Development of a multi-needle fiberoptic Raman spectroscopy technique for simultaneous multi-site deep tissue Raman measurements in the brain

We report on the development of a multi-needle fiberoptic Raman spectroscopy (MNF-RS) technique for simultaneous multi-site deep Raman measurements in brain tissue. The multi-needle fiberoptic Raman probe is designed and fabricated using a number of 100 µm core diameter, aluminum-coated fibers under a coaxial laser excitation and Raman collection scheme, enabling simultaneous collection of deep tissue Raman spectra from a number of tissue sites. We have also developed a Raman retrieval algorithm based on the transformation matrix of each individual needle fiber probe projected to different pixels of a charge-coupled device (CCD) for recovering the tissue Raman spectra collected by each needle fiber probe, allowing simultaneous multi-channel detection by a single Raman spectrometer. High-quality tissue Raman spectra of different tissue types (e.g., muscle, fat, gray matter, and white matter in porcine brain) can be acquired in both the fingerprint (900-1800 cm-1) and high-wavenumber (2800-3300 cm-1) regions within sub-second times using the MNF-RS technique. We also demonstrate that by advancing the multi-needle fiberoptic Raman probe into deep porcine brain, tissue Raman spectra can be acquired simultaneously from different brain regions (e.g., cortex, thalamus, midbrain, and cerebellum). The significant biochemical differences across different brain tissues can also be distinguished, suggesting the promising potential of the MNF-RS technique for label-free neuroscience study at the molecular level.

The significance of neural inter-frequency power correlations

It is of great interest in neuroscience to determine what frequency bands in the brain have covarying power. This would help us robustly identify the frequency signatures of neural processes. However to date, to the best of the author's knowledge, a comprehensive statistical approach to this question that accounts for intra-frequency autocorrelation, frequency-domain oversampling, and multiple testing under dependency has not been undertaken. As such, this work presents a novel statistical significance test for correlated power across frequency bands for a broad class of non-stationary time series. It is validated on synthetic data. It is then used to test all of the inter-frequency power correlations between 0.2 and 8500 Hz in continuous intracortical extracellular neural recordings in Macaque M1, using a very large, publicly available dataset. The recordings were Current Source Density referenced and were recorded with a Utah array. The results support previous results in the literature that show that neural processes in M1 have power signatures across a very broad range of frequency bands. In particular, the power in LFP frequency bands as low as 20 Hz was found to almost always be statistically significantly correlated to the power in kHz frequency ranges. It is proposed that this test can also be used to discover the superimposed frequency domain signatures of all the neural processes in a neural signal, allowing us to identify every interesting neural frequency band.

Expansion microscopy: A powerful nanoscale imaging tool for neuroscientists

One of the biggest unsolved questions in neuroscience is how molecules and neuronal circuitry create behaviors, and how their misregulation or dysfunction results in neurological disease. Light microscopy is a vital tool for the study of neural molecules and circuits. However, the fundamental optical diffraction limit precludes the use of conventional light microscopy for sufficient characterization of critical signaling compartments and nanoscopic organizations of synapse-associated molecules. We have witnessed rapid development of super-resolution microscopy methods that circumvent the resolution limit by controlling the number of emitting molecules in specific imaging volumes and allow highly resolved imaging in the 10-100 nm range. Most recently, Expansion Microscopy (ExM) emerged as an alternative solution to overcome the diffraction limit by physically magnifying biological specimens, including nervous systems. Here, we discuss how ExM works in general and currently available ExM methods. We then review ExM imaging in a wide range of nervous systems, including Caenorhabditis elegans, Drosophila, zebrafish, mouse, and human, and their applications to synaptic imaging, neuronal tracing, and the study of neurological disease. Finally, we provide our prospects for expansion microscopy as a powerful nanoscale imaging tool in the neurosciences.

Emerging Frontier of Peripheral Nerve and Organ Interfaces

The development of new tools to interface with the nervous system, empowered by advances in electronics and materials science, has transformed neuroscience and is informing therapies for neurological and mental conditions. Although the vast majority of neural engineering research has focused on advancing tools to study the brain, understanding the peripheral nervous system and other organs can similarly benefit from these technologies. To realize this vision, the neural interface technologies need to address the biophysical, mechanical, and chemical challenges posed by the peripheral nerves and organs. In this Perspective, we discuss design considerations and recent technological advances to modulate electrical signaling outside the central nervous system. The innovations in bioelectronics borne out of interdisciplinary collaborations between biologists and physical scientists may not only advance fundamental study of peripheral (neuro)physiology but also empower clinical interventions for conditions including neurological, gastrointestinal, and immune dysfunction.

Developing Collaborative Platforms to Advance Neurotechnology and Its Translation

Neurotechnological devices are failing to deliver on their therapeutic promise because of the time it takes to translate them from bench to clinic. In this Perspective, we reflect on lessons learned from medical device successes and failures and consider how such lessons might shape a strategic vision for translating neurotechnologies in the future. We articulate how the intentional design and deployment of "scientific platforms," from the technology stack of hardware and software through the supporting ecosystem, could catalyze a new wave of innovation, discovery, and therapy. We also identify specific actions that could promote future neurotechnology roadmaps and industrial-academic-government collaborative activities. We believe that community-supported neurotechnology platforms will prove to be transformational in accelerating ideas from bench to bedside, maximizing scientific discovery and improving patient care.

