What ethologically based models have taught us about the neural systems underlying fear and anxiety

Classical Pavlovian fear conditioning to painful stimuli has provided the generally accepted view of a core system centered in the central amygdala to organize fear responses. Ethologically based models using other sources of threat likely to be expected in a natural environment, such as predators or aggressive dominant conspecifics, have challenged this concept of a unitary core circuit for fear processing. We discuss here what the ethologically based models have told us about the neural systems organizing fear responses. We explored the concept that parallel paths process different classes of threats, and that these different paths influence distinct regions in the periaqueductal gray - a critical element for the organization of all kinds of fear responses. Despite this parallel processing of different kinds of threats, we have discussed an interesting emerging view that common cortical-hippocampal-amygdalar paths seem to be engaged in fear conditioning to painful stimuli, to predators and, perhaps, to aggressive dominant conspecifics as well. Overall, the aim of this review is to bring into focus a more global and comprehensive view of the systems organizing fear responses.

Morphological development of Corydoras aff. paleatus (Siluriformes, Callichthyidae) and correlation with the emergence of motor and social behaviors

Here we examine major anatomical characteristics of Corydoras aff. paleatus (Jenyns, 1842) post-hatching development, in parallel with its neurobehavioral evolution. Eleutheroembryonic phase, 4.3-8.8 days post-fertilization (dpf); 4.3-6.4 mm standard length (SL) encompasses from hatching to transition to exogenous feeding. Protopterygiolarval phase (8.9-10.9 dpf; 6.5-6.7 mm SL) goes from feeding transition to the commencement of unpaired fin differentiation, which marks the start of pterygiolarval phase (11-33 dpf; 6.8-10.7 mm SL) defined by appearance of lepidotrichia in the dorsal part of the median finfold. This phase ends with the full detachment and differentiation of unpaired fins, events signaling the commencement of the juvenile period (34-60 dpf; 10.8-18.0 mm SL). Eleutheroembryonic phase focuses on hiding and differentiation of mechanosensory, chemosensory and central neural systems, crucial for supplying the larval period with efficient escape and nutrient detection-capture neurocircuits. Protopterygiolarval priorities include visual development and respiratory, digestive and hydrodynamic efficiencies. Pterygiolarval priorities change towards higher swimming efficacy, including carangiform and vertical swimming, necessary for the high social interaction typical of this species. At the end of the protopterygiolarval phase, simple resting and foraging aggregations are seen. Resting and foraging shoals grow in complexity and participant number during pterygiolarval phase, but particularly during juvenile period.

The brain decade in debate: III. Neurobiology of emotion

This article is a transcription of an electronic symposium in which active researchers were invited by the Brazilian Society of Neuroscience and Behavior (SBNeC) to discuss the advances of the last decade in the neurobiology of emotion. Four basic questions were debated: 1) What are the most critical issues/questions in the neurobiology of emotion? 2) What do we know for certain about brain processes involved in emotion and what is controversial? 3) What kinds of research are needed to resolve these controversial issues? 4) What is the relationship between learning, memory and emotion? The focus was on the existence of different neural systems for different emotions and the nature of the neural coding for the emotional states. Is emotion the result of the interaction of different brain regions such as the amygdala, the nucleus accumbens, or the periaqueductal gray matter or is it an emergent property of the whole brain neural network? The relationship between unlearned and learned emotions was also discussed. Are the circuits of the former the underpinnings of the latter? It was pointed out that much of what we know about emotions refers to aversively motivated behaviors, like fear and anxiety. Appetitive emotions should attract much interest in the future. The learning and memory relationship with emotions was also discussed in terms of conditioned and unconditioned stimuli, innate and learned fear, contextual cues inducing emotional states, implicit memory and the property of using this term for animal memories. In a general way it could be said that learning modifies the neural circuits through which emotional responses are expressed.

Determination of the influence of the variation of reducing and non-reducing sugars on coffee quality with use of artificial neural network

The present study aimed at evaluating the use of Artificial Neural Network to correlate the values resulting from chemical analyses of samples of coffee with the values of their sensory analyses. The coffee samples used were from the Coffea arabica L., cultivars Acaiá do Cerrado, Topázio, Acaiá 474-19 and Bourbon, collected in the southern region of the state of Minas Gerais. The chemical analyses were carried out for reducing and non-reducing sugars. The quality of the beverage was evaluated by sensory analysis. The Artificial Neural Network method used values from chemical analyses as input variables and values from sensory analysis as output values. The multiple linear regression of sensory analysis values, according to the values from chemical analyses, presented a determination coefficient of 0.3106, while the Artificial Neural Network achieved a level of 80.00% of success in the classification of values from the sensory analysis.

Detection of Horizontal Two-Phase Flow Patterns Through a Neural Network Model

One of the main problems related to the transport and manipulation of multiphase fluids concerns the existence of characteristic flow patterns and its strong influence on important operation parameters. A good example of this occurs in gas-liquid chemical reactors in which maximum efficiencies can be achieved by maintaining a finely dispersed bubbly flow to maximize the total interfacial area. Thus, the ability to automatically detect flow patterns is of crucial importance, especially for the adequate operation of multiphase systems. This work describes the application of a neural model to process the signals delivered by a direct imaging probe to produce a diagnostic of the corresponding flow pattern. The neural model is constituted of six independent neural modules, each of which trained to detect one of the main horizontal flow patterns, and a last winner-take-all layer responsible for resolving when two or more patterns are simultaneously detected. Experimental signals representing different bubbly, intermittent, annular and stratified flow patterns were used to validate the neural model.

