Kevin A. Mazurek

Feed forward and feedback control for over-ground locomotion in anaesthetized cats

The biological central pattern generator (CPG) integrates open and closed loop control to produce over-ground walking. The goal of this study was to develop a physiologically based algorithm capable of mimicking the biological system to control multiple joints in the lower extremities for producing over-ground walking. The algorithm used state-based models of the step cycle each of which produced different stimulation patterns. Two configurations were implemented to restore over-ground walking in five adult anaesthetized cats using intramuscular stimulation (IMS) of the main hip, knee and ankle flexor and extensor muscles in the hind limbs. An open loop controller relied only on intrinsic timing while a hybrid-CPG controller added sensory feedback from force plates (representing limb loading), and accelerometers and gyroscopes (representing limb position). Stimulation applied to hind limb muscles caused extension or flexion in the hips, knees and ankles. A total of 113 walking trials were obtained across all experiments. Of these, 74 were successful in which the cats traversed 75% of the 3.5 m over-ground walkway. In these trials, the average peak step length decreased from 24.9 ± 8.4 to 21.8 ± 7.5 (normalized units) and the median number of steps per trial increased from 7 (Q1 = 6, Q3 = 9) to 9 (8, 11) with the hybrid-CPG controller. Moreover, within these trials, the hybrid-CPG controller produced more successful steps (step length ≤ 20 cm; ground reaction force ≥ 12.5% body weight) than the open loop controller: 372 of 544 steps (68%) versus 65 of 134 steps (49%), respectively. This supports our previous preliminary findings, and affirms that physiologically based hybrid-CPG approaches produce more successful stepping than open loop controllers. The algorithm provides the foundation for a neural prosthetic controller and a framework to implement more detailed control of locomotion in the future.

Configuring silicon neural networks using genetic algorithms

There are various neuron models which can be used to emulate the neural networks responsible for cortical and spinal processes. One example is the Central Pattern Generator (CPG) networks, which are spinal neural circuits responsible for controlling the timing of periodic systems in vertebrates. In order to model the CPG effectively, it is necessary to model not just multiple individual neurons, but also the interactions between them. Due to the complexity of these types of systems, CPG models typically require large numbers (> 10) of parameters making them difficult to understand and control. Genetic Algorithms (GAs) provide a means for optimizing systems with many parameters. We present an automated method that uses a GA to And sets of parameters for a silicon implementation of a neural network capable of producing CPG type signals. This methodology can be used to configure large silicon neural circuits. In this work, constructed networks involving an 18-parameter space, can be used for controlling legged robots and neuroprosthetic devices.

Restoring stepping after spinal cord injury using intraspinal microstimulation and novel control strategies

The overall objective of this project is to develop a feedback-driven intraspinal microstimulation (ISMS) system. We hypothesize that ISMS will enhance the functionality of stepping by reducing muscle fatigue and producing synergistic movements by activating neural networks in the spinal cord. In the present pilot study, the controller was tested with ISMS and external sensors (force plates, gyroscopes, and accelerometers). Cats were partially supported in a sling and bi-laterally stepped overground on a 4-m instrumented walkway. The walkway had variable friction. Limb angle was controlled to within 10° even in the presence of variable friction. Peak ground reaction forces in each limb were approximately 12% of body weight (12.5% was full load bearing in this experimental setup); rarely, the total supportive force briefly decreased to as low as 4.1%. Magnetic resonance images were acquired of the excised spinal cord and the implanted array. The majority of electrodes (75%) were implanted successfully into their target regions. This represents the first successful application of ISMS for overground walking.

A Mixed-Signal VLSI System for Producing Temporally Adapting Intraspinal Microstimulation Patterns for Locomotion.

Neural pathways can be artificially activated through the use of electrical stimulation. For individuals with a spinal cord injury, intraspinal microstimulation, using electrical currents on the order of 125 μA, can produce muscle contractions and joint torques in the lower extremities suitable for restoring walking. The work presented here demonstrates an integrated circuit implementing a state-based control strategy where sensory feedback and intrinsic feed forward control shape the stimulation waveforms produced on-chip. Fabricated in a 0.5 μm process, the device was successfully used in vivo to produce walking movements in a model of spinal cord injury. This work represents progress towards an implantable solution to be used for restoring walking in individuals with spinal cord injuries.

