Víctor J López-Madrona

Agnès Trébuchon

Ioana Mindruta

Emmanuel J Barbeau

Andrei Barborica

Costi Pistol

Irina Oane

F Xavier Alario

Christian G Bénar

Identification of an early hippocampal recognition system using intracerebral evoked potentials in humans

ABSTRACT

The role of the hippocampal formation in memory recognition has been well studied in animals, with different pathways and structures linked to specific memory processes. In contrast, the hippocampus is commonly analyzed as a unique responsive area in most electrophysiological studies in humans, and the specific activity of its subfields remains unexplored. We combined intracerebral electroencephalogram recordings from epileptic patients with independent component analysis (ICA) during a memory recognition task involving the recognition of old and new images to disentangle the activities of multiple neuronal sources within the hippocampus.

We identified two sources with different responses emerging from the hippocampus: a fast one (maximum at ~250 ms) that could not be directly identified from raw recordings, and a later one, peaking at ~400 ms. The earliest component was found in 12 out of 15 electrodes, with different amplitudes for old and new items in half of the electrodes. The latter component, identified in 13 out of 15 electrodes, had different delays for each condition, with a faster activation (~290 ms after stimulus onset) for recognized items. We hypothesize that both sources represent two steps of hippocampal memory recognition, the faster reflecting the input from other structures and the latter the hippocampal internal processing. Recognized images evoking early activations would facilitate neural computation in the hippocampus, accelerating memory retrieval of complementary information. Overall, our results suggest that hippocampal activity is composed by several sources, including an early system for memory recognition, that can be disentangled with ICA methods.

SIGNIFICANCE STATEMENT

In the human memory circuit, the hippocampus is considered as a relatively late structure, associated to the retrieval of elaborated memories. In most electrophysiological studies, it is analyzed as a unique responsive area, and the specific activity of its subfields remains unexplored.

In this work, we combined intracerebral recordings with independent component analysis to separate the electrophysiological activity from two different substructures of the hippocampus.

We analyzed the responses of both sources in a memory task involving the recognition of old and new images. Our results revealed new hippocampal dynamics associated to different subfields, with memory recognition occurring much faster than previously reported. Importantly, we confirmed the potential of independent component analysis, which can be extended to other brain areas.

INTRODUCTION

The interaction between the different regions of the hippocampal formation plays a key role in many memory processes [START_REF] Bird | The hippocampus and memory: insights from spatial processing[END_REF][START_REF] Carr | Hippocampal replay in the awake state: a potential substrate for memory consolidation and retrieval[END_REF][START_REF] Hainmueller | Dentate gyrus circuits for encoding, retrieval and discrimination of episodic memories[END_REF]. Inputs from CA3 and the entorhinal cortex (EC) integrate distinct information to CA1. Previous studies on rats have related the trisynaptic pathway and the autoassociation network of CA3 with memory retrieval [START_REF] Rolls | A computational theory of episodic memory formation in the hippocampus[END_REF], and EC with the process of environmental information and the encoding of memories [START_REF] Hasselmo | A proposed function for hippocampal theta rhythm: separate phases of encoding and retrieval enhance reversal of prior learning[END_REF]. In the dentate gyrus, inputs from two different streams of the EC (lateral and medial EC) converge, reflecting the "what" and the "where" pathways, respectively [START_REF] Fernández-Ruiz | Gamma rhythm communication between entorhinal cortex and dentate gyrus neuronal assemblies[END_REF]. Despite the large knowledge of these structures in animals, their specific electrophysiological activities in humans remain comparatively much less well understood.

Intracerebral recordings (stereotaxic electroencephalography, SEEG) are considered an excellent approach to analyze the dynamics of the hippocampus, with a millisecond time scale and fine spatial specificity [START_REF] Fernández | Real-time tracking of memory formation in the human rhinal cortex and hippocampus[END_REF][START_REF] Halgren | Intracerebral potentials to rare target and distractor auditory and visual stimuli. II. Medial, lateral and posterior temporal lobe[END_REF]. Event-related potentials (ERPs) are elicited in the hippocampus during recognition of known faces or words, with larger amplitudes between 400 and 600 ms post-stimulus (hippocampal P600) for successful item recognition [START_REF] Barbeau | Spatio temporal dynamics of face recognition[END_REF][START_REF] Fernández | Real-time tracking of memory formation in the human rhinal cortex and hippocampus[END_REF][START_REF] Trautner | Recognition of famous faces in the medial temporal lobe: an invasive ERP study[END_REF]. The timing of this response, together with the activation of other brain areas, allows a rough estimation of the memory circuit of the brain. Early potentials are generated along the whole ventral pathway [START_REF] Allison | Electrophysiological studies of human face perception. I: Potentials generated in occipitotemporal cortex by face and non-face stimuli[END_REF]. However, the first structure showing a modulation by the old/new status of the stimuli is the perirhinal cortex, with higher responses for known items at ~200-300 ms, followed by the supplementary motor area, and by various frontal and parietal regions [START_REF] Despouy | A Fast Visual Recognition Memory System in Humans Identified Using Intracerebral ERP[END_REF][START_REF] Gonzalez | Electrocorticography reveals the temporal dynamics of posterior parietal cortical activity during recognition memory decisions[END_REF]. In this context, the hippocampus has been considered as one of the latest structures involved in recognition [START_REF] Despouy | A Fast Visual Recognition Memory System in Humans Identified Using Intracerebral ERP[END_REF], with the first difference between new and old elements occurring almost 500 ms after stimulus onset. This result has suggested that the hippocampus is not involved in the earliest stages of memory recognition, but rather in a slower process that retrieves complementary information [START_REF] Despouy | A Fast Visual Recognition Memory System in Humans Identified Using Intracerebral ERP[END_REF].

Despite the high spatial resolution of SEEG, it is extremely difficult to identify and analyze the different substructures of the hippocampus, namely CA1, CA3 or the DG. These structures and SEEG contacts are roughly of the same size -a few millimeters. A single sensor may spatially cover more than one structure, while others may not be sampled. In addition, SEEG sampling is constrained by clinical indications. Only a limited number of contacts are placed in the hippocampus and their location varies across patients. The different substructures of the hippocampus are folded one in the other, resulting in overlapped field potentials. Therefore, the hippocampus is most commonly analyzed as a single responsive area in SEEG, blurring the contribution of each substructure to the recordings [START_REF] Barbeau | Hippocampus duality: Memory and novelty detection are subserved by distinct mechanisms[END_REF][START_REF] Ludowig | Two P300 generators in the hippocampal formation[END_REF].

When the activities of various structures overlap in time and space, independent component analysis (ICA) may be used to separate the time-courses of the different current generators contributing to the recorded field potentials [START_REF] Herreras | Site-dependent shaping of field potential waveforms[END_REF][START_REF] Herreras | New uses of LFPs: Pathway-specific threads obtained through spatial discrimination[END_REF][START_REF] Makarova | Parallel readout of pathwayspecific inputs to laminated brain structures[END_REF]. The efficiency of ICA to disentangle hippocampal pathways has been well established in animal studies, where it has helped isolating the different inputs to CA1 [START_REF] Benito | The right hippocampus leads the bilateral integration of gamma-parsed lateralized information[END_REF][START_REF] López-Madrona | Different theta frameworks coexist in the rat hippocampus and are coordinated during memory-guided and novelty tasks[END_REF] and to the DG [START_REF] Benito | Spatial modules of coherent activity in pathway-specific LFPs in the hippocampus reflect topology and different modes of presynaptic synchronization[END_REF][START_REF] Fernández-Ruiz | Gamma rhythm communication between entorhinal cortex and dentate gyrus neuronal assemblies[END_REF], 2013).

In humans, methods such as bipolar montages or current source density analysis are commonly used to measure the local inflow and outflow of currents in a specific location [START_REF] Mitzdorf | Current source-density method and application in cat cerebral cortex: investigation of evoked potentials and EEG phenomena[END_REF][START_REF] Nicholson | Theory of current source-density analysis and determination of conductivity tensor for anuran cerebellum[END_REF]. However, these methods may not recover the correct timecourses of the local sources (Fernández-Ruiz and Herreras, 2013;[START_REF] Martín-Vázquez | Determining the true polarity and amplitude of synaptic currents underlying gamma oscillations of local field potentials[END_REF][START_REF] Michelmann | Data-driven re-referencing of intracranial EEG based on independent component analysis (ICA)[END_REF]. For example, in the case of two co-localized sources (i.e., close to the same SEEG contact), the bipolar montage would measure a combination of the activities of both structures. This overlap can have critical consequences. If the sources have anticorrelated activities, one or both signals would be cancelled. ICA has been recently proposed as an optimal solution for re-referencing intracerebral EEG data and identifying neural generators in LFP recordings [START_REF] Michelmann | Data-driven re-referencing of intracranial EEG based on independent component analysis (ICA)[END_REF], an approach that has so far been restricted to animal studies.

Using such approach, we clearly identified for the first-time different sources of the electrophysiological signal in the hippocampus during a memory recognition task. First, we applied ICA on SEEG recordings, identifying two different hippocampal current generators and disentangling their time courses from other close or distant activities. Then, we examined the spatial topography of each hippocampal component and corroborated the local origin of the sources. Finally, we characterized the dynamics of the hippocampal responses during a memory recognition task.

