Neural activity classification with machine learning models trained on interspike interval series data

The flow of information through the brain is reflected by the activity patterns of neural cells. Indeed, these firing patterns are widely used as input data to predictive models that relate stimuli and animal behavior to the activity of a population of neurons. However, relatively little attention was paid to single neuron spike trains as predictors of cell or network properties in the brain. In this work, we introduce an approach to neuronal spike train data mining which enables effective classification and clustering of neuron types and network activity states based on single-cell spiking patterns. This approach is centered around applying state-of-the-art time series classification/clustering methods to sequences of interspike intervals recorded from single neurons. We demonstrate good performance of these methods in tasks involving classification of neuron type (e.g. excitatory vs. inhibitory cells) and/or neural circuit activity state (e.g. awake vs. REM sleep vs. nonREM sleep states) on an open-access cortical spiking activity dataset

Mechanisms of Supralinear Calcium Integration in Dendrites of Hippocampal CA1 Fast-Spiking Cells

In fast-spiking (FS), parvalbumin-expressing interneurons of the CA1 hippocampus, activation of the GluA2-lacking Ca2+-permeable AMPA receptors (CP-AMPARs) in basal dendrites is coupled to Ca2+-induced Ca2+-release (CICR), and can result in a supralinear summation of postsynaptic Ca2+-transients (post-CaTs). While this mechanism is important in controlling the direction of long-term plasticity, it is still unknown whether it can operate at all excitatory synapses converging onto FS cells or at a set of synapses receiving a particular input. Using a combination of patch-clamp recordings and two-photon Ca2+ imaging in acute mouse hippocampal slices with computational simulations, here we compared the generation of supralinear post-CaTs between apical and basal dendrites of FS cells. We found that, similar to basal dendrites, apical post-CaTs summated supralinearly and relied mainly on the activation of the CP-AMPARs, with a variable contribution of other Ca2+ sources, such as NMDA receptors, L-type voltage-gated Ca2+-channels and Ca2+ release. In addition, supralinear post-CaTs generated in apical dendrites had a slower decay time and a larger cumulative charge than those in basal, and were associated with a stronger level of somatic depolarization. The model predicted that modulation of ryanodine receptors and of the Ca2+ extrusion mechanisms, such as the Na+/Ca2+-exchanger and SERCA pump, had a major impact on the magnitude of supralinear post-CaTs. These data reveal that supralinear Ca2+ summation is a common mechanism of Ca2+ signaling at CP-AMPAR-containing synapses. Shaped in a location-specific manner through modulation of ryanodine receptors and Ca2+ extrusion mechanisms, CP-AMPAR/CICR signaling is suitable for synapse-specific bidirectional modification of incoming inputs in the absence of active dendritic conductances

YOLOBench: Benchmarking Efficient Object Detectors on Embedded Systems

We present YOLOBench, a benchmark comprised of 550+ YOLO-based object detection models on 4 different datasets and 4 different embedded hardware platforms (x86 CPU, ARM CPU, Nvidia GPU, NPU). We collect accuracy and latency numbers for a variety of YOLO-based one-stage detectors at different model scales by performing a fair, controlled comparison of these detectors with a fixed training environment (code and training hyperparameters). Pareto-optimality analysis of the collected data reveals that, if modern detection heads and training techniques are incorporated into the learning process, multiple architectures of the YOLO series achieve a good accuracy-latency trade-off, including older models like YOLOv3 and YOLOv4. We also evaluate training-free accuracy estimators used in neural architecture search on YOLOBench and demonstrate that, while most state-of-the-art zero-cost accuracy estimators are outperformed by a simple baseline like MAC count, some of them can be effectively used to predict Pareto-optimal detection models. We showcase that by using a zero-cost proxy to identify a YOLO architecture competitive against a state-of-the-art YOLOv8 model on a Raspberry Pi 4 CPU. The code and data are available at https://github.com/Deeplite/deeplite-torch-zo

Approches d'apprentissage automatique et de modélisation informatique pour la détection de marqueurs d'activité pathologique dans le cortex

