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DOE OSTI2022

Super-resolution and segmentation deep learning for breast cancer histopathology image analysis

Traditionally, a high-performance microscope with a large numerical aperture is required to acquire high-resolution images. However, the images’ size is typically tremendous. Therefore, they are not conveniently managed and transferred across a computer network or stored in a limited computer storage system. As a result, image compression is commonly used to reduce image size resulting in poor image resolution. Here, we demonstrate custom convolution neural networks (CNNs) for both super-resolution image enhancement from low-resolution images and characterization of both cells and nuclei from hematoxylin and eosin (H&E) stained breast cancer histopathological images by using a combination of generator and discriminator networks so-called super-resolution generative adversarial network-based on aggregated residual transformation (SRGAN-ResNeXt) to facilitate cancer diagnosis in low resource settings. The results provide high enhancement in image quality where the peak signal-to-noise ratio and structural similarity of our network results are over 30 dB and 0.93, respectively. The derived performance is superior to the results obtained from both the bicubic interpolation and the well-known SRGAN deep-learning methods. In addition, another custom CNN is used to perform image segmentation from the generated high-resolution breast cancer images derived with our model with an average Intersection over Union of 0.869 and an average dice similarity coefficient of 0.893 for the H&E image segmentation results. Finally, we propose the jointly trained SRGAN-ResNeXt and Inception U-net Models, which applied the weights from the individually trained SRGAN-ResNeXt and inception U-net models as the pre-trained weights for transfer learning. The jointly trained model’s results are progressively improved and promising. We anticipate these custom CNNs can help resolve the inaccessibility of advanced microscopes or whole slide imaging (WSI) systems to acquire high-resolution images from low-performance microscopes located in remote-constraint settings.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

A Heterogeneous System for Eagle Detection, Deterrent, and Wildlife Collision Detection for Wind Turbines (Final Technical Report)

This report summarizes the design, implementation, and test of an integrated system for automated detection and deterrence of eagles, with included wind turbine blade strike detection and imaging functionality. A machine learning approach was used in conjunction with a 360° camera system for automated detection and classification of golden eagles. This was developed using footage obtained from trained golden eagles and other raptors, in collaboration with wildlife biologists and professional bird handlers. Oregon State University developed a visual deterrent system, which uses inflatable anthropomorphic sculptures with random, kinetic motion to deter eagles, and conducted limited field testing on live eagles; the deterrent can be triggered by the visual detection of eagles using the vision system. Finally, a multi-sensor module was developed that is mounted at the turbine blade root. This module measures vibration and other motions to detect blade strikes, and an integrated on-blade camera captures an image of any impacting objects. Long-term, this blade strike detection system is intended to support an automatic monitoring and certification system for the eagle detection and deterent system. Independent field testing of each system component is described. Testing of the integrated system on an operational wind turbine was conducted across three separate field tests. This includes multi-day fields tests on a General Electric 1.5MW wind turbine at the National Renewable Energy Laboratory (NREL) National Wind Technology Center (NWTC) in Boulder, CO in October 2018 and July 2019; installation procedures, test procedures, and a summary of collected data are presented. A third multi-day on-turbine field test is also presented, which was performed using a General Electric 1.5MW wind turbine at the North American Wind Research and Training Center (NAWRTC) at Mesalands Community College, Tucumcari, NM in April 2019. Across these field tests, the vision system was demonstrated using unmanned aerial vehicles (UAV), and the eagle classification algorithm was not tested; the visual deterrent system was demonstrated, including automatic, remote deployment following surrogate visual detections; and, multi-sensor on-blade data was recorded across multiple wind turbine operational conditions and through more than 100 surrogate blade strikes using soft projectiles, including the successful demonstration of automatic image capture of striking objects. This data set was also used for offline development and validation of enhanced collision detection algorithms. As summarized in this report, the development and field validation of an integrated detection, deterrent, and blade collision detection system represents a critical proof of concept for future technology development of related detection and deterrent technologies, where both deterrent as well as collision detection recording devices are needed for future siting, monitoring, and operation of wind turbine installations, both onshore and offshore.

