Thierry Baron

Claire Agraffeil

Florian Dhalluin

Martin Kogelschtaz

Gilles Cunge

Thierry Chevolleau

Bassem Salem

Billel Salhi

Hicehm Abed

A Potie

L Latu-Romain

C Ternon

K Aissou

L Montes

P Mur

G Molas

B De Salvo

E Jalaguier

T Ernst

P Ferret

P Gentile

N Pauc

Emerging Nanotechnology for integration of Nanostructures in Nanoelectronic devices

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Scaling down of semiconductor devices is the driving force for the development of new applications (mobile phone, memory cards, sensors, etc.). Up to now, device downscaling has been enabled by the top-down approach, combining lithography and etching technologies. However, we will soon reach the limitation of this approach to form nanostructures with uniform properties. In this context, the bottom-up approach, i.e. elaborating nanostructures in a self-ordered manner and integrating them directly into devices, seems to be a promising way to push towards miniaturization of microelectronics components and to create new functionalities. ' In this chapter, we will focus our attention on two emerging nanotechnologies we have developed to fabricate, organize and integrate nanomaterials (nanodots, nanowires) in nanoelectronics devices.

The first one uses the natural properties of diblock copolymers 2 to create (i) a hexagonal lattice array of vertical PMMA cylinders in a polystyrene (PS) matrix, or (ii) an ordered array of horizontal PMMA cylinders in a PS matrix. These films are then used as deposition and/or etching masks to produce dense lattices of 20 nm diameter nanoparticles and nanowires with a density of around IO" cm• 2 • Examples of integration with an 8-inch wafer memory technology will be shown.

The second technology is based on chemical vapor deposition (CVD) 3 of basic nanomaterial building blocks for future microelectronics. We focus on CVD growth of Si nanostructures using a fully compatible CMOS process. The resulting nanoobjects are integrated in test devices and measured electrically.

2.

Diblock copolymer technology for nano-objects fabrication

In this chapter, we use an asymmetric polystyrene-block-methyl methacrylate (PS b-PMMA) diblock copolymer having a PMMA weight fraction of 0.3 with a molecular weight (MW) of 67100 g•mor 1 and a polydispersity of 1.09. A thin -39 nm film of PS-b-PMMA was deposited on the substrate by spin-coating a toluene diluted solution, followed by annealing at 170 °C for 24 hours. In order to employ this film as a template for nanodot or nanowire fabrication, the samples were exposed to ultraviolet (UV) light (HgXe with a -1.2 J/cm 2 fluence) for 15 minutes.

The PMMA is degraded via a chain scission under UV irradiation whereas the PS is cross-linked and becomes insoluble. Rinsing with acetic acid then removes the PMMA phase, leaving an array of holes in the PS matrix. Then, a 5 nm thick layer of metal, Pt for example, was deposited onto the deposition mask by de magnetron sputtering under an Ar pressure of 2.0 mT at ambient temperature. The formation of self-organized nanodots and nanowires is obtained by a lift-off process based mainly on 02 plasma. The nanodots could for example be integrated in a capacitor or served as a hard mask to fabricate an ordered array of nanopillars. 4

This technology was applied to form nanopillars in a multilayer made of a Si/Si0.8Ge0.2 10/10 nm superlattice epitaxially grown on a Si (100) substrate by reduced pressure CVD. Here, the Pt nanodots provided a hard mask and Cli/02 inductively coupled plasma (ICP) was used to anisotropically etch our Si/SiGe stacks. The entire processing sequence is schematically illustrated in Fig. 1.

pt(2)

(3) etching the multilayer with a Cl2/02 ICP plasma.

Pt Ar deposition Plasma Sio.aGeo.2 ... - jPm� Clz103 Plasma (5) (4)
Figure 2 shows the nanopillars etched from a two-layer Si/Si0.8Ge0.2 stack with a Cli/02 (180 sccm/25 seem) ICP plasma during 15 s. The nanopillars have a cylindrical shape with the same height (-50 nm) and diameter (-20 nm). The Pt dots are still present after the etching step. This means that the increase of the 02 flow rate in the etching gas limits the lateral etching of Si0.8Ge0.2 quantum dots. Nevertheless, Fig. 2 shows that the diameter of the pillar is lower at the locations of the Si0.8Ge0.2 layers. The significant decrease of the lateral etching of Si0.8Ge0 .2 layers with the increasing 02 flow is due to sidewall passivation by an oxide layer. [START_REF] Monget | X-ray photoelectron spectroscopy analyses of oxide-masked polycrystalline SiGe features etched in a high-density plasma source[END_REF] The thinner diameter of the Si0.8Ge0.2 regions of the nanopillars is attributed to a thinner passivation layer than on the Si regions of the nanopillars. This effect was also observed by Monget et al. [START_REF] Monget | X-ray photoelectron spectroscopy analyses of oxide-masked polycrystalline SiGe features etched in a high-density plasma source[END_REF] By analyzing the composition of the passivation layer by x-ray photoelectron spectroscopy (XPS), they determined that it contained Si, 0, and Cl. As no Ge could be detected, they concluded that germanium etch products desorb into the gas phase without re-depositing on the sidewalls. This means that only silicon contributes to formation of the passivation layer, which is consequently thinner on the SiGe layers than on the Si layers.

In order to access to the physico-chemical nature of the nanopillars after the etching process, XPS measurements were performed on the samples (not shown). The Si spectrum shows two contributions at 99.6 eV and 104 eV, commonly attributed to Si-Si and Si-0 bonds, respectively. The Ge 2p electronic level shows Ge-Ge and Ge-0 contributions at 1218 eV and 1221.5 eV respectively. 6 An interesting point to note here is that the 1221.5 eV peak corresponds to a completely oxidized Ge state 7 with no significant sub-oxide. The presence of the Ge-Ge and Si-Si peaks shows that the Ge structure of the nanopillar is still present after the etching, whereas the Ge-0 and Si-0 contributions are likely due to the sidewall passivation layers. The high reactivity of the oxygen radicals present in the gas phase during ICP explains the completely oxidized Ge state observed here.

3.

Chemical vapor deposition of nanodots and nanowires

During the last few years, silicon quantum dots (Si-QDs) have been studied for nanoelectronics applications. Their unique physical properties, due to size confinement effects and Coulomb blockade phenomena, make Si-QDs suitable for use in new silicon based devices like single electron transistors 8 or quantum dot floating gate memories. 9 Low pressure CVD is a good way to obtain Si-QDs for industrial applications because of its MOSFET technology compatibility. By controlling the early stages of the Si film growth, silicon crystallites of nanometer size (5 nm) are obtained -a size that has proven of interest to nonvolatile memory devices. 10 In order to obtain reliable devices, size, size uniformity and Si-QDs density must be controlled with a great precision and reproducibility. We have shown that a two-step CVD process makes it possible to control the QD size distribution while obtaining densities up to 10 1 2 cm• 2 -see Fig. 3.

Traditionally, Si nanowires (NWs) are grown using the vapor-liquid-solid (VLS) technique 11 where a metallic catalyst aids in the decomposition of a Si containing gas. The metal and Si form a liquid eutectic droplet that moves along with the growing end of the wire as solid Si precipitates from the melt. Gold is the most frequently used catalyst because the Au-Si eutectic point occurs at a low temperature (363 °C). 1 2 We show that Si NWs can be grown with a solid-phase epitaxy process using fully CMOS-compatible catalysts: PtSi, NiSi, and PdSi. Thus we have grown silicon nanowires by CVD using silicide catalysts and SiH4 diluted in H2 as the precursor gas. The resulting NWs are shown in Fig. 4. The growth temperature varied between 400 and 1000 °C, with silane fluxes in the 10-50 seem range. Extra HCl gas could be added to favor catalytic growth.

4.

Integration of nanoobjects

We have deposited Pt nanoparticles on p-type Si (I 00) substrate covered by a thin tunnel silicon dioxide (5 nm) via the diblock copolymer technology. This layer is integrated as a floating gate in a CMOS capacitor on an 8-inch pilot CMOS fab line. The density of nanoparticles was around 10 11 cm• 2 with the mean size centered around 20 nm. The Pt particles were covered by a 20 nm layer of Si02 deposited at low temperature. Aluminum was used as a metal gate. Figure 5 presents pictures representing the different steps of the process. Capacitance-voltage characterization has been performed for devices with and without Pt nanodots. After a gate stress of 12 V during for 500 ms, a flatband voltage shift of about 1 V was only observed in devices with nanoparticles, indicating charge storage in the Pt nanocrystals.

