Wireless Neural/EMG Telemetry Systems for Small Freely Moving Animals

Tracking technologies: advances driving new insights into monarch migration

Understanding the rules of how monarch butterflies complete their annual North American migration will be clarified by studying them within a movement ecology framework. Insect movement ecology is growing at a rapid pace due to the development of novel monitoring systems that allow ever-smaller animals to be tracked at higher spatiotemporal resolution for longer periods of time. New innovations in tracking hardware and associated software, including miniaturization, energy autonomy, data management, and wireless communication, are reducing the size and increasing the capability of next-generation tracking technologies, bringing the goal of tracking monarchs over their entire migration closer within reach. These tools are beginning to be leveraged to provide insight into different aspects of monarch biology and ecology, and to contribute to a growing capacity to understand insect movement ecology more broadly and its impact on human life.

Behavioral algorithms and neural mechanisms underlying odor-modulated locomotion in insects

Odors released from mates and resources such as a host and food are often the first sensory signals that an animal can detect. Changes in locomotion in response to odors are an important mechanism by which animals access resources important to their survival. Odor-modulated changes in locomotion in insects constitute a whole suite of flexible behaviors that allow insects to close in on these resources from long distances and perform local searches to locate and subsequently assess them. Here, we review changes in odor-mediated locomotion across many insect species. We emphasize that changes in locomotion induced by odors are diverse. In particular, the olfactory stimulus is sporadic at long distances and becomes more continuous at short distances. This distance-dependent change in temporal profile produces a corresponding change in an insect's locomotory strategy. We also discuss the neural circuits underlying odor modulation of locomotion.

Recent approaches to study the neural bases of complex insect behavior

Recent advances in biocompatible materials, miniaturized instrumentation, advanced computational algorithms, and genetic tools have enabled the development of novel methods and approaches to quantify the behavior of individuals or groups of animals. In conjunction with technologies that allow simultaneous monitoring of neural responses, quantitative studies of complex behaviors can reveal tighter links between the external sensory cues in the vicinity of the organism and neural responses they elicit, and how internal neural representations finally get mapped onto the behavior generated. In this review, we examine a few approaches that are beginning to be widely exploited for understanding neural-behavioral response transformations.

A Flying Platform to Investigate Neuronal Correlates of Navigation in the Honey Bee (Apis mellifera)

Navigating animals combine multiple perceptual faculties, learn during exploration, retrieve multi-facetted memory contents, and exhibit goal-directedness as an expression of their current needs and motivations. Navigation in insects has been linked to a variety of underlying strategies such as path integration, view familiarity, visual beaconing, and goal-directed orientation with respect to previously learned ground structures. Most works, however, study navigation either from a field perspective, analyzing purely behavioral observations, or combine computational models with neurophysiological evidence obtained from lab experiments. The honey bee (Apis mellifera) has long been a popular model in the search for neural correlates of complex behaviors and exhibits extraordinary navigational capabilities. However, the neural basis for bee navigation has not yet been explored under natural conditions. Here, we propose a novel methodology to record from the brain of a copter-mounted honey bee. This way, the animal experiences natural multimodal sensory inputs in a natural environment that is familiar to her. We have developed a miniaturized electrophysiology recording system which is able to record spikes in the presence of time-varying electric noise from the copter's motors and rotors, and devised an experimental procedure to record from mushroom body extrinsic neurons (MBENs). We analyze the resulting electrophysiological data combined with a reconstruction of the animal's visual perception and find that the neural activity of MBENs is linked to sharp turns, possibly related to the relative motion of visual features. This method is a significant technological step toward recording brain activity of navigating honey bees under natural conditions. By providing all system specifications in an online repository, we hope to close a methodological gap and stimulate further research informing future computational models of insect navigation.

Light Modulation of Brain and Development of Relevant Equipment

Light modulation plays an important role in understanding the pathology of brain disorders and improving brain function. Optogenetic techniques can activate or silence targeted neurons with high temporal and spatial accuracy and provide precise control, and have recently become a method for quick manipulation of genetically identified types of neurons. Photobiomodulation (PBM) is light therapy that utilizes non-ionizing light sources, including lasers, light emitting diodes, or broadband light. It provides a safe means of modulating brain activity without any irreversible damage and has established optimal treatment parameters in clinical practice. This manuscript reviews 1) how optogenetic approaches have been used to dissect neural circuits in animal models of Alzheimer's disease, Parkinson's disease, and depression, and 2) how low level transcranial lasers and LED stimulation in humans improves brain activity patterns in these diseases. State-of-the-art brain machine interfaces that can record neural activity and stimulate neurons with light have good prospects in the future.

