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Figure 14: Adjunctive square diagram-Brain Functors: A mathematical model of intentional perception and action
<p>Finally, a brain functor is a functor F: X→A that is a left semiadjunction for Het(X, A) and a right semiadjunction for Het(A, X), i.e., HomA(F(X),A) ≅ Het(X,A)<br> and Het(A,X) ≅ HomA(A,F(X)).<br> For each d in Het(X, A), there is a unique hom f(d) in HomA(F(X), A) so that the upper triangular ‘wing’ in the butterfly diagram commutes. For each d' in Het(A, X), there is a unique hom g(d') in HomA(A, F(X)) so that the lower triangular ‘wing’ commutes.</p>
Figure 13: Composition of hets and homs-Brain Functors: A mathematical model of intentional perception and action
<p>The cross-category object-to-object hets d : X→A will be indicated by thin arrows (→) rather than thick arrows (⇒). The first question is how do heteromorphisms compose with one another? But that is not necessary. Chimera do not need to ‘mate’ with other chimera to form a ‘species’ or category; they only need to mate with the intra-category morphisms on each side to form other chimera.8 Given a het d : X→A from an object in a category X to an object in a category A, and homs h : X'⇒X in X and k : A⇒A' in A, the composition dh : X'⇒X→A is another het X'→A and the composition kd : X→A⇒A' is another het X→A'.</p>
Figure 12: Language faculty as two-way determination through a universal-Brain Functors: A mathematical model of intentional perception and action
<p>A brain functor, broadly put, is any universal mechanism of determination that can factor determination either way through a universal–rather than an adjunction that factors one way determination through two (receiving and sending) universals. In some contexts in the life sciences, determination is strictly one way so one might expect to find a semiadjunction but not a two-way system like a brain functor. An application of the scheme for a brain functor in the cognitive sciences is to model the language faculty where there is two way determination between vocal stimuli and internal representations. The previous semiadjunctions for language understanding and language action can be merged to arrive at the brain-like function of the language faculty.</p>
Figure 11: Coding and decoding Cartesian coordinates of geometrical points-Brain Functors: A mathematical model of intentional perception and action
<p>The simplest form of a brain "functor" is just a two-way representation or coding system that constructs and implements a set of codes. Given some set of objects, it is encoded using some isomorphic set of representations or codes for the objects, and then given an instance of the code, it is decoded to determine the object. Coordinatizing is a form of coding. The geometrical plane is a collection of points, and the Cartesian coordinate system represents each point P by a pair (xP, yP) of coordinates. Given a point P , the "coordinate" function selects the coordinates (xP, yP) of the point which is the recognized or coded output, and given the coordinates or code for a point (xP, yP) as an input, the "plot" function designates the point.</p>
Figure 8: Language production through a sending universal-Brain Functors: A mathematical model of intentional perception and action
<p>The dual to "language understanding" is language production or linguistic action (e.g., "speech acts"). The role of the specific het is played by some auditory output such as utterances (Humboldt’s "vocal stimulus"). But the corresponding internal specific hom is the speech act (i.e., internal speech with intentionality) that through the language faculty produces the same outputs but as intentional speech.</p>
Figure 1: Het d: X→ A-Brain Functors: A mathematical model of intentional perception and action
<p>In the body of this paper, I will try to keep the mathematics at a minimal conceptual level– which the mathematical formulations restricted to the Appendix. Category theory lends itself to visualization in diagrams so that non-mathematical style of presentation is emphasized by an abundant use of diagrams. A category is intuitively a set of objects of the same type. Morphisms between objects should be thought of as a type of determining relation or cause-effect relation between the objects. When a morphism is between objects of the same category, it is called a homomorphism or hom, and when between objects of different categories it is a heteromorphism or het.4 One of the problems in the conventional treatment of category theory5 is that it tries to ignore heteromorphisms even though hets are a natural part of working mathematics. This leads to certain definitions being rather contrived (to avoid mentioning hets), the usual treatment of the universal mapping properties in adjunctions being the case in point. Adjunctions will be introduced informally and in the natural manner using hets. The general setting is how the objects in one category (e.g., the "environment" in a life sciences context), the "sending" category, will "affect" or "determine" objects in another category (e.g., "organisms"), the "receiving" category. We start with an object X in the sending category, an object A in the receiving category, and a specific het determination d : X → A from X to A.6</p>
Figure 4: The Adjunctive Square Diagram-Brain Functors: A mathematical model of intentional perception and action
<p>Dually, we can define the above situation, given by the association of the sending universal G(A) with each object A in the receiving category along with the canonical isomorphism Het(X,A) ≅ Homsending(X,G(A)), as a right semiadjunction. Now we are prepared to define an adjunction essentially as:<br> adjunction = left semiadjunction + right semiadjunction Homreceiving(F(X),A) ≅ Het(X,A) ≅ Homsending(X,G(A)).</p>
