No abstract

This paper demonstrates initial results with a novel instrument for nanoparticle detection and quantization, called the "qNano." The qNano instrument provides a label-free method for detection of charged particles passing through a nanopore (a nanopore scale channel that separates two volumes) via electrophoresis. The instrument incorporates an elastomeric membrane in which a nano-scale pore has been produced by mechanical puncturing, and stretching of the membrane allows control of the nanopore size. Trans-membrane voltage drives electrophoresis and particle translocations through the nanopore, as measured by the ionic current that flows through the pore. Pressure control is also available to increase the rates of capture and translocation. We demonstrate quantization of liposome and polystyrene particles ranging from 200-400 nm. Capture rate (translocation events per second) is shown to be linear with respect to applied pressure and membrane stretching distance. Additionally, translocation event amplitude is shown to decrease with increasing pressure, but remains invariant to changes in the membrane stretching distance.

24Decades after the motor homunculus was first proposed, it is still unknown how different body 25 parts are intermixed and interrelated in human motor cortex at single-neuron resolution. Using 26 microelectrode arrays, we studied how face, head, arm and leg movements on both sides of the 27 body are represented in hand knob area of precentral gyrus in people with tetraplegia. Contrary 28 to the traditional somatotopy, we found strong representation of all movements. Probing further, 29 we found that ipsilateral and contralateral movements, and homologous arm and leg 30 movements (e.g. wrist and ankle), had a correlated representation. Additionally, there were 31 neural dimensions where the limb was represented independently of the movement. Together, 32 these patterns formed a "modular" code that might facilitate skill transfer across limbs. We also 33 investigated dual-effector movement, finding that more strongly represented effectors 34 suppressed the activity of weaker effectors. Finally, we leveraged these results to improve 35 discrete brain-computer interfaces by spreading targets across all limbs. 42intermixing between widely distinct areas of the body within any one individual (Leyton and 43 Sherrington 1917; W. Penfield and Boldrey 1937; Wilder Penfield and Rasmussen 1950). fMRI 44 studies also support the existence of an orderly map with largely separate face, arm and leg 45 areas along the precentral gyrus and the anterior bank of the central sulcus (Lotze et al. 2000; computer interface (BCI) that can decode movements across all four limbs. We show that this 86 "full body" BCI improves information throughput relative to a single-effector approach. 87 Results 89Tuning to Face, Head, Arm and Leg Movements 90 91We used microelectrode array recordings from participants T5 and T7 to assess tuning to face, 92 head, arm and leg movements in hand knob area of precentral gyrus. Participant T5 had a C4 93 spinal cord injury and was paralyzed from the neck down; he could move his face and head, but 94 attempted arm and leg movements resulted in little or no overt motion. Participant T7 had ALS 95and could move all joints tested, although some of his arm movements were limited due to 96 weakness. 98In this experiment, T5 and T7 made (or attempted to make) movements in sync with visual cues 99 displayed on a computer screen ( Fig. 1A). T5 completed an instructed delay version of the task 100 where each trial randomly cued one of 32 possible movements spanning the face, head, arm 101 and legs. For face and head movements, T5 was instructed to move normally; for arm and leg 102 movements, T5 was instructed to attempt to make the movement as if he were not paralyzed. 103T7, whose more restricted data was collected earlier in a different study (and who is no longer 104 enrolled), completed an alternating paired movement task with a block design. Each block 105 tested a different movement pair, during which T7 alternated between making each of the paired 106 movements every 3 seconds. 108Despite recording from micr...

Advances in deep learning have given rise to neural network models of the relationship between movement and brain activity that appear to far outperform prior approaches. Brain-computer interfaces (BCIs) that enable people with paralysis to control external devices, such as robotic arms or computer cursors, might stand to benefit greatly from these advances. We tested recurrent neural networks (RNNs) on a challenging nonlinear BCI problem: decoding continuous bimanual movement of two computer cursors. Surprisingly, we found that although RNNs appeared to perform well in offline settings, they did so by overfitting to the temporal structure of the training data and failed to generalize to real-time neuroprosthetic control. In response, we developed a method that alters the temporal structure of the training data by dilating/compressing it in time and re-ordering it, which we show helps RNNs successfully generalize to the online setting. With this method, we demonstrate that a person with paralysis can control two computer cursors simultaneously, far outperforming standard linear methods. Our results provide evidence that preventing models from overfitting to temporal structure in training data may, in principle, aid in translating deep learning advances to the BCI setting, unlocking improved performance for challenging applications.