Recent Advances in Electrical Neural Interface Engineering: Minimal Invasiveness, Longevity, and Scalability

Electrical neural interfaces serve as direct communication pathways that connect the nervous system with the external world. Technological advances in this domain are providing increasingly more powerful tools to study, restore, and augment neural functions. Yet, the complexities of the nervous system give rise to substantial challenges in the design, fabrication, and system-level integration of these functional devices. In this review, we present snapshots of the latest progresses in electrical neural interfaces, with an emphasis on advances that expand the spatiotemporal resolution and extent of mapping and manipulating brain circuits. We include discussions of large-scale, long-lasting neural recording; wireless, miniaturized implants; signal transmission, amplification, and processing; as well as the integration of interfaces with optical modalities. We outline the background and rationale of these developments and share insights into the future directions and new opportunities they enable.

A 340 nW/Channel 110 dB PSRR Neural Recording Analog Front-End Using Replica-Biasing LNA, Level-Shifter Assisted PGA, and Averaged LFP Servo Loop in 65 nm CMOS

This paper presents an 8-channel energy-efficient analog front-end (AFE) for neural recording, with improvements in power supply rejection ratio (PSRR) and dynamic range. The input stage in the low noise amplifier (LNA) adopts low voltage supply (0.35 V) and current-reusing to achieve ultralow power. To maintain a high PSRR performance while using such a low-voltage supply, a replica-biasing scheme is proposed to generate a stable bias current for the input stage of the LNA despite large supply interference. By exploiting the signal characteristics in the tetrode recording, an averaged local field potential (A-LFP) servo loop is introduced to extend the dynamic range without consuming too much extra power and chip area. The A-LFP signal is generated by integrating the four-channel PGA outputs from the same tetrode. Furthermore, the outputs of the programmable gain amplifier (PGA) are level shifted to bias the input nodes of the amplifier through large pseudo resistors, thus increase the maximum output range without distortion under the low-voltage supply. The proof-of-concept prototype is fabricated in a 65 nm CMOS process. Each recording channel including an LNA and a PGA occupies 0.04 mm ² and consumes 340 nW from the 0.35 V and 0.7 V supply. Each A-LFP servo loop, which is shared by four recording channels, occupies 0.04 mm ² and consumes 190 nW. The maximum gain of the AFE is 54 dB, and the input-referred noise is 6.7 μV over the passband from 0.5 Hz to 6.5 kHz. Measurement also shows that the 0.35 V replica-biasing input stage can tolerate a large interferer up to 200 mVpp with a PSRR of 74 dB, which has been improved to 110 dB with a silicon respin that shields critical wires in the layout.

Accuracy and precision of stimulus timing and reaction times with Unreal Engine and SteamVR

The increasing interest in Virtual Reality (VR) as a tool for neuroscientific research contrasts with the current lack of established toolboxes and standards. In several recent studies, game engines like Unity or Unreal Engine were used. It remains to be tested whether these software packages provide sufficiently precise and accurate stimulus timing and time measurements that allow inferring ongoing mental and neural processes. We here investigated the precision and accuracy of the timing mechanisms of Unreal Engine 4 and SteamVR in combination with the HTC Vive VR system. In a first experiment, objective external measures revealed that stimulus durations were highly accurate. In contrast, in a second experiment, the assessment of the precision of built-in timing procedures revealed highly variable reaction time measurements and inaccurate determination of stimulus onsets. Hence, we developed a new software-based method that allows precise and accurate reaction time measurements with Unreal Engine and SteamVR. Instead of using the standard timing procedures implemented within Unreal Engine, time acquisition was outsourced to a background application. Timing benchmarks revealed that the newly developed method allows reaction time measurements with a precision and accuracy in the millisecond range. Overall, the present results indicate that the HTC Vive together with Unreal Engine and SteamVR can achieve high levels of precision and accuracy both concerning stimulus duration and critical time measurements. The latter can be achieved using a newly developed routine that allows not only accurate reaction time measures but also provides precise timing parameters that can be used in combination with time-sensitive functional measures such as electroencephalography (EEG) or transcranial magnetic stimulation (TMS).

A platform for semiautomated voluntary training of common marmosets for behavioral neuroscience

Generally behavioral neuroscience studies of the common marmoset employ adaptations of well-established training methods used with macaque monkeys. However, in many cases these approaches do not readily generalize to marmosets indicating a need for alternatives. Here we present the development of one such alternate: a platform for semiautomated, voluntary in-home cage behavioral training that allows for the study of naturalistic behaviors. We describe the design and production of a modular behavioral training apparatus using CAD software and digital fabrication. We demonstrate that this apparatus permits voluntary behavioral training and data collection throughout the marmoset's waking hours with little experimenter intervention. Furthermore, we demonstrate the use of this apparatus to reconstruct the kinematics of the marmoset's upper limb movement during natural foraging behavior.NEW & NOTEWORTHY The study of marmosets in neuroscience has grown rapidly and presents unique challenges. We address those challenges with an innovative platform for semiautomated, voluntary training that allows marmosets to train throughout their waking hours with minimal experimenter intervention. We describe the use of this platform to capture upper limb kinematics during foraging and to expand the opportunities for behavioral training beyond the limits of traditional training sessions. This flexible platform can easily incorporate other tasks.

