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Memristors
The memristive synapse set-up consisted of an array of memristive devices positioned inside an ArC memristor characterisation and testing instrument33 (Supplementary Fig.5. http:www.arc-instruments.co.uk).
The instrument is controlled by a PC, which handles all the communications over UDP; all through a python-based user interface.
The software is configured to react to UDP packets carrying information about the firing of either artificial or biological neurons (who fired when).
Once a packet is received,
the ID of the neuron that emitted it and the time of spiking are both retrieved from the packet payload and the neural connectivity matrix is consulted in order to determine which neurons are pre- and which are post-synaptic to the firing cell.
Then, if the plasticity conditions are met, the ArC instrument applies programming pulses that cause the memristive synapses to change their resistive states.
Importantly, the set-up can control whether LTP- or LTD-type plasticity is to be applied in each case, but once the pulses have been applied it is the device responses that determine the magnitude of the plasticity.
Notably, resistivity transitions of the device are non-volatile, they hold over at least hours27 as also exemplified in our prototype experiment and are therefore fully compatible with typical LTP and LTD time scales of natural synapses.
The system is sustained by a specific methodology for handling timing within the overall network (Zurich, Southampton, Padova).
The set-up in Southampton being the node that links Zurich and Padova together, controls the overall handling of time.
--
Once a packet is received, the ID of the neuron that emitted it and the time of spiking are retrieved from the neural connectivity matrix (held at the Southampton set-up) is consulted
the ID of the neuron that emitted it and the time of spiking are both retrieved from the packet payload and the neural connectivity matrix (held at the Southampton set-up) is consulted
789:YAMAGUTIseisei
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Under this system, one of the partners (in our case Zurich) is labelled as the “primary partner” and all timing information arriving from that partner is treated as a ground truth.
Every timing information sent by other partners then has to be related to this ground truth, for example if the primary partner says that neuron 12 fires a spike at time 305, then the secondary partner(s) is informed of this (through Southampton).
If then a neuron in the secondary partner set-up fires 5 time units (as measured by a wall-clock) after being informed of the firing of neuron 12, it emits a packet informing Southampton that e.g. neuron 55 fired at time 310.
This way the relative timing between spikes arriving from the primary partner and the spikes triggered by the secondary partner(s) in response is maintained despite any network delays.
The price is that if the secondary partners wish to communicate spikes to the primary partner, network delays for the entire round-trip are then burdening the secondary-to-primary pathway.
The details of timing control at each partner site are fairly complicated and constrained by the set-ups at each partner, but all timing information is eventually encoded in an “absolute time” record held at Southampton.
The rationale behind this design decision was to ensure that at least in the pathway from primary to secondary partner(s) timing control is sufficiently tight to sustain plasticity in the face of network delays.
Neuronal culture and electrophysiology
Embryonic (E18) rat hippocampal neurons were plated and cultured on the CMEA according to procedures described in detail in34.
Recordings were performed on 812 DIV neurons.
The experimental setup in UNIPD(Supplementary Fig.1)enabled UDP-triggered capacitive stimulation of neurons13 while simultaneously recording and communicating via UDP the occurrence of depolarisations that were measured by patch-clamp whole-cell recording
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The CMEA (20 × 20 independent TiO2 capacitors, each one of area 50 × 50 μm2) was controlled by a dedicated stimulation board and all the connections to partners, Southampton and Zurich, were managed by a PC running a LabVIEW-based software
(National Instruments Corp, Austin, TX, USA).
The stimulation protocol was derived from13 and further optimized for non-invasive adjustable stimulation of the neurons.
In brief, capacitive stimulation was adjusted to the memristor’s resistance (i.e. the synaptor weight) by varying the repetition number of appropriate stimulation waveforms (Supplementary Fig.1).
Patch-Clamp recordings were performed in whole-cell current-clamp configuration using an Axopatch 200B amplifier ( USA) connected to the PC through a BNC-2110 Shielded Connector Block ( TX, USA) along with a PCI-6259 PCI Card ( TX, USA).
