International IEEE/EMBS Conference on Neural Engineering : [proceedings]. International IEEE EMBS Conference on Neural Engineering最新文献

During the induction of general anesthesia there is a shift in power from the posterior regions of the brain to the frontal cortices; this shift in power is called anteriorization. For many anesthetics, a prominent feature of anteriorization is a shift specifically in the alpha band (8-13 Hz) from posterior to frontal cortices. Here we present a biophysical computational model that describes thalamocortical circuit-level dynamics underlying anteriorization of the alpha rhythm in the case of halothane. Halothane potentiates GABA_A and increases potassium leak conductances. According to our model, an increase in potassium leak conductances hyperpolarizes and silences the high-threshold thalamocortical (HTC) cells, a specialized subset of thalamocortical cells that fire at the alpha frequency at relatively depolarized membrane potentials (>-60 mV) and are thought to be the generators of quiet awake occipital alpha. At the same time the potentiation of GABA_A imposes an alpha time scale on both the cortical and the thalamic component of the frontal portion of our model. The alpha activity in the frontal component is further strengthened by reciprocal thalamocortical feedback. Thus, we argue that the dual molecular targets of halothane induce the anteriorization of the alpha rhythm by increasing potassium leak conductances, which abolishes occipital alpha, and by potentiating GABA_A, which induces frontal alpha. These results provide a computational modeling formulation for studying highly detailed biophysical mechanisms of anesthetic action in silico.

Neural control of movement is typically studied in constrained environments where there is a reduced set of possible behaviors. This constraint may unintentionally limit the applicability of findings to the generalized case of unconstrained behavior. We hypothesize that examining the unconstrained state across multiple behavioral contexts will lead to new insights into the neural control of movement and help advance the design of neural prosthetic decode algorithms. However, to pursue electrophysiological studies in such a manner requires a more flexible framework for experimentation. We propose that head-mounted neural recording systems with wireless data transmission, combined with markerless computer-vision based motion tracking, will enable new, less constrained experiments. As a proof-of-concept, we recorded and wirelessly transmitted broadband neural data from 32 electrodes in premotor cortex while acquiring single-camera video of a rhesus macaque walking on a treadmill. We demonstrate the ability to extract behavioral kinematics using an automated computer vision algorithm without use of markers and to predict kinematics from the neural data. Together these advances suggest that a new class of "freely moving monkey" experiments should be possible and should help broaden our understanding of the neural control of movement.

The Leaky Integrate and Fire (LIF) model of a neuron is one of the best known models for a spiking neuron. A current limitation of the LIF model is that it may not accurately reproduce the dynamics of an action potential. There have recently been some studies suggesting that a LIF coupled with a multi-timescale adaptive threshold (MAT) may increase LIF's accuracy in predicting spikes in cortical neurons. We propose a mechanotransduction process coupled with a LIF model with multi-timescale adaptive threshold to model slowly adapting type I (SAI) mechanoreceptor in monkey's glabrous skin. In order to test the performance of the model, the spike timings predicted by this MAT model are compared with neural data. We also test a fixed threshold variant of the model by comparing its outcome with the neural data. Initial results indicate that the MAT model predicts spike timings better than a fixed threshold LIF model only.

We used a spiking neural network (SNN) to decode neural data recorded from a 96-electrode array in premotor/motor cortex while a rhesus monkey performed a point-to-point reaching arm movement task. We mapped a Kalman-filter neural prosthetic decode algorithm developed to predict the arm's velocity on to the SNN using the Neural Engineering Framework and simulated it using Nengo, a freely available software package. A 20,000-neuron network matched the standard decoder's prediction to within 0.03% (normalized by maximum arm velocity). A 1,600-neuron version of this network was within 0.27%, and run in real-time on a 3GHz PC. These results demonstrate that a SNN can implement a statistical signal processing algorithm widely used as the decoder in high-performance neural prostheses (Kalman filter), and achieve similar results with just a few thousand neurons. Hardware SNN implementations-neuromorphic chips-may offer power savings, essential for realizing fully-implantable cortically controlled prostheses.

Developmental brain malformations often cause intractable and in many cases generalized and/or multifocal seizures. Surgical intervention is not possible in these cases as it is difficult to isolate the epileptogenic foci. Scalp EEG signals recorded during such seizures include coupled contributions from different sources. If it was possible to decouple these contributions based on differences in both their signatures and inter-arrival times at different electrodes, it would subsequently be possible to estimate the locations of the seizure foci. For this purpose, we applied matched filtering to scalp EEG data from 3 patients with multifocal seizures, using patient-specific source-related short EEG segments as the template waveforms. These segments were assumed to be seizure-related based on distinct sets of inter-arrival times at different channels and alternating signal polarities. We present preliminary results and demonstrate that matched filtering can be successfully applied to extract decoupled signal components from the EEG, generated by potentially distinct sources, and thus with distinct inter-arrival times but partially overlapping spectra.