5.

Conclusions

We believe that the fabrication technologies based on the bottom-up approach are promising to push forward the limits of conventional technology. Ordered arrays of dense nanostructures could be obtained and integrated in devices with CMOS process compatibility. Adding new functionalities to a CMOS integrated circuit via a 3D integration technology could enable new bottom-up applications.

Figure 1 .

 1 Figure 1. Schematic process flow for Pt dot fabrication by lift-off technique and realization of pillars by using Pt dots as a hard mask: (1) PS porous template on a Si02/Si/Si0.8Ge0 2/Si/Si0.8Ge0.2/Si multilayer; (2) Pt deposition onto the mask; (3) Ar plasma to selectively remove the metal in excess on top of the PS layer; (4) 02 plasma to remove the residual polymer and side-walls metal-coated PS; and (5)

Figure 2 .

 2 Figure 2. SEM images of pillars obtained by using the Pt dot array as an etch mask in a Cl2/02 ICP etch process of Si/Si0.8Ge0.2 10/10 nm two-period superlattice on a Si substrate: (a) multiple pillars; (b) isolated -50 nm tall pillar. The arrows in (b) indicate that the diameter of the SiGe layers is lower due to lateral etching.

Figure 3 .

 3 Figure 3. Typical SEM picture of Si nanocrystals after deposition on a Si02 surface: QD density is -1012 cm• 2 , mean QD diameter is -8 nm.

Figure 4 .

 4 Figure 4. SEM picture of Si NWs obtained with different silicide catalysts: PtSi, NiSi and PdSi. The inset in the PdSi-catalyzed NW SEM shows a TEM analysis confirming the crystalline nature of the NWs.

Figure 5 .

 5 Figure 5. SEM pictures of the organized Pt nanocrystals: (a) top view; (b) cross sectional view after the low temperature Si02 deposition.

Figure 6 .

 6 Figure 6. (a) SEM image of Si NW FET, the inset shows the axial silicidation of the Si NW; (b) output characteristics of the back-gated Si NW FET.

Acknowledgments

This work was supported by the ANR French agency project PREAANS.


To develop comprehensive models and tools that allow to achieve consistently and verifiable excellent quality, safe, healthy and tasty animal-based products from both extensive and intensive husbandry systems.

Our goal?

Is all about combining the high quality of animal products to the sustainability of production. This concept, named "One Quality", allows to reward farmers and other operators in the agri-food chain who already transitioned to sustainable husbandry practices.

Our Methodology?

The INTAQT project is a five-year European Horizon 2020 project that aims to assess the relationships between animal production systems and their product quality, in the poultry, dairy and beef sectors.


Keywords: Unimodal auditory priming, Cross-modal priming, Transposed-phoneme effects, Lexical competition

We examined the contributions of phoneme-to-word facilitation and word-to-word inhibition to transposed-phoneme priming effects under unimodal and cross-modal presentations. Experiments 1A and 1B showed that the presentation of an auditory prime formed by transposing two phonemes in a given target word facilitated lexical decisions to auditory targets. This facilitation was independent of the lexicality of the primes. In Experiment 2 the targets were presented visually rather than auditorily. We found an inhibitory priming effect, which, in contrast to Experiment 1, was influenced by the lexicality of the primes, with an effect emerging only with word primes. These findings point to a greater impact of phoneme-to-word facilitation under unimodal presentation and a greater role for word-to-word inhibition under cross-modal presentation. Hence, by simply manipulating the modality of target presentation, it is possible to separately probe two central mechanisms postulated in models of spoken word recognition, namely phoneme-to-word activation and lexical competition.

How do speaker-hearers of a given language recognize the spoken words of that language? Most current models of spoken word recognition assume that several stages of processing are involved in, via which listeners succeed in mapping the speech signal onto the phonological representations of words stored in long-term memory. First, listeners extract from the speech signal units smaller than the words which may be, for instance, features [START_REF] Gaskell | Integrating form and meaning: A distributed model of speech perception[END_REF], phonemes [START_REF] Marslen-Wilson | Processing interaction and lexical access during word recognition in continuous speech[END_REF][START_REF] Norris | SHORTLIST: a connectionist model of continuous speech recognition[END_REF], syllables [START_REF] Mehler | The role of syllables in speech processing: Infant and adult data[END_REF], features and phonemes [START_REF] Mcclelland | The TRACE model of speech perception[END_REF], or phonemes and diphones [START_REF] Hannagan | Spoken word recognition without a TRACE[END_REF]. These pre-lexical units are then mapped onto whole-word phonological representations that best match the unfolding speech signal, and it is further hypothesized that not only exact matches, but also partial matches constitute potential candidates for word recognition. In Interactive-Activation models like TRACE [START_REF] Mcclelland | The TRACE model of speech perception[END_REF], all co-activated lexical representations compete for selection as the best match for a given portion of the speech signal via a process of lateral inhibition. Another key characteristic of this class of model is the cascaded nature of processing. Thus, for example, as soon as phonemes become activated this activity is fed-forward to word representations in a continuous manner. The combination of cascaded processing, interactivity, and lateral inhibitory processes, means that different words can dominate processing at different time windows, and thus word recognition may be best characterized as a succession of activation peaks rather than a succession of discrete word identifications.

The phonological priming paradigm has been often used to investigate the various stages of spoken word recognition. In this paradigm, two stimuli, the prime and target, that share phonemes in the related prime condition, are presented in close temporal succession, and participants have to perform a task (e.g., lexical decision or shadowing) on the target. Of interest is the effect related auditory primes have relative to unrelated primes on the processing of the auditory target. In the vast literature on phonological priming, two opposing effects have been reported (see [START_REF] Mcqueen | Cleaving automatic processes from strategic biases in phonological priming[END_REF][START_REF] Norris | Bias effects in facilitatory phonological priming[END_REF] for a review).

The first one is a facilitatory priming effect, that is faster responses on target words when preceded by a phonologically related prime than by an unrelated control prime, and the second one is an inhibitory priming effect, that is slower responses on target words when preceded by a phonologically related prime than by an unrelated control prime. Facilitatory effects have been interpreted as reflecting bottom-up facilitation operating between sublexical and lexical phonological representations, while inhibitory effects have been explained as reflecting competition/inhibition processes operating between co-activated lexical candidates.

One key issue in current research on spoken word recognition is how to tease apart the contributions of pre-lexical facilitation and lexical competition on phonological priming effects? One well-established method is to examine whether the phonological priming effects found with auditory primes and targets transfer under cross-modal presentation. The logic behind cross-modal presentation is that a priming effect which involves lexical representations should be modality independent and should be of similar magnitude when tested in the same (auditory) modality as across different modalities (see [START_REF] Dumay | Behavioral and electrophysiological study of phonological priming between bisyllabic spoken words[END_REF][START_REF] Spinelli | Phonological priming in spoken word recognition with bisyllabic targets[END_REF]. In contrast, an effect which involves pre-lexical representations should not be observed when primes and targets are presented in different modalities, since in this case the sublexical units activated during prime processing are not the same as those used during target processing. Such a manipulation has already proved useful in examining the precise locus of phonological priming effects when primes and targets share either their initial or final phonemes. For example, the facilitation observed when primes and targets share some of their initial phonemes [START_REF] Spinelli | Phonological priming in spoken word recognition with bisyllabic targets[END_REF] and the inhibition found when primes and targets share all of their phonemes except the last one (e.g. DRESS-DREAD; see Dufour & Peereman, 2003a;2003b;[START_REF] Radeau | Phonological priming between monosyllabic spoken words[END_REF][START_REF] Slowiaczek | Prelexical facilitation and lexical interference in auditory word recognition[END_REF] transfers under cross-modal presentation [START_REF] Radeau | Facilitatory and inhibitory phonological priming effects as a function of overlap position[END_REF][START_REF] Slowiaczek | Prelexical facilitation and lexical interference in auditory word recognition[END_REF][START_REF] Spinelli | Phonological priming in spoken word recognition with bisyllabic targets[END_REF], and these two effects have been respectively interpreted as reflecting lexical activation and competition between co-activated lexical candidates (e.g. Dufour & Peereman, 2003a;2003b;[START_REF] Radeau | Phonological priming between monosyllabic spoken words[END_REF][START_REF] Slowiaczek | Prelexical facilitation and lexical interference in auditory word recognition[END_REF][START_REF] Spinelli | Phonological priming in spoken word recognition with bisyllabic targets[END_REF].