WinWCP (Strathclyde Electrophysiology Software, University of Strathclyde, Glasgow, UK) was used for data acquisition.
Micropipettes were pulled from borosilicate glass capillaries (GB150T-10, Science Products GmbH, Hofheim, Germany) using a P-97 Flaming/Brown Micropipette Puller (Sutter Instruments Corp., Novato, CA, USA).
Intracellular pipette solution and extracellular solution used during the experiments were respectively (in mM): 6.0 KCl, 120 K gluconate, 10 HEPES, 3.0 EGTA, 5 MgATP, 20 Sucrose (K); 135.0 NaCl, 5.4 KCl, 1.0 MgCl2, 1.8 CaCl2, 10.0 Glucose, 5.0 HEPES (N).
Digitised recordings were analysed by a custom LabVIEW software running on the PC, allowing detection and discrimination of firing and EPSP activity through a thresholding approach.
All experiments were performed in accordance with the Italian and European legislation for the use of animals for scientific purposes and protocols approved by the ethical committee of the University of Padova and by the Italian Ministry of Health
(authorisation number 522/2018-PR).
--
Molecular Devices, USA
National Instruments Corp, Austin, TX, USA
adjusted to pH 7.3 with 1N KOH
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References
1.
O’Doherty, J. E. et al.
Active tactile exploration using a brain-machine-brain interface.
Nature 479, 228-231 (2011).
* ADS * Article * Google Scholar
2.
Hampson, R. E. et al.
Developing a hippocampal neural prosthetic to facilitate human memory encoding and recall.
J. Neural Eng. 15, 036014 (2018).
* ADS * Article * Google Scholar
3.
Thakor, N. V.
Translating the Brain-Machine Interface.
Sci. Transl. Med. 5, 210ps17-210ps17 (2013).
* Article * Google Scholar
4.
Mead, C. Neuromorphic electronic systems.
Proc. IEEE 78, 1629-1636 (1990).
* Article * Google Scholar
5.
Vassanelli, S. & Mahmud, M.
Trends and Challenges in Neuroengineering: Toward “Intelligent” Neuroprostheses through Brain-“Brain Inspired Systems” Communication.
Front. Neurosci. 10 (2016).
6.
Boi, F. et al. A Bidirectional Brain-Machine Interface Featuring a Neuromorphic Hardware Decoder.
Front. Neurosci. 10 (2016).
>>748
Recordings were performed on 8-12 DIV neurons.
792:YAMAGUTIseisei
20/08/28 01:45:59.67 STE/0glun
7.
Wei, S. L. et al.
Emulating long-term synaptic dynamics with memristive devices.
ArXiV. 1509, 01998 (2015).
* Google Scholar
8.
Berdan, R. et al.
Emulating short-term synaptic dynamics with memristive devices.
Scientific reports. 6 (2015).
9.
Burr, G. W. et al.
Experimental Demonstration and Tolerancing of a Large-Scale Neural Network (165 000 Synapses) Using Phase-Change Memory as the Synaptic Weight Element.
IEEE Trans. Electron Devices 62, 34983507 (2015).
* ADS * Article * Google Scholar
10.
Yang, J. J., Strukov, D. B. & Stewart, D. R.
Memristive devices for computing.
Nat. Nanotechnol. 8, 13-24 (2013).
* ADS * CAS * Article * Google Scholar
11.
Gupta, I. et al.
Real-time encoding and compression of neuronal spikes by metal-oxide memristors.
Nat. Commun. 7, 12805 (2016).
* ADS * CAS * Article * Google Scholar
12.
Birmingham, K. et al.
Bioelectronic medicines: a research roadmap.
Nat. Rev. Drug Discov. 13, 399-400 (2014).
* CAS * Article * Google Scholar
>>749
6.0 KCl, 120 K gluconate, 10 HEPES, 3.0 EGTA, 5 MgATP, 20 Sucrose (adjusted to pH 7.3 with 1N KOH); 135.0 NaCl, 5.4 KCl, 1.0 MgCl2, 1.8 CaCl2, 10.0 Glucose, 5.0 HEPES (adjusted to pH 7.4 with 1N NaOH).
