Author: Erin Masterson Category: Disaster Recovery, Data Centers, Infrastructure Availability
Disaster recovery planning is an integral part of any business’s IT strategy, and is becoming more prevalent as security breaches and network outages have become common threats, and the cost of downtime has steadily increased.
In the beginning stages of disaster recovery planning, decision makers are often mistaken about what constitutes a disaster recovery plan. Many times they are misled by the idea that data backup is sufficient precaution in the event of a disaster.
“Customers often come to us seeking disaster recovery services without realizing that simply backing up their data is not enough,” says Joe Palian, Regional Account Executive at Expedient.
While having a backup strategy is important, it is not the same as a disaster recovery strategy; rather, the beginning stages of establishing a proper DR plan. A backup is a copy of your data; a disaster recovery plan is insurance that guarantees its recovery.
So, what makes backups and disaster recovery different?
1.) Data retention requirements Backups are typically performed on a daily basis to ensure necessary data retention at a single location, for the single purpose of copying data.
Disaster recovery requires the determination of the RTO (recovery time objective) in order to designate the maximum amount of time the business can be without IT systems post-disaster. Traditionally, the ability to meet a given RTO requires at least one duplicate of the IT infrastructure in a secondary location to allow for replication between the production and DR site. 2.) Recovery ability Disaster recovery is the process of failing over your primary environment to an alternate environment that is capable of sustaining your business continuity.
Backups are useful for immediate access in the event of the need to restore a document, but does not facilitate the failover of your total environment should your infrastructure become compromised. They also do not include the physical resources required to bring them online.
3.) Additional resource needs A backup is simply a copy of data intended to be restored to the original source.
DR requires a separate production environment where the data can live. All aspects of the current environment should be considered, including physical resources, software, connectivity and security. 4.) Planning process Planning a backup routine is relatively simple, since typically the only goals are to meet the RPO (recovery point objective) and data retention requirements.
A complete disaster recovery strategy requires additional planning, including determining which systems are considered mission critical, creating a recovery order and communication process, and most importantly, a way to perform a valid test.
The overall benefits and importance of a DR plan are to mitigate risk and downtime, maintain compliance and avoid outages. Backups serve a simpler purpose. Make sure you know which solution makes sense for your business needs.
Looking to improve your DR preparedness? Follow these 6 Steps.
the cost of downtime https://thecloudcalculator.com/calculators/cost-of-downtime/ often mistaken about what constitutes a disaster recovery plan https://www.expedient.com/blog/the-differences-between-backups-and-disaster-recovery/ a disaster recovery strategy https://www.expedient.com/blog/what-steps-have-you-left-out-of-your-dr-strategy/ Disaster recovery https://www.expedient.com/services/managed-services/disaster-recovery/ the failover of your total environment https://www.expedient.com/blog/expedient-push-button-dr-zertocon2018/ a way to perform a valid test https://www.expedient.com/blog/with-push-button-dr-disaster-recovery-testing-doesnt-have-to-be-a-four-letter-word/ Follow these 6 Steps bit.ly/1SFm5yp
A scalable pipeline for designing reconfigurable organisms View ORCID ProfileSam Kriegman, Douglas Blackiston, Michael Levin, and Josh Bongard PNAS first published January 13, 2020 doi.org/10.1073/pnas.1910837117
Sam Kriegman aDepartment of Computer Science, U
773 名前：niversity of Vermont, Burlington, VT 05405;
Douglas Blackiston bDepartment of Biology, Tufts University, Medford, MA 02153;cAllen Discovery Center, Tufts University, Medford, MA 02153;
Michael Levin bDepartment of Biology, Tufts University, Medford, MA 02153;cAllen Discovery Center, Tufts University, Medford, MA 02153;dWyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115
Josh Bongard aDepartment of Computer Science, University of Vermont, Burlington, VT 05405;
Phase offsets stored in the genotype were mutated by adding a number that was drawn randomly from a normal distribution with mean zero and SD s = 0.4π.
Also, contractile tissue incurs a much higher metabolic cost compared to nonmuscle tissue (the human heart consumes 1 mM ATP per second; ref. 31 ). Also, contractile tissue incurs a much higher metabolic cost compared to nonmuscle tissue (the human heart consumes ∝1 mM ATP per second; ref. 31 ). []
The influence of one memory retrieval on a subsequent memory retrieval *
GEOFFREY R. LOFTUS and ELIZABETH F. LOFTUS University of Washington, Seattle, Washington 98195
*Requests for reprints may be sent to either Loftus, Department of Psychology, University of Washington, Seattle, Washington 98195. The research was supported by a National Institute of Mental Health grant to E. Loftus and a National Science Foundation grant to G. Loftus. Appreciation is expressed to Thomas 0. Nelson for his comments on the manuscript.