17 WIND ENERGY↗

NASA NTRS2009

Expert system for controlling plant growth in a contained environment

In a system for optimizing crop growth, vegetation is cultivated in a contained environment, such as a greenhouse, an underground cavern or other enclosed space. Imaging equipment is positioned within or about the contained environment, to acquire spatially distributed crop growth information, and environmental sensors are provided to acquire data regarding multiple environmental conditions that can affect crop development. Illumination within the contained environment, and the addition of essential nutrients and chemicals are in turn controlled in response to data acquired by the imaging apparatus and environmental sensors, by an ''expert system'' which is trained to analyze and evaluate crop conditions. The expert system controls the spatial and temporal lighting pattern within the contained area, and the timing and allocation of nutrients and chemicals to achieve optimized crop development. A user can access the ''expert system'' remotely, to assess activity within the growth chamber, and can override the ''expert system''.

May, George A.↗

NASA NTRS2011

Expert system for controlling plant growth in a contained environment

In a system for optimizing crop growth, vegetation is cultivated in a contained environment, such as a greenhouse, an underground cavern or other enclosed space. Imaging equipment is positioned within or about the contained environment, to acquire spatially distributed crop growth information, and environmental sensors are provided to acquire data regarding multiple environmental conditions that can affect crop development. Illumination within the contained environment, and the addition of essential nutrients and chemicals are in turn controlled in response to data acquired by the imaging apparatus and environmental sensors, by an "expert system" which is trained to analyze and evaluate crop conditions. The expert system controls the spatial and temporal lighting pattern within the contained area, and the timing and allocation of nutrients and chemicals to achieve optimized crop development. A user can access the "expert system" remotely, to assess activity within the growth chamber, and can override the "expert system".

May, George A.↗

NASA NTRS2018

Next-Generation Sensing Technologies for Exploring Ocean Worlds

Dr. Ved Chirayath's plenary presentation will highlight two instrument technologies he invented at NASA including Fluid Lensing, the first remote sensing technology capable of imaging through ocean waves in 3D at sub-cm resolutions, and MiDAR (Multispectral Imaging, Detection and Active Reflectance), a next-generation active hyperspectral remote sensing and optical communications instrument. Fluid Lensing has been used to provide the first 3D multispectral imagery of shallow marine systems from unmanned aerial vehicles (UAVs, or drones), including coral reefs in American Samoa and stromatolite reefs in Hamelin Pool, Western Australia. MiDAR is being deployed on aircraft, and underwater remotely operated vehicles (ROVs) as a new method to remotely sense living and nonliving structures in extreme environments. MiDAR images targets with high-intensity narrowband structured optical radiation to measure an object's non-linear spectral reflectance, image through fluid interfaces such as ocean waves with active fluid lensing, and simultaneously transmit high-bandwidth data. As an active instrument, MiDAR is capable of remotely sensing reflectance at the centimeter (cm) spatial scale with a signal-to-noise ratio (SNR) multiple orders of magnitude higher than passive airborne and spaceborne remote sensing systems with significantly reduced integration time. This allows for rapid video-frame-rate hyperspectral sensing into the far ultraviolet and VNIR wavelengths. Finally, Chirayath will present preliminary results from NASA NeMO-Net (Neural Multi-Modal Observation and Training Network), the first neural network for global coral reef classification using fluid lensing and MiDAR.