793:YAMAGUTIseisei
20/08/28 01:49:08.05 STE/0glun
13.
Schoen, I. & Fromherz, P.
Extracellular Stimulation of Mammalian Neurons Through Repetitive Activation of Na+ Channels by Weak Capacitive Currents on a Silicon Chip.
J. Neurophysiol. 100, 346-357 (2008).
* Article * Google Scholar
14.
George, R., Mayr, C., Indiveri, G. & Vassanelli, S.
Event-based softcore processor in a biohybrid setup applied to structural plasticity.
In 2015 International Conference on Event-based Control, Communication, and Signal Processing (EBCCSP) 1-4, URLリンク(doi.org) (IEEE, 2015).
15.
Rast, A. D. et al.
A location-independent direct link neuromorphic interface.
In The 2013 International Joint Conference on Neural Networks (IJCNN) 1-8, URLリンク(doi.org) (IEEE, 2013).
16.
Keren, H., Partzsch, J., Marom, S. & Mayr, C. G.
A Biohybrid Setup for Coupling Biological and Neuromorphic Neural Networks.
Front. Neurosci. 13 (2019).
17.
Dudek, S. M. & Bear, M. F.
Homosynaptic long-term depression in area CA1 of hippocampus and effects of N-methyl-D-aspartate receptor blockade.
Proc. Natl. Acad. Sci. USA 89, 4363-4367 (1992).
* ADS * CAS * Article * Google Scholar
18.
Cooper, L. N. & Bear, M. F.
The BCM theory of synapse modification at 30: interaction of theory with experiment.
Nat. Rev. Neurosci. 13, 798-810 (2012).
* CAS * Article * Google Scholar
794:YAMAGUTIseisei
20/08/28 01:51:23.51 STE/0glun
19.
Vassanelli, S., Mahmud, M., Girardi, S. & Maschietto, M.
On the Way to Large-Scale and High-Resolution Brain-Chip Interfacing.
Cogn. Comput. 4, 71-81 (2012).
* Article * Google Scholar
20.
Giacomello, M. et al.
Stimulation of Ca2+ signals in neurons by electrically coupled electrolyte-oxide-semiconductor capacitors.
J. Neurosci. Methods 198, 1-7 (2011).
* CAS * Article * Google Scholar
21.
Spira, M. E. & Hai, A.
Multi-electrode array technologies for neuroscience and cardiology.
Nat. Nanotechnol. 8, 83 (2013).
* ADS * CAS * Article * Google Scholar
22.
Alivisatos, A. P. et al.
Nanotools for Neuroscience and Brain Activity Mapping.
ACS Nano 7, 1850-1866 (2013).
* CAS * Article * Google Scholar
23.
Angle, M. R., Cui, B. & Melosh, N. A.
Nanotechnology and neurophysiology.
Curr. Opin. Neurobiol. 32, 132-140 (2015).
* CAS * Article * Google Scholar
24.
Duan, X. & Lieber, C. M.
Nanoscience and the nano-bioelectronics frontier.
Nano Res. 8, 1-22 (2015).
* Article * Google Scholar
795:YAMAGUTIseisei
20/08/28 01:52:26.41 STE/0glun
25.
Brivio, S. et al.
Experimental study of gradual/abrupt dynamics of HfO2-based memristive devices.
Appl. Phys. Lett. 109, 133504 (2016).
* ADS * Article * Google Scholar
26.
Serrano-Gotarredona, T., Masquelier, T., Prodromakis, T., Indiveri, G. & Linares-Barranco, B.
STDP and STDP variations with memristors for spiking neuromorphic learning systems.
Front. Neurosci. 7 (2013).
27.
Serb, A. et al.
Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses.
Nat. Commun. 7 (2016).
28.
Qiao, N. et al.
A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128K synapses.
Front. Neurosci. 9, 141 (2015).
* Article * Google Scholar
29.