Augmented reality systems for weed economic thresholds applications

The augmented reality (AR) technology has applications in many fields as diverse as aeronautics, tourism, medicine, and education. In this review are summarized the current status of AR and it is proposed a new application of it in weed science. The basic algorithmic elements for AR implementation are already available to develop applications in the area of weed economic thresholds. These include algorithms for image recognition to identify and quantify weeds by species and software for herbicide selection based on weed density. Likewise, all hardware necessary for AR implementation in weed science are available at an affordable price for the user. Thus, the authors propose weed science can take a leading role integrating AR systems into weed economic thresholds software, thus, providing better opportunities for science and computer-based weed control decisions.

Synergistic control of forearm based on accelerometer data and artificial neural networks

In the present study, we modeled a reaching task as a two-link mechanism. The upper arm and forearm motion trajectories during vertical arm movements were estimated from the measured angular accelerations with dual-axis accelerometers. A data set of reaching synergies from able-bodied individuals was used to train a radial basis function artificial neural network with upper arm/forearm tangential angular accelerations. The trained radial basis function artificial neural network for the specific movements predicted forearm motion from new upper arm trajectories with high correlation (mean, 0.9149-0.941). For all other movements, prediction was low (range, 0.0316-0.8302). Results suggest that the proposed algorithm is successful in generalization over similar motions and subjects. Such networks may be used as a high-level controller that could predict forearm kinematics from voluntary movements of the upper arm. This methodology is suitable for restoring the upper limb functions of individuals with motor disabilities of the forearm, but not of the upper arm. The developed control paradigm is applicable to upper-limb orthotic systems employing functional electrical stimulation. The proposed approach is of great significance particularly for humans with spinal cord injuries in a free-living environment. The implication of a measurement system with dual-axis accelerometers, developed for this study, is further seen in the evaluation of movement during the course of rehabilitation. For this purpose, training-related changes in synergies apparent from movement kinematics during rehabilitation would characterize the extent and the course of recovery. As such, a simple system using this methodology is of particular importance for stroke patients. The results underlie the important issue of upper-limb coordination.

Central chemoreceptors and neural mechanisms of cardiorespiratory control

The arterial partial pressure (P CO2) of carbon dioxide is virtually constant because of the close match between the metabolic production of this gas and its excretion via breathing. Blood gas homeostasis does not rely solely on changes in lung ventilation, but also to a considerable extent on circulatory adjustments that regulate the transport of CO2 from its sites of production to the lungs. The neural mechanisms that coordinate circulatory and ventilatory changes to achieve blood gas homeostasis are the subject of this review. Emphasis will be placed on the control of sympathetic outflow by central chemoreceptors. High levels of CO2 exert an excitatory effect on sympathetic outflow that is mediated by specialized chemoreceptors such as the neurons located in the retrotrapezoid region. In addition, high CO2 causes an aversive awareness in conscious animals, activating wake-promoting pathways such as the noradrenergic neurons. These neuronal groups, which may also be directly activated by brain acidification, have projections that contribute to the CO2-induced rise in breathing and sympathetic outflow. However, since the level of activity of the retrotrapezoid nucleus is regulated by converging inputs from wake-promoting systems, behavior-specific inputs from higher centers and by chemical drive, the main focus of the present manuscript is to review the contribution of central chemoreceptors to the control of autonomic and respiratory mechanisms.

Neural regulation of the stress response: glucocorticoid feedback mechanisms

The mammalian stress response is an integrated physiological and psychological reaction to real or perceived adversity. Glucocorticoids are an important component of this response, acting to redistribute energy resources to both optimize survival in the face of challenge and to restore homeostasis after the immediate challenge has subsided. Release of glucocorticoids is mediated by the hypothalamo-pituitary-adrenal (HPA) axis, driven by a neural signal originating in the paraventricular nucleus (PVN). Stress levels of glucocorticoids bind to glucocorticoid receptors in multiple body compartments, including the brain, and consequently have wide-reaching actions. For this reason, glucocorticoids serve a vital function in negative feedback inhibition of their own secretion. Negative feedback inhibition is mediated by a diverse collection of mechanisms, including fast, non-genomic feedback at the level of the PVN, stress-shut-off at the level of the limbic system, and attenuation of ascending excitatory input through destabilization of mRNAs encoding neuropeptide drivers of the HPA axis. In addition, there is evidence that glucocorticoids participate in stress activation via feed-forward mechanisms at the level of the amygdala. Feedback deficits are associated with numerous disease states, underscoring the necessity for adequate control of glucocorticoid homeostasis. Thus, rather than having a single, defined feedback ‘switch’, control of the stress response requires a wide-reaching feedback ‘network’ that coordinates HPA activity to suit the overall needs of multiple body systems.