dataesr/scientific-paragraphs-categorization huggingface dataset:
In these mutants, the decreased innervation of Merkel cells in the glabrous skin and the decreased sensitivity in SA-LTMR electrophysiological (Zheng et al. , 2019)5)Zheng et al. , 2019;Sharma et al. , 2020)ry large receptive fields(Shin et al. , 2003;Li et al. , 2011;Walcher et al. , 2018). They form lanceolate endings, are thinly myelinated and are activated by movement of the smaller zigzag hairs(Lechner and Lewin, 2013). Consistent with the lack of Meis2 expression in this population reported by singlecell RNA-seq databases, Aδ fibers D-hair in the hairy skin showed similar vibration responses in WT inactivation in post-mitotic sensory neurons differentially impaired LTMRs projections and functions without affecting their survival and molecular identity. Combined single cell RNAseq (scRNAseq) analysis and genetic tracing have accurately resolved Meis2 expression(Usoskin et al.
MEA recordings From DIV6 of the neuronal culture on, electrical activity of the cultures was recorded using the Maestro Edge apparatus of Axion Biosystems. During recordings, the plates were kept at 37 • C and 5% CO 2. Recordings were controlled via Axis Navigator software (version 2. 0. 4. 21) and were all performed for 10 min. The obtained raw recording data were further analyzed by the same software to generate numeric results. The settings of the analysis were the following: Spike detection: 6 x SD from voltage baseline. Minimal burst criterion: minimum 5 spikes, maximum inter-spike interval 100 ms. If the mean firing rate was 0, burst percentage and SI were put to 0 as well. For further analysis, the weighted mean firing rate was calculated (mean firing rate per well, corrected for the number of active electrodes detecting electrical activity).
Top 10 fragment ions were chosen for synchronous precursor selection and fragmented with higher energy CID (HICD, 2 Da MS2 isolation, 60% collision energy) for detection of reporter ions in the Orbitrap analyzer (100-500 m/z, resolution 15,000). Data processing and database search were further performed with Proteome Discoverer software 2. 5. 0. 400 (Thermo Fisher). An in-house Mascot server (version 2. 8. 2, Matrix Science Ltd) was used for peptide identification. The data were searched against the Swissprot mouse database (version 2022/05) and two contamination databases (Contaminants_Hao_lab and cRFP,  (Frankenfield et al. , 2022; Shin et al. , 2019). Proteins identified by MS were submitted to Gene Ontology analysis by using PANTHER Enrichment Analysis tool  (Thomas et al. , 2022).
Acknowledgments We thank  Sabine Opitz ,  Pia Trebing  and  Shayne Gilgenbach  for excellent technical assistance. We also thank  Prof. Gilles Huberfeld  for his valuable feedback. Protein identification was performed at the  Core Facility Analytical Proteomics, Institute of Biochemistry and Molecular Biology, Medical Faculty, University of Bonn , funded by the  Deutsche Forschungsgemeinschaft (DFG ,  German Research Foundation ) - Projektnummer 386936527. The present work fulfills the first author obligations for PM's written thesis to obtain the MD degree according to the 'Doctoral  Degree Regulations ' at the  University of Bonn, Medical Faculty.
If the reason for ending participation in the study was lack of satisfaction with treatment, the imputation of the data was the worst-case imputation, meaning that the treatment was considered as having failed the patient. This analysis was prespecified in our statistical analysis plan (ie, before we started this analysis). The nonmissing at random jump to reference option assumed that after dropout, the participant's conditional outcomes deteriorate similarly to those of placebo. Secondary end points and pitolisant safety compared with placebo were analyzed according to the main aforementioned model for the primary end point. Statistical tests were performed with a two-sided 5% level of significance.