Boegerhausen, M., Suter, P. & Liu, S.-C.
Modeling Short-Term Synaptic Depression in Silicon. Neural Comput.
15, 331-348 (2003).
* Article * Google Scholar
30.
Mitra, S., Fusi, S. & Indiveri, G.
Real-Time Classification of Complex Patterns Using Spike-Based Learning in Neuromorphic VLSI.
IEEE Trans. Biomed. Circuits Syst. 3, 32-42 (2009).
* CAS * Article * Google Scholar
796:YAMAGUTIseisei
20/08/28 01:54:02.52 STE/0glun
31.
Livi, P. & Indiveri, G.
A current-mode conductance-based silicon neuron for address-event neuromorphic systems.
In 2009 IEEE International Symposium on Circuits and Systems 2898-2901 URLリンク(doi.org) (IEEE, 2009).
32.
Deiss, S., Douglas, R. & Whatley, A.
A pulse-coded communications infrastructure for neuromorphic systems.
Pulsed Neural Netw. 157-178 (1999).
33.
Berdan, R. et al.
A u-Controller-Based System for Interfacing Selectorless RRAM Crossbar Arrays.
IEEE Trans. Electron Devices 62, 2190-2196 (2015).
* ADS * CAS * Article * Google Scholar
34.
Antonucci, D. E., Lim, S. T., Vassanelli, S. & Trimmer, J. S.
Dynamic localization and clustering of dendritic Kv2.1 voltage-dependent potassium channels in developing hippocampal neurons.
Neuroscience 108, 69-81 (2001).
* CAS * Article * Google Scholar
35.
Indiveri, G. et al.
Neuromorphic silicon neuron circuits.
Front. Neurosci. 5, 73 (2011).
* PubMed * PubMed Central * Google Scholar
36.
Stathopoulos, S. et al.
Multibit memory operation of metal-oxide bi-layer memristors.
Sci. Rep. 7 (2017).
797:YAMAGUTIseisei
20/08/28 01:54:56.67 STE/0glun
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Author information
Affiliations
1.
Centre for Electronics Frontiers, University of Southampton, Southampton, SO17 1BJ, UK
* Alexantrou Serb
* , Ali Khiat
* & Themistoklis Prodromakis
2.
Biomedical Sciences and Padua Neuroscience Center, University of Padova, Padova, 35131, Italy
* Andrea Corna
* , Federico Rocchi
* , Marco Reato
* , Marta Maschietto
* & Stefano Vassanelli
3.
Institute of Circuits and Systems, TU Dresden, Dresden, 01062, Germany
* Richard George
* & Christian Mayr
4.
Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, 8057, Switzerland
* Giacomo Indiveri
798:YAMAGUTIseisei
20/08/28 01:55:43.29 STE/0glun
Contributions
The experiments were jointly conceived by T.P., S.V. and G.I., who share senior authorship.
The experiments were jointly designed and ran by A.S., A.C., R.G., who are acknowledged as shared first authors.
A.K. manufactured the memristive devices.
FR and MR assisted with the biological system set-up and operation.
MM cultured neurons on chips.
C.M. provided valuable feedback and guidance during the write-up of the paper.
The paper was jointly written by all co-authors.
Corresponding authors
Correspondence to Stefano Vassanelli or Themistoklis Prodromakis.
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The authors declare no competing interests.
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Serb, A., Corna, A., George, R. et al. Memristive synapses connect brain and silicon spiking neurons. Sci Rep 10, 2590 (2020). URLリンク(doi.org)
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* Received: 22 October 2019
* Accepted: 21 January 2020
* Published: 25 February 2020
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800:YAMAGUTIseisei
21/09/07 11:21:59.91 Sg5KSVwHZ
sage
801:オーバーテクナナシー
21/09/14 07:52:03.64 lSdSBXgiV
UNIVERSAL TRANSFORMERS. Published as a conference paper at ICLR 2019. URLリンク(arxiv-vanity.com) URLリンク(arxiv.org)
Mostafa Dehghani* † Stephan Gouws* Oriol Vinyals
University of Amsterdam DeepMind DeepMind
dehghani@uva.nl sgouws@google.com vinyals@google.com
Jakob Uszkoreit ukasz Kaiser
Google Brain Google Brain
usz@google.com lukaszkaiser@google.com
D.4. LEARNING TO EXECUTE (LTE).
