Real World article (written from a Production point of view) A stand-in is a person who substitutes for an actor on set while the lighting is adjusted, the action and.
Table of contents
- 5 P.M. INTERVIEW: Stand in the Light Memory Choir Concert
- Filmography
- 5 P.M. INTERVIEW: Stand in the Light Memory Choir Concert
The read request won't be able to tell where the data is, but will see the full view of data loaded at this point in time.
5 P.M. INTERVIEW: Stand in the Light Memory Choir Concert
After enough documents are inserted, the in-memory stand will fill up and be flushed to disk, written out as an on-disk stand. Each new stand gets its own subdirectory under the forest directory, with names that are monotonically-increasing hexadecimal numbers. The first stand gets the lovely name That on-disk stand contains all the data and indexes for the documents loaded thus far. It's written from the in-memory stand out to disk as a sequential write for maximum efficiency. Once it's written, the in-memory stand's allocated memory is freed.
As more documents are inserted, they go into a new in-memory stand. At some point this in-memory stand fills up as well, and the in-memory stand gets written as a new on-disk stand, probably named and about the same size as the first. Sometimes under heavy load you have two in-memory stands at once, when the first stand is still writing to disk as a new stand is created for additional documents. At all times an incoming request can see all the data across all the stands. The mechanism continues with in-memory stands filling up and writing to on-disk stands.
As the total number of on-disk stands grows, an efficiency issue threatens to emerge. To read an index, MarkLogic must read the index data from each individual stand and unify the results. To keep the number of stands to a manageable level where that unification isn't a performance concern, MarkLogic runs merges in the background. A merge takes some of the stands on disk and creates a new singular stand out of them, coalescing and optimizing the indexes and data, as well as removing any previously deleted documents, a topic we'll discuss shortly.
After the merge finishes and the new on-disk stand has been fully written, and after all the current requests using the old on-disk stands have completed, MarkLogic deletes the old on-disk stands. MarkLogic uses an algorithm to determine when to merge, based on the size of each stand.
In a normal server running under constant load you'll usually see a few large stands, a few more mid-sized stands, and several more small stands. Over time the smaller stands get merged into ever-larger stands. However, it's generally best to let MarkLogic manage this process. Think of it as constant background housekeeping.
Each forest has its own in-memory stand and set of on-disk stands. A new document gets assigned to a forest algorithmically, unless you override the selection as part of the insert. Inserting documents and indexing their content is a largely parallelizable activity so splitting the effort across forests and potentially across machines in a cluster can help scale the ingestion work. What happens if you delete or change a document? If you delete a document, MarkLogic marks the document as deleted but does not immediately remove it from disk. The deleted document will be ignored by queries based on its deletion markings, and the next merge of the stand holding the document will bypass the deleted document when writing the new stand.
If you change a document, MarkLogic marks the old version of the document as deleted in its current stand and creates a new version of the document in the in-memory stand. MarkLogic distinctly avoids modifying the document in place. If you consider the updates to an index required to reflect the changes in a single document, you will see that updates in place are an entirely inefficient proposition. The stand-ins can be instanced many times with little overhead to rendering the original model has around 20, polygons so without instancing using stand-ins the following scene with hundreds of soldiers would be quite large.
Arnold, of course, has no trouble rendering the scene. It's worth pointing out that this introductory tutorial has only covered the basics of using stand-ins. For example, as well as using. Stand-ins can also be recursive, and you can defer loading of the procedural geometry until it is needed during rendering i.
These techniques make it possible for you to assemble scenes in a modular way. You can see the creative potential that stand-ins can give you. Below are some further examples that will hopefully inspire you to create your own. Have fun with stand-ins!
Thanks to Angel Jiminez for the use of his facial co-ordinates. Pages Blog Space Tools. At present, we have only a very limited answer to this question. As noted above, we can reach a target that we gaze at directly or one that is only in our peripheral vision. Given that information about gaze angle is available to the brain, visual information could be corrected for gaze angle. Indeed, recordings in the parietal lobe indicate that such correction is done; objects are represented in a coordinate system in which the position of an object is defined relative to the body and does not depend on gaze angle Andersen and Mountcastle, Biologically plausible network operations that can produce such a coordinate transformation have been suggested Andersen and Mountcastle, ; Salinas and Sejnowski, This requires knowledge of the position of the target and the position of the limbs.
It has only recently become clear that there is indeed a population code that represents hand position Hauschild et al. Once the coordinate system for specifying a reach is established, the muscles needed to produce the reach must be triggered by activity in the primary motor cortex. Despite considerable effort, there remains no consensus about what motor cortex is specifying about the reach.