In sensitivity analyses, we demonstrated a homogeneous effect of the treatment irrespective of the baseline characteristics (age, sex, BMI, and country). These data enhance the generalizability of our results in a real-world setting and complete the homogeneous response between participants who were adherent and nonadherent to primary OSA therapy. This remains even though follow-up management pathways of patients treated with CPAP do vary between countries, but also because residual sleepiness and the perception of sleepiness are influenced by sex, age, and cultural or environmental contexts. In data from the European Sleep Apnea Database,  21  EDS appeared heterogeneous among different European countries both at baseline and under CPAP treatment, suggesting that it is influenced by cultural and lifestyle factors.
1 AE 0. 4 after 1 year of treatment. Another limitation of the study was related to the participants who dropped out during the 1-year treatment period, with a potential selection bias in favor of good responders with good tolerance. Our imputation of missing data for dropouts considered all the reasons for dropout, including patients stopping for lack of efficacy, or for a drug-related adverse effect. Despite the existence of dropouts at 1 year, this analysis can be considered as conducted on a full intent-to-treat basis, and therefore included all the 512 patients starting at baseline. We did not plan a double-anonymized randomized withdrawal phase during the open-label study, which prevents us from formally demonstrating long-term maintenance of the efficacy of pitolisant. Finally, BP and heart rate were not assessed using 24-h ambulatory BP monitoring.
Acknowledgments Author contributions: J. -L. P. takes responsibility for (is the guarantor of) the content of the manuscript, including the data and analysis. Y. D. , J. -L. P. , and  P. Lehert  conceived and designed the project. C. C. and I. L. managed the database. P. Lehert performed the statistical analysis. J. -L. P. and Y. D. wrote the first draft of the manuscript. All authors participated in data acquisition, participated in data interpretation, reviewed the article, and approved its submission to CHEST.
Following this analogy, we transform the offline pattern categorization method into an online and event-driven layer. This classifier uses the spiking output of the network to define new time surfaces and we then perform the online classification with a neuromimetic implementation of a multinomial logistic regression. These improvements not only consistently increase the performance of the network, but also bring this event-driven pattern recognition algorithm fully online. The results have been validated on different datasets: Poker-DVS (Serrano-Gotarredona and Linares-Barranco, 2015), N-MNIST (Orchard et al. , 2015a) and DVS Gesture (Amir et al. , 2017). This demonstrates the efficiency of this bio-realistic SNN for ultra-fast object categorization through an event-by-event decision making process.
(2017). This particular model performs object recognition thanks to a feedforward hierarchical architecture using time surfaces, an event-driven analog representation of the local dynamics of a scene. These are then combined into a Hierarchy Of Time Surfaces (HOTS). Using a form of Hebbian learning, the network is able to learn, in an unsupervised way, progressively more complex spatio-temporal features that appear in the event stream. This algorithm has been shown to make accurate predictions on a letter and digit dataset  (Orchard et al. , 2015b) , on a flipped card dataset  (Pérez-Carrasco et al. , 2013)  and on a dataset of scenes with faces. We have identified two main limitations of the HOTS algorithm. First, the unsupervised clustering of the kernels is highly dependent on initialization, and this can affect the performance of the network.
We have recently proposed to include a biologically plausible homeostatic gain control mechanism  (Grimaldi et al. , 2021). We showed there that unsupervised feature learning is qualitatively improved by balancing the activity of the different neurons within the same layer. We also tested the classification accuracy on different datasets by injecting different amounts of spatial and temporal noise into the stream of input events, demonstrating that efficiency was increased by homeostasis. The second limitation that we identified in HOTS is the classifier. Indeed, it is based on computing a histogram of the neuronal activations in the last layer of the network to perform the classification. Such a method ignores the fine-grained temporal dynamics of the stream of events produced by the final layer of the network.
More importantly, a major drawback is that the classification can only be performed post hoc, once all the events of the tested sample have been received. This offline layer was chosen because it provides an accurate response once all events have been emitted. In this study, to achieve a full end-to-end event-driven and online pattern categorization, we incorporate and test an online classification method in the final layer of the network. We formally demonstrate that the overall structure of the proposed model corresponds to a biologically plausible spiking neural network (SNN). To our knowledge, it is the first always-on, event-driven object recognition method, and we validate it on different datasets designed for categorizing symbol (Serrano-Gotarredona and Linares-  Barranco, 2015) , digit  (Orchard et al.