Wireless and battery-free platforms for collection of biosignals

Recent progress in biosensors have quantitively expanded current capabilities in exploratory research tools, diagnostics and therapeutics. This rapid pace in sensor development has been accentuated by vast improvements in data analysis methods in the form of machine learning and artificial intelligence that, together, promise fantastic opportunities in chronic sensing of biosignals to enable preventative screening, automated diagnosis, and tools for personalized treatment strategies. At the same time, the importance of widely accessible personal monitoring has become evident by recent events such as the COVID-19 pandemic. Progress in fully integrated and chronic sensing solutions is therefore increasingly important. Chronic operation, however, is not truly possible with tethered approaches or bulky, battery-powered systems that require frequent user interaction. A solution for this integration challenge is offered by wireless and battery-free platforms that enable continuous collection of biosignals. This review summarizes current approaches to realize such device architectures and discusses their building blocks. Specifically, power supplies, wireless communication methods and compatible sensing modalities in the context of most prevalent implementations in target organ systems. Additionally, we highlight examples of current embodiments that quantitively expand sensing capabilities because of their use of wireless and battery-free architectures.

Adaptive quantization of local field potentials for wireless implants in freely moving animals: an open-source neural recording device

OBJECTIVE

Modern neuroscience research requires electrophysiological recording of local field potentials (LFPs) in moving animals. Wireless transmission has the advantage of removing the wires between the animal and the recording equipment but is hampered by the large number of data to be sent at a relatively high rate.

APPROACH

To reduce transmission bandwidth, we propose an encoder/decoder scheme based on adaptive non-uniform quantization. Our algorithm uses the current transmitted codeword to adapt the quantization intervals to changing statistics in LFP signals. It is thus backward adaptive and does not require the sending of side information. The computational complexity is low and similar at the encoder and decoder sides. These features allow for real-time signal recovery and facilitate hardware implementation with low-cost commercial microcontrollers.

MAIN RESULTS

As proof-of-concept, we developed an open-source neural recording device called NeRD. The NeRD prototype digitally transmits eight channels encoded at 10 kHz with 2 bits per sample. It occupies a volume of 2  ×  2  ×  2 cm³ and weighs 8 g with a small battery allowing for 2 h 40 min of autonomy. The power dissipation is 59.4 mW for a communication range of 8 m and transmission losses below 0.1%. The small weight and low power consumption offer the possibility of mounting the entire device on the head of a rodent without resorting to a separate head-stage and battery backpack. The NeRD prototype is validated in recording LFPs in freely moving rats at 2 bits per sample while maintaining an acceptable signal-to-noise ratio (>30 dB) over a range of noisy channels.

SIGNIFICANCE

Adaptive quantization in neural implants allows for lower transmission bandwidths while retaining high signal fidelity and preserving fundamental frequencies in LFPs.

Telemetry-controlled simultaneous stimulation-and-recording device (SRD) to study interhemispheric cortical circuits in rat primary somatosensory (SI) cortex

Background

A growing need exists for neuroscience platforms that can perform simultaneous chronic recording and stimulation of neural tissue in animal models in a telemetry-controlled fashion with signal processing for analysis of the chronic recording data and external triggering capability. We describe the system design, testing, evaluation, and implementation of a wireless simultaneous stimulation-and-recording device (SRD) for modulating cortical circuits in physiologically identified sites in primary somatosensory (SI) cortex in awake-behaving and freely-moving rats. The SRD was developed using low-cost electronic components and open-source software. The function of the SRD was assessed by bench and in-vivo testing.

Results

The SRD recorded spontaneous spiking and bursting neuronal activity, evoked responses to programmed intracortical microstimulation (ICMS) delivered internally by the SRD, and evoked responses to external peripheral forelimb stimulation.

Conclusions

The SRD is capable of wireless stimulation and recording on a predetermined schedule or can be wirelessly synchronized with external input as would be required in behavioral testing prior to, during, and following ICMS.

An Impulse Radio PWM-Based Wireless Data Acquisition Sensor Interface

A sensor interface circuit based on impulse radio pulse width modulation (IR-PWM) is presented for low power and high throughput wireless data acquisition systems (wDAQ) with extreme size and power constraints. Two triple-slope analog-to-time converters (ATC) convert two analog signals, each up to 5 MHz in bandwidth, into PWM signals, and an impulse radio (IR) transmitted (Tx) with an all-digital power amplifier (PA) combines them while preserving the timing information by transmitting impulses at the PWM rising and falling edges. On the receiver (Rx) side, an RF-LNA followed by an envelope detector recovers the incoming impulses, and a T-flipflop reverts the impulse sequence back to PWM to be digitized by a time-to-digital converter (TDC). Detailed analysis and design guideline on ATC was introduced, and a proof-of-concept prototype was fabricated for a capacitive micromachined ultrasound transducer (CMUT) imaging system in a 0.18-μm HV CMOS process, occupying 0.18 mm² active area and consuming 3.94 mW from a 1.8 V supply. The proposed TDC in this prototype yielded 7-bit resolution, while the entire wDAQ achieved 5.8 effective number of bits (ENOB) at 2 × 10 MS/s.