Technologies↗

NASA NTRS2017

Osiris-REx Spacecraft Current Status and Forward Plans

The NASA New Frontiers OSIRIS-REx spacecraft executed a flawless launch on September 8, 2016 to begin its 23-month journey to near-Earth asteroid (101955). The primary objective of the OSIRIS-REx mission is to collect and return to Earth a pristine sample of regolith from the asteroid surface. The sampling event will occur after a two-year period of remote sensing that will ensure a high probability of successful sampling of a region on the asteroid surface having high science value and within well-defined geological context. The OSIRIS-REx instrument payload includes three high-resolution cameras (OCAMS), a visible and near-infrared spectrometer (OVIRS), a thermal imaging spectrometer (OTES), an X-ray imaging spectrometer (REXIS), and a laser altimeter (OLA). As the spacecraft follows its nominal outbound-cruise trajectory, the propulsion, power, communications, and science instruments have undergone basic functional tests, with no major issues. Outbound cruise science investigations include a search for Earth Trojan asteroids as the spacecraft approaches the Sun-Earth L4 Lagrangian point in February 2017. Additional instrument checkouts and calibrations will be carried out during the Earth gravity assist maneuver in September 2017. During the Earth-moon flyby, visual and spectral images will be acquired to validate instrument command sequences planned for Bennu remote sensing. The asteroid Bennu remote sensing campaign will yield high resolution maps of the temperature and thermal inertia, distributions of major minerals and concentrations of organic matter across the asteroid surface. A high resolution 3d shape model including local surface slopes and a high-resolution gravity field will also be determined. Together, these data will be used to generate four separate maps that will be used to select the sampling site(s). The Safety map will identify hazardous and safe operational regions on the asteroid surface. The Deliverability map will quantify the accuracy with which the navigation team can deliver the spacecraft to and from specific sites on the asteroid surface. The Sampleability map quantifies the regolith properties, providing an estimation of how much material would be sampled at different points on the surface. The final Science Value map synthesizes the chemical, mineralogical, and geological, observations to identify the areas of the asteroid surface with the highest science value. Here, priority is given to organic, water-rich regions that have been minimally altered by surface processes. Asteroid surface samples will be acquired with a touch-and-go sample acquisition system (TAGSAM) that uses high purity pressurized N2 gas to mobilize regolith into a stainless steel canister. Although the mission requirement is to collect at least 60 g of material, tests of the TAGSAM routinely exceeded 300 g of simulant in micro-gravity tests. After acquiring the sample, the spacecraft will depart Bennu in 2021 to begin its return journey, with the sample return capsule landing at the Utah Test and Training Range on September 23, 2023. The OSIRIS-REx science team will carry out a series of detailed chemical, mineralogical, isotopic, and spectral studies that will be used to determine the origin and history of Bennu and to relate high spatial resolution sample studies to the global geological context from remote sensing. The outline of the sample analysis plan is described in a companion abstract.

Messenger, Scott↗

NASA NTRS2024

Vertiport Management from Simulation to Flight: Continued Human Factors Assessment of Vertiport Operations

The Urban Air Mobility concept envisions a new future for transportation in urban environments, primarily focused around vertiport operations. A vertiport refers to an identifiable ground or elevated area used for the vertical takeoff and landing of an aircraft. One critical role within vertiport operations that has been proposed but is under-researched is the vertiport manager. The vertiport manager is expected to manage the ground-to-air operations at the vertiport, but their precise responsibilities, tasks, and challenges are currently undefined. The High Density Vertiplex Subproject at NASA is responsible for developing and testing technologies, concepts, and architectures that will support the infrastructure needed for terminal environments around vertiports. Our previous work investigated ground control station operators performing simulated and live flight operations within a remote operations environment. In the current study, we extended from our previous work to explore the role of a vertiport manager. Specifically, we investigated five participants serving as a vertiport manager across both simulated and live flight operations. We performed multiple knowledge elicitation techniques, a thematic content analysis of qualitative data, and naturalistic observations to create a list of insights, design and training recommendations, and a simplified cognitive task diagram of the vertiport manager’s primary task. Findings from this study are discussed within the context of the current debate on the degree to which this role should be automated. A third alternative is suggested to allow a human vertiport manager to be successful by teaming with increasingly autonomous systems, designing the role to improve safety and efficiency of Urban Air Mobility operations.

AAM↗

NASA NTRS2009

Low Cost Rapid Response Spacecraft, (LCRRS): A Research Project in Low Cost Spacecraft Design and Fabrication in a Rapid Prototyping Environment