, 2015a)  or gesture  (Amir et al. , 2017)  categorization. Given the simplicity of the proposed network's architecture, its eventbased formalism and its local learning rules, this method is easily transferable to neuromorphic hardware to exploit the efficiency of event-based computing. From this perspective, the structure of this paper will be as follows. First, we present the HOTS algorithm using a novel mathematical formalization and the improvements brought by our method. Then, we extend the categorization algorithm by adding a simple biologically plausible online classification layer. We prove that our method corresponds to a SNN with Leaky Integrate-and-Fire (LIF) neuron models, which can be implemented in a neuromorphic chip. Finally, we show the quantitative improvements of the resulting classification performance, and how its dynamics can vary for different datasets.
Materials and methods In this section, we first describe the datasets used in this study and present the method we designed to test the robustness of our algorithm to spatial and temporal jitter. After giving an overview of existing object recognition algorithms, we generalize the event-based HOTS model, already described in  Lagorce et al. (2017) , and extend its formalism to the continuous time domain. We then present the improvements that make the algorithm fully online and biologically plausible. We introduce the homeostasis regulation rule, which allows for a better learning of the weights of the different layers  (Grimaldi et al. , 2021) , and we describe a new classifier using Multinomial Logistic Regression (MLR) to propose an end-to-end event-driven classification algorithm. We conclude this section by providing a formal analogy of our architecture with a SNN and local correlationbased learning rules to propose a unified theoretical framework between neuromorphic engineering and computational neuroscience.
Datasets To load the events, we use the community-built tonic python package  (Lenz et al. , 2021). It currently provides the ability to load 12 different event-based vision datasets and is based on the PyTorch language  (Paszke et al. , 2019). This allows to load event streams in a standard way and to optionally apply data augmentation methods to the event streams. Once loaded, an event-based camera recording is a N ev × 4 matrix where N ev represents the number of events and the 4 columns represent respectively the x and y positions on the pixel grid, the timestamp value, and the polarity. Timestamps are given in microseconds and polarities are 0 and 1 for OFF and ON events respectively. Of these datasets, 6 are labelled for object classification tasks. We choose to test the performance of our method on 3 different datasets: • Poker-DVS dataset (Serrano-Gotarredona and Linares-  Barranco, 2015) , one of the first publicly available DVS recordings from a real-world scene that was used to test the performance of HOTS  Lagorce et al.
(2017). It consists of 131 occurrences of the four different symbols of playing poker cards (clubs, diamonds, hearts and spades). • N-MNIST dataset  (Orchard et al. , 2015a) , a widely used dataset that was recorded by moving an event-based camera in front of a screen onto which digitized MNIST digits  (LeCun et al. , 1998)  were projected. • DVS128 Gesture dataset  (Amir et al. , 2017) , which consists of more complex and naturalistic recordings of realworld scenes. In this dataset, 29 subjects perform hand and arm gestures of different categories ("hand clap", "arm roll",. ). These were recorded with an iniLabs DVS128 event-based camera (with a resolution of 128 × 128 pixels) and under different lighting conditions. The samples provided by the dataset are 6 seconds long and, to reduce the computational load of the training, we keep only the first 3 seconds of the recording.
To test for the robustness of the proposed algorithm, we also used the tonic package to transform and augment the datasets. In particular, this allows spatial or temporal jitter to be added to the input stream. Since the relevant information is supposed to be represented within the timing and position of the input events, we can assume that the classification performance should deteriorate as the jitter increases. Therefore, we will use this module to test the robustness of the algorithm by progressively adding some noise to the input signal. To account for the variability of the random jitter applied, we repeat the prediction 10 times for each amount of jitter and derive a statistical quantification from these repetitions. To reduce the simulation time, we compute this study on Poker-DVS, on a subset of N-MNIST and do not perform this analysis on DVSGesture.