In contrast, the facilitation found when the primes and the targets share their final phonemes (e.g. LAMP-RAMP) does not transfer under cross-modal presentation [START_REF] Dumay | Behavioral and electrophysiological study of phonological priming between bisyllabic spoken words[END_REF][START_REF] Radeau | Facilitatory and inhibitory phonological priming effects as a function of overlap position[END_REF][START_REF] Spinelli | Phonological priming in spoken word recognition with bisyllabic targets[END_REF] and has been largely taken as reflecting processes that occur before lexical access (e.g., [START_REF] Radeau | Phonological priming between monosyllabic spoken words[END_REF][START_REF] Spinelli | Phonological priming in spoken word recognition with bisyllabic targets[END_REF], and in particular the repeated activation of the same pre-lexical units during prime and target processing.

Although the unimodal procedure is more widespread than the cross-modal procedure, the presentation of the primes and the targets in different modalities appears well suited to probe competition between lexical candidates. In fact, in a recent study, [START_REF] Dufour | Investigating the locus of transposedphoneme effects using cross-modal priming[END_REF] suggested that the cross-modal priming paradigm could be even better than the unimodal priming paradigm for capturing the competition process operating between coactivated lexical candidates (i.e., lexical inhibition). In several studies using the phonological priming paradigm, [START_REF] Dufour | Phoneme-order encoding during spoken word recognition: A priming investigation[END_REF][START_REF] Dufour | The influence of word frequency on the transposedphoneme priming effect. Attention, Perception, and Pyschophysics[END_REF]2022) observed that the prior presentation of an auditory prime word (e.g., /ʀͻb/ ROBE "dress") facilitated the subsequent processing of an auditory transposed-phoneme target word (e.g., /bͻʀ/ BORD "edge"). This unimodal facilitatory transposed-phoneme priming effect was observed when both words (e.g., /ʀͻb/-/bͻʀ/; [START_REF] Dufour | Phoneme-order encoding during spoken word recognition: A priming investigation[END_REF][START_REF] Dufour | The influence of word frequency on the transposedphoneme priming effect. Attention, Perception, and Pyschophysics[END_REF] and nonwords (e.g., /biksͻt/-/biskͻt/ BISCOTTE "toasted bread"; Dufour & Grainger, 2022) were used as primes, and was found to be stronger when the target words had a higher frequency than the prime words (e.g., /ʀͻb/-/bͻʀ/) compared with when targets words had a lower frequency than primes (/bͻʀ/-/ʀͻb/; [START_REF] Dufour | The influence of word frequency on the transposedphoneme priming effect. Attention, Perception, and Pyschophysics[END_REF]. This pattern of results was interpreted as reflecting partial activation of the transposed-phoneme target word during nonword or word prime processing, with more activation generated when words increase in frequency. In a follow-up study, Dufour et al. (2022) changed the modality of target presentation (from auditory to visual) and observed clear transposed priming effects, but in the opposite direction. Thus, the prior presentation of an auditory word prime slowed-down the subsequent processing of a visual transposed-phoneme target word.

At a theoretical level, Dufour et al. (2022) concluded that in accordance with interactive-activation models of spoken word recognition [START_REF] Hannagan | Spoken word recognition without a TRACE[END_REF][START_REF] Mcclelland | The TRACE model of speech perception[END_REF], transposed-phoneme target words are not only activated during prime processing, but when the auditory prime is a word, a competition also occurs between the lexical representations of the prime and the transposed-phoneme target. As a result, the transposed-phoneme target word receives inhibition from the prime word, thus slowing-down its subsequent identification as a visual target. At a methodological level, the authors concluded that the cross-modal priming procedure could give more weight to word-to-word inhibition than to phoneme-to-word facilitation, and thus inhibitory influences are more likely to dominate than facilitatory influences under cross-modal presentation of the primes and targets. Of course, the corollary of this is that the unimodal priming procedure would give more weight to phoneme-to-word facilitation than to word-to-word inhibition, and thus facilitatory influences would be more likely to dominate than inhibitory influences under unimodal presentation of primes and targets.

Here, we tested this claim by directly comparing the influence of phoneme-to-word facilitation and word-to-word inhibition in transposed-phoneme priming under unimodal presentation (Experiments 1A & 1B) and under cross-modal presentation (Experiment 2).

More specifically, in each experiment we manipulated the lexical status of the primes, which permitted us for the first time to compare the amount of priming caused by word and nonword primes on exactly the same target words. We reasoned as follows: If as suggested by Dufour et al. (2022) the unimodal priming procedure gives more weight to phoneme-to-word facilitation than to word-to-word inhibition, then we expect to observe a facilitatory transposed-phoneme priming effect when both words and nonwords are used as primes, since in both cases the presentation of the primes cause activation of the transposed-phoneme target words. In contrast, if as suggested by Dufour et al. (2022), the cross-modal priming procedure gives more weight to word-to-word inhibition than to phoneme-to-word facilitation, then we expect to observe an inhibitory transposed-phoneme priming effect only when words are used as primes since, because by definition, nonwords having no lexical representations, they cannot directly inhibit the lexical representation of the transposed-phoneme target. Also, we predicted that the facilitatory effect which is expected with nonword primes under unimodal presentation should be reduced under cross-modal presentation, since the latter procedure is hypothesized to give less weight to phoneme-to-word facilitation.

Experiment 1A

Experiment 1A used the unimodal priming procedure in which primes and targets were presented auditorily. As auditory-auditory presentation is hypothesized to give more weight to phoneme-to-word facilitation, we predicted that a facilitatory transposed-phoneme effect would be observed when both words and nonwords are used as primes.

Method

Participants: A total of 100 participants were recruited on-line via the Prolific platform [START_REF] Palan | Prolific.ac-A subject pool for online experiments[END_REF] and were paid 8 Euros for their participation. All participants selfreported to be native speakers of French and their reported age was between 18 and 61 years.

16 participants were over 40 years old, and only one was over 60.

Materials: Sixty monosyllabic target words with a CVC syllabic structure were selected from Vocolex, a lexical database for French [START_REF] Dufour | VoCoLex: A lexical database on phonological similarity between French words[END_REF]. Half of the target words were preceded by word primes, and the other half by nonword primes. In each condition of prime lexicality (words, nonwords), two primes were associated with each of target word. Transposed prime shared the same CVC phonemes as the target word, but with the two consonants in a different order (e.g. DIGUE /dig/ "sea wall" -GUIDE /gid/ "guide" for the word priming condition; PUGE /pyʒ/ -JUPE /ʒyp/ "skirt" for the nonword priming condition), and the corresponding control prime shared only the medial vowel with the target (e.g. fiche /fiʃ/ "sheet" -GUIDE /gid/ "guide" for the word priming condition; DUVE /dyv/ -JUPE /ʒyp/ "skirt" for the nonword priming condition). The target words in each of the prime lexicality conditions, as well as the transposed and control primes were matched in frequency (i.e., for word primes), number of phonemes and durations. The main characteristics of the primes and the targets are given in Table 1. The complete set of primes and targets are given in the Appendix.

<Insert Table 1

about here>

For each condition of prime lexicality, two experimental lists were created so that each of the 60 target words was preceded by the two types of prime (transposed, control), and participants were presented with each target word only once. For the purpose of the lexical decision task, 60 target nonwords were added to each list. So that the nonwords mimicked the words, 30 of them were paired with a word prime and the 30 other were paired with a nonword prime. Among the 30 word primes, half shared the same phonemes as the target nonword but in a different order (e.g. the word prime BANQUE /bãk/ "bank" and the target nonword /kãb/), and the other half shared only the medial vowel with the target nonword (e.g. the word prime TIR /tiʀ/ "shot" and the target nonword FIPE /fip/). Also, among the 30 nonword primes, half shared the same phonemes as the target nonword but in a different order (e.g. the nonword prime VIME /vim/ and the target nonword MIVE /miv/), and the other half shared only the medial vowel with the target nonword (e.g. the nonword prime SUPE/syp/ and the target nonword BUDE/byd/). In addition, 120 unrelated prime-target pairs having no phoneme in common were added to each list. Again, for the purpose of the lexical decision task, half of the filler targets were words and the other half were nonwords. Among the 60 filler target words, 30 were paired with a word prime and the 30 other target words were paired with a nonword prime. Also, among the 60 filler target nonwords, 30 were paired with a word prime and the 30 other target nonwords were paired with a nonword prime. All the targets with their primes were recorded by a female native speaker of French, in a sound attenuated room, and digitized at a sampling rate of 44 kHz with 16-bit analog to digital recording. The durations of the primes and the targets are given in Table 1.