The Low Cost Rapid Response Spacecraft (LCRRS) is an ongoing research development project at NASA Ames Research Center (ARC), Moffett Field, California. The prototype spacecraft, called Cost Optimized Test for Spacecraft Avionics and Technologies (COTSAT) is the first of what could potentially be a series of rapidly produced low-cost satellites. COTSAT has a target launch date of March 2009 on a SpaceX Falcon 9 launch vehicle. The LCRRS research system design incorporates use of COTS (Commercial Off The Shelf), MOTS (Modified Off The Shelf), and GOTS (Government Off The Shelf) hardware for a remote sensing satellite. The design concept was baselined to support a 0.5 meter Ritchey-Chretien telescope payload. This telescope and camera system is expected to achieve 1.5 meter/pixel resolution. The COTSAT team is investigating the possibility of building a fully functional spacecraft for $500,000 parts and $2,000,000 labor. Cost is dramatically reduced by using a sealed container, housing the bus and payload subsystems. Some electrical and RF designs were improved/upgraded from GeneSat-1 heritage systems. The project began in January 2007 and has yielded two functional test platforms. It is expected that a flight-qualified unit will be finished in December 2008. Flight quality controls are in place on the parts and materials used in this development with the aim of using them to finish a proto-flight satellite. For LEO missions the team is targeting a mission class requiring a minimum of six months lifetime or more. The system architecture incorporates several design features required by high reliability missions. This allows for a true skunk works environment to rapidly progress toward a flight design. Engineering and fabrication is primarily done in-house at NASA Ames with flight certifications on materials. The team currently employs seven Full Time Equivalent employees. The success of COTSATs small team in this effort can be attributed to highly cross trained engineering team. The engineers on the team are capable of functioning in two to three engineering disciplines which allows highly efficient interdisciplinary engineering collaboration. NASA Ames is actively proposing mission concepts to use the COTSAT platform to accomplish science. If the COTSAT team validates this approach, it will allow the possibility for remote sensing missions to produce a high science yield for minimal cost and reduced schedule. Another aim of this approach is to yield an accelerated pathway from a Phase A study to mission launch. Leaders in the aerospace industry have shown interest in this methodology. Several visits and tours have been given in the lab. Although the concept of low-cost development is initially met with skepticism from some within the prohibitive aerospace industry, the project's efforts have been highly praised for the accomplishments met within a limited time and budget. Overall the development has progressed tremendously well and the team is answering critical questions for current and future low-cost small satellite developments. COTSAT subsystems are not limited to a specific weight class and could be adapted to produce smaller platforms and to fit various launch vehicles.

Spremo, Stevan↗

NASA NTRS2024

Vertiport Management from Simulation to Flight: Continued Human Factors Assessment of Vertiport Operations

AAM↗

NASA NTRS1980

MOD-0A 200 kW wind turbine generator design and analysis report

The design, analysis, and initial performance of the MOD-OA 200 kW wind turbine generator at Clayton, NM is documented. The MOD-OA was designed and built to obtain operation and performance data and experience in utility environments. The project requirements, approach, system description, design requirements, design, analysis, system tests, installation, safety considerations, failure modes and effects analysis, data acquisition, and initial performance for the wind turbine are discussed. The design and analysis of the rotor, drive train, nacelle equipment, yaw drive mechanism and brake, tower, foundation, electricl system, and control systems are presented. The rotor includes the blades, hub, and pitch change mechanism. The drive train includes the low speed shaft, speed increaser, high speed shaft, and rotor brake. The electrical system includes the generator, switchgear, transformer, and utility connection. The control systems are the blade pitch, yaw, and generator control, and the safety system. Manual, automatic, and remote control are discussed. Systems analyses on dynamic loads and fatigue are presented.

Anderson, T. S.↗

NASA NTRS1980

The MOD-OA 200 kilowatt wind turbine generator design and analysis report

The project requirements, approach, system description, design requirements, design, analysis, system tests, installation safety considerations, failure modes and effects analysis, data acquisition, and initial performance for the MOD-OA 200 kw wind turbine generator are discussed. The components, the rotor, driven train, nacelle equipment, yaw drive mechanism and brake, tower, foundation, electrical system, and control systems are presented. The rotor includes the blades, hub and pitch change mechanism. The drive train includes the low speed shaft, speed increaser, high speed shaft, and rotor brake. The electrical system includes the generator, switchgear, transformer, and utility connection. The control systems are the blade pitch, yaw, and generator control, and the safety system. Manual, automatic, and remote control and Dynamic loads and fatigue are analyzed.

Andersen, T. S.↗

NASA NTRS2004

[Activities of Center for Lidar and Atmospheric Sciences Students, Hampton University]

The mission of CLASS was to provide education and training in NASA-related mathematics, technology and science to US. students who are underrepresented. In these areas and to encourage them to pursue advanced degrees. The project has three goals which support this mission: research training, curriculum development and outreach. All project activities are designed to meet a concrete objective which directly advances one of these goals. The common theme of all project activities is NASA's Earth Science Enterprise, in particular, the use of laser-based remote sensing systems (lidars) to monitor and understand the earth's environment