Real-time cortical simulation on neuromorphic hardware

Real-time simulation of a large-scale biologically representative spiking neural network is presented, through the use of a heterogeneous parallelization scheme and SpiNNaker neuromorphic hardware. A published cortical microcircuit model is used as a benchmark test case, representing ≈1 mm² of early sensory cortex, containing 77 k neurons and 0.3 billion synapses. This is the first hard real-time simulation of this model, with 10 s of biological simulation time executed in 10 s wall-clock time. This surpasses best-published efforts on HPC neural simulators (3 × slowdown) and GPUs running optimized spiking neural network (SNN) libraries (2 × slowdown). Furthermore, the presented approach indicates that real-time processing can be maintained with increasing SNN size, breaking the communication barrier incurred by traditional computing machinery. Model results are compared to an established HPC simulator baseline to verify simulation correctness, comparing well across a range of statistical measures. Energy to solution and energy per synaptic event are also reported, demonstrating that the relatively low-tech SpiNNaker processors achieve a 10 × reduction in energy relative to modern HPC systems, and comparable energy consumption to modern GPUs. Finally, system robustness is demonstrated through multiple 12 h simulations of the cortical microcircuit, each simulating 12 h of biological time, and demonstrating the potential of neuromorphic hardware as a neuroscience research tool for studying complex spiking neural networks over extended time periods. This article is part of the theme issue 'Harmonizing energy-autonomous computing and intelligence'.

ChaRTr: An R toolbox for modeling choices and response times in decision-making tasks

BACKGROUND

Decision-making is the process of choosing and performing actions in response to sensory cues to achieve behavioral goals. Many mathematical models have been developed to describe the choice behavior and response time (RT) distributions of observers performing decision-making tasks. However, relatively few researchers use these models because it demands expertise in various numerical, statistical, and software techniques.

NEW METHOD

We present a toolbox - Choices and Response Times in R, or ChaRTr - that provides the user the ability to implement and test a wide variety of decision-making models ranging from classic through to modern versions of the diffusion decision model, to models with urgency signals, or collapsing boundaries.

RESULTS

In three different case studies, we demonstrate how ChaRTr can be used to effortlessly discriminate between multiple models of decision-making behavior. We also provide guidance on how to extend the toolbox to incorporate future developments in decision-making models.

COMPARISON WITH EXISTING METHOD(S)

Existing software packages surmounted some of the numerical issues but have often focused on the classical decision-making model, the diffusion decision model. Recent models that posit roles for urgency, time-varying decision thresholds, noise in various aspects of the decision-formation process or low pass filtering of sensory evidence have proven to be challenging to incorporate in a coherent software framework that permits quantitative evaluation among these competing classes of decision-making models.

CONCLUSION

ChaRTr can be used to make insightful statements about the cognitive processes underlying observed decision-making behavior and ultimately for deeper insights into decision mechanisms.

Methods for single-cell recording and labeling in vivo

Targeting individual neurons in vivo is a key method to study the role of single cell types within local and brain-wide microcircuits. While novel technological developments now permit assessing activity from large number of cells simultaneously, there is currently no better solution than glass micropipettes to relate the physiology and morphology of single-cells. Sharp intracellular, juxtacellular, loose-patch and whole-cell approaches are some of the configurations used to record and label individual neurons. Here, we review procedures to establish successful electrophysiological recordings in vivo followed by appropriate labeling for post hoc morphological analysis. We provide operational recommendations for optimizing each configuration and a generic framework for functional, neurochemical and morphological identification of the different cell-types in a given region. Finally, we highlight emerging approaches that are challenging our current paradigms for single-cell recording and labeling in the living brain.

Nongenetic optical neuromodulation with silicon-based materials

Optically controlled nongenetic neuromodulation represents a promising approach for the fundamental study of neural circuits and the clinical treatment of neurological disorders. Among the existing material candidates that can transduce light energy into biologically relevant cues, silicon (Si) is particularly advantageous due to its highly tunable electrical and optical properties, ease of fabrication into multiple forms, ability to absorb a broad spectrum of light, and biocompatibility. This protocol describes a rational design principle for Si-based structures, general procedures for material synthesis and device fabrication, a universal method for evaluating material photoresponses, detailed illustrations of all instrumentation used, and demonstrations of optically controlled nongenetic modulation of cellular calcium dynamics, neuronal excitability, neurotransmitter release from mouse brain slices, and brain activity in the mouse brain in vivo using the aforementioned Si materials. The entire procedure takes ~4-8 d in the hands of an experienced graduate student, depending on the specific biological targets. We anticipate that our approach can also be adapted in the future to study other systems, such as cardiovascular tissues and microbial communities.

Shady: A software engine for real-time visual stimulus manipulation

BACKGROUND

Precise definition, rendering and manipulation of visual stimuli are essential in neuroscience. Rather than implementing these tasks from scratch, scientists benefit greatly from using reusable software routines from freely available toolboxes. Existing toolboxes work well when the operating system and hardware are painstakingly optimized, but may be less suited to applications that require multi-tasking (for example, closed-loop systems that involve real-time acquisition and processing of signals).

NEW METHOD

We introduce a new cross-platform visual stimulus toolbox called Shady (https://pypi.org/project/Shady)-so called because of its heavy reliance on a shader program to perform parallel pixel processing on a computer's graphics processor. It was designed with an emphasis on performance robustness in multi-tasking applications under unforgiving conditions. For optimal timing performance, the CPU drawing management commands are carried out by a compiled binary engine. For configuring stimuli and controlling their changes over time, Shady provides a programmer's interface in Python, a powerful, accessible and widely-used high-level programming language.

RESULTS

Our timing benchmark results illustrate that Shady's hybrid compiled/interpreted architecture requires less time to complete drawing operations, exhibits smaller variability in frame-to-frame timing, and hence drops fewer frames, than pure-Python solutions under matched conditions of resource contention. This performance gain comes despite an expansion of functionality (e.g. "noisy-bit" dithering as standard on all pixels and all frames, to enhance effective dynamic range) relative to previous offerings.

CONCLUSIONS

Shady simultaneously advances the functionality and performance available to scientists for rendering visual stimuli and manipulating them in real time.

