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10 results for “growing functional modules”
Figure 10b. The control loop's information displayed by the controller-Designing a Growing Functional Modules "Artificial Brain"
<p>Once performed the design and configuration, the controller and the local application are<br> executed by pressing the corresponding button. In case of a local simulation, two terminal windows<br> are generated. The first one, localized on the left side of the screen (figure 10.a), displays the<br> behavior of the simulation; the second one, localized on the right side of the screen (figure 10.b),<br> displays the behavior of the controller. Both application run concurrently and their respective<br> contents allow the user to observe and monitor the control session. The last text line of figure 10.b<br> displays the set of commands at cycle 201.</p>
Figure 10.a. Simulation's display: The characters "\__/" represents the front part of the car that should pass through the string "....==..=.==..==" representing the range of obstacles-Designing a Growing Functional Modules "Artificial Brain"
<p>Once performed the design and configuration, the controller and the local application are<br> executed by pressing the corresponding button. In case of a local simulation, two terminal windows<br> are generated. The first one, localized on the left side of the screen (figure 10.a), displays the<br> behavior of the simulation; the second one, localized on the right side of the screen (figure 10.b),<br> displays the behavior of the controller. Both application run concurrently and their respective<br> contents allow the user to observe and monitor the control session. The last text line of figure 10.b<br> displays the set of commands at cycle 201.</p>
Figure 8. Configuration of the controller-Designing a Growing Functional Modules "Artificial Brain"
<p>Before running the controller, it is necessary to specify the communication's configuration.<br> When pressing the “configure” button, two text windows appear (figure 8). The upper one allows to<br> specify the application's localization in order to run it previously to the controller. Presently, the<br> corresponding simulation will run on the same machine, thus the name of the corresponding<br> executable file must be specified. Otherwise, in the same field, the user should specify the IP<br> address of the server where the controlled system is running.</p>
Figure 7. Adding an Acting Module, its configuration values and its input connections-Designing a Growing Functional Modules "Artificial Brain"
<p>The fourth step consists of adding an Acting Module, its configuration values and input<br> connection as shown in figure 7. A type “CI” is assigned because it functionality will consist of<br> triggering a steering command in accordance with the perception from the Sensing Module and in<br> order to satisfy the input request from the Global Goal. Consequently, the feedback is set to “1 18”<br> where “1” is the reference to the Sensation “free” and “18” to the perception in output of the<br> Sensing Module. The identifier “18” for this perception is computed as at the total number of<br> Sensation plus one (first sensing module). Identifiers and their references are automatically updated<br> when a Sensation is added or deleted.</p>
Figure 6. Adding a Global Goal with its required type-Designing a Growing Functional Modules "Artificial Brain"
<p>The next step consists of adding a Global Goal expressing a motivation required by the<br> controller. The goal is to keep the vehicle's front free of obstacles, thus the Sensation “free” should<br> stay equal to “1”. After adding a new Global Goal, its assigned type should be “Cst” corresponding<br> to a constant output request (see figure 6). In the parameter field, its specified value is “1”.</p>
Figure 4. Adding a Sensing Module and selecting its type-Designing a Growing Functional Modules "Artificial Brain"
<p>GFM controllers learn to satisfy some predefined goals<br> while interacting with the environment and thus should be considered as artificial brains. An<br> example of the design process of a simple controller is provided herein to explain the inherent<br> methodology, to exhibit the components' interconnections and to demonstrate the control process.</p>
Figure 5. Adding connections from Sensations 2-17 to the Sensing Module 1-Designing a Growing Functional Modules "Artificial Brain"
<p>The next step consists of connecting the sixteen sensations in the input of the Sensing<br> Module. To do this, the user must right-click on each Sensation, then on “new connection” and<br> indicate the Sensing Module identifier. The resulting design is presented on figure 5. Finally, the<br> Acting Module's field is set to “1” (indicating the number of the Acting Module that later will assess<br> the correctness of the perception) and the unique extra-parameter set to “20” (related with the<br> module's behavior).</p>
Figure 2. The editor's components from left to right: a) Sensation, b) Sensing Module, c) Global Goal, d) Acting Module.-Designing a Growing Functional Modules "Artificial Brain"
<p>Each “Sensation” corresponds to an integer value corresponding to a specific system's<br> sensor. Sensations are symbolized by a green rectangle on the editor's canvas (figure 2.a). Each<br> newly created sensation is assigned an identifier previously incremented. Its unique field, initially<br> filled with question-marks, allows it to associate a mnemonic in order to facilitate its interpretation.</p>
Figure 1. The GFM controller and its control loop-Designing a Growing Functional Modules "Artificial Brain"
<p>The GFM control loop has many similarities to a standard one as shown in figure 1. The<br> controller, delimited by a dotted line, sends during each cycle an output command in order to trigger<br> some mechanical or virtual actuators and in return, receives a feedback composed of a sequence of<br> sensors' values. However, the concept of reference value is replaced by “Global Goals” which are<br> integrated to the controller.</p>
Figure 3. Describing the sequence of input sensations sent by the system.-Designing a Growing Functional Modules "Artificial Brain"
<p>To design the controller, first of all, the user should click the button “new” on the toolbar to<br> create an empty canvas and to assign it a controller's name, presently “pathfinder”. Then, the first<br> convenient step is to describe the feedback given by the system as a sequence of sensations (see<br> figure 3). The first sensation named “free”, indicates if the vehicle has been able or not to cross the<br> range of obstacles. Each of the next sixteen values is associated to its corresponding proximity<br> sensors that points out the presence of an obstacle. They are given a name beginning with “s”<br> followed by a number. The red color of the last sensation's field indicates that the given name is<br> invalid; presently, because it contains a blank character.</p>
ScienceDex guides
Understand access before you commit
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.