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NASA NTRS2000

Trainable Gene Regulation Networks with Applications to Drosophila Pattern Formation

This chapter will very briefly introduce and review some computational experiments in using trainable gene regulation network models to simulate and understand selected episodes in the development of the fruit fly, Drosophila melanogaster. For details the reader is referred to the papers introduced below. It will then introduce a new gene regulation network model which can describe promoter-level substructure in gene regulation. As described in chapter 2, gene regulation may be thought of as a combination of cis-acting regulation by the extended promoter of a gene (including all regulatory sequences) by way of the transcription complex, and of trans-acting regulation by the transcription factor products of other genes. If we simplify the cis-action by using a phenomenological model which can be tuned to data, such as a unit or other small portion of an artificial neural network, then the full transacting interaction between multiple genes during development can be modelled as a larger network which can again be tuned or trained to data. The larger network will in general need to have recurrent (feedback) connections since at least some real gene regulation networks do. This is the basic modeling approach taken, which describes how a set of recurrent neural networks can be used as a modeling language for multiple developmental processes including gene regulation within a single cell, cell-cell communication, and cell division. Such network models have been called "gene circuits", "gene regulation networks", or "genetic regulatory networks", sometimes without distinguishing the models from the actual modeled systems.

Mjolsness, Eric↗

NASA NTRS2002

An amphioxus nodal gene (AmphiNodal) with early symmetrical expression in the organizer and mesoderm and later asymmetrical expression associated with left-right axis formation

The full-length sequence and zygotic expression of an amphioxus nodal gene are described. Expression is first detected in the early gastrula just within the dorsal lip of the blastopore in a region of hypoblast that is probably comparable with the vertebrate Spemann's organizer. In the late gastrula and early neurula, expression remains bilaterally symmetrical, limited to paraxial mesoderm and immediately overlying regions of the neural plate. Later in the neurula stage, all neural expression disappears, and mesodermal expression disappears from the right side. All along the left side of the neurula, mesodermal expression spreads into the left side of the gut endoderm. Soon thereafter, all expression is down-regulated except near the anterior and posterior ends of the animal, where transcripts are still found in the mesoderm and endoderm on the left side. At this time, expression also begins in the ectoderm on the left side of the head, in the region where the mouth later forms. These results suggest that amphioxus and vertebrate nodal genes play evolutionarily conserved roles in establishing Spemann's organizer, patterning the mesoderm rostrocaudally and setting up the asymmetrical left-right axis of the body.

Non-NASA Center↗

NASA NTRS1995

Neural tuning characteristics of auditory primary afferents in the chicken embryo

Primary afferent activity was recorded from the cochlear ganglion in chicken embryos (Gallus domesticus) at 19 days of incubation (E19). The ganglion was accessed via the recessus scala tympani and impaled with glass micropipettes. Frequency tuning curves were obtained using a computerized threshold tracking procedure. Tuning curves were evaluated to determine characteristics frequencies (CFs), CF thresholds, slopes of low and high frequency flanks, and tip sharpness (Q10dB). The majority of tuning curves exhibited the typical 'V' shape described for older birds and, on average, appeared relatively mature based on mean values for CF thresholds (59.6 +/- 20.3 dBSPL) and tip sharpness (Q10dB = 5.2 +/- 3). The mean slopes of low (61.9 +/- 37 dB/octave) and high (64.6 +/- 33 dB/octave) frequency flanks although comparable were somewhat less than those reported for 21-day-old chickens. Approximately 14% of the tuning curves displayed an unusual 'saw-tooth' pattern. CFs ranged from 188 to 1623 Hz. The highest CF was well below those reported for post-hatch birds. In addition, a broader range of Q10dB values (1.2 to 16.9) may related to a greater variability in embryonic tuning curves. Overall, these data suggest that an impressive functional maturity exists in the embryo at E19. The most significant sign of immaturity was the limited expression of high frequencies. It is argued that the limited high CF in part may be due to the developing middle ear transfer function and/or to a functionally immature cochlear base.

