We show that an ongoing locomotor pattern can be modulated by application of discrete electrical stimuli to the spinal cord at specific phases of the locomotor cycle. Data is presented from a series of experiments on in vitro lamprey spinal cords, which were used as an animal model for severe spinal cord injury. For any given stimulus, the effects on frequency, length, and symmetry of locomotor output show a strong dependence on the phase at which stimulation is applied. The most significant changes are seen when stimulation occurs during motor bursting: stimuli applied to the ipsilateral spinal hemicord increase the burst length, while stimuli applied to the contralateral spinal hemicord decrease the burst length. Simulations using experimentally-measured phase-dependent responses indicate that by monitoring the state of the neural system, it should be possible to apply stimuli at the appropriate times to modulate the lamprey "gait" on a cycle-by-cycle basis. Eventually, this approach could lead to development of a neuroprosthetic device for restoring locomotion after paralysis.

The locomotor controller for walking, running, swimming, and flying animals is based on a central pattern generator (CPG). Models of CPGs as systems of coupled nonlinear oscillators have been proposed and have been used for the control of robots. In this paper we describe the implementation of an adaptive CPG model in a compact, custom analog VLSI circuit. We demonstrate the function of the chip by controlling an underactuated, running robotic leg. This circuit has adaptive properties that allow it to tune its behavior based on sensory feedback. To our knowledge this is the first instance of an adaptive CPG chip. This approach supports the construction of extremely inexpensive, low power and compact controllers for walking, flying and swimming machines.

We developed an analog very large-scale integrated system of two mutually inhibitory silicon neurons that display several different stable oscillations. For example, oscillations can be synchronous with weak inhibitory coupling and alternating with relatively strong inhibitory coupling. All oscillations observed experimentally were predicted by bifurcation analysis of a corresponding mathematical model. The synchronous oscillations do not require special synaptic properties and are apparently robust enough to survive the variability and constraints inherent in this physical system. In biological experiments with oscillatory neuronal networks, blockade of inhibitory synaptic coupling can sometimes lead to synchronous oscillations. An example of this phenomenon is the transition from alternating to synchronous bursting in the swimming central pattern generator of lamprey when synaptic inhibition is blocked by strychnine. Our results suggest a simple explanation for the observed oscillatory transitions in the lamprey central pattern generator network: that inhibitory connectivity alone is sufficient to produce the observed transition.