Advances in Penetrating Multichannel Microelectrodes Based on the Utah Array Platform

The Utah electrode array (UEA) and its many derivatives have become a gold standard for high-channel count bi-directional neural interfaces, in particular in human subject applications. The chapter provides a brief overview of leading electrode concepts and the context in which the UEA has to be understood. It goes on to discuss the key advances and developments of the UEA platform in the past 15 years, as well as novel wireless and system integration technologies that will merge into future generations of fully integrated devices. Aspects covered include novel device architectures that allow scaling of channel count and density of electrode contacts, material improvements to substrate, electrode contacts, and encapsulation. Further subjects are adaptations of the UEA platform to support IR and optogenetic simulation as well as an improved understanding of failure modes and methods to test and accelerate degradation in vitro such as to better predict device failure and lifetime in vivo.

Integration of Small- and Wide-Field Visual Features in Target-Selective Descending Neurons of both Predatory and Nonpredatory Dipterans

For many animals, target motion carries high ecological significance as this may be generated by a predator, prey, or potential mate. Indeed, animals whose survival depends on early target detection are often equipped with a sharply tuned visual system, yielding robust performance in challenging conditions. For example, many fast-flying insects use visual cues for identifying targets, such as prey (e.g., predatory dragonflies and robberflies) or conspecifics (e.g., nonpredatory hoverflies), and can often do so against self-generated background optic flow. Supporting these behaviors, the optic lobes of insects that pursue targets harbor neurons that respond robustly to the motion of small moving objects, even when displayed against syn-directional background clutter. However, in diptera, the encoding of target information by the descending neurons, which are more directly involved in generating the behavioral output, has received less attention. We characterized target-selective neurons by recording in the ventral nerve cord of male and female predatory Holcocephala fusca robberflies and of male nonpredatory Eristalis tenax hoverflies. We show that both species have dipteran target-selective descending neurons that only respond to target motion if the background is stationary or moving slowly, moves in the opposite direction, or has un-naturalistic spatial characteristics. The response to the target is suppressed when background and target move at similar velocities, which is strikingly different to the response of target neurons in the optic lobes. As the neurons we recorded from are premotor, our findings affect our interpretation of the neurophysiology underlying target-tracking behaviors.

SIGNIFICANCE STATEMENT Many animals use sensory cues to detect moving targets that may represent predators, prey, or conspecifics. For example, birds of prey show superb sensitivity to the motion of small prey, and intercept these at high speeds. In a similar manner, predatory insects visually track moving prey, often against cluttered backgrounds. Accompanying this behavior, the brains of insects that pursue targets contain neurons that respond exclusively to target motion. We here show that dipteran insects also have target-selective descending neurons in the part of their nervous system that corresponds to the vertebrate spinal cord. Surprisingly, and in contrast to the neurons in the brain, these premotor neurons are inhibited by background patterns moving in the same direction as the target.

Systematic node management mechanism using ZigBee-based real-time vital sign information monitoring system to manage large numbers of patients

BACKGROUND

Local hospitals must deal with large numbers of patients during mass casualty incidents, and the wireless sensor networks (WSNs) can help in these situations by monitoring vital signs. Conventional ZigBee nodes can obtain the ID of a device by assigning a unique 16-bit short address or by burning firmware into an IC. These methods tend to complicate node management and lack portability.

OBJECTIVE

The study developed a node management mechanism to deal with a large number of patients in real-time, through the wireless monitoring of physiological signals. The mechanism proposed for the ZigBee WSN is based on a three-layer (Coordinator, Control Router, and End Device) tree topology.

METHODS

The proposed system includes a node deployment process to formulate a ZigBee WSN as a tree topology, an algorithm to automatically number ZigBee nodes for monitoring and control system (MCS), and an algorithm to automatically obtain the short addresses of nodes for data collection. Specifically, an algorithm automatically collects data from ZigBee nodes for display on a computer graphical user interface (GUI). We also developed a reliable data transmission method capable of resolving the problem of packet loss.

RESULTS

The proposed method has been applied in a local hospital. Our research findings provide a valuable reference for the development of ZigBee-based MCS.

CONCLUSIONS

The proposed node management mechanism is faster, more reliable, and more intuitive to use, than traditional methods.