Spikeling: A low-cost hardware implementation of a spiking neuron for neuroscience teaching and outreach

Understanding how neurons encode and compute information is fundamental to our study of the brain, but opportunities for hands-on experience with neurophysiological techniques on live neurons are scarce in science education. Here, we present Spikeling, an open source in silico implementation of a spiking neuron that costs £25 and mimics a wide range of neuronal behaviours for classroom education and public neuroscience outreach. Spikeling is based on an Arduino microcontroller running the computationally efficient Izhikevich model of a spiking neuron. The microcontroller is connected to input ports that simulate synaptic excitation or inhibition, to dials controlling current injection and noise levels, to a photodiode that makes Spikeling light sensitive, and to a light-emitting diode (LED) and speaker that allows spikes to be seen and heard. Output ports provide access to variables such as membrane potential for recording in experiments or digital signals that can be used to excite other connected Spikelings. These features allow for the intuitive exploration of the function of neurons and networks mimicking electrophysiological experiments. We also report our experience of using Spikeling as a teaching tool for undergraduate and graduate neuroscience education in Nigeria and the United Kingdom.

Ionic Direct Current Modulation for Combined Inhibition/Excitation of the Vestibular System

OBJECTIVE

Prosthetic electrical stimulation delivered to the vestibular nerve could provide therapy for people suffering from bilateral vestibular dysfunction. Common encoding methods use pulse-frequency modulation (PFM) to stimulate the semicircular canals of the vestibular system. We previously showed that delivery of ionic direct current (iDC) can also modulate the vestibular system. In this study, we compare the dynamic range of head velocity encoding from iDC modulation to that of PFM controls.

METHODS

Gentamicin-treated wild-type chinchillas were implanted with microcatheter tubes that delivered ionic current to the left ear vestibular canals and stimulated with steps of anodic/cathodic iDC or PFM. Evoked vestibulo-ocular reflex eye velocity was used to compare PFM and iDC vestibular modulation.

RESULTS

Cathodic iDC steps effectively elicited eye rotations consistent with an increased firing rate of the implanted semicircular canal afferents. Anodic iDC current steps elicited eye rotations in the opposite direction that, when paired with an adapted cathodic offset, increased the dynamic range of eye rotation velocities in comparison to PFM controls.

CONCLUSION

Our results suggest that iDC modulation can effectively modulate the vestibular system across a functional range of rotation vectors and velocities, with a potential benefit over a PFM stimulation paradigm.

SIGNIFICANCE

In conjunction with a safe dc delivery system, iDC modulation could potentially increase the range of simulated head rotation velocities available to neuroelectric vestibular prostheses.

Expansion microscopy: development and neuroscience applications

Many neuroscience questions center around understanding how the molecules and wiring in neural circuits mechanistically yield behavioral functions, or go awry in disease states. However, mapping the molecules and wiring of neurons across the large scales of neural circuits has posed a great challenge. We recently developed expansion microscopy (ExM), a process in which we physically magnify biological specimens such as brain circuits. We synthesize throughout preserved brain specimens a dense, even mesh of a swellable polymer such as sodium polyacrylate, anchoring key biomolecules such as proteins and nucleic acids to the polymer. After mechanical homogenization of the specimen-polymer composite, we add water, and the polymer swells, pulling biomolecules apart. Due to the larger separation between molecules, ordinary microscopes can then perform nanoscale resolution imaging. We here review the ExM technology as well as applications to the mapping of synapses, cells, and circuits, including deployment in species such as Drosophila, mouse, non-human primate, and human.

Current technical approaches to brain energy metabolism

Neuroscience is a technology-driven discipline and brain energy metabolism is no exception. Once satisfied with mapping metabolic pathways at organ level, we are now looking to learn what it is exactly that metabolic enzymes and transporters do and when, where do they reside, how are they regulated, and how do they relate to the specific functions of neurons, glial cells, and their subcellular domains and organelles, in different areas of the brain. Moreover, we aim to quantify the fluxes of metabolites within and between cells. Energy metabolism is not just a necessity for proper cell function and viability but plays specific roles in higher brain functions such as memory processing and behavior, whose mechanisms need to be understood at all hierarchical levels, from isolated proteins to whole subjects, in both health and disease. To this aim, the field takes advantage of diverse disciplines including anatomy, histology, physiology, biochemistry, bioenergetics, cellular biology, molecular biology, developmental biology, neurology, and mathematical modeling. This article presents a well-referenced synopsis of the technical side of brain energy metabolism research. Detail and jargon are avoided whenever possible and emphasis is given to comparative strengths, limitations, and weaknesses, information that is often not available in regular articles.

Jan Evangelista Purkyně (1787-1869) and his instruments for microscopic research in the field of neuroscience

The findings obtained by the famous nineteenth-century Czech scientist Jan Evangelista Purkyně (1787-1869) in the field of microscopic structure of animal and human tissues, including the brain, spinal cord, and nerves, have already been described in depth in a number of older and newer publications. The present article contains an overview of the instruments and tools that Purkyně and his assistants used for microscopic research of tissue histology. Some of these instruments were developed either by Purkyně alone, such as the microtomic compressor, or together with his assistant Adolph Oschatz, such as the microtome. A brief overview of the development of the cutting engines suggests that the first microtome, a prototype of modern sliding microtomes, was designed and constructed under the supervision of Purkyně at the Institute of Physiology in Wrocław. Purkyně and his assistants, thus, not only obtained important findings of animal and human nervous and other tissues but also substantially contributed to the development of instruments and tools for their study, a fact often forgotten today.

