Tədbirlər : Qızıl Zhang Onu da Lei Ma Xanım Liu J.J. Johannes Hjorth haqqında Aleksandr Kozlov Yuxarıda Şeyx Zhang Jeanette Hellgren Əhmədov Yonghong Tian Kərim Grillner Nə qədər Tərcümə Huang Tədbirlər : Qızıl Zhang Onu da Məmmədyar Xanım Liu J.J. Johannes Hjorth haqqında Aleksandr Kozlov Yuxarıda Şeyx Zhang Jeanette Hellgren Əhmədov Yongqong Tian Kərim Grillner Nə qədər Tərcümə Huang Abstraksiya VVD - Hollandiyada futbolçu bu adla tanımır, orada VVD daha çox mərkəz-sağı təmsilən edən siyasi partiyanın adının qısaltması kimi bilinir - artıq sorğu-suala ehtiyacı olmayan ulduzdu. Endritiq İranda Biz teorik olaraq DHS implementasiyasının hesabatla optimal və doğru olduğunu göstəririk. Bu GPU bazlı metod normal CPU platformasında klasik seriya Hines metodundan 2-3 sıra daha yüksək sürətlə işləyir. Biz DHS metodunu və NEURON simulatörünün GPU kompüter motorunu birləşdirən DeepDendrite çərşənbəyi yaratırıq və DeepDendrite'in neuroscience əməllərinə uyğunlaşdırılması göstəririk. Biz 25,000 spin-lik bir human piramidal nöron modelində neuronal excitability-ni necə etkiləyəcəyini araşdırırıq. D H S Introduksiya Neuronların kodlaşdırma və hesablanma prinsiplərini açıqlama neuroscience üçün vacibdir. mamut beyinləri öz-özəl morfoloji və biofiziki xüsusiyyətləri olan binlerce fərqli tipdən daha çox neuronlardan ibarətdir. “Neftçi”nin “Neftçi”nin “Neftçi”nin “Neftçi”nin “Neftçi”nin “Neftçi”nin “Neftçi”nin “Neftçi”nin “Neftçi”nin “Neftçi”nin “Neftçi”nin “Neftçi”nin “Neftçi”nin “Neftçi”nin “Neftçi”nin “Neftçi”nin “Neftçi”nin “Neftçi”nin “Neftçi”nin “Neftçi”nin “Neftçi”nin “Neftçi”nin “Neftçi”nin “Neftçi”nin “Neftçi”nin “Neftçi”in “Neftçi”nin “Neftçi”in “Neftçi”in “Neftçi”in “Neftçi”in” “Neftçi”in “Neftçi” Bununla yanaşı, bir-birinin neuron düzeyində bütünlüklü hesabatların yanı sıra, neuronal dendritlər kimi subcellular bölmələr bağımsız hesabat birimləri olaraq da nonlinear operasiyalar edə bilər. , , , , Bundan başqa, dendritik spinlar, dendritlərin dənizi dəniz nöronlarında örtülən kiçik protrusionlar, sinaptik sinyalləri bölünə bilər, bu da onların valideyn dendritlərindən ex vivo və in vivo ayrılmalarına imkan verir. , , , . 1 2 3 4 5 6 7 8 9 10 11 Biologically detailed neurons using simulations provide a theoretical framework for linking biological details to computational principles.Biofizikally detailed multi-compartment model frameworkin qəlbində biofizikally detailed multi-compartment model framework var. , Bizə realist dendritik morfologiyalar, intrinsic ionik konduktans və extrinsic sinaptik inputlarla nöronları model etmək imkan verir. Dendritlərin biofiziki membran xüsusiyyətlərini pasiv kabellər kimi modelləyir, elektron sinyallərin kompleks neuronal proseslərdə necə invaziv və yayılacağına dair matematiksel bir təsvir verir.İyon kanalları, excitatory və inhibitory sinaptic currents, etc. kimi aktiv biofiziki mekanizmaları ilə kabellə teoriyası daxil edərkən, ayrıntılı bir çox bölmənin modeli eksperimental limitlərin üstündə hüceyrəli və subcellular neuronal hesabatlar əldə edə bilər. , . 12 13 12 4 7 Növbəti xəbərNeftçi.az-a istinadən xəbər verir ki, ABŞ-ın ABŞ-ın ABŞ-ın ABŞ-ın ABŞ-ın İspaniya və Ukrayna ölkələri arasında işsizlik artımının artmasına səbəb olduqca böyük problemlər yaşanıb. , İnsan beyni daha dinamik və gürcü çevrələr içində ANN-lərdən daha yaxşı işləyir. , Əvvəllərki teoriyalı araşdırmalar dendrit integrasiyasının paralel məlumat işlətməsində potansiyallığı aşan effektiv öyrənmə algoritmalarının yaradılması üçün kritik rol oynadığını göstərir. , , Bundan başqa, tek bir ayrıntılı multi-kompartment model yalnız sinaptik gücü ayarlayaraq punct neuronlar üçün ağıllı nonlinear hesabatları öyrənə bilər. , Beləliklə, beyin kimi AI-nin paradigmalarını tək bir detallı nöron modellərindən genişləndirmək böyük ölçekli bioloji detallı ağlara qədər yüksək prioritetdir. 14 15 16 17 18 19 20 21 22 Detaylı simülasiya metodunun uzun sürən bir meydançası çox yüksək hesablanma qiymətindədir, bu da neurologiya və AI-ya istifadəini ciddi şəkildə azaldır. , , Qiymətini artırmaq üçün, klassik Hines metodu O(n3)-dən O(n)-ə düzəltmək üçün vaxtın kompleksliyini azaldır, bu, Neuron kimi popüler simulatörlərdə əsas algoritmadır. Yəni genetik VVD - Hollandiyada futbolçu bu adla tanımır, orada VVD daha çox mərkəz-sağı təmsilən edən siyasi partiyanın adının qısaltması kimi bilinir - artıq çox adi bir hala çevrilib. ), Hines metodu artıq praktik deyil, çünki bu bütün simülasiya üçün çox ağır yük yaradır. 12 23 24 25 26 1 və Layer-5 piramidal nöron modeli və detalllı nöron modelləri ilə istifadə edilən matematiki formulu. Xatırladaq ki, bu problemin yaranması ilə bağlı problemlər də həll olunacaq. Simulasiya vasitəsilə linear eşitmələr aparılır. Hines metodunun linear eşitmələrin çözüldüyündə veriliş bağlaması İŞİD Hines matrisinin ölçüsü model kompleksliyi ilə ölçülür. Çözülməli linear eşitmə sisteminin sayı modelləri daha ayrıntılılaşdıqca böyük bir artımla qarşılaşır. Kompüter maliyyəsi (ağırlıq çözmə fəzasında alınan adımlar) Hines seriya metodunun fərqli tipli neuron modelləri. VVD - Hollandiyada futbolçu bu adla tanımır, orada VVD daha çox mərkəz-sağı təmsilən edən siyasi partiyanın adının qısaltması kimi bilinir - artıq sorğu-suala ehtiyacı olmayan ulduzdu. Üç metodun qiyməti Piramidal modelin düzəldilməsi ilə bağlı problemlər həll olunmalıdır. VVD - Hollandiyada futbolçu bu adla tanımır, orada VVD daha çox mərkəz-sağı təmsilən edən siyasi partiyanın adının qısaltması kimi bilinir - artıq sorğu-suala ehtiyacı olmayan ulduzdu. a b c d c e f g h g i Son illərdə, hüceyrəli düzeydə paralel metodlar istifadə edərək Hines metodunu sürətləndirmək üçün böyük uğurlar əldə edilmişdir, bu da hər bir hücrenin fərqli parçalarının hesablanmasını paralelləşdirməyə imkan verir. , , , , , Bununla yanaşı, günümüz hüceyrəli paralel metodları çox vaxt effektiv paralelizasiya strategiyası verməz və ya orijinal Hines metoduna görə kifayət qədər saylı həddi verməzlər. 27 28 29 30 31 32 Burada, tam avtomatik, sayı ilə doğru və optimize edilmiş simülasiya alətini yaradırıq ki, bu simülasiya alətini mathematik planlaşdırma problemi kimi Hines metodunun paralel hesablanmasını formalaşırıq və kombinasiyalı optimizasiya ilə bağlı Dendritic Hierarchical Scheduling (DHS) metodunu yaratırıq. Paralel kompüter teoriyası Biz algoritmamızın optimalaşdırılmasını, həddi aşmaq olmadan optimallaşdırdıqlarını göstəririk.Daha çox, DHS-i GPU memoriyasını və memoriya quraşdırma mekanizmalarını istifadə edərək günümüzdə ən ilkin GPU çipinə optimize etdik.Daha çox, DHS-in hesablanmasını 60-1500 dəfə daha sürətləndirə bilər. Klassik simulatör Neuron Təsadüfi həddi tutub. 33 34 1 25 AI-da istifadə etmək üçün ayrıntılı dendritik simülasyonları imkanlandırmaq üçün, daha sonra DHS-ə daxil olan CoreNEURON platformasını (NEURON üçün optimizasiya edilmiş kompüter motoru) birləşdirərək DeepDendrite çerçevəsini yaradırıq. Simulasiya motoru və iki köməkçi modulu (I/O modulu və öyrənmə modulu) simulasiyalar sırasında dendritik öyrənmə algoritmalarını dəstəkləyir. 35 DeepDendrite (full-spin modelləri) ilə ANN-lər yaratmaq üçün, DeepDendrite-in dendriti içərisində dendriti içən neuronların neuronal aktivliyi ilə nəyi necə etkiləyəcəyini göstərəcəyik.DeepDendrite, ~25000 dendriti içərisində simülasiya edilmiş insan piramidal nöron modeli ilə nöron hesabatını araşdırmağa imkan verir.DeepDendrite-in analizində, daha konkret olaraq, morfolojik olaraq ayrıntılı insan piramidal nöronları ilə ANN-lər yaratmaq üçün DeepDendrite-in AI kontekstində olan potansiyallara da baxırıq. DeepDendrite üçün bütün source kod, full-spine modelləri və ayrıntılı dendritik ağ modelləri açıq-aşkar online istifadə olunur (Code Availability).Our open-source learning framework can easily be integrated with other dendritic learning rules, such as learning rules for nonlinear (full-active) dendrites “Burst-dependent synaptic plasticity” ilə bağlı , and learning with spike prediction Xatırladaq ki, bizim araşdırmamız bugünkü kompüter neuroscience topluluğu ekosistemini dəyişmək üçün potensialı olan bütün alətlərin bir hissəsini təmin edir. GPU kompüterinin gücünü istifadə edərək, bu alətlərin beyin fin strukturlarının kompüter prinsiplərinin sistem düzeyində araşdırılmasını asanlaşdıracağına və neuroscience və modern AI arasındakı mübahisəyi dəstəkləyəcəyini düşünürük. 21 20 36 Results Dendritic Hierarchical Scheduling (DHS) metodları Computing ionic currents and solving linear equations are two critical phases when simulating biophysically detailed neurons, which are time-consuming and pose severe computational burdens. Fortunately, computing ionic currents of each compartment is a fully independent process so that it can be naturally parallelized on devices with massive parallel-computing units like GPUs VVD - Hollandiyada futbolçu bu adla tanımır, orada VVD daha çox mərkəz-sağı təmsilən edən siyasi partiyanın adının qısaltması kimi bilinir - artıq sorğu-suala ehtiyacı olmayan ulduzdu. İŞİD 37 1a – F Bu barelə qarşılaşmaq üçün hücresel düzeydə paralel metodlar geliştirilmişdir, bu, bir hücrenin paralel olaraq hesabat edilə bilər ki, bir hücrenin bir neçə bölməyə bölünməsi ilə tek hücrenin hesablanmasını sürətləndirir. , , VVD - Hollandiyada futbolçu bu adla tanımır, orada VVD daha çox mərkəz-sağı təmsilən edən siyasi partiyanın adının qısaltması kimi bilinir - artıq sorğu-suala ehtiyacı olmayan ulduzdu. • Təsadüfi sifarişlər. Bu səbəbdən, asimetrik morfologiya olan neuronlar üçün, bəlkə, piramidal neuronlar və Purkinje neuronları üçün daha az effektiv olur. 27 28 38 1 g 1 We aim to develop a more efficient and precise parallel method for the simulation of biologically detailed neural networks. First, we establish the criteria for the accuracy of a cellular-level parallel method. Based on the theories in parallel computing 2013-cü ildə aparılmış arxeoloji tədqiqat işləri burada 120 kv.metrlik ərazidə yaşayış yerinin qalığının olduğunu söyləməyə əsas verir (1).Az xəbər verir ki, bu ərazidə yaşayış yerinin qalığının 60 kv.metrlik ərazidə yaşayış yerinin qalığının olduğunu söyləməyə əsas verir (1).Az xəbər verir ki, bu ərazidə yaşayış yerinin qalığının 60 kv.metrlik ərazidə qalığının olduğunu söyləməyə əsas verir (1). 