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6,059 results for “Journale”
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 19. AnyLogic Implementation of Decision-Making Architechture in an Virtual Autonomous Agent Environment
<p>Figure 19 shows a screenshot of the test implementation. The picture in the middle shows the modules and interfaces<br> of the decision-making architecture which were realized by so-called “active objects” and “ports”.<br> On the left side, the implemented modules are listed. In the right lower corner of the figure, the<br> agents and the virtual environment are displayed. The environment comprises different “objects”<br> (food sources, obstacles, predators, other agents, etc.). In order to survive, the agents have to access<br> food sources. However, the accessing of food sources bears difficulties and risks.</p>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 16. Architecture for Affective Situation Assessment of Perceptual Images (Internal Connections between Emotions are not Depicted for Better Clarity of the Graphic)
<p>Based on the concept of affective neuro-symbols, a model was developed according to<br> which emotions can be represented by affective neuro-symbolic networks (see right half of Figure<br> 16, referred to as architecture of “internal perception” in contrast to the “external perception”<br> architecture of the left half of Figure 16, which has already been presented in Section 4.2).<br> The individual affective neuro-symbols (depicted as circles) represent different emotions<br> (fear, anger, guilt, joy, rage, panic, love, happiness, etc.).</p>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 13. Implementation of Neuro-Symbols and their Communication in AnyLogic
<p>Figure 13 and Figure 14 show screenshots of the model implementation in AnyLogic. Figure<br> 13a shows how individual neuro-symbols were implemented. Neuro-symbols are realized by socalled<br> active objects with an input port and an output port via which information is exchanged with<br> other elements. Additionally, variables are used for calculating the activation of the neuro-symbols<br> (not depicted) and for storing properties of neuro-symbols (e.g., the location property). Timers and<br> state charts serve for processing information that arrives in a certain time window or in a certain<br> temporal succession at the input port. Whenever new input information arrives at the input port, the<br> activation degree of the neuro-symbol is recalculated and checked against the threshold value.<br> Based on this, the neuro-symbol is either activated or deactivated and the corresponding<br> information is sent via the output port by using “message objects” (see Figure 13b).</p>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 17. Autonomous Decision-Making Architecture
<p>An overview of the decision-making architecture is presented in Figure 17. The architecture was<br> guided by two core concepts. The first core concept is that human intelligence bases on a<br> combination of low-level and high-level mechanisms. Low-level mechanisms are mainly<br> predefined. They are not in all situations completely accurate but have the advantage of being fast.<br> High-level mechanisms are not predefined and thus slower but more accurate. The second core<br> concept concerns the use of so-called emotions as mechanism for the evaluation of information</p>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 15. Input Sources of an Affective Neuro-Symbol Representing an Emotion
<p>Based on the descriptions given above and the concept of neuro-symbolic information<br> processing outlined in Section 4.2, so-called “affective neuro-symbols” were defined for the<br> affective situation assessment architecture (see Figure 15). These affective neuro-symbols can<br> principally receive information from four different sources: (1) body states, (2) objects and events<br> perceived in the environment (external perception), (3) from other emotions and (4) cognitive<br> (reasoning) processes. An input from one of these sources can in certain circumstances already be<br> sufficient to activate an affective neuro-symbol. Different sources can either have an exhibitory or<br> inhibitory effect on the activation of an affective neuro-symbol.</p>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 14. Implementation of Overall Architecture of the Perception Model in AnyLogic 4.3
<p>To perform complex functions, individual neuro-symbols are then connected to networks.<br> Figure 14 shows a screenshot of the AnyLogic implementation of the overall system at the<br> beginning of the learning phase. The lowest neuro-symbolic levels receive the direct sensor<br> information as input. The higher neuro-symbolic levels are originally not interconnected amongst<br> each other. Instead, they are connected to so-called “learning ports”, which additionally receive<br> control information needed for the supervised learning process. Details about the multi-stage multilevel<br> learning process can be found in.</p>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 12. Affinities and Differences of Neuro-Symbolic Networks in Comparison to Classical Neural Networks