LTE is a set of tasks indicating the ability of a model to learn to execute computer programs and was proposed by Zaremba & Sutskever (2015).
These tasks include two subsets:
1) program evaluation tasks (program, control, and addition) that are designed to assess the ability of models for understanding numerical operations, if-statements, variable assignments, the compositionality of operations, and more, as well as
2) memorization tasks (copy, double, and reverse).
The difficulty of the program evaluation tasks is parameterized by their length and nesting.
The length parameter is the number of digits in the integers that appear in the programs (so the integers are chosen uniformly from [1, length]), and the nesting parameter is the number of times we are allowed to combine the operations with each other.
Higher values of nesting yield programs with deeper parse trees.
For instance, here is a program that is generated with length = 4 and nesting = 3.
Input:
j=8584
for x in range(8):
j+=920
b=(1500+j)
print((b+7567))
Target:
25011
1) program evaluation tasks (A) that are designed to assess the ability of models for understanding numerical operations, if-statements, variable assignments, the compositionality of operations, and more, as well as 2) memorization tasks (B).
802:オーバーテクナナシー
21/09/14 08:02:31.32 lSdSBXgiV
>>760
ukasz Kaiser.
--
Input:
j=8584
for x in range(8):
j+=920
b=(1500+j)
print((b+7567))
Target:
25011
803:YAMAGUTIseisei
22/05/29 03:53:57.30 npUmdHxq/
URLリンク(webcache.googleusercontent.com)
This is the html version of the file URLリンク(www.lst.ethz.ch)
Google automatically generates html versions of documents as we crawl the web.
Page 1
CellVM: A Homogeneous Virtual Machine Runtime System for a Heterogeneous Single-Chip Multiprocessor
Albert Noll ETH Zurich albert.nollaTinf.ethz ch
Andreas Gal University of California, Irvine galATuci edu
Michael Franz University of California, Irvine franzATuci edu
804: The assign and method benchmark, on the other hand, include CellVM’s worst case scenario: synchronized methods and data structures.Figure 4. Performance evaluation of low-level VM operations.Values are normalized to JamVM running on the PPE
805:オーバーテクナナシー
23/05/17 11:16:29.45 O3RhOyekU
要するに少孑化対策ってのは本来て゛あれば孑なんか産んた゛ら遺棄罪て゛逮捕懲役にされるへ゛き貧乏人に孑を産ませようという遺棄の幇助だろ
男は6Ο代て゛も妊孕能あるか゛女はз○才て゛妊娠困難.ひと昔前なら女学校時代に孑を産んだり.許嫁か゛いたり.行き遅れとか言われたりと
女性の特性に合致した社會風土によって多くの孑が作られていたわけだが、そんな大事な時期を資本家階級の家畜にする目的て゛.洗脳して
竒妙な社会的圧迫を加えて子を産めなくしてるのか゛最大原因た゛ろうに更に子供手当た゛のと憲法の下の平等すら無視した社會的歪みを加えて
余裕か゛あるから孑を作るという真っ当な家庭の子か゛10万のiPhone(笑)とか持ってて.私は買ってもらえないから始まって,公明党斉藤鉄夫
国土破壞省によるクソ航空騷音に勉強妨害されて精神的圧迫されて、路上で幸せそうな親子に斬りつけた不幸JСみたいのを増やそうとか
奨学金カ゛─なんてミ二ハ゛ン飲洒運転して事故って死んて゛る某大生とか典型た゛か゛そいつらに遊ぶ金くれてやることに何の意味があるってんた゛か
やることなすことすべてか゛てめえの私利私欲のために賄賂癒着してる資本家階級の利益を追求してるた゛けなのか゛ハ゛レハ゛レて゛キモチワルイな
創価学會員は.何百万人も殺傷して損害を与えて私腹を肥やし続けて逮捕者まで出てる世界最悪の殺人腐敗組織公明党を
池田センセ━か゛囗をきけて容認するとか本気で思ってるとしたら侮辱にもほと゛か゛あるぞ!
