Cortico-Spinal Neural Interface to Restore Hindlimb Movements in Spinally-Injured Rabbits

Neurophysiology - This study aimed to design a neural interface that extracts motor commands from the cerebral cortex and generates fuzzy-based intraspinal stimulations to restore the hindlimb...     

Movements of Indian Flying Fox in Myanmar as a Guide to Human-Bat Interface Sites

EcoHealth - Frugivorous bats play a vital role in tropical ecosystems as pollinators and seed dispersers but are also important vectors of zoonotic diseases. Myanmar sits at the intersection of...     

Use of Shallow Basins to Restore Cutover Peatlands: Hydrology

Basins 20‐, 10‐, and 4‐m wide were excavated 15 to 20 cm into cutover peat fields near Lac Saint Jean, Québec, Canada to facilitate the establishment of Sphagnum mosses. Sphagnum diaspores (fragments) and straw mulch were spread over the excavated surfaces, a control peat field, and a mulch‐protected site without basins. Mean water tables in the 20‐, 10‐, and 4‐m wide basins and the mulch‐protected site were 27.2, 8.3, 11.4, and 9.7 cm higher, respectively, than in the control peat field in May to August 1996. Similar improvements were observed in 1997 (a drier summer). The higher water table was due to lowering of the peat surface with respect to the local water table, retention of meltwater and stormwater by the peripheral ridges formed during excavation, retention of water during drier periods by the groundwater mound beneath the ridges, and mulch. Soil moisture was always higher in the experimental basins than in the control peat field or in the mulch‐protected site, demonstrating the superior soil wetness characteristic of sites with basins and straw mulch. Water tension data signaled the absence of the capillary fringe (i.e., capillary drainage) near the surface for some finite period, thus possibly limiting water for best Sphagnum growth. At the experimental basins and mulch‐protected site, 100% of these periods lasted four or fewer days. In the control peat field, 20% of the periods when capillary drainage had occurred lasted more than four days, with one period of 17 days. The mulch protection alone provided considerable improvement in hydrological conditions compared with the control peat field, but the additional water retained in the experimental basins protected against Sphagnum desiccation and loss during more extreme dry periods.     

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.     

Using Reinforcement Learning to Provide Stable Brain-Machine Interface Control Despite Neural Input Reorganization

Brain-machine interface (BMI) systems give users direct neural control of robotic, communication, or functional electrical stimulation systems. As BMI systems begin transitioning from laboratory settings into activities of daily living, an important goal is to develop neural decoding algorithms that can be calibrated with a minimal burden on the user, provide stable control for long periods of time, and can be responsive to fluctuations in the decoder’s neural input space (e.g. neurons appearing or being lost amongst electrode recordings). These are significant challenges for static neural decoding algorithms that assume stationary input/output relationships. Here we use an actor-critic reinforcement learning architecture to provide an adaptive BMI controller that can successfully adapt to dramatic neural reorganizations, can maintain its performance over long time periods, and which does not require the user to produce specific kinetic or kinematic activities to calibrate the BMI. Two marmoset monkeys used the Reinforcement Learning BMI (RLBMI) to successfully control a robotic arm during a two-target reaching task. The RLBMI was initialized using random initial conditions, and it quickly learned to control the robot from brain states using only a binary evaluative feedback regarding whether previously chosen robot actions were good or bad. The RLBMI was able to maintain control over the system throughout sessions spanning multiple weeks. Furthermore, the RLBMI was able to quickly adapt and maintain control of the robot despite dramatic perturbations to the neural inputs, including a series of tests in which the neuron input space was deliberately halved or doubled.