Related work Poker-DVS is included as it was tested using the original HOTS method  (Lagorce et al. , 2017). Due to its small size, this experiment acts as a toy model to test the different methods. In this paper, we focus on performing pattern recognition. A widely used event-based dataset: N-MNIST  (Orchard et al. , 2015a). Some related approaches have used standard artificial neural networks that have been converted to SNN, resulting in overall good classification results  (Neil and Liu, 2016; Patino-Saucedo et al. , 2020). In 2020,  Patino-Saucedo et al. (2020)  is the first neuromorphic hardware implementation of the event-based N-MNIST benchmark. Alternatively, some other competitive event-driven algorithms are developed using Back-Propagation (BP) adapted to SNN  (Shrestha and Orchard, 2018; Lee et al.
, 2016; Wu et al. , 2018). More recently, it has been proposed to introduce biomimetic saccades to improve object recognition  (Yousefzadeh et al. , 2018). All these contributions saturate the digit recognition problem introduced by the N-MNIST dataset with accuracies around 99% but none of them addressed the question of ultra-fast object recognition, i. e. the ability to recognize a digit with only the first events. We report that online inference on event-based data has been developed in previous studies  (Thiele et al. , 2018; Giannone et al. , 2020; Zhou et al. , 2021). However, the model proposed by  Zhou et al. (2021)  accumulates spikes as input to reconstruct an image frame,  Giannone et al. (2020)  uses a sample-and-hold approach, and freezes events during a defined time step. Thiele et al.
(2018)  proposes to use Spike-Timing Dependent Plasticity (STDP) for unsupervised learning of spatio-temporal features. For this latter study, they also construct a supervised classifier that can learn in an online fashion and which should be able to make an inference for each event. However, they perform a classification based on the strongest response of a neuron during the time window in which the sample is presented. In this way, they do not take advantage of the event-driven nature of the signal for classification. Then, the DVS128 Gesture dataset provides a more complex recognition task as it consists of real-world scenes with natural movements performed by subjects. Zhang et al. (2021)  presents a non-local synaptic modification method with spiking and artificial neurons inspired by natural networks called self-BP.
A spiking version of the deep ResNet architecture (STS-ResNet) also achieves good performance for gesture recognition as well  (Samadzadeh et al. , 2020). A recent study develops a bio-plausible method using SNN and STDP that achieves very good results  (Safa et al. , 2021). The classifier is a Support Vector Machine (SVM) applied to the feature vectors as output from the SNN. A promising method involving Spiking Recurrent Neural Networks (SRNN) is introduced in  Yin et al. (2021)  and achieves high online performances on several datasets. Accuracy on the DVS128 Gesture dataset reaches 97. 61%, but this method requires an additional preprocessing with a convolution layer on frames computed from the DVS recording and it was not mentioned whether this number reflects an average computed on the online accuracy or not.
SLAYER  (Shrestha and Orchard, 2018)  could also provide an online classification with a SNN using 8 layers trained with BP. However, the network is trained with a target spike count to discriminate the true class and needs to wait until the end of the output spike train to infer a decision. For both datasets, these different methods achieve very good performances, but none of them report the accuracy as a function of the number of events used or as a function of time -thus making it impossible to derive online accuracy results. An exception in the literature is  Sironi et al. (2018) , which reports the accuracy as a function of the latency. It is calculated on the N-CARS dataset created for this study. However, this method uses an accumulation of time surfaces and can not perform eventby-event classification.