Acute In Vivo Electrophysiological Recordings of Local Field Potentials and Multi-unit Activity from the Hyperdirect Pathway in Anesthetized Rats

Converging evidence shows that many neuropsychiatric diseases should be understood as disorders of large-scale neuronal networks. To better understand the pathophysiological basis of these diseases, it is necessary to precisely characterize in which way the processing of information is disturbed between the different neuronal parts of the circuit. Using extracellular in vivo electrophysiological recordings, it is possible to accurately delineate neuronal activity within a neuronal network. The application of this method has several advantages over alternative techniques, e.g., functional magnetic resonance imaging and calcium imaging, as it allows a unique temporal and spatial resolution and does not rely on genetically engineered organisms. However, the use of extracellular in vivo recordings is limited since it is an invasive technique that cannot be universally applied. In this article, a simple and easy to use method is presented with which it is possible to simultaneously record extracellular potentials such as local field potentials and multiunit activity at multiple sites of a network. It is detailed how a precise targeting of subcortical nuclei can be achieved using a combination of stereotactic surgery and online analysis of multi-unit recordings. Thus, it is demonstrated, how a complete network such as the hyperdirect cortico-basal ganglia loop can be studied in anesthetized animals in vivo.

Independent component analysis-based source-level hyperlink analysis for two-person neuroscience studies

Two-person neuroscience, a perspective in understanding human social cognition and interaction, involves designing immersive social interaction experiments as well as simultaneously recording brain activity of two or more subjects, a process termed “hyperscanning.” Using newly developed imaging techniques, the interbrain connectivity or hyperlink of various types of social interaction has been revealed. Functional near-infrared spectroscopy (fNIRS)-hyperscanning provides a more naturalistic environment for experimental paradigms of social interaction and has recently drawn much attention. However, most fNIRS-hyperscanning studies have computed hyperlinks using sensor data directly while ignoring the fact that the sensor-level signals contain confounding noises, which may lead to a loss of sensitivity and specificity in hyperlink analysis. In this study, on the basis of independent component analysis (ICA), a source-level analysis framework is proposed to investigate the hyperlinks in a fNIRS two-person neuroscience study. The performance of five widely used ICA algorithms in extracting sources of interaction was compared in simulative datasets, and increased sensitivity and specificity of hyperlink analysis by our proposed method were demonstrated in both simulative and real two-person experiments.

Of the Artistic Nude and Technological Behaviorism. Leon Harmon and the First Steps towards Neuromorphic Hardware

Neuromorphic technologies lie at the core of 21st century neuroscience, especially in the “big brain science” projects started in 2013 – i.e. the BRAIN Initiative and the Human Brain Project. While neuromorphism and the “reverse engineering” of the brain are often presented as a “methodological revolution” in the brain sciences, these concepts have a long history which is strongly interconnected with the developments in neuroscience and the related field of bioengineering since the end of World War II. In this paper I provide a short review of the first generation of “neuromorphic devices” created in the 1960s, by focusing on the work of Leon Harmon and his “neuromime,” whose material history overlapped in a very interesting sense with the visual and artistic culture of the second half of the 20th century.

A Neuromorphic Event-Based Neural Recording System for Smart Brain-Machine-Interfaces

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 μV, and consumes 90 μW . 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.

A High Performance Delta-Sigma Modulator for Neurosensing

Recorded neural data are frequently corrupted by large amplitude artifacts that are triggered by a variety of sources, such as subject movements, organ motions, electromagnetic interferences and discharges at the electrode surface. To prevent the system from saturating and the electronics from malfunctioning due to these large artifacts, a wide dynamic range for data acquisition is demanded, which is quite challenging to achieve and would require excessive circuit area and power for implementation. In this paper, we present a high performance Delta-Sigma modulator along with several design techniques and enabling blocks to reduce circuit area and power. The modulator was fabricated in a 0.18-μm CMOS process. Powered by a 1.0-V supply, the chip can achieve an 85-dB peak signal-to-noise-and-distortion ratio (SNDR) and an 87-dB dynamic range when integrated over a 10-kHz bandwidth. The total power consumption of the modulator is 13 μW, which corresponds to a figure-of-merit (FOM) of 45 fJ/conversion step. These competitive circuit specifications make this design a good candidate for building high precision neurosensors.

A 1024-channel 6 mW/mm2 optical stimulator for in-vitro neuroscience experiments

Recent optical stimulation technologies allow improved selectivity and have been widely used in neuroscience research. This paper presents an optical stimulator based on high power LEDs. It has 1024 channels and can produce flexible stimulation patterns in each frame, refreshed at above 20 Hz. To increase the light intensity, each LED has an optical package that directs the light into a small angle. To ensure the light of each LED can reach the lens, the LEDs have been specially placed and oriented to the lens. With these efforts, the achieved power efficiency (defined as the mount of LED light power passing through the lens divided by the LED total power consumption) is 5 × 10(-5). In our current prototype, an individual LED unit can source 60 mW electrical power, where the induced irradiance on neural tissues is 6 mW/mm(2) integrating from 460 nm to 480 nm. The light spot is tunable in size from 18 μm to 40 μm with an extra 5-10 μm separation for isolating two adjacent spots. Through both bench-top measurement and finite element simulation, we found the cross channel interference is below 10%. A customized software interface has been developed to control and program the stimulator operation.