Procedure: The experiment was programmed using LabVanced software [START_REF] Finger | LabVanced: A Unified JavaScript Framework for Online Studies[END_REF]. Participants were instructed to put on their headphones and adjust the volume to a comfortable sound level. The primes and the targets were presented auditorily, and an interval (ISI) of 20 ms separated the offset of the primes and the onset of the targets. Participants were asked to make a lexical decision as quickly and accurately as possible by pressing the right arrow of their keyboard for the word response and the left arrow for the nonword response.

Reaction Time (RT) recording was triggered by the presentation of the target and was stopped by the response. The prime-target pairs were presented randomly, and an inter-trial interval of 2000 ms elapsed between the participant's response and the presentation of the next pair.

Participants were tested on only one experimental list and began the experiment with 12 practice trials.

Results and Discussion

Eight participants were excluded from the analyses. Among them five participants had an error rate above 50%, and the three others had RTs greater than 2,000 ms on average. One target word used in the word prime condition that gave rise to an error rate of more than 40% was also removed. The mean RT and percentage of correct responses to target words in each condition are presented in Table 2.

<Insert Table 2

about here>

RTs to target words (available at https://osf.io/c589d/; Open Science Framework;

Foster & Deardorff, 2017) were analyzed using linear mixed effects models with participants and items as crossed random factors, using R software (R Development Core Team, 2016) and the lme4 package [START_REF] Baayen | Mixed-effects modeling with crossed random effects for subjects and items[END_REF][START_REF] Bates | lme4: Linear mixed-effects models using S4 classes, R package version 2.6[END_REF]. The RT analysis was performed on correct responses, thus removing 473 data points out of 5428 (8.71%). Three extremely long RTs > 6000 ms and one short RT <300 ms were considered as outliers, and were excluded from the analysis. Moreover, in each condition RTs greater than three standard deviations above the mean were excluded from the analysis (1.90%). For the model to meet the assumptions of normally-distributed residuals and homogeneity of variance, a log transformation was applied to the RTs [START_REF] Baayen | Analyzing reaction times[END_REF] prior to running the model.

The model was run on 4858 data points. We tested a model with the variables Prime Lexicality (words, nonwords), Prime Type (transposed, control) and their interaction entered as fixed effects. The model failed to converge when random participant and item slopes were included (see [START_REF] Barr | Random effects structure for confirmatory hypothesis testing: Keep it maximal[END_REF]. Therefore the final model included only random intercepts for participants and items. We applied orthogonal contrast coding for the independent variables, namely 0.5 for one condition and -0.5 for the other condition, which allows an estimation of main effects.

The main effect of Prime Lexicality failed to reach significance (b =-0.0424, SE = 0.0222, t = -1.92, p=.06). The main effect of Prime Type was significant (b =-0.0237, SE = 0.0050, t = -4.74, p<.001) with RTs on target words being faster when preceded by transposed primes in comparison to control primes. The interaction between Prime Type and Prime Lexicality was not significant (b =-0.0013, SE = 0.0010, t = -0.13, p>.20).

The percentage of correct responses was analyzed using a mixed-effects logit model [START_REF] Jaeger | Categorical data analysis: away from ANOVAs (transformation or not) and towards logit mixed models[END_REF] following the same procedure as for RTs. The main effect of Prime type was significant (b =0.2768, SE = 0.1018, z = 2.72, p<.01) with more correct responses in the transposed priming condition than in the control priming condition. No other effect was significant.

To sum-up, in line with our predictions, both nonword and word primes generated a facilitatory transposed-phoneme priming effect that was not influenced by the lexicality of the primes.

Experiment 1B

Because RTs in Experiment 1A were somewhat longer than those generally observed in laboratory experiments, we decided to conduct a replication of Experiment 1A using a different system for programming online experiments.

Method

Participants: A total of 100 participants were recruited on-line for the experiment via the Prolific platform [START_REF] Palan | Prolific.ac-A subject pool for online experiments[END_REF] and were paid 8 Euros for their participation.

All participants self-reported to be native speakers of French. Their reported age was between 18 and 65 years. 8 participants were over 40 years old, and only one was over 60.

Materials and Procedure: The materials were the same as in Experiment 1A. The procedure was identical to Experiment 1A except that the experiment was programmed using html and php protocols and then uploaded onto the lab server.

Results and Discussion

Six participants were excluded from the analyses. Among them three participants had an error rate above 50% and the three others had RTs greater than 2000 ms on average. Three target words, one used in the nonword prime condition and the two others used in the word prime condition, that gave rise to an error rate of more than 40% were also removed. The mean RT and percentage of correct responses to target words in each condition are presented in Table 3.

<Insert Table 3 about here> RTs to target words (available at https://osf.io/c589d/) were analyzed using linear mixed effects models following the same procedure as in Experiment 1A. The RT analysis was performed on correct responses, thus removing 499 data points out of 5358 (9.31%).

Seven extremely long RTs > 6000 ms and one short RT <500 ms were considered as outliers, and were excluded from the analysis. Moreover, in each condition RTs greater than three standard deviations above the mean were excluded from the analysis (2.68%). For the model to meet the assumptions of normally-distributed residuals and homogeneity of variance, a log transformation was applied to the RTs [START_REF] Baayen | Analyzing reaction times[END_REF] prior to running the model.

The model was run on 4721 data points. We tested a model with the variables Prime Lexicality (words, nonwords), Prime Type (transposed, control) and their interaction entered as fixed effects. The model failed to converge when random participant and item slopes were included (see [START_REF] Barr | Random effects structure for confirmatory hypothesis testing: Keep it maximal[END_REF]. Therefore the final model included only random intercepts for participants and items. We applied orthogonal contrast coding for the independent variables, namely 0.5 for one condition and -0.5 for the other condition, which allows an estimation of main effects.

The main effect of Prime Lexicality was significant (b =-0.0477, SE = 0.0198, t = -2.40, p<.05) with RTs on target words being shorter when preceded by nonword primes in comparison to word primes. The main effect of Prime Type was significant (b =-0.0286, SE = 0.0054, t = -5.34, p<.001) with RTs on target words being faster when preceded by transposed primes in comparison to control primes. The interaction between Prime Type and Prime Lexicality was not significant (b =0.0109, SE = 0.0107, t = 1.01, p>.20).

The percentage of correct responses was analyzed using a mixed-effects logit model [START_REF] Jaeger | Categorical data analysis: away from ANOVAs (transformation or not) and towards logit mixed models[END_REF] following the same procedure as for RTs. The main effect of Prime Lexicality was significant (b =0.5624, SE = 0.2675, z = 2.10, p<.05) with more correct responses in the nonword prime condition than in the word prime condition. The main effect of Prime type was also significant (b =0.2812, SE = 0.0996, z = 2.82, p<.01) with more correct responses in the transposed condition than in the control condition. The interaction between Prime Type and Prime Lexicality was not significant (b =0.1389, SE = 0.1991, z = 0.70, p>.20).

In sum, we successfully replicated the results of Experiment 1A using a different procedure for programming online experiments. We again found a facilitatory transposedphoneme priming effect under unimodal (auditory-auditory) presentation, that did not depend on prime lexicality 1 . Again, RTs are on average greater than 1100ms, and thus are substantially longer than have been observed in previous auditory-auditory priming studies with monosyllabic words where average RTs typically lie between 700 and 1100 ms (see for example, [START_REF] Dufour | Phoneme-order encoding during spoken word recognition: A priming investigation[END_REF][START_REF] Dufour | The influence of word frequency on the transposedphoneme priming effect. Attention, Perception, and Pyschophysics[END_REF][START_REF] Radeau | Phonological priming between monosyllabic spoken words[END_REF][START_REF] Luce | Phonetic priming, neighborhood activation, and PARSYN[END_REF]. This is likely due to differences between in-lab experimentation and experiments run on-line. One advantage of on-line experimentation is that it enables the testing of participants from various backgrounds (not just psychology students for example) as well as being able to rapidly obtain sample sizes much larger than those typical of laboratory experiments. Thus, the greater variability in the participants that are tested, and perhaps the lower measurement precision associated with on-line experimentation might well be the source of the longer RTs.