Non-NASA Center↗

NASA NTRS1990

Non-Lipschitzian neural dynamics

A novel approach is presented which is motivated by an attempt to remove one of the most fundamental limitations of artificial neural networks: their rigid behavior as compared with even the simplest biological systems. It is demonstrated that non-Lipschitzian dynamics, based on the faliure of the Lipschitz conditions at repellers, displays a new qualitative effect, i.e., a multichoice response to periodic external excitations. This makes it possible to construct unpredictable systems, represented in the form of coupled activation and learning dynamical equations. It is shown that unpredictable systems can be controlled by sign strings which uniquely define the system behavior by specifying the direction of the motions at the critical points. Unpredictable systems driven by sign strings are extremely flexible and can serve as a powerful tool for complex pattern recognition.

Barhen, Jacob↗

NASA NTRS2003

Morphology of the Vestibular Utricule in Toadfish, Opsanus Tau

The uticle is an otolith organ in the vertebrate inner ear that provides gravitoinertial acceleration information into the vestibular reflex pathways. The aim of the present study was to provide an anatomical description of this structure in the adult oyster toadfish, and establish a morphological basis for interpretation of subsequent functional studies. Light, scanning electron and transmission electron microscopy were applied to visualize the sensory epithelium and its neural innervation. Electrophysiological techniques were used to identify utricular afferents by their response to translation stimuli. Similar to nerve afferents supplying the semicircular canals and lagena, utricular afferents commonly exhibit a short-latency increase of firing rate in response to electrical activation of the central efferent pathway. Afferents were labeled with biocytin either intraaxonally or with extracellular bulk deposits. Light microscope images of serial thick sections were used to make three-dimensional reconstructions of individual labeled afferents to identify the dendritic morphology with respect to epithelial location. Scanning electron microscopy was used to visualize the surface of the otolith mass facing the otolith membrane, and the hair cell polarization patterns of strioler and extrastriolar regions. Transmission electron micrographs of serial thin sections were compiled to create a three-dimensional reconstruction of the labeled afferent over a segment of its dendritic field and to examine the hair cell-afferent synaptic contacts.

Bass, L.↗

NASA NTRS1990

30 years of adaptive neural networks - Perceptron, Madaline, and backpropagation

Fundamental developments in feedforward artificial neural networks from the past thirty years are reviewed. The history, origination, operating characteristics, and basic theory of several supervised neural-network training algorithms (including the perceptron rule, the least-mean-square algorithm, three Madaline rules, and the backpropagation technique) are described. The concept underlying these iterative adaptation algorithms is the minimal disturbance principle, which suggests that during training it is advisable to inject new information into a network in a manner that disturbs stored information to the smallest extent possible. The two principal kinds of online rules that have developed for altering the weights of a network are examined for both single-threshold elements and multielement networks. They are error-correction rules, which alter the weights of a network to correct error in the output response to the present input pattern, and gradient rules, which alter the weights of a network during each pattern presentation by gradient descent with the objective of reducing mean-square error (averaged over all training patterns).

Widrow, Bernard↗

NASA NTRS2024

Evaluation of Machine Learning and Deep Learning Algorithms for Fire Prediction in Southeast Asia

Vegetation fires are prevalent in South/Southeast Asian countries, making fire prediction crucial due to their potential environmental, economic, and social impacts. Accurate predictions of fires facilitate timely interventions, helping to mitigate uncontrolled fires that can lead to biodiversity loss and air quality issues. In this study, we utilize VIIRS satellite-derived fire data alongside six machine learning and deep learning models—Simple Persistence, Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), CNN-LSTM, and ConvLSTM—to determine the most effective fire prediction model, using Root Mean Square Error (RMSE) as the metric. Our results indicate that the CNN model is the most reliable in regions with spatial dependencies, such as Brunei, Indonesia, Malaysia, the Philippines, Timor-Leste, and Thailand. Conversely, the ConvLSTM model excels in countries with complex spatiotemporal dynamics like Laos, Myanmar, and Vietnam. The CNN-LSTM hybrid model also performed well in Cambodia, suggesting a need for a balanced approach in areas requiring both spatial and temporal feature extraction. Furthermore, simpler models like Persistence and MLP showed limitations in capturing dynamic patterns and temporal dependencies. Our findings highlight the importance of evaluating models before implementing any decision support systems (DSS) in fire management. By tailoring models to specific regional fire data, we can enhance prediction accuracy and responsiveness, ultimately improving fire risk management in Southeast Asia and beyond.