The OpenPicoAmp: an open-source planar lipid bilayer amplifier for hands-on learning of neuroscience

Understanding the electrical biophysical properties of the cell membrane can be difficult for neuroscience students as it relies solely on lectures of theoretical models without practical hands on experiments. To address this issue, we developed an open-source lipid bilayer amplifier, the OpenPicoAmp, which is appropriate for use in introductory courses in biophysics or neurosciences at the undergraduate level, dealing with the electrical properties of the cell membrane. The amplifier is designed using the common lithographic printed circuit board fabrication process and off-the-shelf electronic components. In addition, we propose a specific design for experimental chambers allowing the insertion of a commercially available polytetrafluoroethylene film. We provide a complete documentation allowing to build the amplifier and the experimental chamber. The students hand-out giving step-by step instructions to perform a recording is also included. Our experimental setup can be used in basic experiments in which students monitor the bilayer formation by capacitance measurement and record unitary currents produced by ionic channels like gramicidin A dimers. Used in combination with a low-cost data acquisition board this system provides a complete solution for hands-on lessons, therefore improving the effectiveness in teaching basic neurosciences or biophysics.

Portable conduction velocity experiments using earthworms for the college and high school neuroscience teaching laboratory

The earthworm is ideal for studying action potential conduction velocity in a classroom setting, as its simple linear anatomy allows easy axon length measurements and the worm's sparse coding allows single action potentials to be easily identified. The earthworm has two giant fiber systems (lateral and medial) with different conduction velocities that can be easily measured by manipulating electrode placement and the tactile stimulus. Here, we present a portable and robust experimental setup that allows students to perform conduction velocity measurements within a 30-min to 1-h laboratory session. Our improvement over this well-known preparation is the combination of behaviorally relevant tactile stimuli (avoiding electrical stimulation) with the invention of minimal, low-cost, and portable equipment. We tested these experiments during workshops in both a high school and college classroom environment and found positive learning outcomes when we compared pre- and posttests taken by the students.

Going from social neuroscience to schizophrenia clinical trials

The goals of the project Social Cognition and Functioning in Schizophrenia (SCAF) were to (1) identify the domains to target from social neuroscience for translation to clinical schizophrenia research, (2) identify the paradigms that represent these domains for which the neural substrates are well documented, (3) adapt these paradigms for use in schizophrenia clinical trials, (4) assess the psychometric properties of these measures, and (5) assess the external validity of these measures. The articles in this theme section present the initial findings from the SCAF project. As more training and psychopharmacological studies evaluate interventions for social cognition, the articles in this theme section are intended to serve as a guide for informed design decisions about possible endpoints in clinical trials.

Adapting social neuroscience measures for schizophrenia clinical trials, Part 1: ferrying paradigms across perilous waters

Social cognitive impairment is prominent in schizophrenia, and it is closely related to functional outcome. Partly for these reasons, it has rapidly become a target for both training and psychopharmacological interventions. However, there is a paucity of reliable and valid social cognitive endpoints that can be used to evaluate treatment response in clinical trials. Also, clinical studies in schizophrenia have benefited rather little from the surge of activity and knowledge in nonclinical social neuroscience. The National Institute of Mental Health-sponsored study, "Social Cognition and Functioning in Schizophrenia" (SCAF), attempted to address this translational challenge by selecting paradigms from social neuroscience that could be adapted for use in schizophrenia. The project also evaluated the psychometric properties and external validity of the tasks to determine their suitability for multisite clinical trials. This first article in the theme section presents the goals, conceptual background, and rationale for the SCAF project.

EnerCage: a smart experimental arena with scalable architecture for behavioral experiments

Wireless power, when coupled with miniaturized implantable electronics, has the potential to provide a solution to several challenges facing neuroscientists during basic and preclinical studies with freely behaving animals. The EnerCage system is one such solution as it allows for uninterrupted electrophysiology experiments over extended periods of time and vast experimental arenas, while eliminating the need for bulky battery payloads or tethering. It has a scalable array of overlapping planar spiral coils (PSCs) and three-axis magnetic sensors for focused wireless power transmission to devices on freely moving subjects. In this paper, we present the first fully functional EnerCage system, in which the number of PSC drivers and magnetic sensors was reduced to one-third of the number used in our previous design via multicoil coupling. The power transfer efficiency (PTE) has been improved to 5.6% at a 120 mm coupling distance and a 48.5 mm lateral misalignment (worst case) between the transmitter (Tx) array and receiver (Rx) coils. The new EnerCage system is equipped with an Ethernet backbone, further supporting its modular/scalable architecture, which, in turn, allows experimental arenas with arbitrary shapes and dimensions. A set of experiments on a freely behaving rat were conducted by continuously delivering 20 mW to the electronics in the animal headstage for more than one hour in a powered 3538 cm(2) experimental area.

Neuronal morphology goes digital: a research hub for cellular and system neuroscience

The importance of neuronal morphology in brain function has been recognized for over a century. The broad applicability of "digital reconstructions" of neuron morphology across neuroscience subdisciplines has stimulated the rapid development of numerous synergistic tools for data acquisition, anatomical analysis, three-dimensional rendering, electrophysiological simulation, growth models, and data sharing. Here we discuss the processes of histological labeling, microscopic imaging, and semiautomated tracing. Moreover, we provide an annotated compilation of currently available resources in this rich research "ecosystem" as a central reference for experimental and computational neuroscience.

Advances in microfluidics-based experimental methods for neuroscience research

The application of microfluidics to neuroscience applications has always appealed to neuroscientists because of the capability to control the cellular microenvironment in both a spatial and temporal manner. Recently, there has been rapid development of biological micro-electro-mechanical systems (BioMEMS) for both fundamental and applied neuroscience research. In this review, we will discuss the applications of BioMEMS to various topics in the field of neuroscience. The purpose of this review is to summarise recent advances in the components and design of the BioMEMS devices, in vitro disease models, electrophysiology and neural stem cell research. We envision that microfluidics will play a key role in future neuroscience research, both fundamental and applied research.

