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Cardiopulmonary sympathetic control is exerted via stellate ganglia (SG); however, little is known about how neuronal firing patterns in the stellate ganglion relate to dynamic physiological function in the heart and lungs. We performed continuous extracellular recordings from SG neurons using multielectrode arrays in chloralose-anesthetized pigs ( n = 6) for 8-9 h. Respiratory and left ventricular pressures (RP and LVP, respectively) and the electrocardiogram (ECG) were recorded concomitantly. Linkages between sampled spikes and LVP or RP were determined using a novel metric to evaluate specificity in neural activity for phases of the cardiac and pulmonary cycles during resting conditions and under various cardiopulmonary stressors. Firing frequency (mean 4.6 ± 1.2 Hz) varied spatially across the stellate ganglion, suggesting regional processing. The firing pattern of most neurons was synchronized with both cardiac (LVP) and pulmonary (RP) activity indicative of cardiopulmonary integration. Using the novel metric to determine cardiac phase specificity of neuronal activity, we found that spike density was highest during diastole and near-peak systole. This specificity was independent of the actual LVP or population firing frequency as revealed by perturbations to the LVP. The observed specificity was weaker for RP. Stellate ganglion neuronal populations exhibit cardiopulmonary integration and profound specificity toward the near-peak systolic phase of the cardiac cycle. This novel approach provides practically deployable tools to probe stellate ganglion function and its relationship to cardiopulmonary pathophysiology. NEW & NOTEWORTHY Activity of stellate ganglion neurons is often linking indirectly to cardiac function. Using novel approaches coupled with extended period of recordings in large animals, we link neuronal population dynamics to mechanical events occurring at near-peak systole. This metric can be deployed to probe stellate ganglion neuronal control of cardiopulmonary function in normal and disease states.

During normal viewing, we direct our eyes between objects in three-dimensional (3D) space many times a minute. To accurately fixate these objects, which are usually located in different directions and at different distances, we must generate eye movements with appropriate versional and vergence components. These combined saccade-vergence eye movements result in disjunctive saccades with a vergence component that is much faster than that generated during smooth, symmetric vergence eye movements. The neural control of disjunctive saccades is still poorly understood. Recent anatomical studies suggested that the central mesencephalic reticular formation (cMRF), located lateral to the oculomotor nucleus, contains premotor neurons potentially involved in the neural control of these eye movements. We have therefore investigated the role of the cMRF in the control of disjunctive saccades in trained rhesus monkeys. Here, we describe a unique population of cMRF neurons that, during disjunctive saccades, display a burst of spikes that are highly correlated with vergence velocity. Importantly, these neurons show no increase in activity for either conjugate saccades or symmetric vergence. These neurons are termed saccade-vergence burst neurons (SVBNs) to maintain consistency with modeling studies that proposed that such a class of neuron exists to generate the enhanced vergence velocities observed during disjunctive saccades. Our results demonstrate the existence and characteristics of SVBNs whose activity is correlated solely with the vergence component of disjunctive saccades and, based on modeling studies, are critically involved in the generation of the disjunctive saccades required to view objects in our 3D world., Competing Interests: The authors declare no competing interest.

Background: Several species of the New World monkeys have been used as experimental models in biomedical and neurophysiological research. However, a method for controlled arm reaching tasks has not been developed for these species., New Method: We have developed a fully automated, pneumatically driven, portable, and reconfigurable experimental apparatus for arm-reaching tasks suitable for these small primates., Results: We have utilized the apparatus to train two owl monkeys in a visually-cued arm-reaching task. Analysis of neural recordings demonstrates directional tuning of the M1 neurons., Comparison With Existing Method(s): Our apparatus allows automated control, freeing the experimenter from manual experiments., Conclusion: The presented apparatus provides a valuable tool for conducting neurophysiological research on New World monkeys., (Copyright © 2016. Published by Elsevier B.V.)

The distributed nature of nervous systems makes it necessary to record from a large number of sites in order to decipher the neural code, whether single cell, local field potential (LFP), micro-electrocorticograms (μECoG), electroencephalographic (EEG), magnetoencephalographic (MEG) or in vitro micro-electrode array (MEA) data are considered. High channel-count recordings also optimize the yield of a preparation and the efficiency of time invested by the researcher. Currently, data acquisition (DAQ) systems with high channel counts (>100) can be purchased from a limited number of companies at considerable prices. These systems are typically closed-source and thus prohibit custom extensions or improvements by end users. We have developed MANTA, an open-source MATLAB-based DAQ system, as an alternative to existing options. MANTA combines high channel counts (up to 1440 channels/PC), usage of analog or digital headstages, low per channel cost (<$90/channel), feature-rich display and filtering, a user-friendly interface, and a modular design permitting easy addition of new features. MANTA is licensed under the GPL and free of charge. The system has been tested by daily use in multiple setups for >1 year, recording reliably from 128 channels. It offers a growing list of features, including integrated spike sorting, PSTH and CSD display and fully customizable electrode array geometry (including 3D arrays), some of which are not available in commercial systems. MANTA runs on a typical PC and communicates via TCP/IP and can thus be easily integrated with existing stimulus generation/control systems in a lab at a fraction of the cost of commercial systems. With modern neuroscience developing rapidly, MANTA provides a flexible platform that can be rapidly adapted to the needs of new analyses and questions. Being open-source, the development of MANTA can outpace commercial solutions in functionality, while maintaining a low price-point.

