A simulated actuator driven by motor cortical signals

Dynamic neural network models of the premotoneuronal circuitry controlling wrist movements in primates

Dynamic recurrent neural networks were derived to simulate neuronal populations generating bidirectional wrist movements in the monkey. The models incorporate anatomical connections of cortical and rubral neurons, muscle afferents, segmental interneurons and motoneurons; they also incorporate the response profiles of four populations of neurons observed in behaving monkeys. The networks were derived by gradient descent algorithms to generate the eight characteristic patterns of motor unit activations observed during alternating flexion-extension wrist movements. The resulting model generated the appropriate input-output transforms and developed connection strengths resembling those in physiological pathways. We found that this network could be further trained to simulate additional tasks, such as experimentally observed reflex responses to limb perturbations that stretched or shortened the active muscles, and scaling of response amplitudes in proportion to inputs. In the final comprehensive network, motor units are driven by the combined activity of cortical, rubral, spinal and afferent units during step tracking and perturbations. The model displayed many emergent properties corresponding to physiological characteristics. The resulting neural network provides a working model of premotoneuronal circuitry and elucidates the neural mechanisms controlling motoneuron activity. It also predicts several features to be experimentally tested, for example the consequences of eliminating inhibitory connections in cortex and red nucleus. It also reveals that co-contraction can be achieved by simultaneous activation of the flexor and extensor circuits without invoking features specific to co-contraction.

Statistical encoding model for a primary motor cortical brain-machine interface

A number of studies of the motor system suggest that the majority of primary motor cortical neurons represent simple movement-related kinematic and dynamic quantities in their time-varying activity patterns. An example of such an encoding relationship is the cosine tuning of firing rate with respect to the direction of hand motion. We present a systematic development of statistical encoding models for movement-related motor neurons using multielectrode array recordings during a two-dimensional (2-D) continuous pursuit-tracking task. Our approach avoids massive averaging of responses by utilizing 2-D normalized occupancy plots, cascaded linear-nonlinear (LN) system models and a method for describing variability in discrete random systems. We found that the expected firing rate of most movement-related motor neurons is related to the kinematic values by a linear transformation, with a significant nonlinear distortion in about 1/3 of the neurons. The measured variability of the neural responses is markedly non-Poisson in many neurons and is well captured by a "normalized-Gaussian" statistical model that is defined and introduced here. The statistical model is seamlessly integrated into a nearly-optimal recursive method for decoding movement from neural responses based on a Sequential Monte Carlo filter.

Extraction algorithms for cortical control of arm prosthetics

Now that recordings of multiple, individual action potentials are being made with chronic electrodes, it seems that previous work showing simple encoding of movement parameters in these spike trains can be used as a real-time control signal for prosthetic arms. Efficient extraction algorithms can compensate for the limited ensemble sample acquired with this emerging technology.

Motor systems

The field of motor control has broadened considerably over the past decade. Increasingly detailed information has accrued about the cellular and molecular processes involved in motor pattern generation and motor learning while, at the other extreme, the comparison of studies in humans and monkeys has begun to bridge the gap between cognitive and motor functions. The most striking feature of recent research has been the intense use of electrophysiological procedures in behaving monkeys and non-invasive imaging procedures in humans to elucidate details of sensory-motor transformations and the functional roles of different brain regions in the learning, planning and execution of movements.

Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex

To determine whether simultaneously recorded motor cortex neurons can be used for real-time device control, rats were trained to position a robot arm to obtain water by pressing a lever. Mathematical transformations, including neural networks, converted multineuron signals into 'neuronal population functions' that accurately predicted lever trajectory. Next, these functions were electronically converted into real-time signals for robot arm control. After switching to this 'neurorobotic' mode, 4 of 6 animals (those with > 25 task-related neurons) routinely used these brain-derived signals to position the robot arm and obtain water. With continued training in neurorobotic mode, the animals' lever movement diminished or stopped. These results suggest a possible means for movement restoration in paralysis patients.

Cortical control of reaching movements

Recent studies provide further support for the hypothesis that spatial representations of limb position, target locations, and potential motor actions are expressed in the neuronal activity in parietal cortex. In contrast, precentral cortical activity more strongly expresses processes involved in the selection and execution of motor actions. As a general conceptual framework, these processes may be interpreted in terms of such formalisms as sensorimotor transformations and 'internal models'.