A 16-Channel Nonparametric Spike Detection ASIC Based on EC-PC Decomposition

In extracellular neural recording experiments, detecting neural spikes is an important step for reliable information decoding. A successful implementation in integrated circuits can achieve substantial data volume reduction, potentially enabling a wireless operation and closed-loop system. In this paper, we report a 16-channel neural spike detection chip based on a customized spike detection method named as exponential component-polynomial component (EC-PC) algorithm. This algorithm features a reliable prediction of spikes by applying a probability threshold. The chip takes raw data as input and outputs three data streams simultaneously: field potentials, band-pass filtered neural data, and spiking probability maps. The algorithm parameters are on-chip configured automatically based on input data, which avoids manual parameter tuning. The chip has been tested with both in vivo experiments for functional verification and bench-top experiments for quantitative performance assessment. The system has a total power consumption of 1.36 mW and occupies an area of 6.71 mm (2) for 16 channels. When tested on synthesized datasets with spikes and noise segments extracted from in vivo preparations and scaled according to required precisions, the chip outperforms other detectors. A credit card sized prototype board is developed to provide power and data management through a USB port.

Implantable neurotechnologies: a review of integrated circuit neural amplifiers

Neural signal recording is critical in modern day neuroscience research and emerging neural prosthesis programs. Neural recording requires the use of precise, low-noise amplifier systems to acquire and condition the weak neural signals that are transduced through electrode interfaces. Neural amplifiers and amplifier-based systems are available commercially or can be designed in-house and fabricated using integrated circuit (IC) technologies, resulting in very large-scale integration or application-specific integrated circuit solutions. IC-based neural amplifiers are now used to acquire untethered/portable neural recordings, as they meet the requirements of a miniaturized form factor, light weight and low power consumption. Furthermore, such miniaturized and low-power IC neural amplifiers are now being used in emerging implantable neural prosthesis technologies. This review focuses on neural amplifier-based devices and is presented in two interrelated parts. First, neural signal recording is reviewed, and practical challenges are highlighted. Current amplifier designs with increased functionality and performance and without penalties in chip size and power are featured. Second, applications of IC-based neural amplifiers in basic science experiments (e.g., cortical studies using animal models), neural prostheses (e.g., brain/nerve machine interfaces) and treatment of neuronal diseases (e.g., DBS for treatment of epilepsy) are highlighted. The review concludes with future outlooks of this technology and important challenges with regard to neural signal amplification.

How animals glide: from trajectory to morphology

Animals that glide produce aerodynamic forces that enable transit through the air in both arboreal and aquatic environments. The relative ease of gliding compared with flapping flight has led to a large diversity of taxa that have evolved some degree of flight capability. Glide paths are curved, reflecting the changing forces on the animal as it progresses through its aerial trajectory. These changing forces can be under control of the glider, which uses specific aspects of anatomy to modulate lift, drag, and rotational moments on the body. However, gliders share no single anatomical or behavioral feature, and some species are unspecialized for gliding, producing aerodynamic forces using posture and orientation alone. Animals use gliding in a broad range of ecological roles, suggesting that multiple performance metrics are relevant for consideration, but we are only beginning to understand how gliders produce and control their flight from takeoff to landing. In this review, we focus on the physical aspects of how glide trajectories are produced, and additionally discuss the range of morphologies and postures that are used to control aerial movements across the broad diversity of animal gliders.

Wireless Flexible Smart Bandage for Continuous Monitoring of Wound Oxygenation

Current methods in treating chronic wounds have had limited success in large part due to the open loop nature of the treatment. We have created a localized 3D-printed smart wound dressing platform that will allow for real-time data acquisition of oxygen concentration, which is an important indicator of wound healing. This will serve as the first leg of a feedback loop for a fully optimized treatment mechanism tailored to the individual patient. A flexible oxygen sensor was designed and fabricated with high sensitivity and linear current output. With a series of off-the-shelf electronic components including a programmable-gain analog front-end, a microcontroller and wireless radio, an integrated electronic system with data readout and wireless transmission capabilities was assembled in a compact package. Using an elastomeric material, a bandage with exceptional flexibility and tensile strength was 3D-printed. The bandage contains cavities for both the oxygen sensor and the electronic systems, with contacts interfacing the two systems. Our integrated, flexible platform is the first step toward providing a self-operating, highly optimized remote therapy for chronic wounds.