Objectives: The development of neurorehabilitation technologies requires the profound understanding of the mechanisms underlying an individual's motor ability and impairment. A major factor limiting this understanding is the difficulty of bridging between events taking place at the neurophysiologic level (i.e., motor neuron firings) with those emerging at the musculoskeletal level (i.e. joint actuation), in vivo in the intact moving human. This review presents emerging model-based methodologies for filling this gap that are promising for developing clinically viable technologies. Methods: We provide a design overview of musculoskeletal modeling formulations driven by recordings of neuromuscular activity with a critical comparison to alternative model-free approaches in the context of neurorehabilitation technologies. We present advanced electromyography-based techniques for interfacing with the human nervous system and model-based techniques for translating the extracted neural information into estimates of motor function. Results: We introduce representative application areas where modeling is relevant for accessing neuromuscular variables that could not be measured experimentally. We then show how these variables are used for designing personalized rehabilitation interventions, biologically inspired limbs, and human–machine interfaces. Conclusion: The ability of using electrophysiological recordings to inform biomechanical models enables accessing a broader range of neuromechanical variables than analyzing electrophysiological data or movement data individually. This enables understanding the neuromechanical interplay underlying in vivo movement function, pathology, and robot-assisted motor recovery. Significance: Filling the gap between our understandings of movement neural and mechanical functions is central for addressing one of the major challenges in neurorehabilitation: personalizing current technologies and interventions to an individual's anatomy and impairment. [ABSTRACT FROM AUTHOR]

Neural recording systems are a central component of Brain-Machince Interfaces (BMIs). In most of these systems the emphasis is on faithful reproduction and transmission of the recorded signal to remote systems for further processing or data analysis. Here we follow an alternative approach: we propose a neural recording system that can be directly interfaced locally to neuromorphic spiking neural processing circuits for compressing the large amounts of data recorded, carrying out signal processing and neural computation to extract relevant information, and transmitting only the low-bandwidth outcome of the processing to remote computing or actuating modules. The fabricated system includes a low-noise amplifier, a delta-modulator analog-to-digital converter, and a low-power band-pass filter. The bio-amplifier has a programmable gain of 45–54 dB, with a Root Mean Squared (RMS) input-referred noise level of 2.1 \mu V, and consumes 90 \mu W . The band-pass filter and delta-modulator circuits include asynchronous handshaking interface logic compatible with event-based communication protocols. We describe the properties of the neural recording circuits, validating them with experimental measurements, and present system-level application examples, by interfacing these circuits to a reconfigurable neuromorphic processor comprising an array of spiking neurons with plastic and dynamic synapses. The pool of neurons within the neuromorphic processor was configured to implement a recurrent neural network, and to process the events generated by the neural recording system in order to carry out pattern recognition. [ABSTRACT FROM PUBLISHER]

Summary Ferrets are a major developmental animal model due to their early parturition. Here we show for the first time that ferrets could be used to study development of higher-level visual processes previously identified in primates. In primates, complex motion processing involves primary visual cortex (V1), which generates local motion signals, and higher-level visual area MT, which integrates these signals over more global spatial regions. Our data show similar transformations in motion signals between ferret V1 and higher-level visual area PSS, located in the posterior bank of the suprasylvian sulcus. We found that PSS neurons, like MT neurons, were tuned for stimulus motion and showed strong suppression between opposing direction inputs. Most strikingly, PSS, like MT, exhibited robust global motion signals when tested with coherent plaids—the classic test for motion integration across multiple moving elements. These PSS responses were described well by computational models developed for MT. Our findings establish the ferret as a strong animal model for development of higher-level visual processing. Highlights • Visual area PSS is a higher-level motion area in ferrets • PSS neurons show signatures of motion integration • PSS responses can be fit by a motion pathway model similar to ones for the primate • Complex motion processing in ferrets parallels that in primates Ferrets are a major animal model for development of early visual stages. Lempel and Nielsen establish signatures of complex motion processing in a higher visual area in the ferret, area PSS. This will allow developmental research in the ferret to expand into higher-level vision. [ABSTRACT FROM AUTHOR]