Ss produced an instance of a category and following zero or two intervening items produced a second instance of the same category. The second instance was produced more quickly than the initial instance. This finding, in conjunction with other data reported in the paper, indicate that the reduction in latency for the second instance is due mostly to a reduction in the rate with which the category is searched.
In an experiment by Freedman and Loftus (1971), Ss were shown a noun category plus a restricting letter or adjective and were asked to name an instance of the category which began with the letter or which was characterized by the adjective. Reaction time to produce the response was measured. The data were discussed in terms of a model that postulated a hierarchical memory composed of noun categories (e.g., animals) with subsets (eg., birds, dogs) and supersets (e.g., living things) of each category. Retrieval from this hierarchical structure was assumed to consist of at least two major steps: (1) entering the appropriate category and (2) searching the category for an appropriate member. The times to execute Step 1 and Step 2 are hereafter denoted t1 and t2, respectively. The duration of t1 was estimated to be about .25 sec by the following reasoning. Ss saw stimuli presented with the category either first (e.g., fruit-P) or second (e.g., P-fruit) and with at least a 1/2-sec interval between the noun and restrictor. Reaction times were measured from the presentation of the second member of the pair. When the category came second, the total retrieval process began only after its presentation and included both t1 and t2, according to the model. When the category came first, however, t1 could be completed before the restrictor was shown. For example, given the stimulus fruit-P, the S could enter the category “fruits” during the interval. Since measured reaction time begins when “P” is presented, measured reaction time excludes t1 in this case. The decrease in reaction time when the category is shown first vs second can therefore be equated with t1, which is excluded in the former case and included in the latter.
More recently, Loftus (1973) asked Ss to produce a member of a category and a short time later asked them to produce a different member of that category. -- This was accomplished by showing a category-letter pair , which asked the S for an appropriate instance, then, following zero, or two intervening items, showing the same category paired with a different letter , which asked for a different instance. This was accomplished by showing a AB pair (eg., cat-P), which asked the S for an appropriate instance, then, following zero, or two intervening items, showing the same BB paired with a different letter (e.g., cat-A), which asked for a different instance. -- Interest centered around the question of whether the speed of retrieving the second instance of a category was affected by the retrieval of the first instance and/or the lag between the two retrievals. The results indicated that response latency for the second instance was shorter than response latency for the first instance and increased monotonically with the number of intervening items. For example, a S’s baseline time to name a fruit beginning with the letter “P” was 1.52 sec. However, it took him 1.22 sec to produce the same response if he had named a different fruit on the previous trial and 1.29 sec to produce the response if he had named a different fruit two trials back.
The results of the Loftus (1973) study thus indicate that the process of retrieving information from a category facilitates a subsequent retrieval from that category. However, in this experiment the S was presented with the category name and restricting letter simultaneously; retrieval time thus included both t1 and t2. Consequently, the facilitation effect could have involved a reduction in t1 or t2 or both. The present experiment is designed to distinguish among these three possibilities.
-- This was accomplished by showing a category-letter pair (eg., fruit-P), which asked the S for an appropriate instance, then, following zero, one, or two intervening items, showing the same category paired with a different letter (e.g., fruit-A), which asked for a different instance. --
In some conditions of the present experiment, an interval was inserted between the category name and the letter and the stimuli were presented either in the order category-letter or in the order letter-category [as in the Freedman & Loftus (1971) study]. As noted above, this procedure allows an estimation of t1 . Additionally in the present experiment, the S was required to name an instance of a category and shortly thereafter was asked to name a second instance of the category [as in the Loftus (1973) study]. This design is sufficient to determine the locus of the reduction in reaction time to name a second category instance.
Figure 1 shows three possible patterns of results. Suppose first that only category entry time, t1, is reduced when a second category instance is produced. In this case, the results shown in Fig.1a should obtain: the letter-category conditions (which include t1) should depend on the prior retrieval, whereas the category-letter conditions (which exclude t1) should not.
467
-- In some conditions of the present experiment, an interval was inserted between the category name and the letter and the stimuli were presented either in the order A in the order B [as in the Freedman & Loftus (1971) study].