METHODS

Participants

Ten patients (three females) with pharmacoresistant epilepsy volunteered for this study.

They were implanted with SEEG depth electrodes along the longitudinal axis of the medial temporal lobe during presurgical evaluation. All these patients had been implanted in the hippocampus, either anterior or posterior, for clinical reasons. Five patients were recruited at La Timone hospital (Marseille, France) and the other five at the Emergency University Hospital Bucharest (Romania). None of the patients presented sclerosis. For those patients recorded in Marseille, the hippocampus exhibited the stereotyped responses during an oddball task, which confirmed the correct functionality of this structure.

Experimental paradigm

Each block of the recognition memory task started with an encoding phase, during which 12 pictures were presented, one after the other, and the participant was asked to memorize them. Each picture was a simple colored drawing of a familiar item (e.g. a dog or a car) on a grey background. The picture database and precise selection criteria are described below. After a distracting video of one minute (silent excerpts from a documentary showing birds and landscapes), the recognition phase involved a set of 24 pictures from the same database. Half of these pictures had been presented during the encoding phase, while the other 12 were new never-presented pictures. Participants were asked to press two different buttons if they recognized the image as having been presented earlier during encoding ("old" condition), or if the image was "new" to them. The latency of this response is the reaction time (RT). Stimuli presentation and response logging were controlled by the software E-prime 3.0 (Psychology Software Tools, Pittsburgh, PA).

Each trial started with a fixation cross presented in the center of the screen for 1000 ms, followed by the experimental picture, presented for 1000 ms in the encoding sub-block and for 1500 ms in the recognition sub-block. The subsequent inter-trial interval was fixed to 1000 ms in both blocks. For each participant, a total of 7 blocks were programmed to be displayed consecutively, using different images.

We selected 24 x 7 = 168 images to be used as experimental materials from the database of [START_REF] Duñabeitia | MultiPic: A standardized set of 750 drawings with norms for six European languages[END_REF]. They were selected as having high name agreement (above 90%). To ensure that the observations were not driven by item-specific properties, different experimental lists were created for each participant. The items were separated into two matched groups of 84 items to serve as old and new, alternatively across patients. Across the "old" and "new" groups of items there were roughly equal numbers of natural and artefact stimuli, with matched visual complexities; their names in French were matched for name agreement, length in syllables, and

(log) lexical frequency of use (normative data from [START_REF] Duñabeitia | MultiPic: A standardized set of 750 drawings with norms for six European languages[END_REF][START_REF] New | Lexique 2: a new French lexical database[END_REF].

The 84-item groups were further broken down into 7 groups to be used in the different blocks, with items matched for visual complexity and (log) lexical frequency across the 7 groups. All matching across picture groups was performed with the MATCH utility [START_REF] Van Casteren | Mix, a program for pseudorandomization[END_REF]. In the encoding phases, the 12 items were presented in a random order; in the recognition phases, the items were presented in a pseudo-random order, with the constraint that there were never more than 3 "old" or "new" items in a row.

SEEG recordings

SEEG recordings were performed using depth electrodes, implanted in stereotactic conditions [START_REF] Talairach | Stereotaxic exploration in frontal epilepsy[END_REF]Alcis, Besançon, France, and Dixi Medical, Chaudefontaine, France). The electrodes of both clinical centers had between 8 and 18 contacts per electrode, a diameter of 0.8 mm, 2 mm contact length and separated from each other by 1.5 mm. We implanted between 89 and 223 SEEG contacts per patient (total contacts recorded: 1590; mean of 159 contacts per patient, SD ± 55). To determine the exact location of each electrode and contact, a CT-scan/MRI data fusion was performed for each patient. SEEG signal from Marseille center was recorded on a digital system (Natus Medical Incorporated) with sampling at 1024 Hz or more with 16-bit resolution, a hardware high-pass filter (cutoff = 0.16 Hz), and an antialiasing low-pass filter (cutoff = 340 Hz). Data from Bucharest hospital was recorded with a Natus

Quantum 128 system (Natus Neuro, Middleton, WI), sampling rate at 4096 Hz, 24-bit resolution and a hardware high-pass filter at 0.08 Hz.

Independent component analysis

Because this work is focused on the analysis of hippocampal sources, we selected for ICA only the electrodes targeting this structure. ICA was run on the continuous traces of each electrode of each patient separately. We thus analyzed a total of 15 electrodes (N=15) obtained from the 10 patients.

ICA aims to solve the 'cocktail party' problem by separating N statistically independent sources that have been mixed in M recording sensors. It is a blind source separation methodology, as the spatial distribution and time-courses of the sources are unknown. To identify the sources, ICA assumes that they are immobile in space, i.e., that the proportional contribution of each source to every sensor is the same throughout the recording session. Each recorded signal 𝑢 𝑚 (𝑡)

is thus modeled as the sum of 𝑁 independent sources (𝑠 𝑛 (𝑡)) multiplied by a constant factor (𝑉 𝑚𝑛 ):

where 𝑢 𝑚 (𝑡) is the SEEG data, 𝑉 𝑚𝑛 the ICA weights or spatial distribution of each source, 𝑀 the number of sensors, 𝑁 the number of sources and 𝑠 𝑛 (𝑡) the obtained independent components ("SEEG-ICs").

In this work, we obtained as many components as sensors per electrode (N=M), without applying any dimension reduction [START_REF] Artoni | Applying dimension to EEG data by Principal Component Analysis reduces the quality of its subsequent Independent Component decomposition[END_REF]. We applied ICA on the continuous data, seeking one mixing matrix per electrode. We used FieldTrip [START_REF] Oostenveld | FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data[END_REF] to compute ICA based on the infomax algorithm, which aims to minimize the mutual information between components [START_REF] Bell | An information-maximization approach to blind separation and blind deconvolution[END_REF], implemented in EEGLAB [START_REF] Delorme | EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis[END_REF].

Although only some of the SEEG-ICs were putative neuronal sources, we did not discard any component at this point.

𝑢 𝑚 (𝑡) = ∑ 𝑉 𝑚𝑛 𝑠 𝑛 (𝑡) 𝑁 𝑛=1 , 𝑚 = 1,2, … , 𝑀 (1)

Analysis of event-related potentials

Only trials associated with the correct behavioral response were considered in the analysis. For each SEEG-IC, we checked if they had a significant ERP in both the "old" and the "new" conditions. To do so, we tested if each time point across trials was significantly different from zero with a t-test, obtaining a t-and p values for each time-point. Then, we corrected these tests for multiple comparisons using a local false discovery rate (LFDR; Benjamini and Heller, 2007) on the t-values with a threshold of 0.2 (Pizzo et al., 2019). LFDR assumes that the distribution of t-values is Gaussian, considering as significant those values that stand out from the distribution.

To have a better estimation of the distribution, we grouped all the t-values across components of each electrode, obtaining a single threshold per electrode. To remove artifactual single points, we selected only those points during the first second after the stimulus and we imposed a minimum number of consecutive significant time samples (10 ms in this work).

To assess if the responses of the components differed in amplitude during "old" vs. "new" trials, we repeated a similar analysis across conditions. For each component and time-point we performed a t-test across trials between the amplitude of the ERP in old versus new conditions.

Then, we corrected the t-values for multiple comparisons using LFDR on the t-values of each dataset (i.e., all the components of one electrode). In this way, we identified statistical differences at the single electrode level.

Identification of hippocampal components

We focused on the putative hippocampal SEEG-ICs that were responding to the protocol.

To identify these components from among all the obtained sources, we first selected the signals with a significant ERP. Then, we considered only the components with a local spatial distribution that was maximal in the hippocampus (i.e., those for which the ICA distribution presents a strong decay across sensors). After visual inspection, we identified two components that had the same ERP across electrodes. We labeled these components "Hc250-IC" and "Hc600-IC" based on the latencies of their responses. The former was present in 12 of 15 electrodes, while the latter was found in 13 electrodes. We only considered the time courses of Hc250-IC and Hc600-IC for further analysis. As ICA does not ensure the correct polarity and amplitude of the sources, we reversed the components when needed to match the same polarity across electrodes and z-scored the continuous traces of each component to facilitate the comparison across subjects.

Spatial distribution across all recording sites

To test whether other brain regions were contributing to the SEEG-ICs components, we repeated the ICA now including all the recording sites in each patient. The computation of a single ICA for all the recordings may affect the resultant time-courses. Low variance hippocampal components may not be singled out, as the addition of many signals far from the hippocampus decreases the relative contribution of this source to the whole dataset. Moreover, the number of sensors and locations strongly differs across patients, hindering the inter-subject comparison.

Therefore, we performed the analysis in an iterative manner, including only one additional contact at a time and evaluating its contribution to the SEEG-ICs. At each iteration, we computed a new ICA on the combination of the original electrode (i.e., the electrode targeting the hippocampus) and one extra contact. We then computed the zero-lag correlation between the original SEEG-IC signal (only the hippocampal electrode) and the new SEEG-ICs (combination of the hippocampal electrode and an additional contact), selecting the component with the highest correlation. This component would represent the same neuronal source in both datasets. Then, we estimated the relative contribution of the additional sensor to the component as the ratio between the ICA weight at the new location divided the highest ICA weight. A value close to 1 would imply that the additional contact strongly contributes to the SEEG-IC, suggesting that the neuronal source is nearby, while a value close to zero would indicate that the additional contact is relatively far from the source. This allows the analysis of the approximated spatial distribution of the components on the whole sampled brain, while minimizing the impact of including more contacts on the estimation of source time courses.