It remains unclear whether cells represent a signal for muscle force, the direction of movement, or a more abstract end-goal of muscle action Adelsberger et al. One promising line of work suggests that subregions of the motor cortex regulate groups of muscles that act synergistically to produce motor primitives multi-joint movements that can be combined in various proportions to produce a variety of actions Dominici et al.
Any explanation of movement will have to specify not only which muscles are activated, but how their activation is precisely timed. The cerebellum appears critical for a range of timing processes that occur in the range of seconds Manto et al. Eye blink conditioning has provided important insights into another timing function of the cerebellum.
In such conditioning, a tone is turned on and a puff of air is given to the eye hundreds of milliseconds later. After such conditioning, the tone produces a protective eye blink just before the air puff reviewed in Thompson, Experiments show that after conditioning, the spontaneous firing of the output cells of the cerebellar cortex, the Purkinje cells, pauses just before the air puff Jirenhed et al.
Recent optogenetic experiments demonstrate that such pauses are sufficient to trigger motor action Heiney et al. It remain controversial whether the pause is due to reduced excitation Ito, or increased inhibition Gao et al. Advances are needed in understanding how cortex, basal ganglia, and cerebellum function together for one possibility, see Shadmehr and Krakauer, One would hope that overall design principles could be elucidated. Indeed, the limit on independent finger movements as in typing is about 10 Hz http: If indeed the motor system is controlled by oscillations, actions longer than msec must be fundamentally discontinuous.
Support for such discontinuity comes from measurements of finger position during simple linear movements Vallbo and Wessberg, This rhythmicity is coherent with signals in the cerebellum and motor cortex Gross et al. Most persuasively, movement onset occurs at a preferred phase of ongoing cortical oscillations Drewes and VanRullen, ; Igarashi et al.
It thus seems likely that elucidating the role of oscillations in the motor system will be an important step toward understanding the still mysterious algorithms of motor control. But what about more complex mental functions, such as executive control, thought, and consciousness?
Below I very briefly outline current efforts to understand these higher functions. Action selection in the laboratory setting involves linking a sensory cue to an action but does not generally involve context. Action selection in real life situations takes an enormous amount of contextual information into consideration, including the existence of prepotent responses Schall and Godlove, , the assessment of what is possible affordances given the current goals Cisek, ; Gibson, , the application of abstract rules Wallis et al.
This strong dependence on multiple constraints is not unlike the dependence of recognition on context Fig. The basal ganglia is also hierarchically organized Haber and Calzavara, ; Yin and Knowlton, ; conceptual breakthroughs are needed to understand how these interacting hierarchies produce executive control. The existence of language raises questions about the generality of the mechanisms of action selection revealed by the study of instrumental condition.
Whereas animals have to be conditioned by the experimenter to make a particular action, a human can simply be asked e. One wonders whether some entirely new mechanism of action selection has evolved in humans or whether, as suggested in a recent paper Kriete et al. According to dualists, consciousness cannot be explained in terms of the physical properties of the brain.
By contrast, neuroscience now assumes that consciousness is a result of specific properties of brain networks Dehaene and Changeux, ; Edelman et al. Studying consciousness is not easy, but there are methods. Reportability is the standard experimental method for verifying human consciousness. Based on experiments that manipulate the ability of subjects to report visual stimuli, Dehaene and Changeux, proposed that sensory cortex does local visual processing before information comes to consciousness. Then, at some critical juncture, information becomes widely communicated from the local source to a global neuronal workspace , thereby producing consciousness.
What might the critical juncture be? There is some evidence that this occurs when a high-level cortical model is chosen that correctly predicts low-level sensory information Graboi and Lisman, ; Pascual-Leone and Walsh, ; Pollen, A further question is how information is communicated to the global neuronal workspace. It is well known that there are direct connections between cortical regions, but Sherman, emphasizes that there is also a thalamic route.
Thus the thalamus, which has been implicated in attention Crick, ; Halassa et al. Another approach to understanding consciousness comes from consideration of unconscious action Lisman and Sternberg, A person can drive to work along a habitual route and also think about a problem. Upon arrival at work, the person may remember how they solved the problem but may be unable to report anything about the commute, thus indicating that driving was done unconsciously by habit. Relating such actions to habit opens new ways to explore the unconscious because habit automatic and non-habit goal-directed behaviors can be distinguished in rodents Dickinson, and experiments show that different brain regions are involved Daw et al.
It may thus be possible to identify mechanisms unique to the regions that mediate non-habit behaviors, thereby providing clues about the mechanism of consciousness. One possible mechanism deserves special mention because of recent progress. In a recent experiment, such activation was demonstrated directly Albers et al.