Fully Integrated Biopotential Acquisition Analog Front-End IC

A biopotential acquisition analog front-end (AFE) integrated circuit (IC) is presented. The biopotential AFE includes a capacitively coupled chopper instrumentation amplifier (CCIA) to achieve low input referred noise (IRN) and to block unwanted DC potential signals. A DC servo loop (DSL) is designed to minimize the offset voltage in the chopper amplifier and low frequency respiration artifacts. An AC coupled ripple rejection loop (RRL) is employed to reduce ripple due to chopper stabilization. A capacitive impedance boosting loop (CIBL) is designed to enhance the input impedance and common mode rejection ratio (CMRR) without additional power consumption, even under an external electrode mismatch. The AFE IC consists of two-stage CCIA that include three compensation loops (DSL, RRL, and CIBL) at each CCIA stage. The biopotential AFE is fabricated using a 0.18 µm one polysilicon and six metal layers (1P6M) complementary metal oxide semiconductor (CMOS) process. The core chip size of the AFE without input/output (I/O) pads is 10.5 mm². A fourth-order band-pass filter (BPF) with a pass-band in the band-width from 1 Hz to 100 Hz was integrated to attenuate unwanted signal and noise. The overall gain and band-width are reconfigurable by using programmable capacitors. The IRN is measured to be 0.94 µV_RMS in the pass band. The maximum amplifying gain of the pass-band was measured as 71.9 dB. The CIBL enhances the CMRR from 57.9 dB to 67 dB at 60 Hz under electrode mismatch conditions.

High order neural correlates of social behavior in the honeybee brain

BACKGROUND

Honeybees are well established models of neural correlates of sensory function, learning and memory formation. Here we report a novel approach allowing to record high-order mushroom body-extrinsic interneurons in the brain of worker bees within a functional colony. New method The use of two 100 cm long twisted copper electrodes allowed recording of up to four units of mushroom body-extrinsic neurons simultaneously for up to 24h in animals moving freely between members of the colony. Every worker, including the recorded bee, hatched in the experimental environment. The group consisted of 200 animals in average.

RESULTS

Animals explored different regions of the comb and interacted with other colony members. The activities of the units were not selective for locations on the comb, body directions with respect to gravity and olfactory signals on the comb, or different social interactions. However, combinations of these parameters defined neural activity in a unit-specific way. In addition, units recorded from the same animal co-varied according to unknown factors. Comparison with existing method(s): All electrophysiological studies with honey bees were performed so far on constrained animals outside their natural behavioral contexts. Yet no neuronal correlates were measured in a social context. Free mobility of recoded insects over a range of a quarter square meter allows addressing questions concerning neural correlates of social communication, planning of tasks within the colony and attention-like processes.

CONCLUSIONS

The method makes it possible to study neural correlates of social behavior in a near-natural setting within the honeybee colony.

Tools for probing local circuits: high-density silicon probes combined with optogenetics

To understand how function arises from the interactions between neurons, it is necessary to use methods that allow the monitoring of brain activity at the single-neuron, single-spike level and the targeted manipulation of the diverse neuron types selectively in a closed-loop manner. Large-scale recordings of neuronal spiking combined with optogenetic perturbation of identified individual neurons has emerged as a suitable method for such tasks in behaving animals. To fully exploit the potential power of these methods, multiple steps of technical innovation are needed. We highlight the current state of the art in electrophysiological recording methods, combined with optogenetics, and discuss directions for progress. In addition, we point to areas where rapid development is in progress and discuss topics where near-term improvements are possible and needed.

Wireless neurosensor for full-spectrum electrophysiology recordings during free behavior

Brain recordings in large animal models and humans typically rely on a tethered connection, which has restricted the spectrum of accessible experimental and clinical applications. To overcome this limitation, we have engineered a compact, lightweight, high data rate wireless neurosensor capable of recording the full spectrum of electrophysiological signals from the cortex of mobile subjects. The wireless communication system exploits a spatially distributed network of synchronized receivers that is scalable to hundreds of channels and vast environments. To demonstrate the versatility of our wireless neurosensor, we monitored cortical neuron populations in freely behaving nonhuman primates during natural locomotion and sleep-wake transitions in ecologically equivalent settings. The interface is electrically safe and compatible with the majority of existing neural probes, which may support previously inaccessible experimental and clinical research.

Feasibility of Energy-Autonomous Wireless Microsensors for Biomedical Applications: Powering and Communication

In this review, biomedical-related wireless miniature devices such as implantable medical devices, neural prostheses, embedded neural systems, and body area network systems are investigated and categorized. The two main subsystems of such designs, the RF subsystem and the energy source subsystem, are studied in detail. Different application classes are considered separately, focusing on their specific data rate and size characteristics. Also, the energy consumption of state-of-the-art communication practices is compared to the energy that can be generated by current energy scavenging devices, highlighting gaps and opportunities. The RF subsystem is classified, and the suitable architecture for each category of applications is highlighted. Finally, a new figure of merit suitable for wireless biomedical applications is introduced to measure the performance of these devices and assist the designer in selecting the proper system for the required application. This figure of merit can effectively fill the gap of a much required method for comparing different techniques in simulation stage before a final design is chosen for implementation.