Temple, Doyle↗

NASA NTRS2007

Desert Research and Technology Studies (RATS) Local and Remote Test Sites

Desert RATS (Research and Technology Studies) is a combined group of inter-NASA center scientists and engineers, collaborating with representatives of industry and academia, for the purpose of conducting remote field exercises. These exercises provide the capability to validate experimental hardware and software, to evaluate and develop mission operational techniques, and to identify and establish technical requirements applicable for future planetary exploration. D-RATS completed its ninth year of field testing in September 2006. Dry run test activities prior to testing at designated remote field site locations are initially conducted at the Johnson Space Center (JSC) Remote Field Demonstration Test Site. This is a multi-acre external test site located at JSC and has detailed representative terrain features simulating both Lunar and Mars surface characteristics. The majority of the remote field tests have been subsequently conducted in various high desert areas adjacent to Flagstaff, Arizona. Both the local JSC and remote field test sites have terrain conditions that are representative of both the Moon and Mars, such as strewn rock and volcanic ash fields, meteorite crater ejecta blankets, rolling plains, hills, gullies, slopes, and outcrops. Flagstaff is the preferred remote test site location for many reasons. First, there are nine potential test sites with representative terrain features within a 75-mile radius. Second, Flagstaff is the location of the United States Geologic Survey (USGS)/Astrogeology Branch, which historically supported Apollo astronaut geologic training and currently supports and provides host accommodations to the D-RATS team. Finally, in considering the importance of logistics in regard to providing the necessary level of support capabilities, the Flagstaff area provides substantial logistics support and lodging accommodations to take care of team members during long hours of field operations.

Janoiko, Barbara↗

NASA NTRS2013

Human Factors in Accidents Involving Remotely Piloted Aircraft

This presentation examines human factors that contribute to RPA mishaps and provides analysis of lessons learned. RPA accident data from U.S. military and government agencies were reviewed and analyzed to identify human factors issues. Common contributors to RPA mishaps fell into several major categories: cognitive factors (pilot workload), physiological factors (fatigue and stress), environmental factors (situational awareness), staffing factors (training and crew coordination), and design factors (human machine interface).

Merlin, Peter William↗

DOE OSTI2020

Automatic Classification of Biological Targets in a Tidal Channel Using a Multibeam Sonar

Multibeam sonars are widely used for environmental monitoring of fauna at marine renewable energy sites. However, they can rapidly accrue vast volumes of data, which poses a challenge for data processing. Here, using data from a deployment in a tidal channel with peak currents of 1–2 m s –1 , we demonstrate the data-reduction benefits of real-time automatic classification of targets detected and tracked in multibeam sonar data. First, we evaluate classification capabilities for three machine learning algorithms: random forests, support vector machines, and k-nearest neighbors. For each algorithm, a hill-climbing search optimizes a set of hand-engineered attributes that describe tracked targets. Here, the random forest algorithm is found to be most effective—in postprocessing, discriminating between biological and nonbiological targets with a recall rate of 0.97 and a precision of 0.60. In addition, 89% of biological targets are correctly classified as either seals, diving birds, fish schools, or small targets. Model dependence on the volume of training data is evaluated. Second, a real-time implementation of the model is shown to distinguish between biological targets and nonbiological targets with nearly the same performance as in postprocessing. From this, we make general recommendations for implementing real-time classification of biological targets in multibeam sonar data and the transferability of trained models.

16 TIDAL AND WAVE POWER↗

NASA NTRS1978

Applicability of the Remote Mobile Emplacement Package (RMEP) design as a mobility aid for proposed post-84 Mars missions, phase O

The results of study to determine the applicability of the Remote Mobile Emplacement Package (RMEP) design concept as a mobility aid for the proposed post-'84 Mars missions are presented. The RMEP wheel and mobility subsystem parameters: wheel tire size, weight, stowed volume, and environmental effects; obstacle negotiation; reliability and wear; motor and drive train; and electrical power demand were reviewed. Results indicated that: (1) the basic RMEP wheel design would be satisfactory, with additional attention to heating, side loading, tread wear and ultraviolet radiation protection; (2) motor and drive train power requirements on Mars would be less than on Earth; and (3) the mobility electrical power requirements would be small enough to offer the option of operating the Mars mini rover untethered. Payload power required for certain sampling functions would preclude the use of battery power for these missions. Hazard avoidance and reverse direction maneuvers are discussed. Limited examination of vehicle payload integration and thermal design was made, pending establishment of a baseline vehicle/payload design.