Parameter Estimation of a Spiking Silicon Neuron

Spiking neuron models are used in a multitude of tasks ranging from understanding neural behavior at its most basic level to neuroprosthetics. Parameter estimation of a single neuron model, such that the models output matches that of a biological neuron is an extremely important task. Hand tuning of parameters to obtain such behaviors is a difficult and time consuming process. This is further complicated when the neuron is instantiated in silicon (an attractive medium in which to implement these models) as fabrication imperfections make the task of parameter configuration more complex. In this paper we show two methods to automate the configuration of a silicon (hardware) neurons parameters. First, we show how a Maximum Likelihood method can be applied to a leaky integrate and fire silicon neuron with spike induced currents to fit the neurons output to desired spike times. We then show how a distance based method which approximates the negative log likelihood of the lognormal distribution can also be used to tune the neurons parameters. We conclude that the distance based method is better suited for parameter configuration of silicon neurons due to its superior optimization speed.

Locomotion Processing Unit

A proposed Locomotion Processing Unit (LPU) is described for generating stimulation patterns for restoring walking in individuals with spinal cord injury (SCI). The LPU operates using sensory and timing based control providing feed forward and feedback information. By breaking down different components of locomotion into states, the LPU activates different muscle groups, or synergies, to recreate the desired functional movements. The LPU circuitry was simulated and compared against another controller designed to restore locomotion in an anesthetized cat to validate its performance.

Injecting Instructions into Premotor Cortex

The premotor cortex (PM) receives inputs from parietal cortical areas representing processed visuospatial information, translates that information into programs for particular movements, and communicates those programs to the primary motor cortex (M1) for execution. Consistent with this general function, intracortical microstimulation (ICMS) in the PM of sufficient frequency, amplitude, and duration has been shown to evoke complex movements of the arm and hand that vary systematically depending on the locus of stimulation. Using frequencies and amplitudes too low to evoke muscle activity, however, we found that ICMS in the PM can provide instructions to perform specific reach, grasp, and manipulate movements. These instructed actions were not fixed but rather were learned through associations between the arbitrary stimulation locations and particular movements. Low-amplitude ICMS at different PM locations thus evokes distinguishable experiences that can become associated with specific movements arbitrarily, providing a novel means of injecting information into the nervous system.

Mirror Neuron Populations Represent Sequences of Behavioral Epochs During Both Execution and Observation

Mirror neurons (MNs) have the distinguishing characteristic of modulating during both execution and observation of an action. Although most studies of MNs have focused on various features of the observed movement, MNs also may monitor the behavioral circumstances in which the movement is embedded, including time periods preceding and following the observed movement. Here, we recorded multiple MNs simultaneously from implanted electrode arrays as two male monkeys executed and observed a reach, grasp, and manipulate task involving different target objects. MNs were recorded from premotor cortex (PM-MNs) and primary motor cortex (M1-MNs). During execution trials, hidden Markov models (HMMs) applied to the activity of either PM-MN or M1-MN populations most often detected sequences of four hidden states, which we named according to the behavioral epoch during which each state began: initial, reaction, movement, and final. The hidden states of MN populations thus reflected not only the movement, but also three behavioral epochs during which no movement occurred. HMMs trained on execution trials could decode similar sequences of hidden states in observation trials, with complete hidden state sequences decoded more frequently from PM-MN populations than from M1-MN populations. Moreover, population trajectories projected in a 2D plane defined by execution trials were preserved in observation trials more for PM-MN than for M1-MN populations. These results suggest that MN populations represent entire behavioral sequences, including both movement and non-movement. PM-MN populations showed greater similarity than M1-MN populations in their representation of behavioral sequences during execution versus observation. SIGNIFICANCE STATEMENT Mirror neurons (MNs) are thought to provide a neural mechanism for understanding the actions of others. However, for an action to be understood, both the movement per se and the non-movement context before and after the movement need to be represented. We found that simultaneously recorded MN populations encoded sequential hidden neural states corresponding approximately to sequential behavioral epochs of a reach, grasp, and manipulate task. During observation trials, hidden state sequences were similar to those identified in execution trials. Hidden state similarity was stronger for MN populations in premotor cortex than for those in primary motor cortex. Execution/observation similarity of hidden state sequences may contribute to understanding the actions of others without actually performing the action oneself.