<p>After having briefly illustrated the basic function principle of neuro-symbolic networks, this<br> section aims at reviewing their affinities and differences to standard neural networks like for<br> example multi-layer perceptrons (MLPs) [58]. A summary of these affinities and differences is<br> given in Figure 12. The affinities concern certain functions of individual nodes of the networks. In<br> both cases, weighted input information is summed up and an activation function is applied to this<br> sum. In both cases, the individual nodes are interconnected to form networks. Much larger than the<br> number of affinities between neuro-symbolic networks and neural network is however the number<br> of differences. The first difference consists in the application domain. Neuro-symbolic networks<br> have so far mainly been applied for complex, large-scale sensor data processing of multimodal data<br> – an application which can so far barely be handled by neural networks.</p>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 11. Activated Neuro-Symbols for Detecting that a Person walks around in the Room
<p>With the example of Figure 11, also the function of feedback connections can be explained.<br> According to the existing feedforward connections, the neuro-symbol “object stands” would be<br> activated together with the neuro-symbol “object moves” whenever the neuro-symbols “motion”<br> and “object moves” are active, because it is activated by a subset of the neuro-symbols that activate<br> the neuro-symbol “object moves”. This activation would however be undesired in this concrete<br> case. For this reason, an inhibitory feedback connection exists from the neuro-symbol “object<br> moves” to the neuro-symbol “object stands” that inhibits the activation of the neuro-symbol “object<br> stands”.</p>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 10. Meeting Room Equipped with Different Sensors
<p>To illustrate the basic working principle of a perceptual neuro-symbolic network in a concrete application, a simplified, concrete example is given in the following. In this example, office meeting room is equipped with different sensors (tactile floor sensors, motion detectors, light barriers, a door contact sensor, a camera, and a microphone) as sketched in Figure 10.</p>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 18. Different Modules involved in the Autonomous Decision-Making Process
<p>The basic functioning of this architecture is now described in the following step by step<br> using Figure 18a-f, where always the relevant modules of the model are highlighted for better<br> comprehension.</p>
BRAIN Journal-Suicide: Neurochemical Approaches-Figure 3. Representative gel electrophoreses showing the mRNA levels of BDNF, Trk B, NGF and Trk A
<p>Our present study, provides the evidence for the first time that two major neurotrophins<br> BDNF and NGF along with their cognitive TrkB and TrkA receptors are not only less expressed in<br> their protein levels but also their transcription levels are also compromised in postmortem brains of<br> suicide subjects as evident from the mRNA studies of the present experiment.</p>
BRAIN Journal-Suicide: Neurochemical Approaches-Figure 1. Representative bands of Western Blot showing the Protein levels of BDNF, Trk B, NGF and Trk A
<p>The molecular weights of BDNF, TrkB, NGF, TrkA and β-actin were 14 kDa, 145 kDa,<br> 13.5 kDa, 140 kDa and 46 kDa, respectively. The expression levels of BDNF, NGF, TrkB, and<br> TrkA proteins were normalized against the β-actin protein level, which was used as an internal<br> control. The results showed that the expression of BDNF, NGF, TrkB and TrkA in the hippocampus<br> decreased significantly in the suicide subjects when compared to the control subjects (P<0.05,<br> Figure 1).</p>
BRAIN Journal-Suicide: Neurochemical Approaches-Figure 2. Hippocampal BDNF and NGF levels of suicide subjects and normal controls
<p>Among the suicidal victims BDNF and NGF levels were significantly reduced in the<br> hippocampus compared to normal control subjects (tBDNF =5.43; df=18; p<0.001; tNGF =6.13; df=18;<br> p<0.001 Figure 2). Such observations clearly indicate the relation of chronic mental depression and<br> hippocampal neurotrophin levels.</p>
BRAIN Journal-An Enhancement over Texture Feature Based Multiclass Image Classification under Unknown Noise-Figure 8. Performance in 1st Approach for three data set