A central question in brain activity decoding is how to detect patterns of abnormal or pathological activity. In this thesis, we apply machine learning methods and computational modelling approaches to analyze neural activity data in the cortex and to learn how to detect markers of abnormal activity in animal models of several common neurodegenerative diseases. First, we identify the machine learning methods that perform well in classification tasks on single-neuron level activity data. We establish a benchmark for neuronal spike train classification and find the time-series features that are highly predictive of the neural circuit state across different tasks and brain areas. Using the established approaches, we analyze a data set of neural activity in the prefrontal cortex (PFC) in animals with mutations leading to dysfunctions of the cholinergic signalling system commonly associated with diseases such as schizophrenia and Alzheimer's disease. We also use computational modelling of the local circuit dynamics in the PFC to mechanistically explain the origins of the activity changes observed in these animals. Finally, we test the machine learning approaches on a multimodal data set of neural activity in an animal model of early amyotrophic lateral sclerosis (ALS). We show that in order to design a system that is capable of accurately detecting the pathological activity inherent to early ALS, one has to train the model to extract information from the interaction between the modalities of cortical activity and animal movement. Overall, we have provided insights into how computational modelling and machine learning could provide tools for detecting pathology from neural circuit activity recordings.Une question centrale dans le décodage de l'activité cérébrale est de savoir comment détecter des schémas d'activité anormale ou pathologique. Dans cette thèse, nous appliquons des méthodes d'apprentissage automatique et des approches de modélisation informatique pour analyser les données d'activité neuronale dans le cortex et apprendre à détecter des marqueurs d'activité anormale dans des modèles animaux de plusieurs maladies neurodégénératives courantes. Tout d'abord, nous identifions les méthodes d'apprentissage automatique qui fonctionnent bien dans les tâches de classification sur des données d'activité au niveau d'un seul neurone. Nous établissons une référence pour la classification des trains de pointes neuronales et trouvons les caractéristiques de séries chronologiques hautement prédictives de l'état du circuit neuronal dans différentes tâches et zones cérébrales. En utilisant les approches établies, nous analysons un ensemble de données d'activité neuronale dans le cortex préfrontal (PFC) chez des animaux présentant des mutations entraînant des dysfonctionnements du système de signalisation cholinergique couramment associés à des maladies telles que la schizophrénie et la maladie d'Alzheimer. Nous utilisons également la modélisation informatique de la dynamique des circuits locaux dans le PFC pour expliquer mécaniquement les origines des changements d'activité observés chez ces animaux. Enfin, nous testons les approches d'apprentissage automatique sur un ensemble de données multimodales d'activité neuronale dans un modèle animal de sclérose latérale amyotrophique (SLA) précoce. Nous montrons que pour concevoir un système capable de détecter avec précision l'activité pathologique inhérente à la SLA précoce, il faut entraîner le modèle à extraire des informations de l'interaction entre les modalités de l'activité corticale et le mouvement animal. Dans l'ensemble, nous avons fourni des informations sur la façon dont la modélisation informatique et l'apprentissage automatique pourraient fournir des outils pour détecter la pathologie à partir des enregistrements d'activité des circuits neuronaux

Machine learning and computational modeling approaches towards detection of pathological activity markers in the cortex