Group analysis of event-related potentials

In order to test for a significative response at the group level, we performed a nonparametric permutation test corrected with cluster-based Statistics [START_REF] Cohen | Analyzing Neural Time Series Data: Theory and Practice[END_REF]. We computed the averaged ERP for each electrode and condition. Then, for each time point we computed a t-test against zero between the ERP values across electrodes in each condition. We kept the t-values of those points with a p-value lower than 0.05. These are the uncorrected values of significance. To correct for multiple comparisons, we selected clusters of significance, i.e., group of consecutive time points with a significant p-value. We assigned to each cluster the sum of the absolute t-values inside the cluster (absolute to also consider negative differences). We computed N=2000 surrogate datasets, by randomly shifting the ERPs of each electrode. The duration of each ERP was 2 seconds, including 500 ms of baseline, and the minimum shift of each surrogate was 250 ms. This way, the ERP signals remained the same, but the temporal alignment between ERPs was broken. We repeated the cluster procedure for each surrogate, keeping only the cluster with maximal t-value at each iteration. Any significance found in these surrogates would be by chance. Finally, we tested whether the t-values of our original clusters were significantly higher that the t-values of the surrogate analysis. The threshold of significance was set at the ninety-fifth percentile of the distribution of surrogate values (p-value < 0.05). The same procedure was followed to compare the amplitudes of the responses across conditions. In this case, the t-test was computed between conditions instead of against zero.

Raster plots

We analyzed the trial-to-trial variability of the responses using raster plots. We selected all trials across electrodes and conditions and sorted them based on the RT to the stimulus. Then, we stacked them in a single matrix with dimensions time x number of trials. To assess whether the ERP was related with the patients' response times, we correlated the latency of the ERP onset with the RT. First, we created super trials of 50 trials pooled across electrodes and conditions to improve the signal to noise ratio [START_REF] Despouy | A Fast Visual Recognition Memory System in Humans Identified Using Intracerebral ERP[END_REF][START_REF] Hebart | The representational dynamics of task and object processing in humans[END_REF]. We tested super trials of different sizes (in numbers of trials) without observing noteworthy differences in the main result. The onset latency of each super trial was estimated using the median rule, i.e., as the first time point with an amplitude higher than the median of the baseline plus 2.3 times the interquartile range [START_REF] Letham | Statistically robust measurement of evoked response onset latencies[END_REF]. Finally, a Pearson correlation was applied between the onset latency and the averaged response time of each super trial.

Detection of slope change points

To better characterize the dynamics the Hc600-IC response, we modeled the averaged ERP for each electrode and condition as a combination of multiple linear segments. The locations of the intersections between segments were automatically selected using the Matlab (Mathworks, Natick, MA) function findchangepts. This function identifies the points where the mean and the slope of the signal change most abruptly [START_REF] Haynes | A computationally efficient nonparametric approach for changepoint detection[END_REF]. The total number of sections was given by a parametric threshold which imposes the minimum required improvement in the residual error for each change point. The residuals are strongly related with the signal to noise of the ERP, which was different for each electrode. Thus, we manually adjusted the threshold value between 0.5 and 3 for each ERP, until the response was clearly modeled with a minimum number of changepoints. The same value was used in both conditions. We determined the t2 value as the change point with maximum amplitude between 200 and 600 ms. Then, the t1 latency was selected as the first change point with a local minimum in amplitude before t2.

RESULTS

Segregation of SEEG time-courses into current generators

We performed electrophysiological recordings from the hippocampus in 10 patients during a recognition memory task (Figure 1a, see Methods). Each patient had between 10 and 20 electrodes in the whole brain, and we selected a total of 15 electrodes (N=15) implanted in this structure among all participants. The stereotactic electrodes had between 8 and 18 contacts that covered an entire path from the lateral to medial brain areas (Figure 1a). For each electrode, we applied the ICA source separation technique to segregate the recordings into the main sources contributing to the SEEG activity (Figure 1). Four different independent components (SEEG-ICs)

were identified in most of the electrodes. The spatial profile of each component (Figure 1c) reflects its contribution to each SEEG contact, allowing to infer the location of the component's source. The first component (labeled 'Cort-IC') is mainly visible on the lateral contacts of the electrode with a progressive reduction towards deeper structures, and presumably reflects a source located in the lateral temporal cortex. Two components had their maximal contribution inside the hippocampus and were labeled as 'Hc250-IC' and 'Hc600-IC' based on the latency of the ERP (Figure 1d). The former had its highest amplitude at ~250 ms, while the latter, with a peak at ~400 ms, may be related with the "hippocampal P600" commonly identified in visual recognition tasks [START_REF] Barbeau | Hippocampus duality: Memory and novelty detection are subserved by distinct mechanisms[END_REF][START_REF] Trautner | Recognition of famous faces in the medial temporal lobe: an invasive ERP study[END_REF]. Both components presented a narrow spatial profile with very low contributions from contacts outside the hippocampus, strongly suggesting that these components represented local sources inside this structure. The fourth component represented a remote source far from the electrode ('Rem-IC'), with a similar contribution to all the contacts. The local components (i.e., Cort-IC, Hc250-IC and Hc600-IC) had a significant ERP compared to baseline (t-test across trials against zero, corrected with LFDR, see methods), but Rem-IC did not (Figure 1d).

The spatial profiles in Figure 1c show that the contribution of the different sources to the SEEG signals present an overlap, indicating that each sensor contains information from several components simultaneously. This is most noticeable in the hippocampus, where a single contact records activities from Rem-IC, Hc250-IC and Hc600-IC (Figure 1c, channel 3). ICA separates the time-courses associated with the sources, removing the contribution from other areas. For sources that are sufficiently separated in space, differences between SEEG-IC and SEEG may seem minor in the averaged ERPs (Figure 1d). However, these differences are remarkable in the continuous traces, where a response to the stimulus that is apparent in the SEEG-IC is hidden in the SEEG (Figure 1e, arrow). This contrast between SEEG-IC and SEEG is most prominent when the sources overlapped in space (Supplementary Figure 1). In this case, the activity of each source cannot be inferred from raw SEEG recordings (not even with bipolar montages, Supplementary

Figure 1), and source separation methods are required to disentangle the time courses of the different components [START_REF] Michelmann | Data-driven re-referencing of intracranial EEG based on independent component analysis (ICA)[END_REF].

Location and modulation of hippocampal sources

The number of local and remote sources retrieved with ICA varies across patients, as it strongly depends on the specific implantation scheme and the number of contacts per electrode.

Thus, we focused our study on the two main hippocampal sources (Hc250-IC and Hc600-IC).

Hc250-IC was present in 12 electrodes and its contribution to the dataset was small (explained variance across electrodes: 4,5%, SD ± 3,2%). Hc600-IC was identified in 13 of the 15 electrodes included in the analysis and represented an important contribution to the total variance of the data (mean explained variance: 29,2%, standard deviation: SD ± 14,2%). These components had a similar spatial distribution across patients; their maximal contribution was in contacts located inside the hippocampus, with little contribution from other sensors (Figure 2a). Within the hippocampus, the spatial profiles of both components presented were slightly different. Hc250-IC was recorded in deeper contacts than Hc600-IC in electrodes placed in the anterior hippocampus (Figure 2a, left). Intriguingly, this location was reversed in electrodes placed in the posterior hippocampus, where Hc250-IC appeared preferentially in more superficial sites (Figure 2a, right). This reversal may reflect rostral to caudal anatomical differences of the human hippocampus, well stratified in its anterior part and with complex folds of its substructures in more posterior sections (Andersen et al., 2006). Conversely, we did not observe notable differences neither in the spatial nor in the ERP between electrodes implanted in the left versus right hemispheres.

Although the spatial distributions of the SEEG-ICs presented a clear peak inside the hippocampus, this does not completely exclude the possibility that they reflect sources from a nearby region. To further test whether the sources were truly located in the hippocampus, we repeated the ICA now including all the contacts available in each patient (average n=159 contacts per patient). Computing a single ICA with all the sensors is not recommended because components with low variance (i.e., Hc250-IC) may be blurred among other sources. Thus, we performed this new analysis in an iterative manner, including only one additional contact at a time and evaluating its contribution to the SEEG-ICs. This allowed us to cover all the sampled brain areas while minimizing distortion of the time-courses identified in the restricted analysis (see Methods). For both Hc250-IC and Hc600-IC, the spatial distribution was in all cases maximal in the contacts within the hippocampus, with negligible contributions from other areas such as the rhinal cortex or the amygdala (Figure 2b). This result reinforces the interpretation of the hippocampus as the origin of the components.

The temporal profile of the components presented similar ERPs across electrodes (Figure 2c). There were no appreciable differences between components in the anterior and posterior hippocampus. Note that ICA does not ensure the correct polarity or amplitude of the sources.