Deep learning↗

NASA NTRS1994

Experiment K-7-18: Effects of Spaceflight in the Muscle Adductor Longus of Rats Flown in the Soviet Biosatellite Cosmos 2044: A Study Employing Neural Cell Adhesion Molecules (N-CAM) Immunocytochemistry and Conventional Morphological Techniques (Light and Electron Microscopy) - Part 1

The effects of spaceflight upon the 'slow' muscle adductor longus was examined in rats flown in the Soviet Biosatellite COSMOS 2044. Three groups - synchronous, vivarium and basal served as controls. The techniques employed included standard methods for light microscopy, N-CAM immunocytochemistry and electron microscopy. Light microscopic observations revealed myofiber atrophy, contraction bands and segmental necrosis accompanied by cellular infiltrates composed of macrophages, leucocytes and mononuclear cells. N-CAM immunoreactivity was seen (N-CAM-IR) on the myofiber surface, satellite cells and in regenerating myofibers reminiscent of myotubes. Ultrastructural alterations included Z band streaming, disorganization of myofibrillar architecture, sarcoplasmic degradation, extensive segmental necrosis with preservation of the basement membrane, degenerative phenomena of the capillary endothelium and cellular invasion of necrotic areas. Regenerating myofibers were identified by the presence of increased amounts of ribosomal aggregates and chains of polyribosomes associated with myofilaments that displayed varied distributive patterns. The principal electron microscopic changes of the neuromuscular junctions consisted of a decrease or absence of synaptic vesicles, degeneration of axon terminals, increased number of microtubules, vacant axonal spaces and axonal sprouting. The present observations indicate that major alterations such as myofibrillar disruption and necrosis, muscle regeneration and denervation and synaptic remodeling at the level of the neuromuscular junction may take place during spaceflight.

Daunton, N. G.↗

NASA NTRS1993

A Novel Optical/digital Processing System for Pattern Recognition

This paper describes two processing algorithms that can be implemented optically: the Radon transform and angular correlation. These two algorithms can be combined in one optical processor to extract all the basic geometric and amplitude features from objects embedded in video imagery. We show that the internal amplitude structure of objects is recovered by the Radon transform, which is a well-known result, but, in addition, we show simulation results that calculate angular correlation, a simple but unique algorithm that extracts object boundaries from suitably threshold images from which length, width, area, aspect ratio, and orientation can be derived. In addition to circumventing scale and rotation distortions, these simulations indicate that the features derived from the angular correlation algorithm are relatively insensitive to tracking shifts and image noise. Some optical architecture concepts, including one based on micro-optical lenslet arrays, have been developed to implement these algorithms. Simulation test and evaluation using simple synthetic object data will be described, including results of a study that uses object boundaries (derivable from angular correlation) to classify simple objects using a neural network.

Boone, Bradley G.↗

NASA NTRS2003

Improved Autoassociative Neural Networks

Improved autoassociative neural networks, denoted nexi, have been proposed for use in controlling autonomous robots, including mobile exploratory robots of the biomorphic type. In comparison with conventional autoassociative neural networks, nexi would be more complex but more capable in that they could be trained to do more complex tasks. A nexus would use bit weights and simple arithmetic in a manner that would enable training and operation without a central processing unit, programs, weight registers, or large amounts of memory. Only a relatively small amount of memory (to hold the bit weights) and a simple logic application- specific integrated circuit would be needed. A description of autoassociative neural networks is prerequisite to a meaningful description of a nexus. An autoassociative network is a set of neurons that are completely connected in the sense that each neuron receives input from, and sends output to, all the other neurons. (In some instantiations, a neuron could also send output back to its own input terminal.) The state of a neuron is completely determined by the inner product of its inputs with weights associated with its input channel. Setting the weights sets the behavior of the network. The neurons of an autoassociative network are usually regarded as comprising a row or vector. Time is a quantized phenomenon for most autoassociative networks in the sense that time proceeds in discrete steps. At each time step, the row of neurons forms a pattern: some neurons are firing, some are not. Hence, the current state of an autoassociative network can be described with a single binary vector. As time goes by, the network changes the vector. Autoassociative networks move vectors over hyperspace landscapes of possibilities.