A new statistical approach for the extraction of adjacency matrix from effective connectivity networks

Graph theory is a powerful mathematical tool recently introduced in neuroscience field for quantitatively describing the main properties of investigated connectivity networks. Despite the technical advancements provided in the last few years, further investigations are needed for overcoming actual limitations in the field. In fact, the absence of a common procedure currently applied for the extraction of the adjacency matrix from a connectivity pattern has been leading to low consistency and reliability of ghaph indexes among the investigated population. In this paper we proposed a new approach for adjacency matrix extraction based on a statistical threshold as valid alternative to empirical approaches, extensively used in Neuroscience field (i.e. fixing the edge density). In particular we performed a simulation study for investigating the effects of the two different extraction approaches on the topological properties of the investigated networks. In particular, the comparison was performed on two different datasets, one composed by uncorrelated random signals (null-model) and the other one by signals acquired on a mannequin head used as a phantom (EEG null-model). The results highlighted the importance to use a statistical threshold for the adjacency matrix extraction in order to describe the real existing topological properties of the investigated networks. The use of an empirical threshold led to an erroneous definition of small-world properties for the considered connectivity patterns.

Neuromorphic atomic switch networks

Efforts to emulate the formidable information processing capabilities of the brain through neuromorphic engineering have been bolstered by recent progress in the fabrication of nonlinear, nanoscale circuit elements that exhibit synapse-like operational characteristics. However, conventional fabrication techniques are unable to efficiently generate structures with the highly complex interconnectivity found in biological neuronal networks. Here we demonstrate the physical realization of a self-assembled neuromorphic device which implements basic concepts of systems neuroscience through a hardware-based platform comprised of over a billion interconnected atomic-switch inorganic synapses embedded in a complex network of silver nanowires. Observations of network activation and passive harmonic generation demonstrate a collective response to input stimulus in agreement with recent theoretical predictions. Further, emergent behaviors unique to the complex network of atomic switches and akin to brain function are observed, namely spatially distributed memory, recurrent dynamics and the activation of feedforward subnetworks. These devices display the functional characteristics required for implementing unconventional, biologically and neurally inspired computational methodologies in a synthetic experimental system.

Design of ultra-low power biopotential amplifiers for biosignal acquisition applications

Rapid development in miniature implantable electronics are expediting advances in neuroscience by allowing observation and control of neural activities. The first stage of an implantable biosignal recording system, a low-noise biopotential amplifier (BPA), is critical to the overall power and noise performance of the system. In order to integrate a large number of front-end amplifiers in multichannel implantable systems, the power consumption of each amplifier must be minimized. This paper introduces a closed-loop complementary-input amplifier, which has a bandwidth of 0.05 Hz to 10.5 kHz, an input-referred noise of 2.2 μ Vrms, and a power dissipation of 12 μW. As a point of comparison, a standard telescopic-cascode closed-loop amplifier with a 0.4 Hz to 8.5 kHz bandwidth, input-referred noise of 3.2 μ Vrms, and power dissipation of 12.5 μW is presented. Also for comparison, we show results from an open-loop complementary-input amplifier that exhibits an input-referred noise of 3.6 μ Vrms while consuming 800 nW of power. The two closed-loop amplifiers are fabricated in a 0.13 μ m CMOS process. The open-loop amplifier is fabricated in a 0.5 μm SOI-BiCMOS process. All three amplifiers operate with a 1 V supply.

Commercialisation of CMOS integrated circuit technology in multi-electrode arrays for neuroscience and cell-based biosensors

The adaptation of standard integrated circuit (IC) technology as a transducer in cell-based biosensors in drug discovery pharmacology, neural interface systems and electrophysiology requires electrodes that are electrochemically stable, biocompatible and affordable. Unfortunately, the ubiquitous Complementary Metal Oxide Semiconductor (CMOS) IC technology does not meet the first of these requirements. For devices intended only for research, modification of CMOS by post-processing using cleanroom facilities has been achieved. However, to enable adoption of CMOS as a basis for commercial biosensors, the economies of scale of CMOS fabrication must be maintained by using only low-cost post-processing techniques. This review highlights the methodologies employed in cell-based biosensor design where CMOS-based integrated circuits (ICs) form an integral part of the transducer system. Particular emphasis will be placed on the application of multi-electrode arrays for in vitro neuroscience applications. Identifying suitable IC packaging methods presents further significant challenges when considering specific applications. The various challenges and difficulties are reviewed and some potential solutions are presented.

My first 20 years in neuroscience

Intriguing facts were obtained in the first electrophysiological investigations (1964) that the action potentials (AP) produced by direct depolarization of the cell membrane in different species of mollusks showed specific relations to changes in external ionic composition. In Helix neurons, the generation of AP was well maintained in sodium-free solutions with high calcium or barium content. The amplitude of the spike overshoot in the case was linearly related to the logarithm of calcium concentration. It is interesting that increase in external calcium ions decreased the ionic conductance of the resting membrane (R0) also in linear relation to the logarithm of Ca2+ or Ba2+ concentration. It was found for the first time (1965) that addition of Ba2+ to the external solution produced in the neurons well-developed prolonged (protracted) APs in all cases. However, the excitability of Planorbis and Limnea neurons was rapidly (during 3 min) reversibly depressed in sodium-free solutions. We found that, after injections of horse radish peroxidase (HRP) in the spinal cord of cats, the enzyme was transported retrogradely to brain stem neurons in the bulbar medial reticular formation, the vestibular complex, and the red nucleus. We obtained (1975) intriguing facts in our investigations: we recorded the labeled neurons in the locus coeruleus and subcoeruleus, as well as in the paraventricular hypothalamic nucleus. The existence of straight pathways from hypothalamus to the spinal cord has not been demonstrated earlier. The next step of our study was to determine the corresponding spinal funiculi where descending fibers (from various brain stem cell groups) are located. Indeed, in our pioneer studies (1977), we found that the fibers from the hypothalamus, which descend throughout the spinal cord, are located mainly in the lateral funiculus, ipsilaterally.