Source record↗

NASA NTRS1998

Remote Sensing Education and Development Countries: Multilateral Efforts through the Committee on Earth Observation Satellites (CEOS)

The Committee on Earth Observation Satellites (CEOS) is an international organization which coordinates space-based Earth observations world wide. Created in 1984, CEOS now comprises 38 national space agencies, regional organizations and international space-related and research groups. The aim of CEOS is to achieve international coordination in the planning of satellite missions for Earth observation and to maximize the utilization of data from these missions world-wide. With regard to developing countries, the fundamental aim of CEOS is to encourage the creation and maintenance of indigenous capability that is integrated into the local decision-making process, thereby enabling developing countries to obtain the maximum benefit from Earth observation. Obtaining adequate access to remote sensing information is difficult for developing countries and students and teachers alike. High unit data prices, the specialized nature of the technology , difficulty in locating specific data, complexities of copyright provisions, the emphasis on "leading edge" technology and research, and the lack of training materials relating to readily understood application are frequently noted obstacles. CEOS has developed an education CD-ROM which is aimed at increasing the integration of space-based data into school curricula, meeting the heretofore unsatisfied needs of developing countries for information about Earth observation application, data sources and future plans; and raising awareness around the world of the value of Earth observation data from space. The CD-ROM is designed to be used with an Internet web browser, increasing the information available to the user, but it can also be used on a stand-alone machine. It contains suggested lesson plans and additional resources for educators and users in developing countries.

Charles, Leslie Bermann↗

NASA NTRS2008

Spiking Neurons for Analysis of Patterns

Artificial neural networks comprising spiking neurons of a novel type have been conceived as improved pattern-analysis and pattern-recognition computational systems. These neurons are represented by a mathematical model denoted the state-variable model (SVM), which among other things, exploits a computational parallelism inherent in spiking-neuron geometry. Networks of SVM neurons offer advantages of speed and computational efficiency, relative to traditional artificial neural networks. The SVM also overcomes some of the limitations of prior spiking-neuron models. There are numerous potential pattern-recognition, tracking, and data-reduction (data preprocessing) applications for these SVM neural networks on Earth and in exploration of remote planets. Spiking neurons imitate biological neurons more closely than do the neurons of traditional artificial neural networks. A spiking neuron includes a central cell body (soma) surrounded by a tree-like interconnection network (dendrites). Spiking neurons are so named because they generate trains of output pulses (spikes) in response to inputs received from sensors or from other neurons. They gain their speed advantage over traditional neural networks by using the timing of individual spikes for computation, whereas traditional artificial neurons use averages of activity levels over time. Moreover, spiking neurons use the delays inherent in dendritic processing in order to efficiently encode the information content of incoming signals. Because traditional artificial neurons fail to capture this encoding, they have less processing capability, and so it is necessary to use more gates when implementing traditional artificial neurons in electronic circuitry. Such higher-order functions as dynamic tasking are effected by use of pools (collections) of spiking neurons interconnected by spike-transmitting fibers. The SVM includes adaptive thresholds and submodels of transport of ions (in imitation of such transport in biological neurons). These features enable the neurons to adapt their responses to high-rate inputs from sensors, and to adapt their firing thresholds to mitigate noise or effects of potential sensor failure. The mathematical derivation of the SVM starts from a prior model, known in the art as the point soma model, which captures all of the salient properties of neuronal response while keeping the computational cost low. The point-soma latency time is modified to be an exponentially decaying function of the strength of the applied potential. Choosing computational efficiency over biological fidelity, the dendrites surrounding a neuron are represented by simplified compartmental submodels and there are no dendritic spines. Updates to the dendritic potential, calcium-ion concentrations and conductances, and potassium-ion conductances are done by use of equations similar to those of the point soma. Diffusion processes in dendrites are modeled by averaging among nearest-neighbor compartments. Inputs to each of the dendritic compartments come from sensors. Alternatively or in addition, when an affected neuron is part of a pool, inputs can come from other spiking neurons. At present, SVM neural networks are implemented by computational simulation, using algorithms that encode the SVM and its submodels. However, it should be possible to implement these neural networks in hardware: The differential equations for the dendritic and cellular processes in the SVM model of spiking neurons map to equivalent circuits that can be implemented directly in analog very-large-scale integrated (VLSI) circuits.

Huntsberger, Terrance↗