Hand, Charles↗

NASA NTRS2021

A Three-Step Semi Analytical Algorithm (3SAA) for Estimating Inherent Optical Properties Over Oceanic, Coastal, and Inland Waters From Remote Sensing Reflectance

We present a three-step inverse model (3SAA) for estimating the inherent optical properties (IOPs) of surface waters from the remote sensing reflectance spectra, Rrs(). The derived IOPs include the total (a()), phytoplankton (aphy()), and colored detrital matter (acdm()), absorption coefficients, and the total (bb()) and particulate (bbp()) backscattering coefficients. The first step uses an improved neural network approach to estimate the diffuse attenuation coefficient of downwelling irradiance from Rrs. a() and bbp() are then estimated using the LS2 model (Loisel et al., 2018), which does not require spectral assumptions on IOPs and hence can assess a() and bb() at any wavelength at which Rrs() is measured. Then, an inverse optimization algorithm is combined with an optical water class (OWC) approach to assess aphy() and acdm() from anw().The proposed model is evaluated using an in situ dataset collected in open oceanic, coastal, and inland waters. Comparisons with other standard semi-analytical algorithms (QAA and GSM), as well as match-up exercises, have also been performed. The applicability of the algorithm on OLCI observations was assessed through the analysis of global IOPs spatial patterns derived from 3SAA and GSM. The good performance of 3SAA is manifested by median absolute percentage differences (MAPD) of 13%, 23%, 34% and 34% for bbp(443), anw(443), aphy(443) and acdm(443), respectively for oceanic waters. Due to the absence of spectral constraints on IOPs in the inversion of total IOPs, and the adoption of an OWC-based approach, the performance of 3SAA is only slightly degraded in bio-optical complex inland waters.

ocean color↗

NASA NTRS2000

Neural processing of gravito-inertial cues in humans. I. Influence of the semicircular canals following post-rotatory tilt

Sensory systems often provide ambiguous information. Integration of various sensory cues is required for the CNS to resolve sensory ambiguity and elicit appropriate responses. The vestibular system includes two types of sensors: the semicircular canals, which measure head rotation, and the otolith organs, which measure gravito-inertial force (GIF), the sum of gravitational force and inertial force due to linear acceleration. According to Einstein's equivalence principle, gravitational force is indistinguishable from inertial force due to linear acceleration. As a consequence, otolith measurements must be supplemented with other sensory information for the CNS to distinguish tilt from translation. The GIF resolution hypothesis states that the CNS estimates gravity and linear acceleration, so that the difference between estimates of gravity and linear acceleration matches the measured GIF. Both otolith and semicircular canal cues influence this estimation of gravity and linear acceleration. The GIF resolution hypothesis predicts that inaccurate estimates of both gravity and linear acceleration can occur due to central interactions of sensory cues. The existence of specific patterns of vestibuloocular reflexes (VOR) related to these inaccurate estimates can be used to test the GIF resolution hypothesis. To investigate this hypothesis, we measured eye movements during two different protocols. In one experiment, eight subjects were rotated at a constant velocity about an earth-vertical axis and then tilted 90 degrees in darkness to one of eight different evenly spaced final orientations, a so-called "dumping" protocol. Three speeds (200, 100, and 50 degrees /s) and two directions, clockwise (CW) and counterclockwise (CCW), of rotation were tested. In another experiment, four subjects were rotated at a constant velocity (200 degrees /s, CW and CCW) about an earth-horizontal axis and stopped in two different final orientations (nose-up and nose-down), a so-called "barbecue" protocol. The GIF resolution hypothesis predicts that post-rotatory horizontal VOR eye movements for both protocols should include an "induced" VOR component, compensatory to an interaural estimate of linear acceleration, even though no true interaural linear acceleration is present. The GIF resolution hypothesis accurately predicted VOR and induced VOR dependence on rotation direction, rotation speed, and head orientation. Alternative hypotheses stating that frequency segregation may discriminate tilt from translation or that the post-rotatory VOR time constant is dependent on head orientation with respect to the GIF direction did not predict the observed VOR for either experimental protocol.