Figure 7: -"Action" as determination through a sending universal-Brain Functors: A mathematical model of intentional perception and action
<p>Dual to the generic model of "perception" is the generic model of "action"–which is the determinative scheme given by a right semiadjunction. In the model of perception, there is the uninterpreted message as just a sensory input (the external het), and then there is the second level where the factorization (the internal hom) through the receiving universal recognizes the interpretation, meaning, or intentionality of the message. In the dual model of "action," the external het specifies the external behavior (which could be even a reflex behavior) while internal hom factoring through a sending universal that supplies the "intentionality" of the "action" (where an "action" is a "behavior" plus the second level of "intentionality"). In each case, we end up with a certain behavior but determined by two different means.</p>
Figure 6: "Perception" as determination through a receiving universal-Brain Functors: A mathematical model of intentional perception and action
<p>Before turning to right semiadjunctions, it might be useful to present a rather generic version of determination through a receiving universal as model of "recognition" or "perception" that captures many of the common features of the various examples. The determination through the receiving universal is the active internal process that supplies the "interpretation" or "intentionality" to the raw sense data. The red blotch is seen as a tomato; the sound "ya" is understood as indicating agreement, and so forth. In the passive/direct alternative, the raw sensory input supplies Lockean "perception" like writing on a blank slate or a stamp making an impression on wax.</p>
Figure 3: Scheme for determination by a sending universal G(A)-Brain Functors: A mathematical model of intentional perception and action
<p>The universal mapping property is: for every het d : X→A, there is a unique hom g(d) : X⇒G(A) in the sending category such that: eAg(d) = X⇒G(A)→A = X→A = d i.e., such that the determination through the universal sending het eA : G(A)→A preceded by the hom g(d) : X⇒G(A) is the same as the original het d : X→A.</p>
Where and how our brain represents the temporal structure of observed action
<p>Dataset includes resting state function and structural scans used in the paper with the same title.</p>
main source codes and files of "Meta-path Based Prioritization of Functional Drug Actions with Multi-Level Biological Networks"
<p>These source codes and their related files are associated the study. "Meta-path Based Prioritization of Functional Drug Actions with Multi-Level Biological Networks"</p> <p>This study is in process of publication.</p>
6TiSCH Open Data Action Datasets
<p>This repository contains the datasets of the 6TiSCH Open Data Action experiment of Fed4FIRE. The datasets cover the execution of three application-level test scenarios, building-automation, home-automation and industrial-monitoring, on two testbeds, w-iLab.t in Ghent and OpenTestbed in Paris. The reference firmware image used was OpenWSN. The data format is documented at https://benchmark.6tis.ch/.</p>
Racionální užití léků ve stáří (rational use of drugs in geriaric age- EU COST Action IS1402 and EUROAGEISM H2020 project)
<p>During the EU COST Action IS1402 video presentations, the information on ageism in medication use, ageist approaches at regulatory level, at the level of service provision and in rationality of drug prescribing in older adults have been emphasized, as well as main aims of FIP7 project in the EUROAGEISM H2020 (FIP project is entitled "Inappropriate prescribing and availability of medication safety and medication management services in older patients in Europe and other countries"). Video presentation is in national language- Czech. </p>
Figure 3 in A new record of the freshwater turtle Mauremys rivulata (Valenciennes, 1833) in the Ofkos river, Cyprus: Conservation actions required
Figure 3. Turtle trap used for the research (upper right and lower left). Trap with two trapped individuals (upper left).
Fig. 1 in Actions of the fire ant Solenopsis saevissima (Smith) (Hymenoptera: Formicidae) on a big-eared opossum carcass
Fig. 1. Carcass of big-eared opossum (Didelphis aurita) colonized by fire ants Solenopsis saevissima (Hymenoptera: Formicidae) and other insects on 12 Nov 2017, Juiz de Fora, Minas Gerais, Brazil. (A) Fire ants initially monopolized the carcass, constructing a dirt mound on the muzzle, thus displaying the burying behavior of the ants. (B) Circled are ants beginning construction of a soil mound near the tail. (C) Diptera depositing eggs on the carcass. Afer the carcass was moved to the periphery of the roadway, the ants lost their dominance to other necrophagous insects. (D) Skeletonized carcass; ants and other necrophagous insects remained until the end of the decomposition process.
Figure 1. – A in First steps towards Live-action Virtual Reality Games
Figure 1. – A: The Clipperton-La Passion Island (France) lies in the NorthEastern Tropical Ocean at a minimum distance of 945 km from the first emerged ground constituted by Socorro Island in the Mexican Exclusive Economic Zone. The closest potential sources for the colonization are Marquesas (French Polynesia) on the Eastern side and Galapagos (Ecuador) on the Western side, respectively (map modified from "Carte de l'île de la Passion", 2004, by C. Jost); B: Approximative location of the adult Caranx ignobilis identified on the 4 February 2016 (+ represent the location of the six dives).