34 Simulasiya həddi və hesabat maliyeti ilə bağlı olaraq, paralelizasiya problemi matematika planlaşdırma problemi kimi formalaşırıq (Metodlar). parallel threads, we can compute at most Hər adımda düymələr var, lakin bir düymə yalnız bütün övlad düymələri işlədikdə hesabatlandırılmasını sağlamalıyıq; bizim hökmümüz bütün proseduru üçün minimum adım sayısı olan bir strateji tapmaqdır. k k To generate an optimal partition, we propose a method called Dendritic Hierarchical Scheduling (DHS) (theoretical proof is presented in the Methods). The key idea of DHS is to prioritize deep nodes (Fig. DHS metodu iki hissəyə bölünür: dendrit topologiya analizi və ən yaxşı bölünmeyi tapmaq: (1) Detaylı bir model verərək, öncə bağlı bağımlılıq ağacını alırıq və ağacda hər bir düymün (bir düymənin derinliyi atası düymlərin sayıdır) dərinliğini hesab edərik (Şəkil 3). (2) Topologiya analizindən sonra, kandidatləri aradan qaldırırıq və ən çox VVD - Hollandiyada futbolçu bu adla tanımır, orada VVD daha çox mərkəz-sağı təmsilən edən siyasi partiyanın adının qısaltması kimi bilinir - artıq sorğu-suala ehtiyacı olmayan ulduzdu. İŞİD 2a 2B və C k 2D DHS prosesləri, DHS prosesləri Hər bir iterasiya üçün ən dərin kandidat düymədir. Xatırladaq ki, bu, bir neçə ildir ki, bir neçə ildir ki, bir neçə ildir ki, bir neçə ildir ki, bir neçə ildir ki, bir neçə ildir ki, bir neçə ildir ki, bir neçə ildir ki, bir neçə ildir ki, bir neçə ildir ki, bir neçə ildir ki, bir neçə ildir ki, bir neçə ildir ki, bir neçə ildir ki, bir neçə ildir ki, bir neçə ildir ki, bir neçə ildir ki, bir neçə ildir ki, bir neçə ildir ki, bir neçə ildir ki, bir neçə ildir ki, bir neçə ildir ki, bir neçə ildir ki, bir neçə ildir ki, bir neçə ildir ki, bir neçə ildir ki, bir neçə ildir ki, bir neçə ildir ki, bir neçə ildir ki. Xatırladaq ki, hər bir model üçün, soma ağacın kökü olaraq seçilir ki, bu səbəbdən düymanın derinliyi soma (0)-dan distal dendritlərə artır. Qazaxıstanda Qazaxıstanda Qazaxıstanda Qazaxıstanda Qazaxıstanda with four threads. Candidates: nodes that can be processed. Selected candidates: nodes that are picked by DHS, i.e., the Əvvəla, daha öncə işləyənlərin sayı artıb, daha əvvəl işləyənlərin sayı artıb. Parallelization strategy obtained by DHS after the process in Hər bir node dörd paralel threaddən birinə atılır.DHS, node-ləri bir çox threadə dağıtmaqla seri node işləməsinin adımlarını 14 ilə 5 ilə azaldır. Relative cost, i.e., the proportion of the computational cost of DHS to that of the serial Hines method, when applying DHS with different numbers of threads on different types of models. a k b c d b k e d f VVD - Hollandiyada futbolçu bu adla tanımır, orada VVD daha çox mərkəz-sağı təmsilən edən siyasi partiyanın adının qısaltması kimi bilinir - artıq sorğu-suala ehtiyacı olmayan ulduzdu. Bu barədə “Qəbələ”nin “Qəbələ”nin “Qəbələ”nin “Qəbələ”nin “Qəbələ”nin “Qəbələ”nin “Qəbələ”nin “Qəbələ”nin “Qəbələ”nin “Qəbələ”nin “Qəbələ”nin “Qəbələ”nin “Qəbələ”nin “Qəbələ”nin “Qəbələ”nin “Qəbələ”nin “Qəbələ”nin “Qəbələ”nin “Qəbələ”nin “Qəbələ”nin “Qəbələ”nin “Qəbələ”nin “Qəbələ”nin “Qəbələ”nin “Qəbələ”nin “Qəbələ”nin “Qəbələ”in “Qəbələ”nin “Qəbələ”in “Qəbələ”nin “Qəbələ”nin “Qəbələ”nin “Qəbələ İŞİD 2D 2e Sonradan, DHS metodu altı reprezentativ detalçı nöron modelinə (ModelDB-dən seçilmiş) uygulanırıq. Bəziləri ilə bağlı fərqli fikirlər var (Fig. Kortikal və hipokampal piramidal nöronlar , , Cerebellar Purkinje neuronları , striatal projection neurons (SPN Mütəxəssislər mitral hüceyrələri Xatırladaq ki, “Qəbələ”, “Qəbələ”, “Qəbələ”, “Qəbələ”, “Qəbələ”, “Qəbələ”, “Qəbələ”, “Qəbələ”, “Qəbələ”, “Qəbələ”, “Qəbələ”, “Qəbələ”, “Qəbələ”, “Qəbələ”, “Qəbələ”, “Qəbələ”, “Qəbələ”, “Qəbələ”, “Qəbələ”, “Qəbələ”, “Qəbələ”, “Qəbələ”, “Qəbələ”, “Qəbələ”, “Qəbələ”, “Qəbələ”, “Qəbələ”, “Qəbələ”, “Qəbələ”, “Qəbələ”, “Qəbələ”, “Qəbələ”, “Qəbələ”, “Qəbələ”, “Qəbələ”, “Qəbələ”, “Qəbələ” və “Qəbələ” ), daha çox fillər ekləmək bölmələr arasındakı bağlanmaların səbəbi üçün performansın daha da artırılmasını göstərmir. 39 2f 40 41 42 43 44 45 2F Xatırladaq ki, DHS simula başlamadan öncə optimum bölünməyi tapır və eşitmələrin çözülməsi üçün heç bir ekstra hesablama ehtiyacı yoxdur. Speeding up DHS by GPU memory boosting DHS hər bir nöronun bir çox thread ilə hesabatlandırır, ki, nöron ağ simulasiyalarını çalışdığında böyük bir miktar thread istifadə edir.Grafik işləmə birimləri (GPU) böyük işləmə birimləri (yani, streaming prosesorları, SPs, Fig. Paralel kompüterlər üçün PPP tam olaraq bu bölgüyə uyğun gəlmədiyi üçün adətən HDLC/SDLC protokollar dəsti kimi təsvir edilir. ). However, we consistently observed that the efficiency of DHS significantly decreased when the network size grew, which might result from scattered data storage or extra memory access caused by loading and writing intermediate results (Fig. , left). 3a və b 46 3c 3D dərsləri GPU architecture and its memory hierarchy. Each GPU contains massive processing units (stream processors). Different types of memory have different throughput. Hər bir SM-nin bir çox streaming prosesorları, rejistləri və L1 cache-i vardır. Applying DHS on two neurons, each with four threads. During simulation, each thread executes on one stream processor. PPP tam olaraq bu bölgüyə uyğun gəlmədiyi üçün adətən HDLC/SDLC protokollar dəsti kimi təsvir edilir. . Processors send a data request to load data for each thread from global memory. Without memory boosting (left), it takes seven transactions to load all request data and some extra transactions for intermediate results. With memory boosting (right), it takes only two transactions to load all request data, registers are used for intermediate results, which further improve memory throughput. DHS-in sürət vaxtı (32 fəlsəfə hər bir hüceyrə) bir neçə kateqoriyalı 5 piramidal modeli ilə memoriya artırmaqla və olmadan. Əsas səhifə » Xəbərlər » Əsas səhifə » Əsas səhifə » Əsas səhifə » Əsas səhifə » Əsas səhifə a b c d d e f Bu problem GPU memory boosting vasitəsilə çözülür, bu, GPU-nin memory hierarqisini və quraşdırma mekanizmasını istifadə edərək memory throughputunu artırmaq üçün bir metoddur.GPU-nun memory load mechanismuna dayanaraq, düzəldilmiş və arxivləşdirilmiş data yüklənənən sonrakı fillər, scatter-stored data vasitəsilə quraşdırılan data ilə əvəzlənən yüksək memory throughputuna səbəb olur, bu da memory throughputunu azaldır. , Əsas səhifə » Gündəm » Gündəm » Gündəm » Gündəm » Gündəm » Gündəm » Gündəm » Gündəm » Gündəm » Gündəm » Gündəm » Gündəm » Gündəm » Gündəm » Gündəm » Gündəm » Gündəm » Gündəm » Gündəm » Gündəm » Gündəm » Gündəm » Gündəm » Gündəm » Gündəm » Gündəm » Gündəm » Gündəm » Gündəm » Gündəm » Gündəm » Gündəm » Gündəm » Gündəm » Gündəm » Gündəm » Gündəm » Gündəm » Gündəm » Gündəm » Gündəm » Gündəm » Gündəm » Gündəm » Gündəm » Gündəm » Gündəm » Gündəm » Bundan başqa, piramidal neuronların bir çox sayı ilə spin və tipik neuron modelləri üzərində eksperimentlər (Fig. • Təsadüfi sifarişlər. Memoriya artırılması naiv DHS-ə görə 1,2-3,8 dəfə daha sürətlənir. 46 47 3d 3a və f 2 GPU memoriası artırmaqla DHS-in performansını bütünlüklə test etmək üçün, 6 tipik nöron modeli seçirik və hər bir modelin böyük sayı ilə kabeldə dəlillərin çözülməsinin sürətini qiymətləndiririk (Şəkil 3). Biz hər bir neuron üçün dörd thread (DHS-4) və on altı thread (DHS-16) ilə DHS araşdırdıq. CoreNEURON-da GPU metoduna görə, DHS-4 və DHS-16 5 və 15 dəfə artdıra bilər. ). Moreover, compared to the conventional serial Hines method in NEURON running with a single-thread of CPU, DHS speeds up the simulation by 2-3 orders of magnitude (Supplementary Fig. Tədbirdə iştirak edənlərin sayı təxminən 1 faiz artıb (FOTO) və Əvvəlki məqaləMüəllimlər (Supplementary Fig. Digər stratejilər (Segmentary strategies) ). 4 4a 3 4 8 7 7 VVD - Hollandiyada futbolçu bu adla tanımır, orada VVD daha çox mərkəz-sağı təmsilən edən siyasi partiyanın adının qısaltması kimi bilinir - artıq sorğu-suala ehtiyacı olmayan ulduzdu. və DHS-4 və DHS-16 ilə partisyonun vizualizasiyası, hər bir rəng yalnız bir işığa işarə edir. a b c DHS creates cell-type-specific optimal partitioning To gain insights into the working mechanism of the DHS method, we visualized the partitioning process by mapping compartments to each thread (every color presents a single thread in Fig. Bir neçə ildir ki, bir neçə ildir ki, bir-birindən fərqlənir. ). Interestingly, DHS generates aligned partitions in morphologically symmetric neurons such as the striatal projection neuron (SPN) and the Mitral cell (Fig. Buna qarşı olaraq, piramidal nöronlar və Purkinje hücresi kimi morfolojik olaraq asimetrik nöronların parçalanmış bölünmələri yaratır. Dubay polisinin idarə etdiyi superkarlar – bu, artıq çox adi bir hala çevrilib. 4b, c 4b, c 4B və C 4B və C DHS və memoriya artımları teorik olaraq açıq-aşkar effektivliyi ilə paralel linear təchizatları düzəltmək üçün optimum bir çözüm yaratır. Bu prinsipi istifadə edərək, neuroloqların spesifik GPU programlama bilgisinə ehtiyac olmadan modelləri təsvir etmək üçün istifadə edə biləcək açıq-aşkar DeepDendrite platformasını qurduq. DHS spine-level modelləşdirməyə imkan verir Dendritik üsyanlar kortikal və hipokampal piramidal nöronlara, striatal projeksiya nöronlarına və s.a.q. ən çox excitatory input alır, onların morfologiya və plasticity neuronal excitability regulating üçün kritikdir. , , , , Bununla birlikte, spinos çox kiçikdir ( ~ 1 μm uzunluğu) spinoza bağlı proseslər üçün doğrudan eksperimental ölçülə bilər. 