Experiment 2

In Experiment 2, we used exactly the same materials as in Experiments 1A and 1B, but the target words were presented visually rather than auditorily. Because cross-modal presentation of the primes and targets is hypothesized to give more weight to word-to-word inhibition, we predicted that an inhibitory transposed-phoneme effect would be observed when words are used as primes. Also, because cross-modal presentation is hypothesized to give less weight to phoneme-to-word facilitation, we expected that the facilitation found in Experiment 1 with nonword primes would be reduced when the transposed-phoneme target words are visually presented. lexical decision task. However, in a different study [START_REF] Dufour | Do you want /ʃoloka/ on a /bistɔk/? On the scope of transposed-phoneme effects with non-adjacent phonemes[END_REF] we reported clear transposed-phoneme effects in an unprimed lexical decision task with nonadjacent transpositions both when the transposed phonemes belonged to a different syllable (/ʃoloka/-/ ʃokola/) and, as in the present study, to the same syllable (/bis.tͻk/-/bis.kͻt/). Post-hoc and correlation analysis in [START_REF] Dufour | Do you want /ʃoloka/ on a /bistɔk/? On the scope of transposed-phoneme effects with non-adjacent phonemes[END_REF] study revealed that the discrepancy between the two studies for the /ʃoloka/ type of stimuli was due to differences in the overall speed of responding of participants, with the effects becoming greater with slower responses.

Participants: As in Experiments 1A and 1B, a total of 100 participants were recruited on-line for the experiment via the Prolific platform [START_REF] Palan | Prolific.ac-A subject pool for online experiments[END_REF] and were paid 8 Euros for their participation. All participants self-reported being native speakers of French and their reported ages were between 18 and 61 years. 10 participants were over 40 years old, and only one was over 60.

Materials: Exactly the same materials and the same experimental lists as in Experiments 1A & 1B were used here. To ensure that the transposed-phoneme effect observed with visual targets is driven by phonological overlap and not orthographic overlap, we calculated the orthographic similarity between our word primes and targets using the Orthographic Levenshtein Distance metric. The Levenshtein distance was 3.5 and 3.4 for the control and transposed-phoneme primes, respectively. Hence, using this metric, the transposed-phoneme primes are not more orthographically similar to target words than the control primes, and this was rendered possible because French has a highly irregular mapping from sound-to-spelling (e.g., the same sound /o/ can be spelt O, Ô, OT, AU, AUX, AUT, AUD, EAU). French is therefore an ideal language for operating this specific manipulation as well as other manipulations that require that orthography and phonology be varied independently (e.g., [START_REF] Ferrand | Phonology and orthography in visual word recognition: Evidence from masked nonword priming[END_REF]. Note also that none of the transposedphoneme prime words were single letter substitution neighbors of the target words (i.e., Coltheart et al.'s, 1977, definition of orthographic neighborhood). Finally, given the evidence for transposed-letter effects (e.g., [START_REF] Perea | Does jugde activate COURT? Transposed-letter similarity effects in masked associative priming[END_REF][START_REF] Perea | Can CANISO activate CASINO? Transposed-letter similarity effects with nonadjacent letter positions[END_REF][START_REF] Perea | Transposed-letter priming effects for close versus distant transpositions[END_REF], we checked that the transposed phoneme primes were not transposed-letter neighbors of the target words. Among the 30 prime-target pairs only two were transposed-letter pairs. Procedure: The same as in Experiments 1A and 1B except that the targets were now visually displayed at the center of the screen.

Results and Discussion

Six participants were excluded from the analyses. Among them three participants had an error rate above 50%, one reported to be more than seventy years old, and the other two had average RTs greater than 2000 ms. The mean RT and percentage of correct responses to target words in each condition are presented in Table 4.

<Insert Table 4 about here> RTs to target words (available at https://osf.io/c589d/) were analyzed using linear mixed effects models following the same procedure as in Experiments 1A and 1B. The RT analysis was performed on correct responses, thus removing 373 data points out of 5640 (6.61%). Three extremely long RTs > 8000 ms were considered as outliers, and were excluded from the analysis. Moreover, in each condition RTs greater than three standard deviations above the mean were excluded from the analysis (1.96%). For the model to meet the assumptions of normally-distributed residuals and homogeneity of variance, a log transformation was applied to the RTs [START_REF] Baayen | Analyzing reaction times[END_REF] prior to running the model.

The model was run on 5161 data points. We tested a model with the variables Prime Lexicality (words, nonwords), Prime Type (transposed, control) and their interaction entered as fixed effects. The model failed to converge when random participant and item slopes were included (see [START_REF] Barr | Random effects structure for confirmatory hypothesis testing: Keep it maximal[END_REF]. Therefore the final model included only random intercepts for participants and items. We applied orthogonal contrast coding for the independent variables, namely 0.5 for one condition and -0.5 for the other condition, which allows an estimation of main effects.

The main effect of Prime Lexicality was significant (b =-0.0624, SE = 0.0251, t = -2.49, p<.05) with RTs on target words being shorter when preceded by nonword primes in comparison to word primes. The main effect of Prime Type was significant (b =0.0171, SE = 0.0064, t = 2.66, p<.01) with RTs on target words being slower when preceded by transposed primes in comparison to control primes. The interaction between Prime Type and Prime Lexicality was significant (b =-0.0284, SE = 0.0128, t = -2.21, p<.05). This interaction was due to a significant priming effect emerging only in the word prime condition (b =0.0316, SE = 0.0097, t = 3.26, p<.01) but not in the nonword prime condition (b =0.0029, SE = 0.0084, t = 0.34, p>.20).

The percentage of correct responses was analyzed using a mixed-effects logit model [START_REF] Jaeger | Categorical data analysis: away from ANOVAs (transformation or not) and towards logit mixed models[END_REF] following the same procedure as for RTs. Only the main effect of Prime Lexicality was significant (b =1.1815, SE = 0.1216, z =9.71; p<.001) with more correct responses in the nonword prime condition than in the word prime condition.

To sum-up, a clear inhibition effect was observed when the target words were presented visually rather than auditorily. Hence, by simply changing the modality of target presentation, we were able to reveal the hypothesized effects of lexical competition operating between prime words and transposed-phoneme target words. Moreover, as predicted, prime lexicality influenced cross-modal priming effects, with an inhibitory effect emerging with word primes but not with nonword primes. It should be noted that the inhibitory priming effect found with transposed-phoneme words does not modulate as a function of prime-target relative frequency (Dufour et al., 2022). In this sense, it clearly differs from the inhibitory priming effect observed in visual studies with unmasked primes that has been found to be stronger when the visually presented target words were of higher frequency that the visually presented prime words [START_REF] Colombo | Activation and inhibition with orthographically similar words[END_REF][START_REF] Lupker | Inhibitory effects in form priming: Evaluating a phonological competition explanation[END_REF][START_REF] Segui | Priming word recognition with orthographic neighbors: effects of relative prime-target frequency[END_REF].

Moreover, as indicated in the materials section, our related primes and targets i) do not involve transposed-letter words, ii) are not orthographic neighbors, iii) and perhaps more crucially, our control and related primes are well-matched in Orthographic Levenshtein Distance. Hence, we are confident that the inhibitory priming effect found with auditory primes and visual targets reflect processes involved during spoken word recognition, and in particular the inhibition of the transposed-phoneme target word during the auditory processing of the prime words.