<p>This work also deals with classification of multi class images under different constraints of<br> data set. The first experiment is carried out on images without noise, second with Gaussian noise<br> and filtered data set in third experiment. Performance of the classifier using statistical texture<br> features for two approaches are presented in the table 3. It is observed that performance n the first<br> experiment is best in the first data set i.e. data set without noise in both the approach, while the<br> performance is decreased if the same images are affected by Gaussian noise.</p>
BRAIN Journal-An Enhancement over Texture Feature Based Multiclass Image Classification under Unknown Noise-Figure 4. Principal stages of image classification system
<p>In computer vision, images or objects are recognised by machine going through two phases<br> shown in the figure 4. First the system is trained with features extracted from sample images in<br> training stage then they are tested on input images in testing stage. The performance of the classifier<br> depends on features extracted from the image. This research work is carried out in three different<br> experiments, first experiment is performed on data set containing the original images of sixteen<br> categories, noisy images are classified in second experiment, and third experiment detects the type<br> of noise affected the image followed by filtering through appropriate filter, then filtered images are<br> classified. The performance of each of the experiment is measured with two approaches. First<br> approach extracts the statistical texture features of the whole image, and the original image of size<br> 128x128 is divided into sixteen blocks of size 32x32 pixels in second approach. Then six statistical<br> texture features discussed in second section are extracted from each of the block producing 96<br> features from each of the images are used for training and testing stage.</p>
BRAIN Journal-An Enhancement over Texture Feature Based Multiclass Image Classification under Unknown Noise-Figure 1. Sample Images of sixteen categories
<p>An image is often corrupted by noise in its acquisition or transmission. Noise is any<br> undesired information that degrades the image and appears in images from a variety of sources.</p> <p>Basically, there are three standard noise models [17], which model the types of noise<br> encountered in most images; they are additive noise, multiplicative noise and impulse noise. In this<br> work we have considered the occurrence of additive noise. An image function is given by f (x, y)<br> where (x, y) is spatial coordinate and f is intensity at point(x, y). Let f (x, y) be the original image,<br> g(x, y) be the noisy version and η(x, y) be the noise function, which returns random values coming<br> from an arbitrary distribution.</p>
BRAIN Journal-Intelligent Continuous Double Auction method For Service Allocation in Cloud Computing-Figure 6. Comparison on resource utilization.
<p>Figure 6 shows resource utilization in different system loads and as shown in it, in ICDA<br> resource utilization is more efficient than other methods especially in higher system load which is<br> due to tradeoff and sharing factors.</p>
BRAIN Journal-Intelligent Continuous Double Auction method For Service Allocation in Cloud Computing-Figure 3. Sharing and merging effect on successful allocation
<p>Fig (3) shows the effect of merging and sharing resources by auctioneer in term of success<br> rate of allocation. As shown in it, these factors improve successful allocation rate especially in<br> higher system load.</p>
BRAIN Journal-Intelligent Continuous Double Auction method For Service Allocation in Cloud Computing-Figure 2. bid value for time factor
<p>Each consumer is looking for utilizing its requested service with minimum price before its<br> deadline. To utilize a service all required resources should be allocated before deadline and<br> otherwise service failed to utilize and consumer must pay penalty to providers for all other<br> resources which is allocated to it. So Consumer should adjust its bid price rapidly to the acceptable<br> price of the market. Since consumers are generally sensitive to deadline in acquiring requested<br> service, it is intuitive to consider deadline time when formulating the bid price. Consumer agent<br> time dependent bid price formula is determined in.</p>
BRAIN Journal-An Enhancement over Texture Feature Based Multiclass Image Classification under Unknown Noise-Figure 7. Performance in experiment 3 for both approaches
<p>Classification of noisy images starts with detection of type of noise followed by appropriate<br> filtering operation. Then similar approaches are followed for feature extraction as discussed in<br> above experiments.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.