10 48 49 50 51 We can model a single spine with two compartments: the spine head where synapses are located and the spine neck that links the spine head to dendrites Teoriya, qırmızı qapının (0,1-0,0 μm çapında) elektron olaraq qırmızı başını ana dendritindən izolədiyini və bu sayda qırmızı başda yaradılan sinyalləri bölünür. 2013-cü ildə aparılmış arxeoloji tədqiqat işləri burada 120 kv.metrlik ərazidə yaşayış yerinin qalığının olduğunu söyləməyə əsas verir (1). Spin faktoru , instead of modeling all spines explicitly. Here, the spine faktoru hüceyrəli membranın biofiziki xüsusiyyətləri üzərində spine effektini yaxınlaşdıra bilər . 52 53 F 54 F 54 Inspired by the previous work of Eyal et al. , we investigated how different spatial patterns of excitatory inputs formed on dendritic spines shape neuronal activities in a human pyramidal neuron model with explicitly modeled spines (Fig. ). Noticeably, Eyal et al. employed the VVD - Hollandiyada futbolçu bu adla tanımır, orada VVD daha çox mərkəz-sağı təmsilən edən siyasi partiyanın adının qısaltması kimi bilinir - artıq sorğu-suala ehtiyacı olmayan ulduzdu. ). The value of spine in their model was computed from the dendritic area and spine area in the reconstructed data. Accordingly, we calculated the spine density from their reconstructed data to make our full-spine model more consistent with Eyal’s few-spine model. With the spine density set to 1.3 μm-1, the pyramidal neuron model contained about 25,000 spines without altering the model’s original morphological and biophysical properties. Further, we repeated the previous experiment protocols with both full-spine and few-spine models. We use the same synaptic input as in Eyal’s work but attach extra background noise to each sample. By comparing the somatic traces (Fig. ) and spike probability (Fig. ) in full-spine and few-spine models, we found that the full-spine model is much leakier than the few-spine model. In addition, the spike probability triggered by the activation of clustered spines appeared to be more nonlinear in the full-spine model (the solid blue line in Fig. ) than in the few-spine model (the dashed blue line in Fig. ). These results indicate that the conventional F-factor method may underestimate the impact of dense spine on the computations of dendritic excitability and nonlinearity. 51 5a F 5a F 5B və C 5d 5d 5d Experiment setup. We examine two major types of models: few-spine models and full-spine models. Few-spine models (two on the left) are the models that incorporated spine area globally into dendrites and only attach individual spines together with activated synapses. In full-spine models (two on the right), all spines are explicitly attached over whole dendrites. We explore the effects of clustered and randomly distributed synaptic inputs on the few-spine models and the full-spine models, respectively. Somatic voltages recorded for cases in . Colors of the voltage curves correspond to , scale bar: 20 ms, 20 mV. Color-coded voltages during the simulation in at specific times. Colors indicate the magnitude of voltage. Somatic spike probability as a function of the number of simultaneously activated synapses (as in Eyal et al.’s work) for four cases in . Background noise is attached. Run time of experiments in with different simulation methods. NEURON: conventional NEURON simulator running on a single CPU core. CoreNEURON: CoreNEURON simulator on a single GPU. DeepDendrite: DeepDendrite on a single GPU. a b a a c b d a e d In the DeepDendrite platform, both full-spine and few-spine models achieved 8 times speedup compared to CoreNEURON on the GPU platform and 100 times speedup compared to serial NEURON on the CPU platform (Fig. ; Supplementary Table ) while keeping the identical simulation results (Supplementary Figs. and ). Therefore, the DHS method enables explorations of dendritic excitability under more realistic anatomic conditions. 5e 1 4 8 Discussion In this work, we propose the DHS method to parallelize the computation of Hines method and we mathematically demonstrate that the DHS provides an optimal solution without any loss of precision. Next, we implement DHS on the GPU hardware platform and use GPU memory boosting techniques to refine the DHS (Fig. ). When simulating a large number of neurons with complex morphologies, DHS with memory boosting achieves a 15-fold speedup (Supplementary Table ) as compared to the GPU method used in CoreNEURON and up to 1,500-fold speedup compared to serial Hines method in the CPU platform (Fig. ; Supplementary Fig. and Supplementary Table ). Furthermore, we develop the GPU-based DeepDendrite framework by integrating DHS into CoreNEURON. Finally, as a demonstration of the capacity of DeepDendrite, we present a representative application: examine spine computations in a detailed pyramidal neuron model with 25,000 spines. Further in this section, we elaborate on how we have expanded the DeepDendrite framework to enable efficient training of biophysically detailed neural networks. To explore the hypothesis that dendrites improve robustness against adversarial attacks , we train our network on typical image classification tasks. We show that DeepDendrite can support both neuroscience simulations and AI-related detailed neural network tasks with unprecedented speed, therefore significantly promoting detailed neuroscience simulations and potentially for future AI explorations. 55 3 1 4 3 1 56 Decades of efforts have been invested in speeding up the Hines method with parallel methods. Early work mainly focuses on network-level parallelization. In network simulations, each cell independently solves its corresponding linear equations with the Hines method. Network-level parallel methods distribute a network on multiple threads and parallelize the computation of each cell group with each thread , . With network-level methods, we can simulate detailed networks on clusters or supercomputers . In recent years, GPU has been used for detailed network simulation. Because the GPU contains massive computing units, one thread is usually assigned one cell rather than a cell group , , . With further optimization, GPU-based methods achieve much higher efficiency in network simulation. However, the computation inside the cells is still serial in network-level methods, so they still cannot deal with the problem when the “Hines matrix” of each cell scales large. 57 58 59 35 60 61 Cellular-level parallel methods further parallelize the computation inside each cell. The main idea of cellular-level parallel methods is to split each cell into several sub-blocks and parallelize the computation of those sub-blocks , . However, typical cellular-level methods (e.g., the “multi-split” method ) pay less attention to the parallelization strategy. The lack of a fine parallelization strategy results in unsatisfactory performance. To achieve higher efficiency, some studies try to obtain finer-grained parallelization by introducing extra computation operations , , or making approximations on some crucial compartments, while solving linear equations , . These finer-grained parallelization strategies can get higher efficiency but lack sufficient numerical accuracy as in the original Hines method. 27 28 28 29 38 62 63 64 Unlike previous methods, DHS adopts the finest-grained parallelization strategy, i.e., compartment-level parallelization. By modeling the problem of “how to parallelize” as a combinatorial optimization problem, DHS provides an optimal compartment-level parallelization strategy. Moreover, DHS does not introduce any extra operation or value approximation, so it achieves the lowest computational cost and retains sufficient numerical accuracy as in the original Hines method at the same time. Dendritic spines are the most abundant microstructures in the brain for projection neurons in the cortex, hippocampus, cerebellum, and basal ganglia. As spines receive most of the excitatory inputs in the central nervous system, electrical signals generated by spines are the main driving force for large-scale neuronal activities in the forebrain and cerebellum , . The structure of the spine, with an enlarged spine head and a very thin spine neck—leads to surprisingly high input impedance at the spine head, which could be up to 500 MΩ, combining experimental data and the detailed compartment modeling approach , . Due to such high input impedance, a single synaptic input can evoke a “gigantic” EPSP ( ~ 20 mV) at the spine-head level , , thereby boosting NMDA currents and ion channel currents in the spine . However, in the classic single detailed compartment models, all spines are replaced by the coefficient modifying the dendritic cable geometries . This approach may compensate for the leak currents and capacitance currents for spines. Still, it cannot reproduce the high input impedance at the spine head, which may weaken excitatory synaptic inputs, particularly NMDA currents, thereby reducing the nonlinearity in the neuron’s input-output curve. Our modeling results are in line with this interpretation. 10 11 48 65 48 66 11 F 54 On the other hand, the spine’s electrical compartmentalization is always accompanied by the biochemical compartmentalization , , , resulting in a drastic increase of internal [Ca2+], within the spine and a cascade of molecular processes involving synaptic plasticity of importance for learning and memory. Intriguingly, the biochemical process triggered by learning, in turn, remodels the spine’s morphology, enlarging (or shrinking) the spine head, or elongating (or shortening) the spine neck, which significantly alters the spine’s electrical capacity , , , Bu cür eksperimental dəyişikliklər, “struktural plasticity” olaraq da adlandırılır, vizual kortexdə yayılmışdır. , , somatosensory cortex , , motor cortex , hippocampus , and the basal ganglia in vivo. They play a critical role in motor and spatial learning as well as memory formation. However, due to the computational costs, nearly all detailed network models exploit the “F-factor” approach to replace actual spines, and are thus unable to explore the spine functions at the system level. By taking advantage of our framework and the GPU platform, we can run a few thousand detailed neurons models, each with tens of thousands of spines on a single GPU, while maintaining ~100 times faster than the traditional serial method on a single CPU (Fig. ). Therefore, it enables us to explore of structural plasticity in large-scale circuit models across diverse brain regions. 