Une question centrale dans le décodage de l'activité cérébrale est de savoir comment détecter des schémas d'activité anormale ou pathologique. Dans cette thèse, nous appliquons des méthodes d'apprentissage automatique et des approches de modélisation informatique pour analyser les données d'activité neuronale dans le cortex et apprendre à détecter des marqueurs d'activité anormale dans des modèles animaux de plusieurs maladies neurodégénératives courantes. Tout d'abord, nous identifions les méthodes d'apprentissage automatique qui fonctionnent bien dans les tâches de classification sur des données d'activité au niveau d'un seul neurone. Nous établissons une référence pour la classification des trains de pointes neuronales et trouvons les caractéristiques de séries chronologiques hautement prédictives de l'état du circuit neuronal dans différentes tâches et zones cérébrales. En utilisant les approches établies, nous analysons un ensemble de données d'activité neuronale dans le cortex préfrontal (PFC) chez des animaux présentant des mutations entraînant des dysfonctionnements du système de signalisation cholinergique couramment associés à des maladies telles que la schizophrénie et la maladie d'Alzheimer. Nous utilisons également la modélisation informatique de la dynamique des circuits locaux dans le PFC pour expliquer mécaniquement les origines des changements d'activité observés chez ces animaux. Enfin, nous testons les approches d'apprentissage automatique sur un ensemble de données multimodales d'activité neuronale dans un modèle animal de sclérose latérale amyotrophique (SLA) précoce. Nous montrons que pour concevoir un système capable de détecter avec précision l'activité pathologique inhérente à la SLA précoce, il faut entraîner le modèle à extraire des informations de l'interaction entre les modalités de l'activité corticale et le mouvement animal. Dans l'ensemble, nous avons fourni des informations sur la façon dont la modélisation informatique et l'apprentissage automatique pourraient fournir des outils pour détecter la pathologie à partir des enregistrements d'activité des circuits neuronaux.A central question in brain activity decoding is how to detect patterns of abnormal or pathological activity. In this thesis, we apply machine learning methods and computational modelling approaches to analyze neural activity data in the cortex and to learn how to detect markers of abnormal activity in animal models of several common neurodegenerative diseases. First, we identify the machine learning methods that perform well in classification tasks on single-neuron level activity data. We establish a benchmark for neuronal spike train classification and find the time-series features that are highly predictive of the neural circuit state across different tasks and brain areas. Using the established approaches, we analyze a data set of neural activity in the prefrontal cortex (PFC) in animals with mutations leading to dysfunctions of the cholinergic signalling system commonly associated with diseases such as schizophrenia and Alzheimer's disease. We also use computational modelling of the local circuit dynamics in the PFC to mechanistically explain the origins of the activity changes observed in these animals. Finally, we test the machine learning approaches on a multimodal data set of neural activity in an animal model of early amyotrophic lateral sclerosis (ALS). We show that in order to design a system that is capable of accurately detecting the pathological activity inherent to early ALS, one has to train the model to extract information from the interaction between the modalities of cortical activity and animal movement. Overall, we have provided insights into how computational modelling and machine learning could provide tools for detecting pathology from neural circuit activity recordings

Spikebench: An open benchmark for spike train time-series classification.

Modern well-performing approaches to neural decoding are based on machine learning models such as decision tree ensembles and deep neural networks. The wide range of algorithms that can be utilized to learn from neural spike trains, which are essentially time-series data, results in the need for diverse and challenging benchmarks for neural decoding, similar to the ones in the fields of computer vision and natural language processing. In this work, we propose a spike train classification benchmark, based on open-access neural activity datasets and consisting of several learning tasks such as stimulus type classification, animal's behavioral state prediction, and neuron type identification. We demonstrate that an approach based on hand-crafted time-series feature engineering establishes a strong baseline performing on par with state-of-the-art deep learning-based models for neural decoding. We release the code allowing to reproduce the reported results

Cholinergic modulation of hierarchical inhibitory control over cortical resting state dynamics: Local circuit modeling of schizophrenia-related hypofrontality

International audienceNicotinic acetylcholine receptors (nAChRs) modulate the cholinergic drive to a hierarchy of inhibitory neurons in the superficial layers of the PFC, critical to cognitive processes. It has been shown that genetic deletions of the various types of nAChRs impact the properties of ultra-slow transitions between high and low PFC activity states in mice during quiet wakefulness. The impact characteristics depend on specific interneuron populations expressing the manipulated receptor subtype. In addition, recent data indicate that a genetic mutation of the α5 nAChR subunit, located on vasoactive intestinal polypeptide (VIP) inhibitory neurons, the rs16969968 single nucleotide polymorphism (α5 SNP), plays a key role in the hypofrontality observed in schizophrenia patients carrying the SNP. Data also indicate that chronic nicotine application to α5 SNP mice relieves the hypofrontality. We developed a computational model to show that the activity patterns recorded in the genetically modified mice can be explained by changes in the dynamics of the local PFC circuit. Notably, our model shows that these altered PFC circuit dynamics are due to changes in the stability structure of the activity states. We identify how this stability structure is differentially modulated by cholinergic inputs to the parvalbumin (PV), somatostatin (SOM) or the VIP inhibitory populations. Our model uncovers that a change in amplitude, but not duration of the high activity states can account for the lowered pyramidal (PYR) population firing rates recorded in α5 SNP mice. We demonstrate how nicotine-induced desensitization and upregulation of the β2 nAChRs located on SOM interneurons, as opposed to the activation of α5 nAChRs located on VIP interneurons, is sufficient to explain the nicotine-induced activity normalization in α5 SNP mice. The model further implies that subsequent nicotine withdrawal may exacerbate the hypofrontality over and beyond one caused by the SNP mutation