A frequency shaping neural recorder with 3 pF input capacitance and 11 plus 4.5 bits dynamic range

This paper presents a frequency-shaping (FS) neural recording architecture and its implementation in a 0.13 μ m CMOS process. Compared with its conventional counterpart, the proposed architecture inherently rejects electrode offset, increases input impedance 5-10 fold, compresses neural data dynamic range (DR) by 4.5-bit, simultaneously records local field potentials (LFPs) and extracellular spikes, and is more suitable for long-term recording experiments. Measured at a 40 kHz sampling clock and ± 0.6 V supply, the recorder consumes 50 μW/ch, of which 22 μW per FS amplifier, 24 μ W per buffer, 4 μ W per 11-bit successive approximation register analog-to-digital converter (SAR ADC). The input-referred noise for LFPs and extracellular spikes are 13 μ Vrms and 7 μVrms, respectively, which are sufficient to achieve high-fidelity full-spectrum neural data. In addition, the designed recorder has a 3 pF input capacitance and allows " 11+4.5"-bit neural data DR without system saturation, where the extra 4.5-bit owes to the FS technique. Its figure-of-merit (FOM) based on data DR reaches 36.0 fJ/conversion-step.

Minimum requirements for accurate and efficient real-time on-chip spike sorting

BACKGROUND

Extracellular recordings are performed by inserting electrodes in the brain, relaying the signals to external power-demanding devices, where spikes are detected and sorted in order to identify the firing activity of different putative neurons. A main caveat of these recordings is the necessity of wires passing through the scalp and skin in order to connect intracortical electrodes to external amplifiers. The aim of this paper is to evaluate the feasibility of an implantable platform (i.e., a chip) with the capability to wirelessly transmit the neural signals and perform real-time on-site spike sorting.

NEW METHOD

We computationally modelled a two-stage implementation for online, robust, and efficient spike sorting. In the first stage, spikes are detected on-chip and streamed to an external computer where mean templates are created and sent back to the chip. In the second stage, spikes are sorted in real-time through template matching.

RESULTS

We evaluated this procedure using realistic simulations of extracellular recordings and describe a set of specifications that optimise performance while keeping to a minimum the signal requirements and the complexity of the calculations.

COMPARISON WITH EXISTING METHODS

A key bottleneck for the development of long-term BMIs is to find an inexpensive method for real-time spike sorting. Here, we simulated a solution to this problem that uses both offline and online processing of the data.

CONCLUSIONS

Hardware implementations of this method therefore enable low-power long-term wireless transmission of multiple site extracellular recordings, with application to wireless BMIs or closed-loop stimulation designs.

Extracellular wire tetrode recording in brain of freely walking insects

Increasing interest in the role of brain activity in insect motor control requires that we be able to monitor neural activity while insects perform natural behavior. We previously developed a technique for implanting tetrode wires into the central complex of cockroach brains that allowed us to record activity from multiple neurons simultaneously while a tethered cockroach turned or altered walking speed. While a major advance, tethered preparations provide access to limited behaviors and often lack feedback processes that occur in freely moving animals. We now present a modified version of that technique that allows us to record from the central complex of freely moving cockroaches as they walk in an arena and deal with barriers by turning, climbing or tunneling. Coupled with high speed video and cluster cutting, we can now relate brain activity to various parameters of the movement of freely behaving insects.

Long-term behavioral tracking of freely swimming weakly electric fish

Long-term behavioral tracking can capture and quantify natural animal behaviors, including those occurring infrequently. Behaviors such as exploration and social interactions can be best studied by observing unrestrained, freely behaving animals. Weakly electric fish (WEF) display readily observable exploratory and social behaviors by emitting electric organ discharge (EOD). Here, we describe three effective techniques to synchronously measure the EOD, body position, and posture of a free-swimming WEF for an extended period of time. First, we describe the construction of an experimental tank inside of an isolation chamber designed to block external sources of sensory stimuli such as light, sound, and vibration. The aquarium was partitioned to accommodate four test specimens, and automated gates remotely control the animals' access to the central arena. Second, we describe a precise and reliable real-time EOD timing measurement method from freely swimming WEF. Signal distortions caused by the animal's body movements are corrected by spatial averaging and temporal processing stages. Third, we describe an underwater near-infrared imaging setup to observe unperturbed nocturnal animal behaviors. Infrared light pulses were used to synchronize the timing between the video and the physiological signal over a long recording duration. Our automated tracking software measures the animal's body position and posture reliably in an aquatic scene. In combination, these techniques enable long term observation of spontaneous behavior of freely swimming weakly electric fish in a reliable and precise manner. We believe our method can be similarly applied to the study of other aquatic animals by relating their physiological signals with exploratory or social behaviors.