Thus, we reversed the components when needed and normalized them (z-score) to ease comparisons. The earliest response was in Hc250-IC, with a single peak at ~260 ms (mean 257±42 ms across patients) post-stimulus during "old" items (Figure 2c, left) that was significantly different from zero in all electrodes (t-test across trials against zero, corrected with LFDR). The response to "new" items presented a similar timing as to recognized elements, but with significant differences in amplitude between both conditions in 6 out of 12 electrodes (old/new contrast; Supplementary Figure 2; t-test across trials between conditions, corrected with LFDR).

However, there were no differences at the group level (p>0.05, permutation test). The response of Hc600-IC elicited by old items was characterized by two peaks of opposite polarity at 405 and 610 ms post stimulus onset (Figure 2c, right), similar to the standard hippocampal response in other memory tasks [START_REF] Barbeau | Hippocampus duality: Memory and novelty detection are subserved by distinct mechanisms[END_REF][START_REF] Barbeau | Spatio temporal dynamics of face recognition[END_REF][START_REF] Trautner | Recognition of famous faces in the medial temporal lobe: an invasive ERP study[END_REF]. Interestingly, the visual comparison between "old" and "new" responses revealed that the changes were not in amplitude, but their temporal dynamics were different, with a slower activation time to "new"

items. Therefore, we further explored the modulation of this component. and Hc600-IC (bottom) across all the recording sites in one patient. The maximal contribution is in the hippocampus, with low values in all the other contacts, including those located in the amygdala and the rhinal cortex. c) Grand average ERP across electrodes contrasting "old" (solid traces) and "new" (dashed traces) items for Hc250-IC and Hc600-IC. Shaded areas illustrate the periods where the response to old trials is significantly different from zero, while the green line indicates the interval with differences between conditions (p<0.05, permutation test; n.s., not significant).

Analysis of hippocampal dynamics

First, to confirm that the differences between conditions were induced by the memory paradigm, we tested whether the latency of the response was related to the behavioral response time (RT) to the protocol. We selected all the single trials for both "old" and "new" conditions and reordered them based on the RT, either for all the electrodes together (Figure 3a) or for each electrode separately (Figure 3b). No correlation between the raster plot and the RT could be visually appreciated. To quantify this observation, we grouped the data into super trials (i.e., the average of 50 trials with adjacent RTs pooled across electrodes and conditions) and estimated the evoked onset time of each one as the first time point with an amplitude significantly higher than baseline (see Methods). We then computed the correlation between such evoked onset time and the averaged RT of each super with no significant relation between the timing of the Hc600-IC response and the RT (Figure 3c; correlation test, N=24 super trials, r 2 =0.006, p=0.71). This null result is in good agreement with previous studies, where the RT did modulate the response of the perirhinal cortex and the motor cortex, but had no discernable effect on the hippocampal response [START_REF] Despouy | A Fast Visual Recognition Memory System in Humans Identified Using Intracerebral ERP[END_REF].

Second, to better analyze the properties of the temporal response, we modeled the averaged ERP of each condition and electrode as a combination of multiple linear sections (Figure 3d). The juncture between sections was statistically defined as the time-points where the mean and the slope of the signal changed most abruptly (see methods). We tested if the timing of these change points differed between conditions. Two points of the ERP were selected for each electrode: the latency at which amplitude starts to increase (t1) and the latency of the maximal response (t2). Responses to "old" images exhibited significantly earlier latencies at both t1 (mean latency ± standard error of the mean, s.e.m.: 287 ± 16.1 ms and 314 ± 17.9 ms for old and new responses, respectively; p<0.05, t-test across electrodes, N=13) and t2 (mean latency ± s.e.m.:

431 ± 21 ms and 490 ± 28.7 ms for old and new responses, respectively; p<0.01, paired t-test between conditions across electrodes, N=13; Figure 3e). This implies that the hippocampus processes memory recognition as soon as 290 ms post stimulus onset, around 120 ms before the latency of its maximal response.

DISCUSSION

We described two different specific hippocampal sources recorded during a recognition memory task in humans. Patients with intracerebral electrodes implanted for focal drug-resistant epilepsy monitoring were asked to classify images as "old" or "new". Using ICA on the SEEG recordings, we disentangled two hippocampal components (i.e., the spatial distribution maximum was inside the hippocampus) from other cortical and remote sources. The first source (Hc250-IC) had a low contribution to the total variance and presented an early response to the stimulus, with significant differences between memory conditions in 6 out of 12 electrodes. The second one (Hc600-IC) had a higher contribution to the total variance and was modulated by the memory condition in all cases, with faster responses for known images compared to "new" items.

The earliest difference was at 290 ms, demonstrating that the hippocampus has an earlier involvement in memory recognition than previously reported.

Identification of two hippocampal sources in SEEG

In this work, we have identified two different hippocampal neural generators, and, thanks to the use of ICA, separated their corresponding electrophysiological activity. The presence of several memory-related sources in the hippocampal formation has been previously reported [START_REF] Barbeau | Hippocampus duality: Memory and novelty detection are subserved by distinct mechanisms[END_REF][START_REF] Ludowig | Two P300 generators in the hippocampal formation[END_REF]. Ludowig et al., 2010, described The two hippocampal components found in our study may be related to the different activities described in [START_REF] Barbeau | Hippocampus duality: Memory and novelty detection are subserved by distinct mechanisms[END_REF] These authors observed that the evoked responses during memory and novelty detection had opposite polarities, raising the possibility of different sources for each response. It is possible that both sources were always present, but with one predominating over the other for each task. In this scenario, Hc600-IC would be the main generator modulated by memory recognition, while Hc250-IC could be related to a hippocampal source also involved in novelty detection. This would explain its faster activation (the response to novelty is believed to be faster than recognition: [START_REF] Barbeau | Hippocampus duality: Memory and novelty detection are subserved by distinct mechanisms[END_REF][START_REF] Norman | Modeling hippocampal and neocortical contributions to recognition memory: a complementary-learning-systems approach[END_REF],

and the absence of clear differences between conditions in memory recognition. The lack of differences between anterior and posterior hippocampus also supports this possibility [START_REF] Poppenk | Long-axis specialization of the human hippocampus[END_REF]. Further studies combining memory and novelty tasks will address this question.

Previous studies have suggested the presence of an early response (~250 ms) from the hippocampus, modulated by stimuli repetition [START_REF] James | Neural transition from short-to long-term memory and the medial temporal lobe: a human evoked-potential study[END_REF][START_REF] Nahum | Rapid consolidation and the human hippocampus: intracranial recordings confirm surface EEG[END_REF][START_REF] Raynal | Early signal from the hippocampus for memory encoding[END_REF]. This response would coincide in time with the Hc250-IC found in our study. However, they are unlikely to be generated by the same source. While in these studies the activity was related to memory encoding, we did not observe such changes for new elements. On the contrary, our results have the opposite tendency, with higher amplitudes during memory recognition at the single level. Moreover, the variance of Hc250-IC was quite low in the intracerebral recordings (4,5%), making its fingerprint on the surface likely to be negligible on raw EEG recordings [START_REF] James | Neural transition from short-to long-term memory and the medial temporal lobe: a human evoked-potential study[END_REF][START_REF] Raynal | Early signal from the hippocampus for memory encoding[END_REF].

Anatomical considerations of hippocampal current generators

The activity recorded at each sensor with SEEG reflects the summation of several close and distant sources [START_REF] Buzsáki | The origin of extracellular fields and currents -EEG, ECoG, LFP and spikes[END_REF]. The main features that determine the field potential of one region are the geometry and the degree of synchronization of the current sources [START_REF] Herreras | Local Field Potentials: Myths and Misunderstandings[END_REF]. Blind source separation methods such as ICA has been proposed as the solution to recover the time-courses associated to specific current generators [START_REF] Herreras | Site-dependent shaping of field potential waveforms[END_REF][START_REF] Herreras | New uses of LFPs: Pathway-specific threads obtained through spatial discrimination[END_REF]. Due to its versatility, ICA has been used to remove the reference signal in intracerebral EEG [START_REF] Hu | Automatic identification and removal of scalp reference signal for intracranial EEGs based on independent component analysis[END_REF] and to separate neural sources in EEG [START_REF] Onton | Imaging human EEG dynamics using independent component analysis[END_REF][START_REF] Tang | Independent components of magnetoencephalography: single-trial response onset times[END_REF], magnetoencephalography (MEG; [START_REF] Barborica | Extracting seizure onset from surface EEG with independent component analysis: Insights from simultaneous scalp and intracerebral EEG[END_REF][START_REF] Malinowska | Interictal networks in magnetoencephalography[END_REF] and local field potentials in rats [START_REF] Fernández-Ruiz | Gamma rhythm communication between entorhinal cortex and dentate gyrus neuronal assemblies[END_REF][START_REF] Makarov | Disentanglement of local field potential sources by independent component analysis[END_REF][START_REF] Torres | Local and Volume-Conducted Contributions to Cortical Field Potentials[END_REF]. In this work, we innovatively used ICA to disentangle multiple generators in SEEG [START_REF] Michelmann | Data-driven re-referencing of intracranial EEG based on independent component analysis (ICA)[END_REF].

