קובץ:Order-Based-Representation-in-Random-Networks-of-Cortical-Neurons-pcbi.1000228.s004.ogv
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Order-Based-Representation-in-Random-Networks-of-Cortical-Neurons-pcbi.1000228.s004.ogv (קובץ וידאו Theora של Ogg, באורך 4 דקות 15 שניות, 320 × 240 פיקסלים, 221 קילו־ביטים בשנייה, גודל הקובץ: 6.74 מ"ב)
זהו קובץ שמקורו במיזם ויקישיתוף. תיאורו בדף תיאור הקובץ המקורי (בעברית) מוצג למטה. |
תקציר
תיאורOrder-Based-Representation-in-Random-Networks-of-Cortical-Neurons-pcbi.1000228.s004.ogv |
English: Braitenberg Vehicle. In a small yet seminal book titled Vehicles: Experiments in Synthetic Psychology (MIT Press, 1986), Valentino Braitenberg describes a set of thought experiments in which agents with simple structure behave in human-like ways; Braitenberg blatantly put forward the hypothesis that the primitives for realizing such machines are cellular and synaptic processes that are amenable for physiological characterization. The reasoning and results presented in this study make the realization of a Braitenberg vehicle that classifies objects in its visual field using a large-scale network of biological neurons a trivial matter. This is demonstrated in the attached clip that was prepared by Danny Eytan, David Ben Shimol and Lior Lev-Tov from the Technion - Israel Institute of Technology. The main text and data of the present study shows that the physical loci from which stimuli are delivered to a recurrent, large scale random network of cortical neurons, albeit causing temporally “noisy” neuronal responses, may be fully classified using the temporal order at which neurons are recruited by the different stimuli. Here, an application of this idea, in the form of a Braitenberg vehicle, is demonstrated: Inputs from the two (Right and Left) ultrasonic “eyes” of a Lego Mindstorms vehicle are sampled at 0.2 Hz and translated into stimulation of a large random network of cortical neurons at two different sites. The side corresponding to the nearest visual object (relative to vehicle's longitudinal axis, depicted by a red arrow) is classified using an Edit-distance metric based on the recruitment order of 8 neurons, similar to procedures shown in Figure 6 of the manuscript. Based on the classified activity, a command is sent to the appropriate motor attached to one of the wheels. The red trace on the left represents the total network activity (points depict evoked activity); the blue numbers in front of vehicle's “eyes” show distances (in cm) from the right and left sensed objects; the Edit distance of the evoked recruitment orders, from a predefined internal representation of the Right and Left objects, is shown in red numbers. Top left: time in seconds.
Deutsch: Braitenberg-Vehikel mit Ultraschallsensoren und künstlichem neuronalen Netz für die Reizverarbeitung und die Steuerung der Antriebsmotoren, realisiert mit Lego Mindstorms. |
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תאריך יצירה | |||
מקור | Video S1 from Shahaf G, Eytan D, Gal A, Kermany E, Lyakhov V, Zrenner C, Marom S (2008). "Order-Based Representation in Random Networks of Cortical Neurons". PLOS Computational Biology. DOI:10.1371/journal.pcbi.1000228. PMID 19023409. PMC: 2580731. | ||
יוצר | Shahaf G, Eytan D, Gal A, Kermany E, Lyakhov V, Zrenner C, Marom S | ||
אישורים והיתרים (שימוש חוזר בקובץ זה) |
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תאריך/שעה | תמונה ממוזערת | ממדים | משתמש | הערה | |
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נוכחית | 20:47, 16 בנובמבר 2012 | 4 דקות 15 שניות, 320 × 240 (6.74 מ"ב) | Open Access Media Importer Bot | Automatically uploaded media file from Open Access source. Please report problems or suggestions here. |
שימוש בקובץ
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שימוש גלובלי בקובץ
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קובץ זה מכיל מידע נוסף, שכנראה הגיע ממצלמה דיגיטלית או מסורק שבהם הקובץ נוצר או עבר דיגיטציה.
אם הקובץ שונה ממצבו הראשוני, כמה מהנתונים להלן עלולים שלא לשקף באופן מלא את הקובץ הנוכחי.
יוצר | Shahaf G, Eytan D, Gal A, Kermany E, Lyakhov V, Zrenner C, Marom S |
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תנאי שימוש | http://creativecommons.org/licenses/by/3.0/ |
כותרת התמונה | Braitenberg Vehicle. In a small yet seminal book titled Vehicles: Experiments in Synthetic Psychology (MIT Press, 1986), Valentino Braitenberg describes a set of thought experiments in which agents with simple structure behave in human-like ways; Braitenberg blatantly put forward the hypothesis that the primitives for realizing such machines are cellular and synaptic processes that are amenable for physiological characterization. The reasoning and results presented in this study make the realization of a Braitenberg vehicle that classifies objects in its visual field using a large-scale network of biological neurons a trivial matter. This is demonstrated in the attached clip that was prepared by Danny Eytan, David Ben Shimol and Lior Lev-Tov from the Technion - Israel Institute of Technology. The main text and data of the present study shows that the physical loci from which stimuli are delivered to a recurrent, large scale random network of cortical neurons, albeit causing temporally ?noisy? neuronal responses, may be fully classified using the temporal order at which neurons are recruited by the different stimuli. Here, an application of this idea, in the form of a Braitenberg vehicle, is demonstrated: Inputs from the two (Right and Left) ultrasonic ?eyes? of a Lego Mindstorms vehicle are sampled at 0.2 Hz and translated into stimulation of a large random network of cortical neurons at two different sites. The side corresponding to the nearest visual object (relative to vehicle's longitudinal axis, depicted by a red arrow) is classified using an Edit-distance metric based on the recruitment order of 8 neurons, similar to procedures shown in Figure 6 of the manuscript. Based on the classified activity, a command is sent to the appropriate motor attached to one of the wheels. The red trace on the left represents the total network activity (points depict evoked activity); the blue numbers in front of vehicle's ?eyes? show distances (in cm) from the right and left sensed objects; the Edit distance of the evoked recruitment orders, from a predefined internal representation of the Right and Left objects, is shown in red numbers. Top left: time in seconds. |
תוכנה בשימוש | Xiph.Org libtheora 1.1 20090822 (Thusnelda) |
התאריך והשעה של הפיכת הקובץ לדיגיטלי | 2008-11 |