Figure 2 in First steps towards Live-action Virtual Reality Games
Figure 2. – Photography of an adult individual of Caranx ignobilis on the eastern side reef slope of the Clipperton atoll on the 4 February 2016. The dorsal profile of steep and nearly straight snout, plus the silvery gray dorsal colour, shading to silvery white ventrally, with numerous scattered, very small black spots, are quite characteristic of this species of jack (Photo E. Clua).
Dataset: Seeking feedback and taking action: An evidence based evaluation of the impact of using practice tests with inbuilt feedback in a first-year business class
<p><strong>This data set includes both qualitative and quantitative data.</strong></p> <p><strong>Qualitative, survey data sought student opinions on the purpose of feedback and the actions taken following receipt of feedback.</strong></p> <p>1. Survey data collection began with a 13 question survey used in classes in early February 2020. This initial feedback survey was conducted via MS Forms. The data from this is available in the following files: a. 2020-2-17_feedbacksurvey_1_rawdata.csv b. 2020-2-17_feedbacksurvey_1_how-many-times-got-feedback.csv c. 2020-2-17_feedbacksurvey_1_take-action-after-feedback.csv d. 2020-2-17_feedbacksurvey_1_type-feedback-you-prefer.csv e. 2020-2-17_feedbacksurvey_1_purpose-of-feedback.csv f. 2020-2-17_feedbacksurvey_1_define-feedback.csv g. 2020-2-17_feedbacksurvey_1_what-makes-feedback-useful.csv h. 2020-2-17_feedbacksurvey_1_know-how-feedback-effective.csv i. 2020-2-17_feedbacksurvey_1_what-can-institute-do-to-improve-feedback.csv</p> <p><br>2. The first practice test was followed by a short survey of approximately 3 questions (created using MS Forms, a mix of closed and open questions). The data from this is available in the following files: a. 2020-3-2_feedbacksurvey_2_rawdata.csv b. 2020-3-2_action-comments-coded.csv c. 2020-3-2_other-comments-coded.csv d. 2020-3-2_open-question-comments.csv</p> <p>3. The second practice test followed by a 3 question short survey (created using MS Forms, all open questions) The data from this is available in the following files: a. 2020-5-12_feedbacksurvey_3_rawdata.csv b. 2020-5-12_feedbacksurvey_3_what-learn-by-participating.csv c. 2020-5-12_feedbacksurvey_3_what-action-taking.csv d. 2020-5-12_feedbacksurvey_3_has-module-changed-view-of-feedback.csv<br><br><strong>Quantitative data was gathered from Learning Management System, Canvas. This data shows the relationship between number of practice tests taken by students and their resultant grades.</strong></p> <p><br>4. I gathered the number of times students had taken the practice test before the midterm summative exam and compared with the student grades from the 30% midterm summative exam. The data from this is available in the following files: a. 2020-11-26_Canvasdata_midterm.csv</p> <p>5. Following the completion of the second summative exam (worth 70%), Canvas data showing the number of times the students had taken the second practice test and the grade outcomes for the students for the summative 70% test was gathered. The data from this is available in the following files: a. 2020-11-26_Canvasdata_endterm.csv</p> <p><br>6. A regression model was created to examine the impact of number of times that the practice test was taken on final grades, whilst controlling for midterm grades. The data from this is available in the following files: a. 2024-2-12_Anonymised_mid_and_final_grades_with_number_of_practices_before_final_exam.csv</p>
Dog neuroimaging data from: Action observation reveals a network with divergent temporal and parietal cortex engagement in dogs compared to humans
<p>Action observation is a fundamental pillar of social cognition. Neuroimaging research has revealed a human and non-human primate action observation network (AON) encompassing fronto-temporo-parietal areas with links to the species’ imitation tendencies and relative lobe expansion. Dogs (Canis familiaris) have good action perception and imitation skills and a less expanded parietal than temporal cortex, but their AON remains unexplored. We conducted a functional MRI study with 28 dogs and 40 humans and found functionally analogous involvement of somatosensory and temporal brain areas of both species’ AONs and responses to transitive and intransitive action observation in line with their imitative skills. Employing a functional localizer, we also identified functionally analogous agent-responsive areas within both species’ AONs. However, activation and task-based functional connectivity measures suggested significantly less parietal cortex involvement in dogs than in humans. These findings advance our understanding of the neural bases of action understanding and the convergent evolution of social cognition, with analogies and differences resulting from similar social environments and divergent brain expansion, respectively.</p> <p>This data set contains:</p> <ul> <li>raw functional neuroimaging data of <em>N</em> = 28 dogs</li> <li>structural scans of the same dogs including brain masks & skull-stripped versions</li> <li>eventfiles containing the condition names, onsets and durations for each dog and task run</li> </ul> <p>Data of the comparative human neuroimaging sample will be made available by the first author upon reasonable request.</p> <p>Please also visit our Open Science Framework project site for group-level imaging data of both species (https://osf.io/z479k/).</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.