Several hippocampal generators have been described in animal studies [START_REF] Benito | Spatial modules of coherent activity in pathway-specific LFPs in the hippocampus reflect topology and different modes of presynaptic synchronization[END_REF][START_REF] Fernández-Ruiz | Gamma rhythm communication between entorhinal cortex and dentate gyrus neuronal assemblies[END_REF][START_REF] Korovaichuk | Minor contribution of principal excitatory pathways to hippocampal LFPs in the anesthetized rat: a combined independent component and current source density study[END_REF][START_REF] López-Madrona | Different theta frameworks coexist in the rat hippocampus and are coordinated during memory-guided and novelty tasks[END_REF], reflecting the inputs to different layers of CA1 and the dentate gyrus (DG). These two structures present a suitable anatomy to generate electric fields. In CA1, the most dominant generators are located in the stratum radiatum, with the input from CA3 through the Schaffer collateral [START_REF] Benito | Spatial modules of coherent activity in pathway-specific LFPs in the hippocampus reflect topology and different modes of presynaptic synchronization[END_REF][START_REF] Korovaichuk | Minor contribution of principal excitatory pathways to hippocampal LFPs in the anesthetized rat: a combined independent component and current source density study[END_REF], and in the stratum lacunosum-moleculare [START_REF] Benito | Spatial modules of coherent activity in pathway-specific LFPs in the hippocampus reflect topology and different modes of presynaptic synchronization[END_REF], where are located the synaptic outputs of the layer III of the EC. In the DG, the highest potential is generated by the projections from the layer II of the EC to the granular cells [START_REF] Benito | Spatial modules of coherent activity in pathway-specific LFPs in the hippocampus reflect topology and different modes of presynaptic synchronization[END_REF][START_REF] Korovaichuk | Minor contribution of principal excitatory pathways to hippocampal LFPs in the anesthetized rat: a combined independent component and current source density study[END_REF][START_REF] Makarova | Parallel readout of pathwayspecific inputs to laminated brain structures[END_REF]. We speculate that the current generators in these two structures might be the origin of our components, with Hc250-IC related with the input of EC to the DG and Hc600-IC reflecting the computations in CA1.

The anatomy of the hippocampus differs from rostral to caudal (Andersen et al., 2006).

The anterior section presents a relative clear distribution of the different layers, with the subiculum, CA1 and CA3 in the lateral part, surrounding the DG in between [START_REF] Amaral | Introduction: what is where in the medial temporal lobe?[END_REF]. This results in well localized components, with similar topographies across electrodes (Figure 2a, anterior hippocampus). In good agreement with our hypothesis, the spatial profiles of Hc600-IC are deeper than those of Hc250-IC, which may relate Hc250-IC with lateral areas (but see below).

On the contrary, the posterior section of the hippocampus has a different anatomy, with several folds of the granular layer, forming small "dents". This geometry impacts the spatial distribution of its current sources, with summation and cancelation of currents caused by the opposite orientation of the cells through the gyrus [START_REF] Buzsáki | The origin of extracellular fields and currents -EEG, ECoG, LFP and spikes[END_REF]. Our results reflect the complexity of this area, with a huge variety in shape and location of the topographies of the two identified hippocampal sources across electrodes/patients (Figure 2a, posterior hippocampus).

It is important to remark the complexity of the hippocampal circuit to avoid simplistic interpretations. To exemplified this, the DG includes a dense inhibitory network, back projections from CA3 and several inputs from lateral and medial EC with different information (Andersen et al., 2006). Therefore, the identified generators cannot be linked to a single pathway or process.

Moreover, the location of the maximal potential may not coincide with the origin of the current source [START_REF] Herreras | Local Field Potentials: Myths and Misunderstandings[END_REF][START_REF] Herreras | Site-dependent shaping of field potential waveforms[END_REF]. Due to the curvature of CA1, a synchronized synaptic input to the whole coronal layer (i.e., from the boundary with CA3 to the subiculum), would generate field potentials across the layer, whose sum would be maximal in the center of the curve. In this situation, an activation of CA1 could be detected inside the DG. This effect has been described in the DG, where the synaptic input of the EC was in the molecular layer, but its field potential was dominant in the hilar region (Fernández-Ruiz et al., 2013). Further information of the gradients of the field potentials across the hippocampus may help to identify the true origin of the components, for example, using microelectrodes to improve the resolution across SEEG sensors [START_REF] Ulbert | Multiple microelectrode-recording system for human intracortical applications[END_REF]. Ultimately, a realistic computational model of the human hippocampus is necessary to understand the origin of the multiple source generators [START_REF] Herreras | New uses of LFPs: Pathway-specific threads obtained through spatial discrimination[END_REF].

Early memory recognition system of the hippocampus

It has been suggested the existence of two different memory recognition systems in the brain [START_REF] Despouy | A Fast Visual Recognition Memory System in Humans Identified Using Intracerebral ERP[END_REF]: one fast, linked to familiarity processes; and another slower, more related to memory recollection [START_REF] Yonelinas | The Nature of Recollection and Familiarity: A Review of 30 Years of Research[END_REF]. According to this theory, the fast system would respond between 200 and 300 ms after stimulus onset and it would involve a number of areas in the frontal, temporal and parietal lobes, leaded by the perirhinal cortex [START_REF] Despouy | A Fast Visual Recognition Memory System in Humans Identified Using Intracerebral ERP[END_REF][START_REF] Gonzalez | Electrocorticography reveals the temporal dynamics of posterior parietal cortical activity during recognition memory decisions[END_REF]. This system may reflect our interaction with the environment, allowing us to rapidly react to any stimulus. The second system would be triggered at ~450 ms after the stimulus, when the hippocampus and other areas of the medial temporal lobe are activated [START_REF] Despouy | A Fast Visual Recognition Memory System in Humans Identified Using Intracerebral ERP[END_REF]. At this stage, more elaborated memories are retrieved with additional information.

As most of previous studies have focused their analysis on the main hippocampal response, the so-called hippocampal P600 [START_REF] Barbeau | Hippocampus duality: Memory and novelty detection are subserved by distinct mechanisms[END_REF][START_REF] Barbeau | Spatio temporal dynamics of face recognition[END_REF][START_REF] Despouy | A Fast Visual Recognition Memory System in Humans Identified Using Intracerebral ERP[END_REF][START_REF] Dietl | Processing of famous faces and medial temporal lobe event-related potentials: a depth electrode study[END_REF][START_REF] Trautner | Recognition of famous faces in the medial temporal lobe: an invasive ERP study[END_REF], it has been suggested that this structure cannot be involved in fast memory processing, which would occur much earlier. However, thanks to the use of ICA in SEEG recordings, we have found that the hippocampal dynamics during memory recognition are very complex, with at least two different generators contributing to the response. Our results, with the peak of Hc250-IC ERP at 250 ms and Hc600-IC presenting the earliest differential activity at 290 ms, challenges the vision of the hippocampus as a "late" structure.

We propose that both components (Hc250-IC and Hc600-IC) represent two steps of processing. The earliest component, Hc250-IC, presents similar delays as the N240 component evoked in the EC and in other mesial structures [START_REF] Barbeau | Hippocampus duality: Memory and novelty detection are subserved by distinct mechanisms[END_REF]. Thus, it may reflect the input from the EC, the main entrance pathway to the hippocampus [START_REF] Fernández-Ruiz | Gamma rhythm communication between entorhinal cortex and dentate gyrus neuronal assemblies[END_REF][START_REF] López-Madrona | Functional Interactions between Entorhinal Cortical Pathways Modulate Theta Activity in the Hippocampus[END_REF]. This activity would already contain a pre-identification of known elements, realized in the perirhinal cortex [START_REF] Despouy | A Fast Visual Recognition Memory System in Humans Identified Using Intracerebral ERP[END_REF]. The preprocessed information from EC may trigger the internal hippocampal circuit (linked to Hc600-IC), facilitating memory retrieval of those elements already recognized. This neuronal facilitation would be reflected in the ERP, with earlier latencies for old items (Figure 3e). Interestingly, we identified a similar facilitation pattern in magnetoencephalography (MEG) during the same task [START_REF] López-Madrona | Magnetoencephalography can reveal deep brain network activities linked to memory processes[END_REF]. In that work, the combined activity of the hippocampus and the rhinal cortex presented earlier latencies for old items. Importantly, the origin of the MEG activity was validated using simultaneous MEG-SEEG recordings. Although the Hc250-IC responses suggest that the hippocampus process could be even faster in identifying known items, the absence of clear differences at the group level cannot confirm a direct link between the hippocampus and the fast memory recognition system. Further work is required, for example pushing the participants to their quickest answer and exploring the hippocampus together with other areas involved in the task. Overall, our results show that the hippocampus is not a late responding structure, but it is activated relatively fast during memory recognition [START_REF] Despouy | A Fast Visual Recognition Memory System in Humans Identified Using Intracerebral ERP[END_REF].