A 0.5 cm(3) four-channel 1.1 mW wireless biosignal interface with 20 m range

This paper presents a self-contained, single-chip biosignal monitoring system with wireless programmability and telemetry interface suitable for mainstream healthcare applications. The system consists of low-noise front end amplifiers, ADC, MICS/ISM transmitter and infrared programming capability to configure the state of the chip. An on-chip packetizer ensures easy pairing with standard off-the-shelf receivers. The chip is realized in the IBM 130 nm CMOS process with an area of 2×2 mm(2). The entire system consumes 1.07 mW from a 1.2 V supply. It weighs 0.6 g including a zinc-air battery. The system has been extensively tested in in vivo biological experiments and requires minimal human interaction or calibration.

Development of new muscle contraction sensor to replace sEMG for using in muscles analysis fields

Nowadays, the technologies for detecting, processing and interpreting bioelectrical signals have improved tremendously. In particular, surface electromyography (sEMG) has gained momentum in a wide range of applications in various fields. However, sEMG sensing has several shortcomings, the most important being: measurements are heavily sensible to individual differences, sensors are difficult to position and very expensive. In this paper, the authors will present an innovative muscle contraction sensing device (MC sensor), aiming to replace sEMG sensing in the field of muscle movement analysis. Compared with sEMG, this sensor is easier to position, setup and use, less dependent from individual differences, and less expensive. Preliminary experiments, described in this paper, confirm that MC sensing is suitable for muscle contraction analysis, and compare the results of sEMG and MC sensor for the measurement of forearm muscle contraction.

Large-scale, high-density (up to 512 channels) recording of local circuits in behaving animals

Monitoring representative fractions of neurons from multiple brain circuits in behaving animals is necessary for understanding neuronal computation. Here, we describe a system that allows high-channel-count recordings from a small volume of neuronal tissue using a lightweight signal multiplexing headstage that permits free behavior of small rodents. The system integrates multishank, high-density recording silicon probes, ultraflexible interconnects, and a miniaturized microdrive. These improvements allowed for simultaneous recordings of local field potentials and unit activity from hundreds of sites without confining free movements of the animal. The advantages of large-scale recordings are illustrated by determining the electroanatomic boundaries of layers and regions in the hippocampus and neocortex and constructing a circuit diagram of functional connections among neurons in real anatomic space. These methods will allow the investigation of circuit operations and behavior-dependent interregional interactions for testing hypotheses of neural networks and brain function.

Statistics of the electrosensory input in the freely swimming weakly electric fish Apteronotus leptorhynchus

The neural computations underlying sensory-guided behaviors can best be understood in view of the sensory stimuli to be processed under natural conditions. This input is often actively shaped by the movements of the animal and its sensory receptors. Little is known about natural sensory scene statistics taking into account the concomitant movement of sensory receptors in freely moving animals. South American weakly electric fish use a self-generated quasi-sinusoidal electric field for electrolocation and electrocommunication. Thousands of cutaneous electroreceptors detect changes in the transdermal potential (TDP) as the fish interact with conspecifics and the environment. Despite substantial knowledge about the circuitry and physiology of the electrosensory system, the statistical properties of the electrosensory input evoked by natural swimming movements have never been measured directly. Using underwater wireless telemetry, we recorded the TDP of Apteronotus leptorhynchus as they swam freely by themselves and during interaction with a conspecific. Swimming movements caused low-frequency TDP amplitude modulations (AMs). Interacting with a conspecific caused additional AMs around the difference frequency of their electric fields, with the amplitude of the AMs (envelope) varying at low frequencies due to mutual movements. Both AMs and envelopes showed a power-law relationship with frequency, indicating spectral scale invariance. Combining a computational model of the electric field with video tracking of movements, we show that specific swimming patterns cause characteristic spatiotemporal sensory input correlations that contain information that may be used by the brain to guide behavior.