Emerging neurotechnologies for lie-detection: promises and perils

Detection of deception and confirmation of truth telling with conventional polygraphy raised a host of technical and ethical issues. Recently, newer methods of recording electromagnetic signals from the brain show promise in permitting the detection of deception or truth telling. Some are even being promoted as more accurate than conventional polygraphy. While the new technologies raise issues of personal privacy, acceptable forensic application, and other social issues, the focus of this paper is the technical limitations of the developing technology. Those limitations include the measurement validity of the new technologies, which remains largely unknown. Another set of questions pertains to the psychological paradigms used to model or constrain the target behavior. Finally, there is little standardization in the field, and the vulnerability of the techniques to countermeasures is unknown. Premature application of these technologies outside of research settings should be resisted, and the social conversation about the appropriate parameters of its civil, forensic, and security use should begin.

High density event-related potential data acquisition in cognitive neuroscience

Functional magnetic resonance imaging (fMRI) is currently the standard method of evaluating brain function in the field of Cognitive Neuroscience, in part because fMRI data acquisition and analysis techniques are readily available. Because fMRI has excellent spatial resolution but poor temporal resolution, this method can only be used to identify the spatial location of brain activity associated with a given cognitive process (and reveals virtually nothing about the time course of brain activity). By contrast, event-related potential (ERP) recording, a method that is used much less frequently than fMRI, has excellent temporal resolution and thus can track rapid temporal modulations in neural activity. Unfortunately, ERPs are under utilized in Cognitive Neuroscience because data acquisition techniques are not readily available and low density ERP recording has poor spatial resolution. In an effort to foster the increased use of ERPs in Cognitive Neuroscience, the present article details key techniques involved in high density ERP data acquisition. Critically, high density ERPs offer the promise of excellent temporal resolution and good spatial resolution (or excellent spatial resolution if coupled with fMRI), which is necessary to capture the spatial-temporal dynamics of human brain function.

Through a scanner darkly: functional neuroimaging as evidence of a criminal defendant's past mental states

As with phrenology and the polygraph, society is again confronted with a device that the media claims is capable of reading our minds. Functional magnetic resonance imaging ("fMRI"), along with other types of functional brain imaging technologies, is currently being introduced at various stages of a criminal trial as evidence of a defendant's past mental state. This Article demonstrates that functional brain images should not currently be admitted as evidence into courts for this purpose. Using the analytical framework provided by Federal Rule of Evidence 403 as a threshold to a Daubert/Frye analysis, we demonstrate that, when fMRI methodology is properly understood, brain images are only minimally probative of a defendant's past mental states and are almost certainly more unfairly prejudicial than probative on balance. Careful and detailed explanation of the underlying science separates this Article from others, which have tended to paint fMRI with a gloss of credibility and certainty for all courtroom-relevant applications. Instead, we argue that this technology may present a particularly strong form of unfair prejudice in addition to its potential to mislead jurors and waste the court's resources. Finally, since fMRI methodology may one day improve such that its probative value is no longer eclipsed by its extreme potential for unfair prejudice, we offer a nonexhaustive checklist that judges and counsel can use to authenticate functional brain images and assess the weight these images are to be accorded by fact finders.

Adaptive spike detection method based on capacitor arrays dedicated to implantable neural recording microsystems

An analog spike detector circuit is presented, which adaptively generates a threshold level for spike detection based on hard-thresholding. Operation of the circuit was tested not only with a neural signal obtained from real in-vivo recording from a live animal, but also with a large sinusoidal baseline variation intentionally added to examine the capability of the circuit to track baseline variations as large as 50mV. The circuit runs at 3.3V supply voltage and dissipates 270 microW.

Dual-monitor deterministic hardware for visual stimuli generation in neuroscience experiments

This article describes the development of a dual-monitor visual stimulus generator that is used in neuroscience experiments with invertebrates such as flies. The experiment consists in the visualization of two fixed images that are displaced horizontally according to the stimulus data. The system was developed using off-the-shelf FPGA kits and it is capable of displaying 640x480 pixels with 256 intensity levels at 200 frames per second (FPS) on each monitor. A Raster plot of the experiment with the superimposed stimuli was generated as the result of this work. A novel architecture was developed, using the same DOT Clock for both monitors, and its implementation generates a perfect synchronism in both devices.

Chapter 9: understanding the nervous system in the 18th century

The 18th century was an age of transition. The time-honored neuropsychology of classical and medieval times, mechanized in Descartes' hydraulic neurophysiology, was undermined by microscopical observations and careful physiological experimentation. Yet it was not until the very end of the century, when work on electric fish and amphibia began to suggest an acceptable successor to "animal spirit," that the old understanding of human neurophysiology began to fade. This chapter traces this slow retreat from the iatrophysics of the early part of the century, with its hollow nerves and animal spirits, through a number of stop-gap explanations involving mysterious subtle fluids or forces described variously as irritability, élan vital, vis viva, vis insita, the spirit of animation etc., or perhaps involving vibrations and vibratiuncles and mysterious magnetic effluvia, to the dawning electrophysiology of the end of the century and the beginning of the next. This developing understanding filtered slowly through to affect medical education, and the 18th century saw the development of strong medical schools at Leiden, Edinburgh, Paris, Bologna and London. Associated with these developments there was a great increase, as a well-known physician looking back at the beginning of the following century noted, in a class of diseases that had little concerned physicians in the preceding century - "nervous disorders."