Concluding remarks

The brain circuit of memory encoding and retrieval is an open question in neuroscience [START_REF] Ferguson | A human memory circuit derived from brain lesions causing amnesia[END_REF][START_REF] Thompson | Memory systems in the brain and localization of a memory[END_REF]. Our results provide new insights in the role of the hippocampus in memory recognition, linking the hippocampal activity to early stages of memory recognition. However, it remains unknown which is the specific contribution of the hippocampus to these fast processes. Future investigations will explore the role of the identified hippocampal sources in different memory processes, as well as the specific substructures contributing to the recorded activity. In addition, we have proved the efficacy of ICA to disentangle neuronal generators in SEEG. This opens new possibilities, not only in the analysis of the human hippocampus, but for all intracerebral studies. The use of ICA in other brain structures may reveal the dynamics of different but spatially overlapped structures, overcoming the limitations of traditional montages [START_REF] Herreras | Site-dependent shaping of field potential waveforms[END_REF][START_REF] Herreras | New uses of LFPs: Pathway-specific threads obtained through spatial discrimination[END_REF]. Importantly, it can be used in clinical applications with, for example, the potential to identify and separate the generators involved in epileptic networks [START_REF] Barborica | Extracting seizure onset from surface EEG with independent component analysis: Insights from simultaneous scalp and intracerebral EEG[END_REF][START_REF] Malinowska | Interictal networks in magnetoencephalography[END_REF]. Further research is granted.

Figure 1 :

 1 Figure 1: Separation of brain sources in SEEG with ICA in one patient

Figure 2 :

 2 Figure 2: Two hippocampal sources during memory recognition.

Figure 3 :

 3 Figure 3: Comparison of Hc600-IC for old and new responses.

  two different generators, in the subiculum and in the posterior hippocampus, during an oddball task. These structures presented a similar ERP with the characteristic P300, but with different voltage gradients along the sensors, which indicated a local origin of the activities. It is unlikely that they are the components of our study, which we found in the anterior and posterior part of the hippocampus. Moreover, Hc250-IC and Hc600-IC had very different response time, in contrast with the generators identified by Ludowig and colleagues.

Table 1

 1

	shows the clinical information for

Table 1 : Clinical information of each patient.

 1

ACKNOWLEDGEMENTS

We would like to thank Cornel Tudor and Aurelia Dabu for performing the surgical procedures, Flavius Bratu, Camelia Lentoiu and Felicia Mihai for assisting with the patient investigation and data collection, Cristian Donos for the development of the computational framework for localizing the SEEG electrodes in common patient space. This study was performed thanks to a FLAG ERA/HBP grant from Agence Nationale de la recherche, ANR-17-HBPR-0005 SCALES and UEFISCDI COFUND-FLAGERA II-SCALES. This work has received support from the French government under the Programme « Investissements d'Avenir », Initiative d'Excellence d'Aix-Marseille Université via A*Midex funding (AMX-19-IET-004), and ANR (ANR-17-EURE-0029). The data were acquired on a platform member of France Life Imaging network (grant ANR-11-INBS-0006), supported in part by grants ANR-16-CONV-0002 (ILCB) and the Excellence Initiative of Aix-Marseille University (A*MIDEX).

SUPPLEMENTARY MATERIAL

Supplementary Figure 1

Supplementary Figure 2: Single-case comparison between old and new responses of Hc250-IC

Each plot represents the averaged ERP of Hc250-IC during old (solid traces) and new (dashed traces) trials for each electrode. Hashes represent patient number and stars indicate significant differences in amplitude between conditions (* p<0.05, t-test across trials corrected with LFDR). Only 6 out of 12 electrodes presented a modulation to the memory protocol. In three cases, this difference was due to higher amplitudes after the presentation of old images at early latencies (electrodes 5, 10 and 12). In the other three electrodes, the responses were different, with the responses to the new images standing high during a longer period (electrodes 2, 4 and 7)


domain: sdv info

text:
Margaux Calice

Julien Ballbé

Lyle J Graham

Efficient approximations for probabilistic synaptic connectivity in large-scale network models

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

Model performance

➢ We present an algorithm for defining large-scale networks, reducing the order from 𝑂(𝑁 2 ) to 𝑂(𝑁×𝑀) while keeping a close the correspondence between M(X) and the theoretical number of connections ➢ Can be applied to simulations of large excitatory and inhibitory networks with biophysically mimetic cell models and densities.

Contact : Margaux Calice (margaux.calice@math.cnrs.fr)

Approximation of the probability function F(source, target) specified by experimental data and/or theoretical considerations : average of the probability over an interval of distance Y.

The volume in which the neurons are placed is divided into a regular grid B with a characteristic dimension X.

For each element of the grid B ij , we obtain for a source neuron S targeting within B ij the # of connections 𝑀 ij thanks to the approximated probability function :

𝑀 ij =𝐹(𝑆, B ij )×[# 𝑜𝑓 𝑛𝑒𝑢𝑟𝑜𝑛𝑠 𝑖𝑛 B ij ]
The explicit connections are assigned by randomly choosing 𝑀_𝑖𝑗 target neurons located within B ij

The network explored here consists of two populations of conductance-driven LIF neurons, thus with basic firing properties to mimic excitatory and (fast-spiking) inhibitory neurons in the cortex.

Several point-neuron network models combine data from anatomy and electrophysiology, with probability or deterministic rules to construct realistic synaptic connections. For reviews of these models and general guidelines for network models see (2,3). In anatomical data-driven models, connectivity between areas can be derived from their architecture and laminar composition (2). For local connectivity, neurons close to each other will have a greater tendency to connect (3,7). We can built models strongly informed by these results where the connection probability dependent of the intersomatic distance for pairs of neurons follows a Gaussian or an exponential decay (3,6). While this approach arguably is the closest simulacrum to the experimental protocols, configuring large-scale models can become computationally onerous due to the pairwise sampling procedure. Derivatives of the distance-dependent probability rule have been proposed to improve this situation, including The Brain Scaffold Builder (BSB) (4), which limits the number of pairwise iterations according to "a maximum search radius derived from the cumulative probability function of the distance distribution". However, other large models, e.g. Schmidt et al., 2018 (5), which produces 2.42 ⋅ 10 10 synapses, and Billeh et al., 2020 (8) rely on supercomputers for practical computing times Here we present preliminary results for an algorithm that promises to extends the capability of more modest hardware to further optimize the distance-dependence framework, while maintaining a certain degree of realism in the network.

Performance tests comparing the new « Shell Method » with a straight-forward implementation of thorough pair-wise search (« Brute Force ») were made on a modest laptop. Various circuit geometries are applicable for the method, ranging from low-dimensional forms (sphere) to anatomically-defined cortical layer/areas.

(1) Braitenberg, V., and Schüz, A., 1991 (2) van Albada, S.J. ➢ As either X or the cellular density of the model become smaller, the performance of the method mimics the brute force approach; for even smaller networks the overhead for setting up the procedure results in an overall longer execution time.

  Gridding of neurons in the cortical plane of the primary visual cortex. A. Layer II/III cortical plane cortex in which the neurons, with a xyz density, were placed in a realistic 3-dimensional volume. B. The cortical plane is divided into a regular grid with a dimension of 100µm. C. The distancedependent connection probability applied to these neurons is averaged over a distance interval of 100µm. D. of 200µm. artefacts as X increases :1. The approximation of the probability function over 𝐵_𝑖𝑗 2. the geometric dis-congruence between the grid and the shape of the probability function•In addition, edge effects are more susceptible for this algorithm

➢

  et al, 2022 (3) Senk, J. et al 2022 (4) De Schepper et al, 2022 (5) Schmidt, M. et al 2018 (6) Markram, H., et al. 2015 (7) Levy, R.B., and Reyes, A.D. 2012 (8) Billeh, Y.N. et al 2020 The authors wish to acknowledge the support by the FIRE doctoral program (for M.C.), the Centre Borrelli, and the Human Brain Project via the Rising Net consortium (led by Egidio D'Angelo) The precision and efficiency of the algorithm scales inversely with X.


Konstantina Margiotoudi

Joel Fagot

Adrien Meguerditchian

Isabelle Dautriche
email: isabelle.dautriche@cnrs.fr

Crossmodal pitch-luminance correspondence in human and non-human primates: a failed replication of Ludwig et al. (2011) in baboons (Papio papio)

Keywords: crossmodal correspondence, comparative cognition, iconicity

Crossmodal pitch-luminance correspondence in human and non-human primates: a failed replication of [START_REF] Ludwig | Visuoauditory mappings between high luminance and high pitch are shared by chimpanzees (Pan troglodytes) and humans[END_REF] in baboons (Papio papio) Humans tend to form automatically cross-modal correspondences, i.e., associations between modality-specific features [START_REF] Spence | Crossmodal correspondences: A tutorial review[END_REF]. For instance, they associate certain sound frequency to shape properties: a high-pitched tone will be preferably associated to objects that are small, spiky and bright, whereas a low-pitched tone would be generally associated to objects that are big, round, and dark [START_REF] Spence | On the relative nature of (pitch-based) crossmodal correspondences[END_REF]. Such cross-modal biases have been demonstrated experimentally across a wide range of modalities: between odors and visual stimuli [START_REF] Hanson-Vaux | Smelling shapes: Crossmodal correspondences between odors and shapes[END_REF], sound and taste [START_REF] Kno | Crossmodal correspondences between sounds and tastes[END_REF], and taste and touch [START_REF] Christensen | Effects of taste quality and intensity on oral perception viscosity[END_REF], among many others [START_REF]The handbook of multisensory processes[END_REF].