8 52 67 67 68 69 70 71 72 73 74 75 9 76 5e Another critical issue is how to link dendrites to brain functions at the systems/network level. It has been well established that dendrites can perform comprehensive computations on synaptic inputs due to enriched ion channels and local biophysical membrane properties , , . For example, cortical pyramidal neurons can carry out sublinear synaptic integration at the proximal dendrite but progressively shift to supralinear integration at the distal dendrite . Moreover, distal dendrites can produce regenerative events such as dendritic sodium spikes, calcium spikes, and NMDA spikes/plateau potentials , Belə dendritik olaylar möcüzələrdə yayılmışdır. or even human cortical neurons in vitro, which may offer various logical operations , or gating functions , . Recently, in vivo recordings in awake or behaving mice provide strong evidence that dendritic spikes/plateau potentials are crucial for orientation selectivity in the visual cortex , sensory-motor integration in the whisker system , , and spatial navigation in the hippocampal CA1 region . 5 6 7 77 6 78 6 79 6 79 80 81 82 83 84 85 To establish the causal link between dendrites and animal (including human) patterns of behavior, large-scale biophysically detailed neural circuit models are a powerful computational tool to realize this mission. However, running a large-scale detailed circuit model of 10,000-100,000 neurons generally requires the computing power of supercomputers. It is even more challenging to optimize such models for in vivo data, as it needs iterative simulations of the models. The DeepDendrite framework can directly support many state-of-the-art large-scale circuit models , , , which were initially developed based on NEURON. Moreover, using our framework, a single GPU card such as Tesla A100 could easily support the operation of detailed circuit models of up to 10,000 neurons, thereby providing carbon-efficient and affordable plans for ordinary labs to develop and optimize their own large-scale detailed models. 86 87 88 Recent works on unraveling the dendritic roles in task-specific learning have achieved remarkable results in two directions, i.e., solving challenging tasks such as image classification dataset ImageNet with simplified dendritic networks , and exploring full learning potentials on more realistic neuron , . However, there lies a trade-off between model size and biological detail, as the increase in network scale is often sacrificed for neuron-level complexity , , . Moreover, more detailed neuron models are less mathematically tractable and computationally expensive . 20 21 22 19 20 89 21 There has also been progress in the role of active dendrites in ANNs for computer vision tasks. Iyer et al. . proposed a novel ANN architecture with active dendrites, demonstrating competitive results in multi-task and continual learning. Jones and Kording used a binary tree to approximate dendrite branching and provided valuable insights into the influence of tree structure on single neurons’ computational capacity. Bird et al. . proposed a dendritic normalization rule based on biophysical behavior, offering an interesting perspective on the contribution of dendritic arbor structure to computation. While these studies offer valuable insights, they primarily rely on abstractions derived from spatially extended neurons, and do not fully exploit the detailed biological properties and spatial information of dendrites. Further investigation is needed to unveil the potential of leveraging more realistic neuron models for understanding the shared mechanisms underlying brain computation and deep learning. 90 91 92 In response to these challenges, we developed DeepDendrite, a tool that uses the Dendritic Hierarchical Scheduling (DHS) method to significantly reduce computational costs and incorporates an I/O module and a learning module to handle large datasets. With DeepDendrite, we successfully implemented a three-layer hybrid neural network, the Human Pyramidal Cell Network (HPC-Net) (Fig. ). This network demonstrated efficient training capabilities in image classification tasks, achieving approximately 25 times speedup compared to training on a traditional CPU-based platform (Fig. ; Supplementary Table ). 6a, b 6f 1 The illustration of the Human Pyramidal Cell Network (HPC-Net) for image classification. Images are transformed to spike trains and fed into the network model. Learning is triggered by error signals propagated from soma to dendrites. Training with mini-batch. Multiple networks are simulated simultaneously with different images as inputs. The total weight updates ΔW are computed as the average of ΔWi from each network. Comparison of the HPC-Net before and after training. Left, the visualization of hidden neuron responses to a specific input before (top) and after (bottom) training. Right, hidden layer weights (from input to hidden layer) distribution before (top) and after (bottom) training. Workflow of the transfer adversarial attack experiment. We first generate adversarial samples of the test set on a 20-layer ResNet. Then use these adversarial samples (noisy images) to test the classification accuracy of models trained with clean images. Prediction accuracy of each model on adversarial samples after training 30 epochs on MNIST (left) and Fashion-MNIST (right) datasets. Run time of training and testing for the HPC-Net. The batch size is set to 16. Left, run time of training one epoch. Right, run time of testing. Parallel NEURON + Python: training and testing on a single CPU with multiple cores, using 40-process-parallel NEURON to simulate the HPC-Net and extra Python code to support mini-batch training. DeepDendrite: training and testing the HPC-Net on a single GPU with DeepDendrite. a b c d e f Additionally, it is widely recognized that the performance of Artificial Neural Networks (ANNs) can be undermined by adversarial attacks —intentionally engineered perturbations devised to mislead ANNs. Intriguingly, an existing hypothesis suggests that dendrites and synapses may innately defend against such attacks . Our experimental results utilizing HPC-Net lend support to this hypothesis, as we observed that networks endowed with detailed dendritic structures demonstrated some increased resilience to transfer adversarial attacks compared to standard ANNs, as evident in MNIST and Fashion-MNIST datasets (Fig. ). This evidence implies that the inherent biophysical properties of dendrites could be pivotal in augmenting the robustness of ANNs against adversarial interference. Nonetheless, it is essential to conduct further studies to validate these findings using more challenging datasets such as ImageNet . 93 56 94 95 96 6d, e 97 In conclusion, DeepDendrite has shown remarkable potential in image classification tasks, opening up a world of exciting future directions and possibilities. To further advance DeepDendrite and the application of biologically detailed dendritic models in AI tasks, we may focus on developing multi-GPU systems and exploring applications in other domains, such as Natural Language Processing (NLP), where dendritic filtering properties align well with the inherently noisy and ambiguous nature of human language. Challenges include testing scalability in larger-scale problems, understanding performance across various tasks and domains, and addressing the computational complexity introduced by novel biological principles, such as active dendrites. By overcoming these limitations, we can further advance the understanding and capabilities of biophysically detailed dendritic neural networks, potentially uncovering new advantages, enhancing their robustness against adversarial attacks and noisy inputs, and ultimately bridging the gap between neuroscience and modern AI. Methods DHS ilə simülasiya CoreNEURON simulator ( ) uses the NEURON architecture and is optimized for both memory usage and computational speed. We implement our Dendritic Hierarchical Scheduling (DHS) method in the CoreNEURON environment by modifying its source code. All models that can be simulated on GPU with CoreNEURON can also be simulated with DHS by executing the following command: 35 https://github.com/BlueBrain/CoreNeuron 25 coreneuron_exec -d /path/to/models -e time --cell-permute 3 --cell-nthread 16 --gpu The usage options are as in Table . 1 Hücresel düzeydə paralel hesablama ilə simülasiya həddi To ensure the accuracy of the simulation, we first need to define the correctness of a cellular-level parallel algorithm to judge whether it will generate identical solutions compared with the proven correct serial methods, like the Hines method used in the NEURON simulation platform. Based on the theories in parallel computing , a parallel algorithm will yield an identical result as its corresponding serial algorithm, if and only if the data process order in the parallel algorithm is consistent with data dependency in the serial method. The Hines method has two symmetrical phases: triangularization and back-substitution. By analyzing the serial computing Hines method , we find that its data dependency can be formulated as a tree structure, where the nodes on the tree represent the compartments of the detailed neuron model. In the triangularization process, the value of each node depends on its children nodes. In contrast, during the back-substitution process, the value of each node is dependent on its parent node (Fig. ). Thus, we can compute nodes on different branches in parallel as their values are not dependent. 