Non-NASA Center↗

NASA NTRS1999

Developmental regulation of the gene for chimeric calcium/calmodulin-dependent protein kinase in anthers

Chimeric Ca(2+)/calmodulin-dependent protein kinase (CCaMK) was cloned from developing anthers of lily (Lilium longiflorum Thumb. cv. Nellie White) and tobacco (Nicotiana tabacum L. cv. Xanthi). Previous biochemical characterization and structure/function studies had revealed that CCaMK has dual modes of regulation by Ca(2+) and Ca(2+)/calmodulin. The unique structural features of CCaMK include a catalytic domain, a calmodulin-binding domain, and a neural visinin-like Ca(2+)-binding domain. The existence of these three features in a single polypeptide distinguishes it from other kinases. Western analysis revealed that CCaMK is expressed in a stage-specific manner in developing anthers. Expression of CCaMK was first detected in pollen mother cells and continued to increase, reaching a peak around the tetrad stage of meiosis. Following microsporogenesis, CCaMK expression rapidly decreased and at later stages of microspore development, no expression was detected. A tobacco genomic clone of CCaMK was isolated and transgenic tobacco plants were produced carrying the CCaMK promoter fused to the beta-glucuronidase reporter gene. Both CCaMK mRNA and protein were detected in the pollen sac and their localizations were restricted to the pollen mother cells and tapetal cells. Consistent results showing a stage-specific expression pattern were obtained by beta-glucuronidase analysis, in-situ hybridization and immunolocalization. The stage- and tissue-specific appearance of CCaMK in anthers suggests that it could play a role in sensing transient changes in free Ca(2+) concentration in target cells, thereby controlling developmental events in the anther.

NASA Discipline Plant Biology↗

NASA NTRS2002

Data Analysis for the SOLIS Vector Spectromagnetograph

The National Solar Observatory's SOLIS Vector Spectromagnetograph (VSM), which will produce three or more full-disk maps of the Sun's photospheric vector magnetic field every day for at least one solar magnetic cycle, is in the final stages of assembly. Initial observations, including cross-calibration with the current NASA/NSO spectromagnetograph (SPM) will soon be carried out at a test site in Tucson. This paper discusses data analysis techniques for reducing the raw data, calculation of line-of-sight magnetograms and both quick-look and high-precision inference of vector fields from Stokes spectral profiles. Existing SPM algorithms, suitably modified to accomodate the cameras, scanning pattern, and polarization calibration optics for the VSM, will be used to "clean" the raw data and to process line-of-sight, magnetograms. A recent. version of the High Altitude Observatory Milne-Eddington (HAO-ME) inversion code (Skumanich and Lites; 1987, 11)J 322, p. 473) will he used for high-precision vector fields since the algorithm has been extensively tested, is well understood, and is fast enough to complete data analysis within 24 hours of data acquisition. The simplified inversion algorithm of Auer, Heasley. arid House (1977, Sol. Phys. 55, p. 47) forms the initial guess for this version of the HAO-ME code and will be used for quick-look vector analysis of VSM data since its performance on simulated Stokes profiles is better than other candidate methods. Improvements (e.g., principal components analysis or neural networks) are under consideration and will be straightforward to implement. However, current resources are sufficient to store the original Stokes profiles only long enough for high-precision analysis. Retrospective reduction of Stokes data with improved methods will not be possible, and modifications will only be introduced when the advantages of doing so are compelling enough to justify discontinuity in the long-term data stream.

Jones, Harrison P.↗

NASA NTRS1993

Visual information processing II; Proceedings of the Meeting, Orlando, FL, Apr. 14-16, 1993

Various papers on visual information processing are presented. Individual topics addressed include: aliasing as noise, satellite image processing using a hammering neural network, edge-detetion method using visual perception, adaptive vector median filters, design of a reading test for low-vision image warping, spatial transformation architectures, automatic image-enhancement method, redundancy reduction in image coding, lossless gray-scale image compression by predictive GDF, information efficiency in visual communication, optimizing JPEG quantization matrices for different applications, use of forward error correction to maintain image fidelity, effect of peanoscanning on image compression. Also discussed are: computer vision for autonomous robotics in space, optical processor for zero-crossing edge detection, fractal-based image edge detection, simulation of the neon spreading effect by bandpass filtering, wavelet transform (WT) on parallel SIMD architectures, nonseparable 2D wavelet image representation, adaptive image halftoning based on WT, wavelet analysis of global warming, use of the WT for signal detection, perfect reconstruction two-channel rational filter banks, N-wavelet coding for pattern classification, simulation of image of natural objects, number-theoretic coding for iconic systems.