Subjects were shown oriented bars and were asked to imagine the bars rotated by a certain amount; using fMRI it was found that the activation pattern in visual cortex was similar to that produced by viewing rotated bars. Let us return to the central question of this Perspective: Through the study of the hippocampal system, it is now possible to directly observe memory sequences being recalled and to optogenetically manipulate memories. Furthermore, specific networks and synaptic connections have been demonstrated to form the associations that underlie some forms of memory. In the case of action selection, the synapses in the amygdala and basal ganglia where conditioning occurs have been identified.
The understanding of Pavlovian and instrumental conditioning is not superficial; the fact that optogenetic stimulation of specific cells can altogether bypass the need for the unconditioned stimulus demonstrates that core processes are now understood. Current models of the hippocampus, amygdala, and basal ganglia are certainly incomplete, but they are unlikely to be fundamentally wrong.
Less progress has been made in understanding visual perception, working memory, and motor control. We cannot yet understand these processes because they are cortical and the role of different cortical layers and cell types has not yet been determined. There are, however, reasons to be optimistic about prospects for understanding cortex. Experimental methods of enormous power are now being brought to bear on the problem. For example, in the somatosensory cortex, it is now possible to study the function of identified cell types in awake animals during sensory-guided decisions Larkum, ; Xu et al.
Furthermore, during such decisions, it is possible to inhibit or excite specific cell types using optogenetics Guo et al. These new methods would seem to be sufficiently powerful to finally crack the cortex problem. To be sure, the sheer number of cytoarchitectonic areas in cortex Fig. There are, however, regularities that may simplify the task. Different regions have similar cell types with common connection motifs Klausberger and Somogyi, ; Pi et al. Furthermore, there are repeating rules for hierarchical organization: Similarly, the study of subcortical structures has identified repeating structural rules Kinkhabwala et al.
The skeptic might pose another ground for pessimism: This seems unlikely given the qualitative similarities of rodent and human neuroanatomy. But there is data in one research area that bears on this question. Recordings from the human hippocampal region made during surgery reveal place cells and grid cells with obvious similarity to those recorded in the rodent Ekstrom et al. Thus, even though there are certainly differences between the human and rodent brain, there is little doubt that what is learned from the study of the rodent brain will take us a long way toward understanding the human brain.
Perhaps twenty years ago, one could have argued that the emergence of cognitive function from interconnected neurons was deeply mysterious. That does not seem true today. What has changed is that we now have a feel for how networks can produce cognitively relevant computations Figs. In many areas of brain research, models of network function are being explored through the interplay of experimentation, theory, and computer modeling.
In summary, there has been demonstrated success in providing an understanding of several brain processes, and there is every reason to expect further rapid progress. History is thus likely to look back on the first half of the twenty-first century as the period during which the brain came to be understood. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form.
Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. National Center for Biotechnology Information , U. Author manuscript; available in PMC May The publisher's final edited version of this article is available at Neuron. See other articles in PMC that cite the published article. Abstract Starting with the work of Cajal more than years ago, neuroscience has sought to understand how the cells of the brain give rise to cognitive functions. Open in a separate window. Examples of simple neural networks that perform useful computations A.
Visual Recognition Let us begin with a discussion of visual recognition, the most extensively studied sensory process. Cortical and subcortical brain regions A.
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Long-Term Episodic Memory You may meet someone and know they are familiar; then a new fact may suddenly allow you to remember their name and the sequence of events when you met. Network and coding mechanisms of the hippocampus A. Network mechanisms of long-term memory The intricate set of connections that define the wiring diagram of the hippocampus Fig. Action Selection We now turn to the problem of how actions are selected. Mechanisms of Pavlovian conditioning An extensively studied example of Pavlovian conditioning involves the emotion of fear.
Mechanisms of instrumental conditioning Whereas fear conditioning involves a hard-wired component of behavior e. Connections of the basal ganglia and premotor cortex that mediate instrumental conditioning a cue leads to the conditioned response Sensory cortex carries cue information to the striatum through synapses onto medium spiny neurons MSN. Conditioning by optogenetic activation of dopamine cells instead of actual reward Channel-rhodopsin was expressed in dopamine cells.
The Motor System Once an action is selected, the motor system must execute the action, and this is no trivial matter. Executive control Action selection in the laboratory setting involves linking a sensory cue to an action but does not generally involve context. Language-based control The existence of language raises questions about the generality of the mechanisms of action selection revealed by the study of instrumental condition.
Consciousness According to dualists, consciousness cannot be explained in terms of the physical properties of the brain. Conclusion Let us return to the central question of this Perspective: Optogenetic interrogation of dopaminergic modulation of the multiple phases of reward-seeking behavior. The Journal of neuroscience: Local domains of motor cortical activity revealed by fiber-optic calcium recordings in behaving nonhuman primates. Shared representations for working memory and mental imagery in early visual cortex.
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A model of V4 shape selectivity and invariance.
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5 P.M. INTERVIEW: Stand in the Light Memory Choir Concert
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