For the time constant τ L , we set it as a function of the number of channels in the time surface so that it is adapted to the average interspike interval on each layer: τ L = N L p. τ L=0. A description of the hyperparameters associated with the experiments on the different datasets is given in 3. 1. As for learning of the weights, this is done in an unsupervised manner. During the unsupervised clustering phase, the kernels are updated with the same learning rule as described in  Lagorce et al. (2017) : KL p L+1 i ← KL p L+1 i + η p L+1 i • β p L+1 i • (S L local (t i ) -KL p L+1 i ) with η p L+1 i = 0. 01 1 + # p L+1 i 20000 where we define # p L+1 i as the number of times kernel KL p L+1 i has been selected and S L local (t i ) is the time surface defined locally, i. e. around the event as input of the layer with a radius R L.
Homeostasis The contribution of homeostasis to the robustness of the HOTS model is the guideline of a previous work  (Grimaldi et al. , 2021). Similar regulation methods on an event-based dataset are used in  Diehl and Cook (2015) ;  Wu et al. (2019)  to balance the firing rate over the neurons of each layer of the SNN. The model of  Diehl and Cook (2015)  uses an adaptive membrane threshold, while  Wu et al. (2019)  adds an auxiliary neuron per layer to regulate the firing rates. In this last paper, they make a comparison of this technique with zeromean batch normalization (Ioffe and Szegedy), which is used for training deep neural networks. These methods are similar in their aims and are well justified in terms of efficient coding  (Perrinet, 2010). Here, we implement homeostasis regulation by adapting the heuristics used in a sparse coding scheme  (Perrinet, 2019).
Online event-based classification In the original HOTS algorithm, classification is performed by comparing the activation histograms across the channels of the last layer of the network with the average observed for each given class. This classification by histogram comparison is performed post hoc, after the encoding of an element from the dataset. Here, we introduce a novel online classification scheme, that is, where classification is performed for each spike that reaches the classifier, and more generally at any time when a classification is required. Following the same strategy used for the construction of time surfaces, each event reaching the last layer L = C of the network can indeed be transformed into a time surface S C (t) using a time constant τ C. This constant can vary from one dataset to another according to the statistics of the samples.
Results The classification results obtained with our method are presented in this section. First, we present the online classification performance of the network, which is the main novelty of our study. We then compare the performance with the state of the art (SOTA) on the different datasets by reporting the accuracy obtained when making one prediction per sample. We complete our analysis by studying the robustness of our algorithm to both temporal and spatial jitter, comparing it to the original method proposed in  Lagorce et al. (2017). Let's start with a detailed description of the parameters and the architecture of the networks tuned for classification on the different datasets. We report these parameters in Table  1. The parameter tuning for L 1 and L 2 , which are layers similar to those in the original HOTS network, was done on subsets of the different datasets by computing the accuracy for each of the different architectures using histogram comparison as done in  Lagorce et al.
(2017). For each dataset, the classification layer is implemented in PyTorch, and we train it using gradient descent and the Adam optimizer. The time surfaces as input to this last layer are defined globally, i. e. on the whole pixel grid, and we adjust the time constant for each dataset. Time constants are also obtained empirically, by testing the performance of the classifier with different parameters, on a subset of the original dataset. For each dataset, we set the number of epochs to 33 and keep the optimizer's default parameters. For both the N-MNIST and DVSGesture datasets, to reduce the number of computations and to avoid reaching a local minimum within the first samples, we perform the learning only on a randomly chosen percentage of the computed time surfaces. We keep 10% and 5% of the time surfaces of a sample for N-MNIST and DVSGesture datasets, respectively.
Online inference We first present the results of the end-to-end event-driven online classification described in section 2. 5. To illustrate the dynamic evolution of the event-based classification performance, we plot the accuracy value as a function of the number of events received by the classifier for each dataset (see Figure  5 ). We present two decision-making modes for the classifier. The first is online HOTS, where a prediction is made for each incoming event without any condition on the probability values corresponding to the different classes. The second is online HOTS with threshold, i. e. when the output of the classifier must reach a probability threshold to make a decision. In this last condition, in order to filter out events in periods of poor information content (especially at the beginning), we select only events with a decision confidence above a threshold.