RT * letter - category * category - letter 0 2 initial LAG
b
RT
0 2 initial LAG
c
RT
0 2 initial LAG
Fig.1. Three possible patterns of results for the relationship between time and the number of intervening items (lag) between two appearances of a critical category.
Conversely, suppose that only category search time, t2, is reduced when the second category instance is produced. Such a situation would lead to the results shown in Fig.1b. Both the category-letter and the letter-category conditions include t2, so they should be affected equally by the initial retrieval.
The final possibility is that both t1 and t2 are reduced. This situation would predict the results shown in Fig.1c. Here, the category-letter condition (which includes t2 but not t1) should be affected by the initial retrieval, but the letter-category condition (which includes both t, and t2) should be affected to a greater degree.
METHOD
Subjects Eighteen Ss from the New School for Social Research received $5 for their participation in two 1-h sessions, which occurred on 2 consecutive days. No S had previously participated in a memory experiment.
Materials Each stimulus was printed in block letters on a 5 x 8 in. index card. A stimulus always consisted of a category name plus a letter (e.g., fruit-P). Eighty critical category names were selected from the Battig and Montague (1969) and Shapiro and Palermo (1970) category norms. Each of the category names was paired with two different letters. If “dominance" is defined as the frequency with which a word is given as an exemplar of a category, then one of the twu category-letter stimuli will be referred to as more dominant than the other.
In addition to the 160 critical stimuli (80 categories each paired with two letters), 80 filler stimuli were used. The filler stimuli also consisted of a category plus a letter. Some of the filler categories were used only once; others appeared twice with two different letters. Thus, each S saw 240 unique stimuli (80 critical categories, each paired with two letters, plus 80 filler stimuli).
Design There were three within-S factors: order (category-letter vs letter-category), interval (simultaneous presentation of the stimuli vs 2.5-sec interval between the category name and the letter), and lag (Lag 0, Lag 2, and initial presentation). These factors were combined factorially, thereby giving a 2 (orders) by 2 (intervals) by 3 (lags) by 18 (Ss) design.
Each S received a different permutation of the 240 items with the following restrictions:
(1)The initial presentation of a critical category-letter pair was followed after zero or two intervening filler items (i.e., at Lag 0 or at Lag 2) by the presentation of the same category paired with a different letter. Each S received 40 stimuli presented at Lag 0 and 40 at Lag 2.
(2) On half of the trials, Ss saw the stimulus corresponding to the high dominant instance before seeing the stimulus corresponding to the low dominant instance. For the remaining trials, the reverse arrangement held. A given category was presented in the order dominant-nondominant for half the Ss and in the reverse order for the remaining half of the Ss.
Procedure Each S was told that he would see items consisting of categories and letters and that he was to respond with a word in the category that began with the given letter. He was given examples and told to respond as quickly as possible, but to avoid errors.
The S sat in front of a screen with a window covered by half-silvered glass. An index card containing the stimulus was placed in a dark enclosure behind the minor and was presented by illuminating the enclosure. A microphone was placed in front of the S, and he responded by speaking into it.
A trial consisted of the following: (a) a card with the item printed in large type was placed in the darkened enclosure; (b) the E said “ready” and pressed a button which illuminated the first member of the stimulus pair; (c) either simultaneously or after a 2.5-sec interval, the second member of the pair was automatically illuminated and an electric timer started; (d) the S’s verbal response activated a voice key that stopped the timer and temrinated the trial. A warm-up period of 20 trials preceded the experimental trials each day.
Only correct responses (96%) to the critical stimuli were included in the following analyses. Median latencies were obtained for each S’s responses in each of the 12 conditions. For each condition, mean latencies were then obtained by averaging the medians from individual Ss; these means are plotted in Figs. 2 and 3. Figure 2 shows the results when the 2.5-sec interval was inserted between the category and the letter. In both the letter-category and category-letter conditions, a second instance of a category is produced faster than the first instance; furthermore, a second instance is produced faster at Lag 0 than at Lag 2. Figure 3 indicates that the same pattern of results obtains when letter and noun are presented simultaneously.
A 2 (orders) by 2 (intervals) by 3 (lags) analysis of variance was done on the latency data. Significant effects were found for lag [F(2,34) = 6.57, p < .05], category-letter order [F(1,17) = 14.71, p< .01],‘and interval [F(1,17) = 33.52, p <01].