Cross-modal biases have been proposed as a key mechanism underlying multisensory integration and language evolution. In perception, cross-modal correspondences may help to constrain the cross-modal binding problem (i.e., the problem of knowing which of many perceptual information should be bound together) [START_REF] Ernst | Learning to integrate arbitrary signals from vision and touch[END_REF]. In language: cross-modal biases to associate sounds with other sensory modalities may have triggered the evolution of spoken language by facilitating the expression of sensory experiences through a unique auditory channel [START_REF] Ramachandran | Synaesthesia--a window into perception, thought and language[END_REF][START_REF] Cuskley | Synesthesia, cross-modality, and language evolution[END_REF].

Surprisingly though, despite the potential evolutionary importance of cross-modal correspondences, the investigation of their phylogenetic origins remains very limited.

One possibility is that cross-modal correspondences may be limited in non-human animals because they are rooted in linguistic experience. For instance, the pitchluminance mapping [START_REF] Ludwig | Visuoauditory mappings between high luminance and high pitch are shared by chimpanzees (Pan troglodytes) and humans[END_REF] -where higher frequencies are associated to bright stimulimight derive from the usage in English of the same adjectives to refer to luminance and pitch, namely 'high' and 'low'. Alternatively, because non-human species would similarly benefit from cross-modal correspondences to facilitate the integration of multisensory inputs coming from their environment [START_REF] Parise | Synesthetic congruency modulates the temporal ventriloquism effect[END_REF], these proclivities may be the result of a natural, structural, feature of our perceptual system that may be shared across species. For example, for the pitch-luminance mapping, both visual high luminance and acoustic high frequency have a shared increase in stimulus intensity which is suggested to be encoded by the same brain structure dedicated to magnitude [START_REF] Walsh | A theory of magnitude: common cortical metrics of time, space and quantity[END_REF].

Comparative studies provide an immediate way to tease apart these different possibilities. To our knowledge, only few studies tested the existence of these mappings in other animals, and report evidence for a pitch-elevation mapping [START_REF] Korzeniowska | Audio-visual crossmodal correspondences in domestic dogs (Canis familiaris)[END_REF] where higher frequencies are associated to high spatial positions--, and a pitch-size mapping in domestic dogs [START_REF] Korzeniowska | Highpitch sounds small for domestic dogs: abstract crossmodal correspondences between auditory pitch and visual size[END_REF], a space-luminance mapping [START_REF] Loconsole | Space-luminance crossmodal correspondences in domestic chicks[END_REF] -where right positions are associated to brighter stimuli--but not a pitch-luminance mapping in chicks [START_REF] Loconsole | Pitch-Luminance Crossmodal Correspondence in the Baby Chick: An Investigation on Predisposed and Learned Processes[END_REF], and finally a pitch-luminance mapping in chimpanzee [START_REF] Ludwig | Visuoauditory mappings between high luminance and high pitch are shared by chimpanzees (Pan troglodytes) and humans[END_REF]. Some of these correspondences could be learn from their co-occuring regularities in the environment.

The pitch-elevation correspondence might be explained by environmental statistics of auditory scenes as higher frequency sounds originate from elevated sources [START_REF] Christensen | Effects of taste quality and intensity on oral perception viscosity[END_REF].

Similarly the pitch-size correspondence might arise from the observation that large objects tend to produce low frequency sounds (e.g., when dropped) while small objects produce high-pitched sounds [START_REF] Gaver | What in the world do we hear?: An ecological approach to auditory event perception[END_REF]. Yet, evidence from the pitch-luminance and the space-luminance mappings in non-human animals would suggest that some of these mappings have a non-linguistic, structural origin.

Of special interest is pitch-luminance correspondence, which is well established in humans [START_REF] Marks | On associations of light and sound: The mediation of brightness, pitch, and loudness[END_REF][START_REF] Mondloch | Do small white balls squeak? Pitch-object correspondences in young children[END_REF][START_REF] Haryu | Are higher-frequency sounds brighter in color and smaller in size? Auditory-visual correspondences in 10-month-old infants[END_REF], that triggered different findings in chicks and chimpanzees. Critically, this is the only study reporting the existence of a cross-modal correspondence that cannot be extracted statistically from the environment, in a non-human primate species. In that study, 6 chimpanzees and 33 human participants were trained in a visual speeded classification task on a touch screen, where they had to match a sample square (black or white) to the matching stimulus (i.e., identical black or white square). During testing, both species performed the same task while passively listening to background sounds (i.e., high and low-pitched tones), which were crossmodally either congruent or incongruent to the luminance of the sample square.

Both species showed better performance when the frequency of the sound matched the luminance of the sample square -white square congruent with the high-pitched tone and black square with the low-pitched tone.

Other attempts on testing sound-shape mappings (i.e., mapping the 'round' vs 'sharp' sounding nonwords, such as 'bouba' and 'kiki', to a curved and 'sharp' shapes, respectively [START_REF] Margiotoudi | Sound symbolic congruency detection in humans but not in great apes[END_REF][START_REF] Margiotoudi | Bo-NO-bouba-kiki: picture-word mapping but no spontaneous sound symbolic speech-shape mapping in a language trained bonobo[END_REF]) in great apes (i.e., gorillas, chimpanzees and bonobos) failed to show any cross-modal effects in these species. Based on the weak experimental evidence on cross-modal mapping in non-human primates, in the current preregistered study, we aimed to carry out a direct replication of Ludwig et al. ( 2011)'s study on the pitch-luminance correspondence, by testing a group of humans and baboons (Papio papio) following the original methods and analyses as closely as possible. Baboons and humans were trained on a visual speeded classification task, where they had to classify squares based on their luminance. During the testing phase, both species repeated the same speeded classification task, while background sounds (e.g., low vs high pitched tones) were presented. We examined whether sound congruency, following a pitch-luminance mapping, would affect the performance of both species on the visual classification task.

Methods

The methods, design and analysis were pre-registered at: https://osf.io/vsnp4. used to be trained with visual feedback when performing touch-screen tasks [START_REF] Margiotoudi | Bo-NO-bouba-kiki: picture-word mapping but no spontaneous sound symbolic speech-shape mapping in a language trained bonobo[END_REF]. After a correct response, a black screen appeared for 3s combined with food reward only for the baboons, whereas after an incorrect response a green screen appeared for 3 s plus a black screen of 3s.

Before starting with the task, participants received written instructions and were asked to be as fast and accurate as possible in performing the visual speeded classification task. The experiment was divided into two phases, a training and a testing phase.

During the training, participants performed 16 matching-to-sample trials without any background sound (for details see supplementary material). By the end of this phase, participants had to reach a performance of 100%, in order to continue to the testing phase. These 16 trials were repeated once in case of mistakes. Two participants did not reach a 100% performance at the first training and repeated the procedure. Once the training was completed, the testing phase followed. Participants received written instructions to focus on the visual speeded classification task, even though sounds would be played in the background. The testing phase started with 4 trials including sounds, which were excluded from the analysis. Then, two blocks of 80 trials each (160 trials in total) followed, with a break in-between. Humans were tested in one single session of 30 minutes.

Baboon testing: Fifteen Guinea baboons (Papio papio), (12 females, age M=11.4 ± 5.64) completed the study. Baboons were tested at the CNRS Primatology center in Rousset-sur-Arc, France where they live in two social groups and had ad libitum access to 13 ALDMs (Automated Computer Learning devices) [START_REF] Fagot | Automated testing of cognitive performance in monkeys: Use of a battery of computerized test systems by a troop of semi-freeranging baboons (Papio papio)[END_REF]. Each device has a food dispenser, a touchscreen and two speakers. The speakers are facing down inside each test chamber. A radio-frequency identification of the subjects by the system facilitated testing, as it doesn't require capturing of the subjects [START_REF] Fagot | Effects of freely accessible computerized test systems on the spontaneous behaviors and stress level of Guinea baboons (Papio papio)[END_REF]. The baboons have voluntary access to all the devices. Baboons performed the same visual speeded classification task as humans, and were exposed to the same training procedure (for details on the training see supplementary material). All baboons completed the testing in three consecutive days. Each day comprised a testing session. During a session, the monkeys completed a block of 16 trials without any background noise, where they had to achieve a performance of at least 80% in both categories (i.e., black/white) (White: M=0.80%; SE=0.02%, Black: M=0.88%; SE=0.02). The block was repeated until the criterion was reached (number of averaged training blocks =2.71; min=1; max=11; SE=0.31). After that, baboons performed 4 practice trials with background sounds. Then, a block of 160 testing trials followed. In total, baboons completed 480 testing trials. All trials and conditions between samples and sounds were equally counterbalanced, following the description by [START_REF] Ludwig | Visuoauditory mappings between high luminance and high pitch are shared by chimpanzees (Pan troglodytes) and humans[END_REF] (for details on the training see supplementary material).

Results

We conducted the analysis described by Ludwig et al., (2011) [START_REF] Ludwig | Visuoauditory mappings between high luminance and high pitch are shared by chimpanzees (Pan troglodytes) and humans[END_REF]. We first looked at the mean median RT and the error rates for congruent and incongruent trials in both species. Our results confirm the findings of Ludwig et al. ( 2011) [START_REF] Ludwig | Visuoauditory mappings between high luminance and high pitch are shared by chimpanzees (Pan troglodytes) and humans[END_REF] on humans but not on non-human primates. Specifically, in the original study, chimpanzees made more errors in incongruent than in congruent trials, while, in baboons, no differences were observed on the mean error rates between congruent (M= 10.58%, SD= 3.16%) and incongruent trials (M = 10.58%, SD= 4.16%). Yet, similarly to the original study, there was also no difference on the mean median RTs between congruent (M = 653ms, SD = 97.6ms) and incongruent trials (M = 657ms, SD = 102.4ms; t(14)=-1.31, p=0.20, r = 0.33).