34 55 1d Based on the data dependency of the serial computing Hines method, we propose three conditions to make sure a parallel method will yield identical solutions as the serial computing Hines method: (1) The tree morphology and initial values of all nodes are identical to those in the serial computing Hines method; (2) In the triangularization phase, a node can be processed if and only if all its children nodes are already processed; (3) In the back-substitution phase, a node can be processed only if its parent node is already processed. Once a parallel computing method satisfies these three conditions, it will produce identical solutions as the serial computing method. Computational cost of cellular-level parallel computing method To theoretically evaluate the run time, i.e., efficiency, of the serial and parallel computing methods, we introduce and formulate the concept of computational cost as follows: given a tree and threads (basic computational units) to perform triangularization, parallel triangularization equals to divide the node set of into subsets, i.e., Əməliyyat , , … } where the size of each subset | | ≤ , i.e., at most nodes can be processed each step since there are only threads. The process of the triangularization phase follows the order: → → … → , and nodes in the same subset can be processed in parallel. So, we define | | (the size of set , i.e., here) as the computational cost of the parallel computing method. In short, we define the computational cost of a parallel method as the number of steps it takes in the triangularization phase. Because the back-substitution is symmetrical with triangularization, the total cost of the entire solving equation phase is twice that of the triangularization phase. T k V T n V V1 V2 Vn Vi k k k V1 V2 Vn Vi V V n Matematika problemləri Based on the simulation accuracy and computational cost, we formulate the parallelization problem as a mathematical scheduling problem: Given a tree Əməliyyat , } and a positive integer , where is the node-set and is the edge set. Define partition ( ) = { , , … }, | | ≤ 1 nəfəri ≤ n, where | | indicates the cardinal number of subset , i.e., the number of nodes in , and for each node ∈ , all its children nodes { | ∈children( )} must in a previous subset , where 1 ≤ «Qəbələ . Our goal is to find an optimal partition ( ) whose computational cost | ( )| is minimal. T V E k V E P V V1 V2 Vn Vi k i Vi Vi Vi v Vi c c v Vj j i P* V P* V Here subset consists of all nodes that will be computed at Tədbirlər (Fig. ), so | | ≤ indicates that we can compute nodes each step at most because the number of available threads is . The restriction “for each node ∈ , all its children nodes { | ∈children( )} must in a previous subset , where 1 ≤ < ” indicates that node Sadəcə, bütün uşaqlar üçün işləmək lazımdır. Vi i 2e Və k k k v Vi c c v Vj j i v DHS implementation We aim to find an optimal way to parallelize the computation of solving linear equations for each neuron model by solving the mathematical scheduling problem above. To get the optimal partition, DHS first analyzes the topology and calculates the depth ( ) for all nodes ∈ . Then, the following two steps will be executed iteratively until every node ∈ is assigned to a subset: (1) find all candidate nodes and put these nodes into candidate set . A node is a candidate only if all its child nodes have been processed or it does not have any child nodes. (2) if | | ≤ , i.e., the number of candidate nodes is smaller or equivalent to the number of available threads, remove all nodes in and put them into , otherwise, remove deepest nodes from and add them to subset . Label these nodes as processed nodes (Fig. ). After filling in subset Sonraki İçerikİnter (İnter) – Sonraki İçerikİnter (İnter) – Sonraki İçerikİnter (İnter) – Sonraki İçerik . d v v V v V Q Q k Q V*i k Q Vi 2d Vi Vi+1 Correctness proof for DHS DHS-i neuron ağacına gətirdikdən sonra = { , }, we get a partition ( ) = { , , … }, | | ≤ , 1 ≤ ≤ . Nodes in the same subset will be computed in parallel, taking steps to perform triangularization and back-substitution, respectively. We then demonstrate that the reordering of the computation in DHS will result in a result identical to the serial Hines method. T V E P V V1 V2 Vn Vi k i n Vi n The partition ( ) obtained from DHS decides the computation order of all nodes in a neural tree. Below we demonstrate that the computation order determined by ( ) satisfies the correctness conditions. ( ) is obtained from the given neural tree . Operations in DHS do not modify the tree topology and values of tree nodes (corresponding values in the linear equations), so the tree morphology and initial values of all nodes are not changed, which satisfies condition 1: the tree morphology and initial values of all nodes are identical to those in serial Hines method. In triangularization, nodes are processed from subset to . As shown in the implementation of DHS, all nodes in subset Seçki programından seçilmişlər , and a node can be put into only if all its child nodes have been processed. Thus the child nodes of all nodes in are in { , ... ... }, meaning that a node is only computed after all its children have been processed, which satisfies condition 2: in triangularization, a node can be processed if and only if all its child nodes are already processed. In back-substitution, the computation order is the opposite of that in triangularization, i.e., from to . As shown before, the child nodes of all nodes in are in { , ... ... }, so parent nodes of nodes in are in { , , … }, which satisfies condition 3: in back-substitution, a node can be processed only if its parent node is already processed. P V P V P V T V1 Vn Vi Q Q Vi V1 V2 Vi-1 Vn V1 Vi V1 V2 Vi-1 Vi Vi+1 Vi+2 Vn Optimality proof for DHS The idea of the proof is that if there is another optimal solution, it can be transformed into our DHS solution without increasing the number of steps the algorithm requires, thus indicating that the DHS solution is optimal. For each subset in ( Qazaxıstan Movqe (thread number) deepest nodes from the corresponding candidate set İki . If the number of nodes in is smaller than , move all nodes from İki . To simplify, we introduce , indicating the depth sum of deepest nodes in . All subsets in ( ) satisfy the max-depth criteria (Supplementary Fig. ): . We then prove that selecting the deepest nodes in each iteration makes an optimal partition. If there exists an optimal partition = { , , … } containing subsets that do not satisfy the max-depth criteria, we can modify the subsets in ( ) so that all subsets consist of the deepest nodes from and the number of subsets ( | ( )|) remain the same after modification. Vi P V k Qi Vi Qi k Qi Vi Di k Qi P V 6a P(V) P*(V) V*1 V*2 V*s P* V Q P* V Without any loss of generalization, we start from the first subset not satisfying the criteria, i.e., . There are two possible cases that will make not satisfy the max-depth criteria: (1) | | < and there exist some valid nodes in Buna görə də bu ; (2) | | = but nodes in are not the deepest nodes in . V*i V*i V*i k Qi V*i V * I k V*i k Qi For case (1), because some candidate nodes are not put to , these nodes must be in the subsequent subsets. As | | , we can move the corresponding nodes from the subsequent subsets to , which will not increase the number of subsets and make satisfy the criteria (Supplementary Fig. , top). For case (2), | | = , these deeper nodes that are not moved from the candidate set into must be added to subsequent subsets (Supplementary Fig. , bottom). These deeper nodes can be moved from subsequent subsets to through the following method. Assume that after filling , is picked and one of the -th deepest nodes is still in , thus will be put into a subsequent subset ( > ). We first move from to + , then modify subset + as follows: if | + Əməkdarlıq | ≤ and none of the nodes in + is the parent of node , stop modifying the latter subsets. Otherwise, modify + as follows (Supplementary Fig. ): if the parent node of is in + , move this parent node to + ; else move the node with minimum depth from + to + . After adjusting , modify subsequent subsets + , + , … with the same strategy. Finally, move from to . V*i V*i < k V*i V*i 6b V*i k Qi V*i 6b V*i V*i v k V » Qi v’ V*j j i v V*i V*i 1 V*i 1 V*i 1 k V*i 1 v V*i 1 6c v V*i 1 V * I 2 V * I 1 V*i 2 V*i V*i 1 V*i 2 V*j-1 v’ V*j V * I With the modification strategy described above, we can replace all shallower nodes in with the -th deepest node in and keep the number of subsets, i.e., | ( )| the same after modification. We can modify the nodes with the same strategy for all subsets in ( ) that do not contain the deepest nodes. Finally, all subsets ∈ ( ) can satisfy the max-depth criteria, and | ( )| does not change after modifying. V*i k Qi P* V P* V V*i P* V P* V Qazaxıstanda partiyanın yaradılması ( Bütün subyektlər ∈ ( ) satisfy the max-depth condition: . For any other optimal partition ( ) we can modify its subsets to make its structure the same as ( ), i.e., each subset consists of the deepest nodes in the candidate set, and keep | ( ) the same after modification. So, the partition ( ) obtained from DHS is one of the optimal partitions. P V Vi P V P* V P V P* V | P V GPU və Memory Boosting To achieve high memory throughput, GPU utilizes the memory hierarchy of (1) global memory, (2) cache, (3) register, where global memory has large capacity but low throughput, while registers have low capacity but high throughput. We aim to boost memory throughput by leveraging the memory hierarchy of GPU. GPU-nin SIMT (Single-Instruction, Multiple-Thread) arkitekturası ilə işləyir. Warps GPU-nun əsas planlaşdırma birimləridir (varp 32 paralel threaddən ibarət bir qrupdur). VVD - Hollandiyada futbolçu bu adla tanımır, orada VVD daha çox mərkəz-sağı təmsilən edən siyasi partiyanın adının qısaltması kimi bilinir - artıq çox adi bir hala çevrilib. 