THE INFLUENCE OF ONE MEMORY RETRIEVAL 469
None of the two-way or three-way interactions was significant (F < 1 for all cases).
Dependence of Memory Retrievals A number of studies have indicated that the time to retrieve information from a semantic category is decreased if that category has been accessed a short time previously. Collins and Quillian (1970), for example, have shown that the time required to answer such questions as “Is a canary a bird?” is decreased by as much as 600 msec if information about canaries has been accessed on the previous trial. Using a somewhat different paradigm, Meyer and Schvaneveldt (Meyer & Schvaneveldt, 1971; Meyer, Schvaneveldt, & Ruddy, 1972', Schvaneveldt & Meyer, 1973; Meyer, 1973) have shown the same thing. In these experiments, Ss were required to classify letter strings as words or nonwords. The general finding was that the reaction time to classify a letter string as a word is faster if the S has just classified 3 semantically similar word as opposed to a semantically dissimilar word. Thus, for example, the time it takes to classify “butter” as a word is faster if “butter” is preceded by “bread” than if it is preceded by “nurse.”
Two general classes of models have been proposed to handle such results. A location shifting model (Meyer & Schvaneveldt, 1971) assumes that when a S has finished processing a member of a particular category An activation model, on the other hand, assumes that when items in a category are processed, other items are “excited” or “activated” to the extent that they are semantically similar to the information being processed. Two further assumptions are made: first (Warren, 1970) that activation decays away over time and second that activated items are more readily accessible than nonactivated items.
-- A location shifting model (1971) assumes that when a S has finished processing a member of a particular category and must then shift to begin processing a second category, the shift time is dependent upon the semantic distance between the two categories.
1.90 : * Letter - Category : * Category - Letter 1.60 1.50
0 2 Initial LAG
Fig. 2. Mean reaction time in seconds as a function of the number of intervening items (lag) between two appearances of a critical category. Items were presented with a 2.5-sec interval between the category and the letter.
simult.
RT
2.20 : * Letter - Category : * Category - Letter 1.90 1.80
0 2 Initial LAG
Fig. 3. Mean reaction time in seconds as a function of the number of intervening items (lag) between two appearances of a critical category. The category and letter were presented simultaneously.
The results of the present experiment together with the data of Meyer et al (1972) and Loftus (1973) disconfirm the location shifting model and support the activation model. All of these experiments involve the following sorts of comparisons. Let T represent target information whose time to be processed is the dependent variable of interest. Let R represent information which is semantically related to T, and finally let U1 and U2 represent information which is semantically unrelated to T. Now consider three conditions:
Condition a: Process U1 ; Process U1 ; Process T. Condition b: Process R ; Process U2 ; Process T. Condition c: Process U1 ; Process R ; Process T.
The data show that T is processed fastest in Condition c, next fastest in Condition b, and slowest in Condition a. Both the location shifting model and the activation model correctly predict that reaction time in Condition c would be faster than reaction time in Conditions a and b. However, the predictions of the two models differ with regard to the relationship between Conditions a and b. A location shifting model incorrectly predicts that reaction time would be the same for Conditions a and b, since in both cases the S is shifting from the unrelated category, U2 to T. An activation model, on the other hand, correctly predicts the obtained pattern of results. This is because in Condition b, T is assumed to have been activated by R, and this activation has not decayed by the time T is processed. In Condition a, on the Other hand, T is not assumed to have been activated at all; therefore, time to process T would be longer.
Processing Stages In the outset of this report, it was noted that the semantic retrieval model proposed by Freedman and Loftus (1971) postulates two major processing stages: entering a category (which takes time t1) and searching the category (which takes time t2).
Another stage, taking time k, is a baseline stage, involving response execution, etc. Unfortunately, these stages are not sufficient to handle the data from the present experiment. To see why this is so, consider the reaction times to initially access a category. These reaction times fall into a 2 by 2 design with order (category-letter vs letter-category) and interval (2.5 sec vs simultaneous) as factors. According to the Freedman-Loftus model, the processing times involved in initial access should be as follows:
Thus reaction times for Conditions 2-4 should be equal to each other and should differ (by t1) from the reaction time to Condition 1. However, the data indicate that all four reaction times differ from one another, thereby necessitating the postulation of additional processing stages. First, in Condition 4, the predisposition to encode the category before the letter may conflict with normal left-to-right reading habits. Thus, an additional eye fixation could sometimes occur in Condition4 relative to the other three conditions. We shall label the time for this additional eye fixation t3. Secondly, when category and letter are presented simultaneously (Conditions 3 and 4), reaction time must include the time to encode both stimuli. With a 2.5-sec interval, on the other hand (Conditions 1 and 2), reaction time includes the time to encode only one of the two stimuli. Let the extra encoding time required in Conditions 3 and 4 be designated by t4.