Humans had no significant difference in the mean error rates between congruent (M = 0.18%, SD = 0.04%) and incongruent trials (M = 0.46%, SD = 0.06%; t( 26 How to account for the difference between chimpanzees [START_REF] Ludwig | Visuoauditory mappings between high luminance and high pitch are shared by chimpanzees (Pan troglodytes) and humans[END_REF] and the current baboons result? Certainly, primate species differences might suggest that the mapping of pitchto-luminance is unique to chimpanzees and humans, but does not extend to more phylogenetically distant relatives such as the Old World monkeys, including the *** baboons. According to this hypothesis, such a cross-modal correspondence might thus have emerged and be inherited from the last common ancestor of the Hominini tribe. Yet, another possibility is that the absence of any systemic pitch-to-luminance correspondence in baboons, questions the existence of this mapping not only in this species but also in any other nonhuman species. There are several reasons to doubt on the robustness of the chimpanzee's result. First, while the study of Ludwig et al.

(2011) showed a pitch interference effect in a visual speeded classification task in chimpanzees, this effect was based on a 2.7% difference between mean error rates for congruent and incongruent trials but was not visible on median response times, contrary to human participants. Second, the size of the chimpanzee sample was relatively low (N=6) compared to the present baboon study (N=15). Third, whether the chimpanzees tested in the Ludwig et al., (2011) [START_REF] Ludwig | Visuoauditory mappings between high luminance and high pitch are shared by chimpanzees (Pan troglodytes) and humans[END_REF] had previous experiences on auditory discrimination between high-and low-pitched tones, is not specified in the original study. Any such previous experience could have increased sensitivity to the auditory stimuli of the present paradigm. This is of particular importance as testing non-human primates on auditory paradigms is not a trivial task [START_REF] Hashiya | Acquisition of auditory-visual intermodal matching-to-sample by a chimpanzee (pan troglodytes): comparison with visualvisual intramodal matching[END_REF][START_REF] Martinez | Visual and auditory conditional position discrimination in chimpanzees (Pan troglodytes)[END_REF].

Our results are consistent with previous results showing no evidence of crossmodal mappings in non-human primates [START_REF] Margiotoudi | Sound symbolic congruency detection in humans but not in great apes[END_REF][START_REF] Margiotoudi | Bo-NO-bouba-kiki: picture-word mapping but no spontaneous sound symbolic speech-shape mapping in a language trained bonobo[END_REF] or in other animals such as chicks [START_REF] Loconsole | Pitch-Luminance Crossmodal Correspondence in the Baby Chick: An Investigation on Predisposed and Learned Processes[END_REF]. This suggests that some crossmodal correspondences might not be shared with other nonhuman species. As we noted, the absence of any crossmodal effects in these species could have plausibly resulted from attention or discriminatory troubles on auditory stimuli. In addition, factors such as previous habituation to visual tasks, training history, protocol design, or age of the individuals appear to play an important role in testing the performance of nonhuman primates on tasks including auditory stimuli [START_REF] Ennaji | Voice perception in non-human primates : A behavioral study of categorization of vocal and non-vocal sounds in Guinea baboon (Papio papio)[END_REF].

To conclude, evidence of the presence of a pitch-luminance mapping in other nonhuman species appears to be weak, suggesting that language, culture or humanspecific neuro-anatomical features may be fundamental for the emergence of this nonstatistically learned mapping. Future studies will be necessary to investigate the reliability of this hypothesis.

Fundings

  /osf.io/ng6y3/?view_only=b3797de197744bb79a1bcc544f0cf69a.All methods for the human study were approved by the Ethics Committee of Aix-Marseille University and for the baboon study received approval from the French Ministère de l'Education Nationale (approval no. APAFIS-2717-2015111708173794-V3).Human testing: Twenty-seven, right-handed human adults (21 females, age M=22.70 ± 5.55) participated in the study. The task was a visual speeded classification task involving the matching of a sample square (black or white) to one of the two alternatives which is identical to the sample (identity matching-to-sample procedure).During the test trials, a tone, either high or low-pitched, was played while participants were performing the same task. The design was identical to the one described in Ludwig et al. (2011) [10], with the sole difference on the feedback screens (see supplementary Figure S1.b). In the present study, we used visual feedback, instead of audio feedback as in Ludwig et al., 2011, since that group of baboons was already

Figure 1 .

 1 Figure 1. Mean IE scores for congruent and incongruent trials in a) humans and b) baboons. Blue circles and errors bars represent the means and standard errors for each condition (SE). Asterisks indicate significant differences between the two conditions (p<0.001).

This work was supported by Fyssen Foundation, by the European Research Council under the European Union's Horizon 2020 research and innovation program grant agreement No 716931 -GESTIMAGE -ERC-2016-STG, by the "Agence Nationale de le Recherche" ANR-16-CONV-0002 (ILCB) and the Excellence Initiative of Aix-Marseille University (A*MIDEX).


text:
Julien Ballbé

Margaux Calice

Marta Gajowa

Daniele Marinazzo

Lyle J Graham

Y Sabaté

Derivation of a protocol agnostic current clamp description for the cross-analysis of diverse neuronal electrophysiological databases

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

Biophysically realistic spiking network models rely on the ability of the cellular models to be coherent with the biological diversity of cellular properties and electrophysiological behavior, for example how synaptic conductance inputs impact neuronal firing, Numerous studies of neuronal biophysics have been made over the last decades, primarily intracellular recordings under the current clamp configuration, thus current input as an approximation of synaptic inputs. Increasingly labs publicly release their experimental databases, often including other cellular information [1,2,3]. Apart from common standards for electrophysiological data (Neurodata Without Borders,[4]), experimental design and data collation is typically unique to a given lab, complicating comparative analysis across databases.

To overcome this limitation, we propose a database-independent analysis pipeline to analyze intracellular recording data and extract features relevant for neuron models. These features constitute a Protocol-Agnostic Current-Clamp (PACC) vector, characteristic of a cell independently of its database.

Examples of the application of the PACC include associating transcriptomic or anatomic data available in one database, to cells without this information from another database. Another application is to associate current-clamp and conductance-clamp data from our lab, allowing the inference of current-clamp data from other labs to conductance dependent properties.

Cortical Model Development

 Part of the variability can be compensated by correcting features by the cell's input resistance, and expressing input current in term of linear voltage (i.e. Gain : Hz/pA  Hz/mV; Threshold : pA  mV)

Neurons' electrophysiological properties differences between different experimental configurations can be compensated by the difference in input resistance (Only cells coming from VIS area are considered) A) Input resistance significantly differs between cells recorded in vivo and in vitro B)

Distributions of neuronal Gain between in vivo and in vitro ( fitted to LogNormal function), when expressed in Bi) current Gain (i.e. Hz/pA) and Bii) linear voltage Gain (i.e. Hz/mV).

A Voltage Gain

In Vitro

Current Gain

In Vivo

Bi Bii

These will serve as a basis for the development of biologically-constrained cell models tuned to the specificities extrapolated from the recordings originating of multiple databases.

Correlation between a PACC vector and neuron's threshold dependence on synaptic inhibition

  independent analysis pipeline -Input resistance correction to gather in vivo and in vitro data -A Rosetta Stone mapping between current and conductance clamp data have been gathered, from 3 different databases [1] Gouwens et al. 2019 ; [2] Gouwens et al. 2020 ; [3] Lantyer et al 2018 [4] Rübel et al., 2022 ; [5] Destexhe et al., 2003 To ensure consistent and relevant comparison of features, we reduce variability arising from differences in curation and experimental paradigms (i.e. in vivo and in vitro) Assumptions: -Neuronal properties scale with cell area -Higher background synaptic activity In vivo can account for many differences in functional properties with In vitro cells [5]  Cell model and synaptic connectivity constrained to experimental data  Must account for biological differences (between areas, layers…) and variability (e.g. within VIS area) A cerebral cortex model in the context of the implementation of a multi-scale model of the mouse brain. A) Scheme of the overall mouse brain model, from neuron model to CC model B) Statistical gain differences between VIS and SS areas, and C) between layers of VIS area : -Designed for long current steps -No pre-processing required -Low-coding effort -2-script calls from TerminalHow to get all databases through the same analysis pipeline ?Organization and the integration of external databases into the data analysis pipelineI 𝑉 𝑉 ( 𝑉)𝑉 𝑉𝑉𝑉 ( 𝑉) =𝑉 𝑉𝑉𝑉 ( 𝑉 𝑉 ( 𝑉) -𝑉 𝑉𝑉𝑉 ) +𝑉 𝑉𝑉𝑉 ( 𝑉 𝑉 ( 𝑉) -𝑉 𝑉𝑉h )Rely on common data analysis pipeline to implement multi-scale cerebral cortex model  I/O features distribution reproduced in cortical models