Huck, Friedrich O.↗

NASA NTRS1996

Unsupervised Pattern Classifier for Abnormality-Scaling of Vibration Features for Helicopter Gearbox Fault Diagnosis

A new unsupervised pattern classifier is introduced for on-line detection of abnormality in features of vibration that are used for fault diagnosis of helicopter gearboxes. This classifier compares vibration features with their respective normal values and assigns them a value in (0, 1) to reflect their degree of abnormality. Therefore, the salient feature of this classifier is that it does not require feature values associated with faulty cases to identify abnormality. In order to cope with noise and changes in the operating conditions, an adaptation algorithm is incorporated that continually updates the normal values of the features. The proposed classifier is tested using experimental vibration features obtained from an OH-58A main rotor gearbox. The overall performance of this classifier is then evaluated by integrating the abnormality-scaled features for detection of faults. The fault detection results indicate that the performance of this classifier is comparable to the leading unsupervised neural networks: Kohonen's Feature Mapping and Adaptive Resonance Theory (AR72). This is significant considering that the independence of this classifier from fault-related features makes it uniquely suited to abnormality-scaling of vibration features for fault diagnosis.

Jammu, Vinay B.↗

NASA NTRS1994

The Influence of Gravito-Inertial Force on Sensorimotor Integration and Reflexive Responses

Sensorimotor responses (e.g.. eye movements, spinal reflexes, etc depend upon the interpretation of the neural signals from the sensory systems. Since neural signals from the otoliths may represent either tilt (gravity) or translation (linear inertial force), sensory signals from the otolith organs are necessarily somewhat ambiguous. Therefore. the neural responses to changing otolith signals depend upon the context of the stimulation (e.g- active vs. passive, relative orientation of gravity, etc.) as well as upon other sensory signals (e.g., vision. canals, etc.). This session will focus upon the -role -played by the sensory signals from the otolith organs in producing efficient sensorimotor and behavioral responses. Curthoys will show the influence of the peripheral anatomy and physiology. Tomko will discuss the influence of tilt and translational otolith signals on eye movements. Merfeld will demonstrate the rate otolith organs play during the interaction of sensory signals from the canals and otoliths. Watt will show the influence of the otoliths on spinal/postural responses. Guedry will discuss the contribution of vestibular information to "path of movement"' perception and to the development of a stable vertical reference. Sensorimotor responses to the ambiguous inertial force stimulation provide an important tool to investigate how the nervous system processes patterns of sensory information and yields functional sensorimotor responses.

Curthoys, Ian S.↗

NASA NTRS1999

Efferent projections of the ectostriatum in the pigeon (Columba livia)

The ectostriatum is a major visual component of the avian telencephalon. The core region of the ectostriatum (Ec) receives visual input from the optic tectum through thalamic nuclei. In the present study, the efferent projections of the ectostriatum were investigated by using the anterograde tracers Phaseolus vulgaris leucoagglutinin and biotinylated dextran amine. Projection patterns resulting from these tracers were confirmed by the retrograde tracer cholera toxin subunit B. When anterograde tracers were injected in Ec, primary projections were seen traveling dorsolaterally to the belt region of the ectostriatum (Ep) and the neostriatal area immediately surrounding Ep (Ep2). Neurons in Ep sent projections primarily to the overlying Ep2. The efferents of Ep2 traveled dorsolaterally to terminate in three telencephalic regions, from anterior to posterior: (1) neostriatum frontale, pars lateralis (NFL), (2) area temporo-parieto-occipitalis (TPO), and (3) neostriatum intermedium, pars lateralis (NIL). A part of the archistriatum intermedium and the lateral part of the neostriatum caudale also received somewhat minor projections. In addition, some neurons in Ec were also the source of direct, but minor, projections to the NFL, TPO, NIL, and archistriatum intermedium. The topographical relationship among the primary (Ec), secondary (Ep and Ep2), and tertiary (NFL, TPO, NIL) areas indicate that the neural populations for visual processing are organized along the rostral-caudal axis. Thus, the anterior Ec sent efferents to the anterior Ep, which in turn sent projections to anterior Ep2. Neurons in the anterior Ep2 sent projections to NFL and the anterior TPO. Similarly, the intermediate and posterior Ec sent projections to corresponding parts of Ep, whose efferents projected to intermediate and posterior Ep2, respectively. The intermediate Ep2 gave rise to major projections to TPO, whereas posterior Ep2 neurons sent efferents primarily to NIL. The organization of this neural circuit is compared with those of other sensory circuits in the avian telencephalon, as well as the laminar arrangement of the mammalian isocortex.

Non-NASA Center↗