We are now in a position to include the two new stages in the four initial reaction times.
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 ). The estimate of 0.27 sec for t1 (category entry time) coincides well with previous estimates obtained by Freedman and Loftus (1971) and Loftus and Freedman (1972). The estimate of 0.22 sec for t4 (encoding time) is far greater than one would expect if “encoding” meant only the process of pattern-recognizing the visual stimulus (cf. Sperling, 1963, who estimated 10msec per item for the pattern-recognition process). Thus the obtained estimate of 0.22 sec must include a great deal more processing, although it is impossible in the present experiment to determine what such encoding might consist of. Finally, since an eye fixation usually lasts on the order of 200-300 msec, the estimate of 0.13 sec for t3 (extra fixation time) is somewhat less than one would expect. A possible reason for this discrepancy is that additional eye fixations may not be made on all of the Condition 4 trials. The notion of an extra eye fixation sometimes occurring in Condition 4 is, of course, easily testable.
One more parenthetical remark should be made. As noted above, the interaction of interval time and category-letter order was not significant. If the null hypothesis of no interaction is accepted, then inspection of Eqs. 1a-4a indicates that t1 = t3. (This can be seen either by the fact that RT3 - RT1 = RT4 - RT2 or by the fact that RT2 - RT1 = RT4 - RT3, both of which are true under the null hypothesis.) However, since nothing in the present experiment necessarily warrants acceptance of the null hypothesis, the equality of t1 and t3 should not be taken very seriously.
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
Battig, W. F., & Montague, W. E. Category norms for verbal items in 56 categories: A replication and extension of the Connecticut category norms. Journal of Experimental Psychology Monograph, 1969. 80(3, Pt.2).
Collins, A. M., & Quillian, M. R. Facilitating retrieval from semantic memory: The effect of repeating part of an inference. In A. F. Sanders (Ed.), Attention and performance III. Amsterdam: North-Holland, 1970.
Freedman, J. L., & Loftus, E. F. Retrieval of words from long-term memory. Journal of Verbal Learning & Verbal Behavior, 1971, 10, 107-115.
Loftus, E. F. Activation of semantic memory. American Journal of Psychology. 1974. in press.
Loftus, E. F., & Freedman, J. L. Effect of category-name frequency on the speed of naming an instance of the category. Journal of Verbal Learning & Verbal Behavior, 1972, 11, 343-347.
Meyer, D. E. Correlated operations in searching stored semantic categories. Journal of Experimental Psychology, 1973, 99, 124-133.
Meyer, D. E., & Schvaneveldt, R. W. Facilitation in recognizing pairs of words: Evidence of a dependence between retrieval operations. Journal of Experimental Psychology, 1971, 90, 227-234.
Meyer, D. E., Schvaneveldt, R. W., & Ruddy, M. G. Activation of lexical memory. Paper presented at the meeting of the Psychonomic Society. St. Louis, November 1972.
Schvaneveldt, R. W., & Meyer, D. E. Retrieval and comparison processes in semantic memory. In S. Kornblum (Ed.), Attention and performance IV. New York: Academic Press, 1973
Shapiro, S. I., & Palermo, D. S. Conceptual organization and class membership: Normative data for representatives of 100 categories. Psychonomic Monograph Supplements, 1970, 3(11, Whole No. 43).
Sperling, G. A model for visual memory tasks. Human Factors, 1963, 5, 19-31.
Warren, R. E. Stimulus encoding and memory. Unpublished doctoral dissertation, University of Oregon, 1970.
(Received for publication September 17, 1973; revision accepted December 6, 1973.)
>>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.
nature.com/articles/s41598-020-58831-9 Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. : * 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
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
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. T
796 名前：he 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 []
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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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
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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Cite this article Serb, A., Corna, A., George, R. et al. Memristive synapses connect brain and silicon spiking neurons. Sci Rep 10, 2590 (2020). https://doi.org/10.1038/s41598-020-58831-9 Download citation
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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).
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