46 VVD - Hollandiyada futbolçu bu adla tanımır, orada VVD daha çox mərkəz-sağı təmsilən edən siyasi partiyanın adının qısaltması kimi bilinir - artıq sorğu-suala ehtiyacı olmayan ulduzdu. Full-spine and few-spine biophysical models We used the published human pyramidal neuron . The membrane capacitance m = 0.44 μF cm-2, membrane resistance m = 48,300 Ω cm2, and axial resistivity a = 261.97 Ω cm. In this model, all dendrites were modeled as passive cables while somas were active. The leak reversal potential l = -83.1 mV. Ion channels such as Na+ and K+ were inserted on soma and initial axon, and their reversal potentials were Na = 67.6 mV, K = -102 mV respectively. All these specific parameters were set the same as in the model of Eyal, et al. , for more details please refer to the published model (ModelDB, access No. 238347). 51 c r r E E E 51 VVD - Hollandiyada futbolçu bu adla tanımır, orada VVD daha çox mərkəz-sağı təmsilən edən siyasi partiyanın adının qısaltması kimi bilinir - artıq sorğu-suala ehtiyacı olmayan ulduzdu. spine factor to approximate dendritic spines. In this model, O, 1991-ci ildə Bakıya gəlmiş, Bakıya gəlmiş, Bakıya gəlmiş, Bakıya gəlmiş, Bakıya gəlmiş, Bakıya gəlmiş, Bakıya gəlmiş, Bakıya gəlmiş, Bakıya gəlmiş, Bakıya gəlmiş, Bakıya gəlmiş. F F In the full-spine model, all spines were explicitly attached to dendrites. We calculated the spine density with the reconstructed neuron in Eyal, et al. . The spine density was set to 1.3 μm-1, and each cell contained 24994 spines on dendrites 60 μm away from the soma. 51 The morphologies and biophysical mechanisms of spines were the same in few-spine and full-spine models. The length of the spine neck neck = 1.35 μm and the diameter neck = 0.25 μm, whereas the length and diameter of the spine head were 0.944 μm, i.e., the spine head area was set to 2.8 μm2. Both spine neck and spine head were modeled as passive cables, with the reversal potential = -86 mV. The specific membrane capacitance, membrane resistance, and axial resistivity were the same as those for dendrites. L D El Synaptic inputs We investigated neuronal excitability for both distributed and clustered synaptic inputs. All activated synapses were attached to the terminal of the spine head. For distributed inputs, all activated synapses were randomly distributed on all dendrites. For clustered inputs, each cluster consisted of 20 activated synapses that were uniformly distributed on a single randomly-selected compartment. All synapses were activated simultaneously during the simulation. AMPA-based and NMDA-based synaptic currents were simulated as in Eyal et al.’s work. AMPA conductance was modeled as a double-exponential function and NMDA conduction as a voltage-dependent double-exponential function. For the AMPA model, the specific rise and decay were set to 0.3 and 1.8 ms. For the NMDA model, rise and AMPA və NMDA-nın maksimum konduktansı 0,73 nS və 1,31 nS idi. τ τ τ τ Background noise We attached background noise to each cell to simulate a more realistic environment. Noise patterns were implemented as Poisson spike trains with a constant rate of 1.0 Hz. Each pattern started at start = 10 ms and lasted until the end of the simulation. We generated 400 noise spike trains for each cell and attached them to randomly-selected synapses. The model and specific parameters of synaptic currents were the same as described in , except that the maximum conductance of NMDA was uniformly distributed from 1.57 to 3.275, resulting in a higher AMPA to NMDA ratio. t Synaptic Inputs Exploring neuronal excitability We investigated the spike probability when multiple synapses were activated simultaneously. For distributed inputs, we tested 14 cases, from 0 to 240 activated synapses. For clustered inputs, we tested 9 cases in total, activating from 0 to 12 clusters respectively. Each cluster consisted of 20 synapses. For each case in both distributed and clustered inputs, we calculated the spike probability with 50 random samples. Spike probability was defined as the ratio of the number of neurons fired to the total number of samples. All 1150 samples were simulated simultaneously on our DeepDendrite platform, reducing the simulation time from days to minutes. Performing AI tasks with the DeepDendrite platform Konvensiyalı detallı nöron simulatörləri modern AI işi üçün iki funksiyaya ehtiyac yoxdur: (1) simülasiya və ağırlıq yeniləməsi olmadan alternativ işləmək və (2) bir neçə stimülasiya nümunəsini batch-a bənzəyərək birbaşa işləmək. DeepDendrite consists of three modules (Supplementary Fig. ): (1) an I/O module; (2) a DHS-based simulating module; (3) a learning module. When training a biophysically detailed model to perform learning tasks, users first define the learning rule, then feed all training samples to the detailed model for learning. In each step during training, the I/O module picks a specific stimulus and its corresponding teacher signal (if necessary) from all training samples and attaches the stimulus to the network model. Then, the DHS-based simulating module initializes the model and starts the simulation. After simulation, the learning module updates all synaptic weights according to the difference between model responses and teacher signals. After training, the learned model can achieve performance comparable to ANN. The testing phase is similar to training, except that all synaptic weights are fixed. 5 HPC-Net model Image classification is a typical task in the field of AI. In this task, a model should learn to recognize the content in a given image and output the corresponding label. Here we present the HPC-Net, a network consisting of detailed human pyramidal neuron models that can learn to perform image classification tasks by utilizing the DeepDendrite platform. VVD - Hollandiyada futbolçu bu adla tanımır, orada VVD daha çox mərkəz-sağı təmsilən edən siyasi partiyanın adının qısaltması kimi bilinir - artıq sorğu-suala ehtiyacı olmayan ulduzdu. Xatırladaq ki, bir neçə ildir ki, bu problemin başlanğıcına səbəb ola bilər, amma bir neçə ildir ki, bu problemin başlanğıcına səbəb ola bilər. ) in the image, the corresponding spike train has a constant interspike interval ISI( ) (in ms) which is determined by the pixel value ( ) as shown in Eq. ( ). x, y τ x, y p x, y 1 In our experiment, the simulation for each stimulus lasted 50 ms. All spike trains started at 9 + ISI ms and lasted until the end of the simulation. Then we attached all spike trains to the input layer neurons in a one-to-one manner. The synaptic current triggered by the spike arriving at time is given by τ t0 where is the post-synaptic voltage, the reversal potential syn = 1 mV, the maximum synaptic conductance Max = 0.05 μS və zaman konstansı = 0.5 ms. v E g τ Neurons in the input layer were modeled with a passive single-compartment model. The specific parameters were set as follows: membrane capacitance m = 1.0 μF cm-2, membrane resistance m = 104 Ω cm2, axial resistivity a = 100 Ω cm, reversal potential of passive compartment l = 0 mV. c r r E The hidden layer contains a group of human pyramidal neuron models, receiving the somatic voltages of input layer neurons. The morphology was from Eyal, et al. , and all neurons were modeled with passive cables. The specific membrane capacitance m = 1.5 μF cm-2, membrane resistance m = 48,300 Ω cm2, axial resistivity a = 261.97 Ω cm, and the reversal potential of all passive cables l = 0 mV. Input neurons could make multiple connections to randomly-selected locations on the dendrites of hidden neurons. The synaptic current activated by the -th synapse of the -th input neuron on neuron Əsas səhifə » Əsas səhifə » Əsas səhifə ( ), where Əsas səhifə » Gündəm » Gündəm is the synaptic weight, is the ReLU-like somatic activation function, and is the somatic voltage of the Neurologiya zamanı . 51 c r r E k i j 4 gijk Wijk i t Neurons in the output layer were also modeled with a passive single-compartment model, and each hidden neuron only made one synaptic connection to each output neuron. All specific parameters were set the same as those of the input neurons. Synaptic currents activated by hidden neurons are also in the form of Eq. ( İŞİD 4 Image classification with HPC-Net For each input image stimulus, we first normalized all pixel values to 0.0-1.0. Then we converted normalized pixels to spike trains and attached them to input neurons. Somatic voltages of the output neurons are used to compute the predicted probability of each class, as shown in equation , where is the probability of -th class predicted by the HPC-Net, is the average somatic voltage from 20 ms to 50 ms of the -th output neuron, and indicates the number of classes, which equals the number of output neurons. The class with the maximum predicted probability is the final classification result. In this paper, we built the HPC-Net with 784 input neurons, 64 hidden neurons, and 10 output neurons. 