Sensory flow shaped by active sensing: sensorimotor strategies in electric fish

Goal-directed behavior in most cases is composed of a sequential order of elementary motor patterns shaped by sensorimotor contingencies. The sensory information acquired thus is structured in both space and time. Here we review the role of motion during the generation of sensory flow focusing on how animals actively shape information by behavioral strategies. We use the well-studied examples of vision in insects and echolocation in bats to describe commonalities of sensory-related behavioral strategies across sensory systems, and evaluate what is currently known about comparable active sensing strategies in electroreception of electric fish. In this sensory system the sensors are dispersed across the animal's body and the carrier source emitting energy used for sensing, the electric organ, is moved while the animal moves. Thus ego-motions strongly influence sensory dynamics. We present, for the first time, data of electric flow during natural probing behavior in Gnathonemus petersii (Mormyridae), which provide evidence for this influence. These data reveal a complex interdependency between the physical input to the receptors and the animal's movements, posture and objects in its environment. Although research on spatiotemporal dynamics in electrolocation is still in its infancy, the emerging field of dynamical sensory systems analysis in electric fish is a promising approach to the study of the link between movement and acquisition of sensory information.

A battery-free multichannel digital neural/EMG telemetry system for flying insects

This paper presents a digital neural/EMG telemetry system small enough and lightweight enough to permit recording from insects in flight. It has a measured flight package mass of only 38 mg. This system includes a single-chip telemetry integrated circuit (IC) employing RF power harvesting for battery-free operation, with communication via modulated backscatter in the UHF (902-928 MHz) band. An on-chip 11-bit ADC digitizes 10 neural channels with a sampling rate of 26.1 kSps and 4 EMG channels at 1.63 kSps, and telemeters this data wirelessly to a base station. The companion base station transceiver includes an RF transmitter of +36 dBm (4 W) output power to wirelessly power the telemetry IC, and a digital receiver with a sensitivity of -70 dBm for 10⁻⁵ BER at 5.0 Mbps to receive the data stream from the telemetry IC. The telemetry chip was fabricated in a commercial 0.35 μ m 4M1P (4 metal, 1 poly) CMOS process. The die measures 2.36 × 1.88 mm, is 250 μm thick, and is wire bonded into a flex circuit assembly measuring 4.6 × 6.8 mm.

A 0.09 μW low power front-end biopotential amplifier for biosignal recording

This work presents a biopotential front-end amplifier in which the MOS transistors are biased in subthreshold region with a supply voltage and current of 0.4-0.8 V and 0.23-1.86 μA, respectively, to reduce the system power. Flicker noise is then removed using a chopping technique, and differential interference produced by electrode impedance imbalance is suppressed using a Gm-C filter. Additionally, the circuit is fabricated using TSMC 0.18 μm CMOS technology with a core area of 0.77 × 0.36 mm². With a minimum supply voltage of 0.4 V, the measured SNR and power consumption of the proposed IC chip are 54.1 dB and 0.09μW, respectively.

Decision Making and Behavioral Choice during Predator Avoidance

One of the most important decisions animals have to make is how to respond to an attack from a potential predator. The response must be prompt and appropriate to ensure survival. Invertebrates have been important models in studying the underlying neurobiology of the escape response due to their accessible nervous systems and easily quantifiable behavioral output. Moreover, invertebrates provide opportunities for investigating these processes at a level of analysis not available in most other organisms. Recently, there has been a renewed focus in understanding how value-based calculations are made on the level of the nervous system, i.e., when decisions are made under conflicting circumstances, and the most desirable choice must be selected by weighing the costs and benefits for each behavioral choice. This article reviews samples from the current literature on anti-predator decision making in invertebrates, from single neurons to complex behaviors. Recent progress in understanding the mechanisms underlying value-based behavioral decisions is also discussed.

Escape behaviors in insects

Escape behaviors are, by necessity, fast and robust, making them excellent systems with which to study the neural basis of behavior. This is especially true in insects, which have comparatively tractable nervous systems and members who are amenable to manipulation with genetic tools. Recent technical developments in high-speed video reveal that, despite their short duration, insect escape behaviors are more complex than previously appreciated. For example, before initiating an escape jump, a fly performs sophisticated posture and stimulus-dependent preparatory leg movements that enable it to jump away from a looming threat. This newfound flexibility raises the question of how the nervous system generates a behavior that is both rapid and flexible. Recordings from the cricket nervous system suggest that synchrony between the activity of specific interneuron pairs may provide a rapid cue for the cricket to detect the direction of an approaching predator and thus which direction it should run. Technical advances make possible wireless recording from neurons while locusts escape from a looming threat, enabling, for the first time, a direct correlation between the activity of multiple neurons and the time-course of an insect escape behavior.

Visual control of prey-capture flight in dragonflies

Interacting with a moving object poses a computational problem for an animal's nervous system. This problem has been elegantly solved by the dragonfly, a formidable visual predator on flying insects. The dragonfly computes an interception flight trajectory and steers to maintain it during its prey-pursuit flight. This review summarizes current knowledge about pursuit behavior and neurons thought to control interception in the dragonfly. When understood, this system has the potential for explaining how a small group of neurons can control complex interactions with moving objects.