General Discussion

On the basis of previous observations that auditory primes facilitate the processing of transposed-phoneme target words when presented auditorily [START_REF] Dufour | Phoneme-order encoding during spoken word recognition: A priming investigation[END_REF][START_REF] Dufour | The influence of word frequency on the transposedphoneme priming effect. Attention, Perception, and Pyschophysics[END_REF], 2022), but inhibit target word processing when presented visually (Dufour et al., 2022), we hypothesized that the reason for these differences is that the cross-modal priming procedure gives more weight to word-to-word inhibition (lexical competition) while the unimodal priming procedure gives more weight to phoneme-to-word facilitation (lexical activation). In order to test this hypothesis, in the present study we manipulated the lexicality of primes and compared transposed-phoneme priming effects under unimodal and cross-modal priming procedures. In the present Experiments 1A and 1B primes and targets were presented auditorily (unimodal presentation) and we found a facilitatory transposed-phoneme priming effect that was independent of prime lexicality. In Experiment 2, in which primes were presented auditorily and the target visually, we found an inhibitory priming effect which, in contrast to Experiment 1, was found to be influenced by the lexicality of the primes, with an effect emerging only when primes were words.

The results of the present study therefore provide strong support for our explanation of the different pattern of transposed-word priming effects found in our prior work as a function of the modality of prime and target presentation (auditory-auditory vs. auditory-visual). That is, that unimodal presentation strengthens the influence of bottom-up phoneme-to-word facilitation effects relative to the effects of lexical competition, whereas cross-modal presentation has the opposite impact. The combined influence of modality of prime and target presentation plus the influence of prime lexicality (nonword vs. word) provides a complete account of the present findings. Under unimodal (auditory) presentation, bottom-up facilitatory influences dominate priming effects and prime lexicality therefore has little impact. Under cross-modal presentation, lexical competition dominates priming effects, with inhibition only emerging when primes are words.

Our account of the two distinct pattern of priming effects obtained under unimodal and cross-modal presentation is compatible with interactive-activation models according to which feed-forward activation (phoneme-to-word) and lateral inhibition (word-to-word) processes are two key mechanisms involved in spoken word recognition. Perhaps the most impressive aspect of the present results is that we have shown that these two key mechanisms can be probed separately with exactly the same stimuli simply by changing the modality of target presentation. As was predicted on the basis of our prior findings concerning transposed-phoneme priming effects, unimodal presentation accentuated the effects of feedforward facilitation, whereas cross-modal presentation enhanced the role of lateral inhibition. A possibility however is that at least part of the difference observed between unimodal and cross-modal priming was driven by differences in the ease of discrimination of the prime from the target in the two priming situations. Indeed, in the unimodal situation of the present study, the same speaker was used for the primes and the targets which could have encouraged participants to attend more carefully to the prime's phonemes in order to distinguish them from the targets. This might have promoted a reliance on phonological information more than in the cross-modal situation in which there is arguably less pressure to attend carefully to the prime's phonemes. Further unimodal priming studies could therefore investigate the impact of the use of same vs. different speakers for the primes and the targets on the magnitude and direction of the transposed-phoneme priming effect. The use of different speakers for the primes and the targets could indeed diminish the reliance on phonological information such that either a smaller facilitation effect than that observed in the present study or even an inhibitory effect might be observed. In the latter scenario, this would suggest that feedforward facilitation and lateral inhibition could also be probed separately under unimodal presentation of primes and the targets simply by changing the acoustic characteristics (i.e., speaker voice) of the primes. It should also be noted that inhibitory phonological priming effects have been observed in prior research using unimodal presentation and the same speaker for the primes and the targets. Such an inhibitory effect has been observed when related primes have a high initial phonemic overlap with targets, sharing all of their phonemes except the last one (e.g. DRESS-DREAD; Dufour & Peereman, 2003a, 2003b;[START_REF] Hamburger | Phonological priming reflects lexical competition[END_REF][START_REF] Radeau | Phonological priming between monosyllabic spoken words[END_REF]. This suggests that initial phoneme overlap between prime words and target words creates stronger inhibition, most likely because the ambiguity between prime and target identity is only resolved at the very end of target processing. This is not the case for transposed-phoneme priming where the difference between prime and target words can be resolved earlier. The results of [START_REF] Toscano | Reconsidering the role of temporal order in spoken word recognition[END_REF] are in accordance with this view. Using the visual world paradigm, these authors examined the eye movements of participants who followed spoken instructions to manipulate pictures of objects presented on a computer screen. They found more fixations on the picture representing a BUS than on a control picture (e.g., the picture of a WELL) when the spoken target was SUB, thus suggesting that SUB induces activation of BUS that shares all phonemes but in a different order. Importantly, they also observed that the picture representing the word SUN, sharing its initial phonemes with the target SUB, attracted even more fixations than the picture of a BUS, thus suggesting that initial phoneme overlap words interfere more with target word identification than transposedphoneme words.

One particular model of spoken word recognition, the TISK model [START_REF] Hannagan | Spoken word recognition without a TRACE[END_REF], provides a principled explanation for the lesser inhibitory impact of related transposedphoneme words compared with initial phoneme overlap words. TISK is an interactiveactivation model similar to the TRACE model [START_REF] Mcclelland | The TRACE model of speech perception[END_REF], but it replaces the position-dependent units in TRACE by both a set of position-independent phoneme units and a set of open-diphone units that represent ordered combinations of contiguous and noncontiguous phonemes. Within such a framework, it is position-independent phoneme units that lead to activation of transposed words while diphone units that encode the order of phonemes allow the model to distinguish between transposed words. Now consider French short words like /lup/ LOUPE "magnifying glass" composed of the three phonemes units /l/, /u/, /p/ and of the three biphone units /lu/, /lp/, /up/, and its two competitors /pul/ POULE "chicken" and /luʃ/ LOUCHE "ladle". During processing of /lup/, the transposed-phoneme competitor /pul/ receives activation from the position-independent phonemes (i.e. /l/, /u/ and /p/) only, while the initial phoneme overlap competitor /luʃ/ receives activation from both the position-independent phoneme representations (i.e. /l/ and /u/) and the diphone representation (i.e. /lu/). Moreover, in TISK connection weights between diphone representations and wholeword representations are stronger than those between single phoneme representations and whole-word representations (see also [START_REF] You | TISK 1.0: An easy-to-use Python implementation of the time-invariant string kernel model of spoken word recognition[END_REF]. Thus, initial phoneme overlap competitors receive more bottom-up support than transposed-phoneme competitors, hence the greater competitive impact of initial phoneme overlap words.

In sum, our study shows that by simply changing the modality of target presentation with auditory primes, we were able to reverse the direction of priming effects. Although prior research has shown that an effect found under unimodal presentation can disappear under cross-modal presentation, to our knowledge, this is the first time in spoken word recognition research that a priming effect reverses as a function of the modality of target presentation.

Perhaps more crucially, with exactly the same materials, we were able to capture separately two key mechanisms postulated in current models of spoken word recognition, namely the bottom-up activation of lexical candidates and the subsequent competition that arises between co-activated lexical representations. On the basis of our results, we conclude that the unimodal priming paradigm is better suited than the cross-modal priming paradigm to probe bottom-up lexical activation, while the cross-modal priming paradigm is better suited than the unimodal priming paradigm to probe lexical competition.

Table 1 :

 1 Characteristics of the primes and the targets used in Experiments 1A, 1B and 2. In number of occurrences per million. 2 phonemes for the control and related primes; phonemes /letters in Experiment 2 for the targets.3 In milliseconds.

		Frequency 1	Number of	Duration 3
			phonemes and
			letters 2
	Word Prime condition
	Control	101	3	565
	Transposed	144	3	564
	Target word	96	3/4.53	579
	Nonword Prime condition
	Control	-	3	561
	Transposed	-	3	560
	Target word	93	3/4.50	582
	Note: 1

Table 2 :

 2 Mean Reaction Times (in ms) and percentages of correct responses for the control and transposed primes in each prime lexicality condition in Experiment 1A (auditory primes and targets).

		Control	Transposed Priming effect
	Word Prime
	RT	1151	1127	24
	Correct Responses	88	91
	Nonword Prime
	RT	1104	1081	23
	Correct Responses	93	94

Table 3 :

 3 Mean Reaction Times (in ms) and percentages of correct responses for the control and transposed primes in each prime lexicality condition in Experiment 1B (auditory primes and targets).

		Control	Transposed Priming effect
	Word Prime
	RT	1227	1179	48
	Correct Responses	87	89
	Nonword Prime
	RT	1164	1132	32
	Correct Responses	92	94

Table 4 :

 4 Mean Reaction Times (in ms) and percentages of correct responses for the control and transposed primes in each prime lexicality condition in Experiment 2 (auditory primes and visual targets).