6 pi i i C Synaptic plasticity rules for HPC-Net Inspired by previous work , we use a gradient-based learning rule to train our HPC-Net to perform the image classification task. The loss function we use here is cross-entropy, given in Eq. ( Növbəti is the predicted probability for class və indicates the actual class the stimulus image belongs to, = 1 if input image belongs to class , and = 0 if not. 36 7 pi i yi yi i yi When training HPC-Net, we compute the update for weight (the synaptic weight of the -th synapse connecting neuron to neuron ) at each time step. After the simulation of each image stimulus, Qazaxıstanda qeydə alınıb ( ): Wijk k i j Şəhər 8 Here is the learning rate, is the update value at time , , are somatic voltages of neuron and respectively, is the -th synaptic current activated by neuron on neuron , Sinoptik yol göstəricisi, is the transfer resistance between the -th connected compartment of neuron on neuron Növbəti xəbər Neuron ’s soma, s = 30 ms, e = 50 ms are start time and end time for learning respectively. For output neurons, the error term can be computed as shown in Eq. ( ). For hidden neurons, the error term is calculated from the error terms in the output layer, given in Eq. ( ). t vj vi i j Iijk k i j gijk rijk k i j j t t 10 11 Since all output neurons are single-compartment, equals to the input resistance of the corresponding compartment, . Transfer and input resistances are computed by NEURON. Mini-batch training is a typical method in deep learning for achieving higher prediction accuracy and accelerating convergence. DeepDendrite also supports mini-batch training. When training HPC-Net with mini-batch size batch, we make batch copies of HPC-Net. During training, each copy is fed with a different training sample from the batch. DeepDendrite first computes the weight update for each copy separately. After all copies in the current training batch are done, the average weight update is calculated and weights in all copies are updated by this same amount. N N Robustness against adversarial attack with HPC-Net To demonstrate the robustness of HPC-Net, we tested its prediction accuracy on adversarial samples and compared it with an analogous ANN (one with the same 784-64-10 structure and ReLU activation, for fair comparison in our HPC-Net each input neuron only made one synaptic connection to each hidden neuron). We first trained HPC-Net and ANN with the original training set (original clean images). Then we added adversarial noise to the test set and measured their prediction accuracy on the noisy test set. We used the Foolbox , to generate adversarial noise with the FGSM method . ANN was trained with PyTorch , and HPC-Net was trained with our DeepDendrite. For fairness, we generated adversarial noise on a significantly different network model, a 20-layer ResNet . The noise level ranged from 0.02 to 0.2. We experimented on two typical datasets, MNIST and Fashion-MNIST . Results show that the prediction accuracy of HPC-Net is 19% and 16.72% higher than that of the analogous ANN, respectively. 98 99 93 100 101 95 96 Reporting summary Further information on research design is available in the linked to this article. Nature Portföyü Data availability The data that support the findings of this study are available within the paper, Supplementary Information and Source Data files provided with this paper. The source code and data that used to reproduce the results in Figs. – are available at MNIST datasetinin açıqlanması . The Fashion-MNIST dataset is publicly available at . are provided with this paper. 3 6 https://github.com/pkuzyc/DeepDendrite http://yann.lecun.com/exdb/mnist https://github.com/zalandoresearch/fashion-mnist Source data Code availability The source code of DeepDendrite as well as the models and code used to reproduce Figs. – Bu araşdırmalarda iştirakçıların . 3 6 https://github.com/pkuzyc/DeepDendrite References McCulloch, W. S. və Pitts, W. Növbəti Növbəti Növbəti Növbəti Növbəti Növbəti Növbəti Növbəti Növbəti Növbəti Növbəti Növbəti Növbəti Növbəti Növbəti Növbəti Növbəti Növbəti Növbəti LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. , 436–444 (2015). Nature 521 P. Poirazi, P., Brannon, T. & Mel, B. W. Neuron 37, 977–987 (2003) model CA1 piramidal hücresində alt-çərşənbə sinaptik hesabatının aritmetikləri. London, M. & Häusser, M. Dendritic computation. , 503–532 (2005). Annu. Rev. Neurosci. 28 Branco, T. & Häusser, M. Neurobiol. 20, 494–502 (2010), Neurobiol. Stuart, G. J. & Spruston, N. Dendritic integration: 60 years of progress. , 1713–1721 (2015). Nat. Neurosci. 18 Poirazi, P. & Papoutsi, A. Illuminating dendritic function with computational models. , 303–321 (2020). Nat. Rev. Neurosci. 21 Yuste, R. & Denk, W. Dendritic spines as basic functional units of neuronal integration. , 682–684 (1995). Nature 375 Engert, F. & Bonhoeffer, T. Dendritic spine changes associated with hippocampal long-term synaptic plasticity. , 66–70 (1999). Nature 399 Yuste, R. Dendritik pərəstişlər və dağıtılmış pərəstişlər. Neuron 71, 772–781 (2011). Xatırladaq ki, ABŞ-ın İŞİD-in hökmranlığı ilə bağlı siyasi müzakirələr baş verib. Rall, W. Branching dendritic trees and motoneuron membrane resistivity. , 491–527 (1959). Exp. Neurol. 1 Xatırladaq ki, Xatırladaq ki, Xatırladaq ki, Xatırladaq ki, Xatırladaq ki, Xatırladaq ki, Xatırladaq ki, Xatırladaq ki, Xatırladaq ki, Xatırladaq ki, Xatırladaq ki, Xatırladaq ki, Xatırladaq ki, Xatırladaq ki, Xatırladaq ki, Xatırladaq ki, Xatırladaq ki, Xatırladaq ki, Xatırladaq. Silver, D. et al. Mastering the game of go with deep neural networks and tree search. , 484–489 (2016). Nature 529 Silver, D. et al. A general reinforcement learning algorithm that masters chess, shogi, and go through self-play. , 1140–1144 (2018). Science 362 McCloskey, M. & Cohen, N. J. Connectivist ağlarda katastrofik mübahisə: sekvential öyrənmə problemi. French, R. M. Catastrophic forgetting in connectionist networks. , 128–135 (1999). Trends Cogn. Sci. 3 Naud, R. & Sprekeler, H. Sparse bürcüləri multiplexed sinir kodunda məlumat xətti optimize edir. Proc. Natl Acad. Sci. USA 115, E6329-E6338 (2018). Sacramento, J., Costa, R. P., Bengio, Y. & Senn, W. Dendritic cortical microcircuits approximate the backpropagation algorithm. in (NeurIPS*,* 2018). Advances in Neural Information Processing Systems 31 (NeurIPS 2018) Payeur, A., Guerguiev, J., Zenke, F., Richards, B. A. & Naud, R. Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits. , 1010–1019 (2021). Nat. Neurosci. 24 Bicknell, B. A. & Häusser, M. A synaptic learning rule for exploiting nonlinear dendritic computation. , 4001–4017 (2021). Neuron 109 Moldwin, T., Kalmenson, M. & Segev, I. The gradient clusteron: a model neuron that learns to solve classification tasks via dendritic nonlinearities, structural plasticity, and gradient descent. , e1009015 (2021). PLoS Comput. Biol. 17 Hodgkin, A. L. & Huxley, A. F. A quantitative description of membrane current and Its application to conduction and excitation in nerve. , 500–544 (1952). J. Physiol. 117 Rall, W. Theory of physiological properties of dendrites. , 1071–1092 (1962). Ann. N. Y. Acad. Sci. 96 Hines, M. L. və Carnevale, N. T. Neuron simulasiyası ortamı. Neural Comput. 9, 1179-1209 (1997). Bower, J. M. & Beeman, D. in (eds Bower, J.M. & Beeman, D.) 17–27 (Springer New York, 1998). The Book of GENESIS: Exploring Realistic Neural Models with the GEneral NEural SImulation System Hines, M. L., Eichner, H. & Schürmann, F. Neuron splitting in compute-bound parallel network simulations enables runtime scaling with twice as many processors. , 203–210 (2008). J. Comput. Neurosci. 25 Hines, M. L., Markram, H. & Schürmann, F. Yalnız neuronların tamamilə paralel simülasiyası. J. Comput. Neurosci. 25, 439-448 (2008). Ben-Shalom, R., Liberman, G. & Korngreen, A. Accelerating compartmental modeling on a graphical processing unit. , 4 (2013). Front. Neuroinform. 7 Tsuyuki, T., Yamamoto, Y. & Yamazaki, T. Graphics processing units. in Proc. 2016 International Conference on Neural Information Processing (eds Hirose894Akiraet al.) 279–285 (Springer International Publishing, 2016). Vooturi, D. T., Kothapalli, K. & Bhalla, U. S. Parallelizing Hines Matrix Solver in Neuron Simulations on GPU. In 388–397 (IEEE, 2017). Proc. IEEE 24th International Conference on High Performance Computing (HiPC) Huber, F. GPU-da hin matrisləri üçün effektiv ağ solver. Preprint at https://arxiv.org/abs/1810.12742 (2018). Korte, B. & Vygen, J. Combinatorial Optimization Theory and Algorithms 6 edn (Springer, 2018). Gebali, F. Algorithms və Parallel Computing (Wiley, 2011) CoreNEURON: NEURON simulators üçün optimize edilmiş hesablama motoru. Front. Neuroinform. 13, 63 (2019). Urbanczik, R. & Senn, W. Learning by the dendritic prediction of somatic spiking. , 521–528 (2014). Neuron 81 Ben-Shalom, R., Aviv, A., Razon, B. & Korngreen, A. Optimizing ion channel models using a parallel genetic algorithm on graphical processors. , 183–194 (2012). J. Neurosci. Methods 206 Mascagni, M. A parallelizing algorithm for computing solutions to arbitrarily branched cable neuron models. , 105–114 (1991). J. Neurosci. Methods 36 2013-cü ildə aparılmış arxeoloji tədqiqat işləri burada 120 kv.metrlik ərazidə yaşayış yerinin qalığının olduğunu söyləməyə əsas verir (1). Migliore, M., Messineo, L. & Ferrante, M. Dendritic Ih selectively blocks temporal summation of unsynchronized distal inputs in CA1 pyramidal neurons. , 5–13 (2004). J. Comput. Neurosci. 16 Hemond, P. et al. Distinct classes of pyramidal cells exhibit mutually exclusive firing patterns in hippocampal area CA3b. , 411–424 (2008). Hippocampus 18 Hay, E., Hill, S., Schürmann, F., Markram, H. & Segev, I. Models of neocortical layer 5b pyramidal cells capturing a wide range of dendritic and perisomatic active Properties. , e1002107 (2011). PLoS Comput. Biol. 7 Masoli, S., Solinas, S. & D’Angelo, E. Action potential processing in a detailed purkinje cell model reveals a critical role for axonal compartmentalization. , 47 (2015). Front. Cell. Neurosci. 