20/04/12 15:22:46.15 juW0pBg5d
What Stage Does Activation Affect?
Using the logic outlined above, it is possible to obtain estimates of t1, (t2 + k), t3, and t4 for second category presentations at Lags 0 and 2.
These estimates, along with the estimates given above for initial presentation, are shown in Table 1.
The statistical analyses of the data indicate that the only parameter which reliably changes over lag condition is t2 + k.
If we make the reasonable assumption that k remains constant over lag conditions, then t2, the category search time, constitutes the locus of the activation effect.
This finding agrees with the conclusion of Meyer (1973, p.30), who noted that “The semantic distance between categories...may affect the search rate for the second category.”
The invariance of encoding time (t4) over lag condition is somewhat at odds with the finding of Meyer et al (1972, Experiment 3) that encoding time appears to be shortened by prior processing of semantically similar information.
The reason for this discrepancy is not entirely clear.
A possible explanation may lie in the fact that the processing delay between the two categories was much shorter in the Meyer et al experiment than in the present experiment,
and the activation decay function for encoding time may be different from the analogous decay function for search rate.
THE INFLUENCE OF ONE MEMORY RETRIEVAL
47l
--
A possible explanation may lie in the fact that the processing delay between the two categories was shorter than the present experiment,and the activation decay function for encoding time may be different from the analogous decay function for search rate
743:YAMAGUTIseisei
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745:YAMAGUTIseisei
20/06/19 20:32:52.46 ZY15djw41
>>727-744
URLﾘﾝｸ(link.springer.com)
URLﾘﾝｸ(link.springer.com)
>>732
If “dominance" is defined as the frequency with which a word is given as an exemplar of a category, then one of the two category-letter stimuli will be referred to as more dominant than the other.
>>741
By appropriate manipulations of Eqs 1a-4a, we find that t1 = 0.27 sec (RT2 - RTI); (t2 + k) = 1.69 sec (RT1), t3 = 0.13 sec (RT4 - RT3); and t4 = 0.22 sec (RT3 - RT2 ).
If the null hypothesis of no interaction is accepted, then inspection of Eqs 1a-4a indicates that t, = t3.
746:YAMAGUTIseisei
20/08/28 00:26:14.31 STE/0glun
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* Article
* Open Access
* Published: 25 February 2020
Memristive synapses connect brain and silicon spiking neurons
* Alexantrou Serb1,
* Andrea Corna2,
* Richard George3,
* Ali Khiat1,
* Federico Rocchi2,
* Marco Reato2,
* Marta Maschietto2,
* Christian Mayr3,
* Giacomo Indiveri ORCID: orcid.org/0000-0002-7109-16894,
* Stefano Vassanelli ORCID: orcid.org/0000-0003-0389-80232 &
* Themistoklis Prodromakis ORCID: orcid.org/0000-0002-6267-69091
Scientific Reports volume 10, Article number: 2590 (2020) Cite this article
:
Subjects
* Bionanoelectronics
* Nanosensors
747:YAMAGUTIseisei
20/08/28 00:34:57.19 STE/0glun
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
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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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Download references
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
757: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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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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759:YAMAGUTIseisei
21/09/07 11:21:59.91 Sg5KSVwHZ
sage
760: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).
761: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
762: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
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