		Control	Transposed Priming effect
	Word Prime
	RT	744	783	-39
	Correct Responses	90	90
	Nonword Prime
	RT	720	718	+2
	Correct Responses	96	97

Note thatDufour and Grainger (2022) did not observe transposed-phoneme effects with nonwords created by transposing nonadjacent phonemes in multisyllabic words (e.g. /ʃoloka/-/ ʃokola/) in both a primed and unprimed


Amelie Gruel
email: amelie.gruel@univ-cotedazur.fr

Antonio Vitale
email: antonio.vitale@pbl.ee.ethz.ch

Jean Martinet
email: jean.martinet@univ-cotedazur.fr

Michele Magno
email: michele.magno@pbl.ee.ethz.ch

Neuromorphic Event-Based Spatio-temporal Attention using Adaptive Mechanisms

Keywords: Event-Based Vision, Online Adaptation, Neuromorphic Hardware, Spiking Neural Networks

Contrary to RGB cameras, Dynamic Vision Sensor (DVS) output visual data in the form of an asynchronous events stream by recording pixel-wise luminance changes at microsecond resolution. While conventional computer vision approaches utilise frame-based input data, thus failing to take full advantage of the high temporal resolution, novel approaches use spiking neural networks Spiking Neural Networks (SNNs) which are more compatible to handle event-based data since these bioinspired neural models intrinsically encode information in a sparse manner using activation spikes trains. This paper presents an attentional mechanism which detects regions with higher event density by using inherent SNN dynamics combined with online weight and threshold adaptation. We implemented the network directly on Intel's research neuromorphic chip Loihi and evaluate our proposed method on the open DVS128 Gesture Dataset. Our system is able to process 1 ms of event-data in 6 ms and reject more than 50% of incoming unwanted events occurring only 20 ms after activity onset.

I. INTRODUCTION

Event-based vision is an emerging technology based on bio-inspired vision sensors that output pixel-level brightness changes instead of standard intensity frames and promises to significantly improve energy efficiency and latency in next generation of computer vision applications [START_REF] Gallego | Event-based vision: A survey[END_REF]. However, classic computer vision algorithms which rely on frame-based data for applications such as object classification [START_REF] Song | Improved convolutional neutral network based model for small visual object detection in autonomous driving[END_REF] and scene segmentation [START_REF] Peng | Multi-task adas system on fpga[END_REF] are unable to handle sparse, asynchronous event data coming from Dynamic Vision Sensors (DVSs). Approaches combining classic methods with event-based data as input rely on pre-processing this data at the cost of losing information from the data stream [START_REF] Zhu | Ev-flownet: Selfsupervised optical flow estimation for event-based cameras[END_REF]. Recent work has shown that SNNs can operate on raw event data and achieve similar performance as conventional methods [START_REF] Paredes-Vallés | Self-supervised learning of event-based optical flow with spiking neural networks[END_REF].

Neuromorphic chips, such as Intel's Loihi [START_REF] Davies | Advancing neuromorphic computing with loihi: A survey of results and outlook[END_REF], implement SNNs directly on hardware. These ultra-fast, low-power processors can be interfaced directly to DVSs and are a promising alternative for handling event-based data. However the maturity of SNN-based vision algorithms is still far from state of the art conventional methods. [START_REF] Gruel | Bio-inspired visual attention for silicon retinas based on spiking neural networks applied to pattern classification[END_REF] presented an overview of attention models which use events as input derived from classical computer vision. In [START_REF] Haessig | Neuromorphic networks on the spinnaker platform[END_REF] the authors interface two Asynchronous Time-based Image Sensor (ATIS) cameras to the neuromorphic platform SpiNNaker [START_REF] Mayr | Spinnaker 2: A 10 million core processor system for brain simulation and machine learning[END_REF] to obtain optical flow and scene depth estimation. The work in [START_REF] Vitale | Event-driven vision and control for uavs on a neuromorphic chip[END_REF] shows ultra-fast control of an Unmanned Aerial Vehicle achieved by interfacing a DVS camera directly to Intel's neuromorphic platform Kapoho Bay, the same one used in this work.

This paper presents a low-latency neuromorphic temporal attention model which detects a dynamic Region of Interest (ROI) in a scene recorded by a DVS, relying solely on the intrinsic dynamics of SNNs. A ROI is a part of the scene which contains useful information and visual attention can be defined as the behavioural and cognitive process of selectively focusing on a discrete aspect of sensory cues while disregarding other perceivable information [START_REF] Borji | State-of-the-art in visual attention modeling[END_REF].

The object tracking SNN implemented by [START_REF] Renner | Event-based attention and tracking on neuromorphic hardware[END_REF] focuses on the same task as this work. There Dynamic Neural Fields (DNF) as a soft Winner-Takes-All (WTA) are used in order to detect the salient activity by manually increasing the activity in a pre-defined region. However, the preovious approach is not applicable in real scenarios where the ROI is not known before hand. In contrast, our proposed method aims at tracking a cluster of activity which occurs first in time. To the best of our knowledge this is the first work which presents this approach and demonstrates it with experimental evaluation.

Our ROI detection mechanism relies on finding activity originating within a spatio-temporal region where many pixels are active. This activity more likely corresponds to an object of interest relevant for the task at hand than to background noise. The contributions of the paper are as follows:

• we propose and evaluate the neuromorphic spatiotemporal attention model for DVS camera using the PyNN simulator • we implement and evaluate the proposed approach on Intel's neuromorphic chip Loihi to demonstrate its benefits.

II. BACKGROUND

A. Spiking Neural Networks

SNNs are artificial neural network which mimic the dynamics of biological neuronal circuits by receiving and processing information in the form of spike trains. We selected the "Leaky Integrate-And-Fire" SNN model because of its simplicity: the membrane potential is at rest when there is no input; otherwise, it increases according to the incoming spikes, and it slowly decays towards the resting value when the input stops (leak). If the membrane potential overcomes a threshold, an output spike is produced and the membrane potential is reset.

B. Neuromorphic Hardware: Intel's Loihi

Intel Loihi is a recent neuromorphic research chip [START_REF] Davies | Advancing neuromorphic computing with loihi: A survey of results and outlook[END_REF] that has been used in this work as processing platform. In particular, the Kapoho Bay platform, which comes in a USB form factor with 2 Loihi chips with a total of 256 neuro-cores able to simulate 262.144 neurons and up to 260Mn synapses. It can be easily interfaced for live communication with a host system1 by programming the three embedded x86 processors, which are used for monitoring and I/O spike management embedded on the Loihi chip.

C. DVS Data

An event camera consists of a pixel array which responds to luminance changes in a sparse and asynchronous fashion. An event e i = {x i , p i , t i }, with polarity p i ∈ {+, -}, is triggered at pixel location x i = {x i , y i } at time t i whenever the change of luminance at the pixel exceeds above or decreases below the contrast sensitivity threshold [START_REF] Gallego | Event-based vision: A survey[END_REF]. To design and evaluate the proposed neuromorphic model, an existing dataset has been used. The DVS128 Gesture Dataset is a commonly used neuromorphic classification dataset of different gestures performed by multiple participants in front of a 128 × 128 DVS [START_REF] Amir | A Low Power, Fully Event-Based Gesture Recognition System[END_REF]. Our target is the hand(s) as the objects of interest. The samples from 3 classes were used in various combinations for the demonstration of this spatio-temporal attention model: "arm roll" (1), "hand clap" (2) and "right hand clockwise" [START_REF] Gruel | Bio-inspired visual attention for silicon retinas based on spiking neural networks applied to pattern classification[END_REF].

The samples are separated into two sub-types, either "large" or "small", according to the number of events in the sample and the ratio of active pixels in the sensor: classes 1 and 2 are "small" and class 7 is "large". New samples with size 128×256 were generated by concatenating samples from both types. We balance our dataset by allowing the first gesture to start either on the right or on the left. A varying time shift to the start of the second sample is added, using shifts of 0.5ms, 1ms, 5ms, 10ms, 20ms and 50ms, to demonstrate the accuracy of the filtering increasing with the time shift.