URLリンク(i.imgur.com)
806:オーバーテクナナシー
23/07/31 07:30:53.81 b2DRs8Y/9
土砂崩れに洪水に灼熱地獄にと殺されまくって損害を受けてるやつらは.テ囗組織自民公明を恨んて゛憎んて゛呪って戦えよ,たた゛の自然災害て゛
運が惡かっただけとか思い込んでる能天氣な被災者に、憲法13条25条29条と公然と違反しながら.カによる─方的な現状変更によって
都心まで数珠つなき゛て゛鉄道の3〇倍以上もの温室効果カ゛スをまき散らす斎藤鉄夫ら世界最悪の殺人テロ組織國土破壞省か゛JALた゛のANΑた゛の
クソアヰ又ト゛ゥた゛のテロリス├と天下り賄賂癒着して私利私欲のために莫大な温室効果ガスまき散らして気侯変動させて海水温上昇させて
かつてない量の水蒸氣を日本列島に供給させているのか゛原因て゛あって曰本と゛ころか世界中て゛災害連發、クソ航空機は核以上に廃絶すへ゛き絶対悪
という強い認識と行動を教えよう!自民公明に入れたバ力の家や農作物が流されて殺されて無━文になるのは当然の報いて゛望ましいことた゛が,
年々気候変動による被災地は国内て゛も拡大している中、次こそ殺されるという正しい理解とともにこの強盗殺人腐敗テ口政府に立ち向かおう!
破防法を適用すべきクソ航空関係者と國土破壊省のテ囗リストと゛もを皆殺しにすることは.正当防衛かつ緊急避難として合法かつ正当な権利な
創価学會員は.何百万人も殺傷して損害を与えて私腹を肥やし続けて逮捕者まて゛出てる世界最惡の殺人腐敗組織公明党を
池田センセ―が囗をきけて容認するとか本氣で思ってるとしたら侮辱にもほと゛があるそ゛!
hΤтρs://i.imgur、cοm/hnli1ga.jpeg
807:オーバーテクナナシー
24/01/16 18:40:25.80 IM8+CJJfv
疲弊してるのは分からんでもないが大川原化工機社長の「できれば謝罪して欲しい』は残念だな
関東全域毎日グルク゛ル何台ものクソヘリ飛ばしまくって望遠カメラで女風呂やらのぞき見して遊び倒して莫大な温室効果ガスまき散らして
気侯変動させて洪水、土砂崩れ、暴風、熱中症,大雪にと災害連発させて住民の生命と財産を破壊して騒音まき散らして威カ業務妨害して
子の学習環境まで破壊しながら暇すき゛るしお前らとっとと犯罪おかせやと住民イライラ犯罪惹起してる上に捏造逮捕までするデタラメ腐敗集団
警視庁や東京地検、共謀した經産省の外道公務員個人に賠償金を求償するのは当然、しかも勾留中に死亡してんだから同じ期間勾留した上に
殺人罪適用して━生かけて償わせて害悪でしかない警視庁解体に向けて運動を繰り広げよう!
5億円もの裏金發覚した腐敗政党自民党は腐敗の隠蔽のために国民の血税をクソ公務員利権に費やしてきたツケが出てる現実を認識しろよ
公務員も原発も制御しきれる悪魔ではないわけだか゛自閉隊利権まで倍増させて、すでに傀儡状態だが名実ともに統治権まで奪われるわ
(ref.) tTрs://www.сall4.jp/info.php?tУpe=items&id=I0000062
ttPs://haneda-project.jimdofree.com/ , Тtps://flighT-route.com/
TTPs://n-souonhigaisosУoudan.amebaownd.com/
808:過去ログ ★
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