplified representation of in vitro results [1] and was capable of reproducing reflex responses as well as rhythmic bursting. Simulation results showed that blocking assistance reflexes reduced the burst frequency of the network and was consistent with other locomotor systems in which
assistance reflexes help to facilitate transitions between stance and swing during step cycles of a leg [2].
In order to determine how sensory feedback changes the output of the sensorimotor circuit, it was necessary to run a large number of simulations of the neuromechanical model and to sample the parameter space in a structured way. Consequently, it was critical to develop an efficient way of classifying network activity as quiescent, bursting, or tonically spiking. In Chapter 4, a novel spike train analysis algorithm is presented. The Extended Hill- Valley analysis method used fluctuations in a smoothed, history-dependent analysis signal that was derived from a neural spike train. Briefly, rapid fluctuations were classified as bursts and
13 slow, maintained fluctuations were determined to be tonic spiking. The algorithm was tested on a variety of spike trains and its performance was qualitatively as well as quantitatively compared to two other methods that are commonly used.
In Chapter 5, a database of simulations was run in order to determine how the network behaved when the motor CPG was activated separately from resistance and assistance
reflexes. This was not possible to do with in vitro experiments. In addition, simulations were run
in one of two biomechanical coupling conditions. The first was when biomechanical feedback was based on movements of the virtual leg that was driven by activity of the MN CPG. The second condition was when biomechanical feedback was decoupled from MN activity.
Consequently, simulation results illustrated a separable contribution of the MN CPG, resistance reflexes, and assistance reflexes to changes in network activity. While the MN CPG produced two activity regimes (quiescence and rhythmic bursting), reducing the strength of resistance reflexes and increasing the strength of assistance reflex interneurons (ARINs) resulted in three activity regimes (quiescence, rhythmic bursting, and tonic spiking). Additional simulations separated the effect of sensory feedback and showed that resistance reflexes mediated short bouts of tonic spiking (different than the tonic spiking regime) in the quiescent and rhythmic bursting regimes while ARIN activation increased the burst frequency of the network and induced a tonic spiking regime.
In Chapter 6, simulations were used to determine whether the balance of resistance and assistance reflexes was able to reorganize network dynamics. Sets of simulations were run under three levels of CPG activation corresponding to quiescence, rhythmic bursting (fictive locomotion), and tonic spiking. In each set of simulations, the strength of resistance and assistance reflexes were varied independently by changing the activation of primary afferent depolarizing interneurons (PADIs) or the activation of ARINs. CPG activity states were classified by analyzing simulations that were run when biomechanical feedback was decoupled from
motor output. When the biomechanical feedback loop was closed, results showed that sensory feedback reorganized network dynamics if the CPG was in a quiescent or rhythmic bursting state. In addition, sensory feedback induced a region of short bouts of tonic spiking when there was an imbalance between resistance and assistance reflexes. There was no clear change in network dynamics when the CPG was in a tonically active state in the
biomechanically decoupled condition.
Finally, in Chapter 7 a mathematical model is presented in order to determine whether sensory feedback is changing the dynamics of the underlying MN CPG. The mathematical model reproduced qualitative features of the neuromechanical model that included the three regimes induced by sensory feedback as well as the two regimes created by the CPG alone as described in Chapter 5. In addition, examples of simplified feedback dynamics were presented to illustrate how the mathematical model can account for a range of behaviors observed for different ratios of positive negative feedback mediated by assistance and resistance reflexes, respectively. A bifurcation analysis of the mathematical model showed that sensory feedback was not changing the underlying structure of the bifurcations, but, instead, was moving the operating point of the network around the bifurcations set up by the central oscillator.
15
2 The Effect of Sensory Feedback on Crayfish Posture and Locomotion: I. Experimental Analysis of Closing the Loop
[23] Chung B, Bacque-Cazenave J, Cofer DW, Cattaert D, Edwards DH. (2015). The effect of sensory feedback on crayfish posture and locomotion: I. Experimental analysis of closing the
loop. J Neurophysiol. 113: 1763-1771. DOI: 10.1152/jn.00248.2014.
As primary author, my contributions to this work include:
- Set up of the hybrid experimental preparation and electrophysiology rig
- Running 4 of the 6 experiments analyzed in the results
Copyright by
the American Physiological Society 2015
17