III. NEUROMORPHIC MODEL

This section describes the main contribution of the paper that is the proposed SNN architecture for the temporal filtering of DVS data. This whole mechanism relies solely on intrinsic SNN dynamics and dynamic adaptation rules applied to synaptic weights and population thresholds. This is a crucial features as this leads to minimise the latency as it doesn't require the conversion of spiking event in a frame. These are modified dynamically over time according to the layers' activation, which allows for a good generalisability of the network, since the ROI detection is not specialised for any specific context. The proposed architecture, shown in Fig. 1, is designed to be lightweight to enable running in real-time.

A. Input layer

The input layer translates sensor events into spikes. The spikes produced by the input layer are sent to ROI detector via an excitatory down-scaling connection. This corresponds to a convolutional layer with a kernel size S × S, a stride S, without padding. The input neurons are segregated into nonoverlapping square regions of size S. The input data is of size 256 × 128 pixels wide, thus S was arbitrarily set at 5. Each neuron at the Input layer's subregions are connected to one neuron in the ROI detector layer. Here, the ROI detector is a 2D layer of size 52 × 26 pixels.

B. ROI detector

The ROI detection aggregates the active regions into distinct segments using a soft WTA by laterally inhibiting the neurons in the same layer: each neuron activation leads to the inhibition of the others, without autapses (self-connections). Since a strong WTA leads to the activation of only one neuron in the layer and multiple ROIs are to be detected by the network, the soft WTA weight has been set experimentally to 0.02.

In the case of the ROI detector, a specific exponential WTA is implemented according to the radial basis function Eq. 1 in order to allow ROIs of arbitrary sizes:

W W T A = max( e d w × h , w max ) (1)
where d corresponds to the Euclidean distance in number of neurons between the active and target neuron subject to inhibition, and w and h to the width and height of the layer. The weight W W T A has an upper bound of w max = 50. Finally, the adaptive detection of ROIs by this layer is enabled by a dynamic weight adaptation rule between the input layer and the ROI detector, inspired by Hebb's rule: "cells that fire together wire together" [START_REF] Hebb | The organization of behavior: A neuropsychological theory[END_REF]. This rule is implemented by

C. Output layer

The output layer is connected to the input layer via excitatory one-to-one connection. The spikes filtering is implemented a threshold adaptation rule. The thresholds are initialised at a high value to reduce the firing rate. The thresholds are updated during runtime according to the activation of the neurons in the ROI detector layer: the thresholds of the neurons in the S × S region in the output layer are decreased whenever the corresponding neurons spike in the ROI detector, while they are increased in the absence of ROI activity. We implement this threshold adaptation rule in order to make the neurons in the Output layer more susceptible to be activated while keeping a correct outline of the object of interest, according to the ROIs detected by the ROIs detector. This mechanism needs to know which neurons correspond to the ROIs in the Output layer before lowering the corresponding thresholds. Thus the initial thresholds are set at a high value in order to let the ROIs detector activate first, meanwhile preventing excessive unwanted output.

Another exponential WTA is applied to ensure the detection of one ROI at the time. This relies on the WTA having a strong weight value to prevents other neurons in the output layer to spike when one segment is already filtered.

D. Implementation on Neuromorphic Hardware

As depicted in Fig. 1, the key elements in the presented SNN are both the weight and threshold adaptation mechanisms. Currently the Loihi platform does not support these features thus the SNN had to be modified to implement it on the neuromorphic chip. The input and the ROI layer were connected to the output in a way such that at any given timestep the neurons in the output layer would spike only if the corresponding neurons were active in both previous layers. The connections between input layer and ROI detector layer, as well as the WTA mechanisms were kept the same. The resulting 3-layer network occupies 68 neurocores and consists of 4.224 neurons and 81.716 synapses.

IV. EXPERIMENTAL RESULTS

The ground truth GT (x, y, t) for each experiment was defined as the event stream of the first gesture only and all other events are GT ′ (x, y, t). We treat events in the same way irrespective of their polarity.

The input, output and ground truth sequence were split into 100 ms windows and the active neurons, corresponding to x, y event coordinates, were stored in arrays with same dimensions as the neuron populations. The weighted Root Mean Square Error (RMSE) was calculated according to Eq. 2:

RM SE = RM SE(R O∩GT , GT ) + RM SE(R ′ O∩GT ′ , GT ) N GT (2)
with O the output active region, R O∩GT (x, y, t) the intersection with the ground truth, R ′ O∩GT ′ (x, y, t) the respective intersection with regions not belonging to the ground truth and N GT the total number of events in the ground truth. Eq. 2 defines both RM SE input and RM SE output . In order to penalise wrongly activated regions, RM SE ′ ∩ was multiplied with the total number of wrongly active events. The total RM SE for the output sequence was obtained by adding RM SE ∩ and RM SE ′ ∩ . The final error value is reported as the ratio between RM SE output and RM SE input such that a lower error value indicates better filtering performance.

A. Temporal filtering

Both results produced using PyNN and Loihi show that this attention model filters the region of primary interest as a function of time with significant performance. As depicted in Fig. 2, when alternating between two overlapping gestures the network focuses only on the one that occurs first, independently of their size. The output is not significantly influenced depending on the size of the different input ROIs: the first gesture is filtered out (see Fig. 2) and the error stays low (see Fig. 3) independently of the characteristics of the first and second occurring input gestures.

B. Loihi vs PyNN simulator 1) Simulation time:

The simulation of the model using PyNN and NEST is limited because it runs on a CPU2 : around 3h26min58s were necessary to process 400 ms of the data used in Fig. 2, which is a ratio of 1:12505, very far from real time.

On Loihi, the event data is collected for 1 ms on the embedded x86 cores before being sent to the corresponding input layer neurons. On average, measurements showed that it takes 6 ms to process 1 ms of input data. This ratio of 1:6 is much closer to real time.

These numbers show that processing event-based data on neuromorphic hardware is more efficient in terms of latency. This is an important advantage not only for running offline simulations, but also paves the way for potential real-time, closed-loop applications.

2) Adaptation rules: Currently, Loihi does not support online threshold adaptation. The results presented in Fig. 3 were obtained from the modified SNN running on the neuromorphic hardware. These experiments were not performed with the PyNN simulator due to the runtime limitations mentioned above. However, to demonstrate the effectiveness of the adaptation rules, we produced some limited results using shorter input data (400 ms instead of 1.5 s) on the PyNN simulator: the RMSE equals 0.03 on average in the context described in Fig. 2 Left, and 0.16 for Fig. 2 Right.

These results point towards significant improvement using adaptation rules. Incorporating such mechanisms in the design of future neuromorphic architectures will give rise to novel event-based attention applications.

  This work was supported by the European Union's ERA-NET CHIST-ERA 2018 research and innovation programme under grant agreement ANR-19-CHR3-0008.

Fig. 1 :

 1 Fig. 1: Architecture of the spatio-temporal attention model.

Fig. 2 :

 2 Fig. 2: Simulation of the attention model applied to two samples with a time shift of 100 ms. The Input, ROI.d and Output plots in the 3 first rows represent the neurons emitting a spike at time t (x axis) in the Input, ROI detector and Output layers respectively. The Weight and Threshold Rule correspond to the evolution of the weights and thresholds at the output layer.

Fig. 3 :

 3 Fig. 3: Study of size and spatial invariance of the attention model. Left and Center: Error variation of different combinations between two "small" gestures (classes 1 and 2, x-axis) and one "large" gesture (class 7, y-axis left side), according to different time shifts (y-axis right size). Irrespective of the gesture size, the error decreases as the time shift increases. Right: Evolution of the error as the time shift increases: the larger time shit, the better filtering from the attention model.

We used an UP board featuring a 64-bit Intel®ATOM™x5-Z8350 processor, 1.92GHz, 4GB RAM; it runs Ubuntu 18.04, Python 3.7 and version 0.9.9 of Intel's NxSDK.

Intel 8-core i9-10885H at 2.4GHz.V. CONCLUSION

This paper introduces a low-latency spatio-temporal attention SNN model, deployed on Intel's Loihi chip, which filters ROIs from a DVS stream. The architecture allows for a quick and efficient preprocessing of event data, which can be used prior to a task-related process focusing on a region of interest.

Future work will exploit this result as a basis for the implementation of real-time tasks such as multi-object tracking and scene segmentation by interfacing DVSs directly to the neuromorphic chip.