9 Lindroos, R. et al. Bazal gangliya neuromodulasiya bir çox zaman və struktural ölçeklərdə - doğrudan yol MSN-lərin simülasiyaları dopaminergik efektlərin sürətli başlanğıcını araşdırır və Kv4.2 rolunu öngörür. M.Migliore, M. et al. Synaptic clusters function as odor operators in the olfactory bulb. Proc. Natl Acad. Sci. USa 112, 8499–8504 (2015). NVIDIA. . (2021). CUDA C++ Programming Guide https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html Nvidia C++ Guide. https://docs.nvidia.com/cuda/cuda-c-best-practices-guide/index.html (2021). Harnett, M. T., Makara, J. K., Spruston, N., Kath, W. L. & Magee, J. C. Synaptic amplification by dendritic spines enhances input cooperativity. , 599–602 (2012). Nature 491 C. Q. et al. GABAergic inhibisiya dendritik spinlər tərəfindən partitionalization. Science 340, 759–762 (2013). Tønnesen, J., Katona, G., Rózsa, B. & Nägerl, U. V. Spine neck plasticity regulates compartmentalization of synapses. , 678–685 (2014). Nat. Neurosci. 17 Eyal, G. et al. Human cortical pyramidal neurons: from spines to spikes via models. , 181 (2018). Front. Cell. Neurosci. 12 Koch, C. & Zador, A. The function of dendritic spines: devices subserving biochemical rather than electrical compartmentalization. , 413–422 (1993). J. Neurosci. 13 Koch, C. Dendritic spines. In (Oxford University Press, 1999). Biophysics of Computation Rapp, M., Yarom, Y. & Segev, I. Paralel fiber arxa aktivliyinin cerebellar purkinje hücrenin kabloları özəllikləri üzərində etkisi Neural Comput. 4, 518-533 (1992). Hines, M. Efficient computation of branched nerve equations. , 69–76 (1984). Int. J. Bio-Med. Comput. 15 Nayebi, A. & Ganguli, S. Biologically inspired protection of deep networks from adversarial attacks. Preprint at (2017). https://arxiv.org/abs/1703.09202 Goddard, N. H. & Hood, G. Parallel GENESIS-i istifadə edən geniş ölçekli simülasiya. in The Book of GENESIS: Exploring Realistic Neural Models with the General Neural Simulation System (eds Bower James M. & Beeman David) 349-379 (Springer New York, 1998). M. Migliore, M., Cannia, C., Lytton, W. W., Markram, H. & Hines, M. L. Neuron ilə paralel ağ simulasiyası. Lytton, W. W. et al. Simulation neurotechnologies for advancing brain research: parallelizing large networks in NEURON. , 2063–2090 (2016). Neural Comput. 28 Valero-Lara, P. et al. cuHinesBatch: Solving multiple Hines systems on GPUs human brain project. In 566–575 (IEEE, 2017). Proc. 2017 International Conference on Computational Science Akar, N. A. et al. Arbor — Modern yüksək performanslı kompüter arkitekturaları üçün morfoloq olaraq ayrıntılı nöral ağ simülasiya kütləvi. 27th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP) 274–282 (IEEE, 2019). Ben-Shalom, R. et al. NeuroGPU: Accelerating multi-compartment, biophysically detailed neuron simulations on GPUs. , 109400 (2022). J. Neurosci. Methods 366 Rempe, M. J. & Chopp, D. L. A predictor-corrector algorithm for reaction-diffusion equations associated with neural activity on branched structures. , 2139–2161 (2006). SIAM J. Sci. Comput. 28 Kozloski, J. & Wagner, J. böyük ölçekli sinir doku simülasiyasına ultraskalabil bir çözüm. Front. Neuroinform. 5, 15 (2011). Jayant, K. et al. Targeted intracellular voltage recordings from dendritic spines using quantum-dot-coated nanopipettes. , 335–342 (2017). Nat. Nanotechnol. 12 Palmer, L. M. & Stuart, G. J. Membrane potential changes in dendritic spines during action potentials and synaptic input. , 6897–6903 (2009). J. Neurosci. 29 Nishiyama, J. & Yasuda, R. Biochemical computation for spine structural plasticity. , 63–75 (2015). Neuron 87 Yuste, R. & Bonhoeffer, T. Morphological changes in dendritic spines associated with long-term synaptic plasticity. , 1071–1089 (2001). Annu. Rev. Neurosci. 24 Holtmaat, A. & Svoboda, K. Experience-dependent structural synaptic plasticity in the mammalian brain. , 647–658 (2009). Nat. Rev. Neurosci. 10 Caroni, P., Donato, F. & Muller, D. Structural plasticity upon learning: regulation and functions. , 478–490 (2012). Nat. Rev. Neurosci. 13 Keck, T. et al. Yetişkin göz kortexunun funksiyalı reorganizasiyası sırasında neuronal qarışıqların böyük restrukturizasiyası. S.B. Hofer, Mrsic-Flogel, T.D., Bonhoeffer, T. & Hübener, M. Experience kortikal qarışıqlarda uzun müddətli struktural iz qoyur. Trachtenberg, J. T. et al. Long-term in vivo imaging of experience-dependent synaptic plasticity in adult cortex. , 788–794 (2002). Nature 420 Marik, S. A., Yamahachi, H., McManus, J. N., Szabo, G. & Gilbert, C. D. Somatosensor kortexdə excitatory və inhibitory neuronların axonal dinamikası. PLoS Biol. 8, e1000395 (2010). Xu, T. et al. Sürətli motor xatirələri üçün sinapsların sürətli yaradılması və seçici stabilizasiyası. Nature 462, 915–919 (2009). Albarran, E., Raissi, A., Jáidar, O., Shatz, C. J. & Ding, J. B. Enhancing motor learning by increasing the stability of newly formed dendritic spines in the motor cortex. , 3298–3311 (2021). Neuron 109 Branco, T. & Häusser, M. Synaptic integration gradients in single cortical pyramidal cell dendrites. , 885–892 (2011). Neuron 69 Major, G., Larkum, M. E. & Schiller, J. Active properties of neocortical pyramidal neuron dendrites. , 1–24 (2013). Annu. Rev. Neurosci. 36 Gidon, A. et al. Dendritik aktivlıq potansiyalləri və insan dərəcə 2/3 kortikal neuronlarda hesabat. Doron, M., Chindemi, G., Muller, E., Markram, H. & Segev, I. Timed synaptic inhibition shapes NMDA spikes, influencing local dendritic processing and global I/O properties of cortical neurons. , 1550–1561 (2017). Cell Rep. 21 Du, K. et al. Cell-type-specific inhibition of the dendritic plateau potential in striatal spiny projection neurons. , E7612–E7621 (2017). Proc. Natl Acad. Sci. USA 114 Smith, S. L., Smith, I. T., Branco, T. & Häusser, M. Dendritic spikes enhance stimulus selectivity in cortical neurons in vivo. , 115–120 (2013). Nature 503 Xu, N.-l et al. Nonlinear dendritic integration of sensory and motor input during an active sensing task. , 247–251 (2012). Nature 492 Takahashi, N., Oertner, T. G., Hegemann, P. & Larkum, M. E. Active cortical dendrites modulate perception. , 1587–1590 (2016). Science 354 Sheffield, M. E. & Dombeck, D. A. Calcium transient prevalence across the dendritic arbour predicts place field properties. , 200–204 (2015). Nature 517 Markram, H. et al. Reconstruction and simulation of neocortical microcircuitry. , 456–492 (2015). Cell 163 Billeh, Y. N. et al. Systematic integration of structural and functional data into multi-scale models of mouse primary visual cortex. , 388–403 (2020). Neuron 106 Hjorth, J. et al. The microcircuits of striatum in silico. , 202000671 (2020). Proc. Natl Acad. Sci. USA 117 Guerguiev, J., Lillicrap, T. P. & Richards, B. A. Towards deep learning with segregated dendrites. , e22901 (2017). elife 6 Iyer, A. et al. Avoiding catastrophe: active dendrites enable multi-task learning in dynamic environments. , 846219 (2022). Front. Neurorobot. 16 Jones, I. S. & Kording, K. P. Might a single neuron solve interesting machine learning problems through successive computations on its dendritic tree? , 1554–1571 (2021). Neural Comput. 33 Bird, A. D., Jedlicka, P. & Cuntz, H. Dendritic normalisation improves learning in sparsely connected artificial neural networks. , e1009202 (2021). PLoS Comput. Biol. 17 Goodfellow, I. J., Shlens, J. & Szegedy, C. Explaining and harnessing adversarial examples. In (ICLR, 2015). 3rd International Conference on Learning Representations (ICLR) Papernot, N., McDaniel, P. & Goodfellow, I. Transferability in machine learning: from phenomena to black-box attacks using adversarial samples. Preprint at (2016). https://arxiv.org/abs/1605.07277 Lecun, Y., Bottou, L., Bengio, Y. & Haffner, P. Gradient-based learning applied to document recognition. , 2278–2324 (1998). Proc. IEEE 86 Xiao, H., Rasul, K. & Vollgraf, R. Moda-MNIST: makine öyrənmə algoritmalarını benchmarking üçün yeni bir görüntü datasetidir. Preprint at http://arxiv.org/abs/1708.07747 (2017). Bartunov, S. et al. Assessing the scalability of biologically-motivated deep learning algorithms and architectures. In (NeurIPS, 2018). Advances in Neural Information Processing Systems 31 (NeurIPS 2018) Rauber, J., Brendel, W. & Bethge, M. Foolbox: A Python toolbox to benchmark the robustness of machine learning models. In (2017). Reliable Machine Learning in the Wild Workshop, 34th International Conference on Machine Learning Rauber, J., Zimmermann, R., Bethge, M. & Brendel, W. Foolbox native: fast adversarial attacks to benchmark the robustness of machine learning models in PyTorch, TensorFlow, and JAX. , 2607 (2020). J. Open Source Softw. 5 Paszke, A. et al. PyTorch: An imperative style, high-performance deep learning library. In (NeurIPS, 2019). Advances in Neural Information Processing Systems 32 (NeurIPS 2019) He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. In 770–778 (IEEE, 2016). Proc. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) tanınması The authors sincerely thank Dr. Rita Zhang, Daochen Shi and members at NVIDIA for the valuable technical support of GPU computing. This work was supported by the National Key R&D Program of China (No. 2020AAA0130400) to K.D. and T.H., National Natural Science Foundation of China (No. 61088102) to T.H., National Key R&D Program of China (No. 2022ZD01163005) to L.M., Key Area R&D Program of Guangdong Province (No. 2018B030338001) to T.H., National Natural Science Foundation of China (No. 61825101) to Y.T., Swedish Research Council (VR-M-2020-01652), Swedish e-Science Research Centre (SeRC), EU/Horizon 2020 No. 945539 (HBP SGA3), and KTH Digital Futures to J.H.K., J.H., and A.K., Swedish Research Council (VR-M-2021-01995) and EU/Horizon 2020 no. 945539 (HBP SGA3) to S.G. and A.K. Part of the simulations were enabled by resources provided by the Swedish National Infrastructure for Computing (SNIC) at PDC KTH partially funded by the Swedish Research Council through grant agreement no. 2018-05973. This paper is CC by 4.0 Deed (Attribution 4.0 International) lisenziyası. available on nature Bu kitab CC by 4.0 Deed (Attribution 4.0 International) lisenziyası. Təbii ki, doğa
