Inferring neuronal network connectivity from spike data: A temporal data mining approach

Abstract. Understanding the functioning of a neural system in terms of its underlying circuitry is an important problem in neuroscience. Recent developments in electrophysiology and imaging allow one to simultaneously record activities of hundreds of neurons. Inferring the underlying neuronal connectivity patterns from such multi-neuronal spike train data streams is a challenging statistical and computational problem. This task involves finding significant temporal patterns from vast amounts of symbolic time series data. In this paper we show that the frequent episode mining methods from the field of temporal data mining can be very useful in this context. In the frequent episode discovery framework, the data is viewed as a sequence of events, each of which is characterized by an event type and its time of occurrence and episodes are certain types of temporal patterns in such data. Here we show that, using the set of discovered frequent episodes from multi-neuronal data, one can infer different types of connectivity patterns in the neural system that generated it. For this purpose, we introduce the notion of mining for frequent episodes under certain temporal constraints; the structure of these temporal constraints is motivated by the application. We present algorithms for discovering serial and parallel episodes under these temporal constraints. Through extensive simulation studies we demonstrate that these methods are useful for unearthing patterns of neuronal network connectivity

Modeling and Analysis of Electrical Network Activity in Neuronal Systems.

Electrical activity in networks of neurons is an essential part of most brain functions. This dissertation deals with two different aspects in modeling and analysis of such activity in neuronal systems. Part I develops the first detailed mathematical model of the electrophysiology of the specific neuronal network responsible for the generation of circadian (~24-hour) rhythms in mammals. Part II is concerned with methods for inferring the functional connectivity of neuronal networks from multi-neuronal spike train data. Mammalian circadian rhythms are controlled by a group of about 20,000 neurons in the hypothalamus called the suprachiasmatic nucleus (SCN). We have developed a model of action potential firing in the SCN network. With this model we can simulate and track the action potentials of thousands of model SCN neurons, while experimentally it is only possible to record the activity of a few dozen SCN neurons at the same time. Our simulations predict that subgroups, or clusters, of SCN neurons form, within which neurons synchronize their firing at a millisecond time scale. Furthermore, our simulations demonstrate how this clustering leads to the silencing or adjustment of neurons whose firing is out of phase with the rest of the population at the 24-hour time scale, giving insight into how the circadian clock may operate at the network level. Temporal patterns of firing that are more complex than synchrony, such as precise firing sequences with fixed time delays between neurons, have been observed in multi-neuronal recordings from other brain areas. To determine whether the patterns detected are meaningful, it is important to know whether they are occurring more or less often than would be expected due to chance alone. To address this question, we have developed statistical methods for assessing when the number of occurrences of a precise firing sequence is significantly different from randomness and for estimating the magnitude of the connection strength. Our approach is computationally efficient and can discover patterns involving many neurons. The significant patterns discovered in multi-neuronal spike trains can be used to infer the functional connectivity between neurons and potentially identify circuits in the underlying neural tissue.Ph.D.Industrial and Operations Engineering and BioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/78984/1/diekman_1.pd

Fouille de séquences temporelles pour la maintenance prédictive : application aux données de véhicules traceurs ferroviaires

In order to meet the mounting social and economic demands, railway operators and manufacturers are striving for a longer availability and a better reliability of railway transportation systems. Commercial trains are being equipped with state-of-the-art onboard intelligent sensors monitoring various subsystems all over the train. These sensors provide real-time flow of data, called floating train data, consisting of georeferenced events, along with their spatial and temporal coordinates. Once ordered with respect to time, these events can be considered as long temporal sequences which can be mined for possible relationships. This has created a neccessity for sequential data mining techniques in order to derive meaningful associations rules or classification models from these data. Once discovered, these rules and models can then be used to perform an on-line analysis of the incoming event stream in order to predict the occurrence of target events, i.e, severe failures that require immediate corrective maintenance actions. The work in this thesis tackles the above mentioned data mining task. We aim to investigate and develop various methodologies to discover association rules and classification models which can help predict rare tilt and traction failures in sequences using past events that are less critical. The investigated techniques constitute two major axes: Association analysis, which is temporal and Classification techniques, which is not temporal. The main challenges confronting the data mining task and increasing its complexity are mainly the rarity of the target events to be predicted in addition to the heavy redundancy of some events and the frequent occurrence of data bursts. The results obtained on real datasets collected from a fleet of trains allows to highlight the effectiveness of the approaches and methodologies usedDe nos jours, afin de répondre aux exigences économiques et sociales, les systèmes de transport ferroviaire ont la nécessité d'être exploités avec un haut niveau de sécurité et de fiabilité. On constate notamment un besoin croissant en termes d'outils de surveillance et d'aide à la maintenance de manière à anticiper les défaillances des composants du matériel roulant ferroviaire. Pour mettre au point de tels outils, les trains commerciaux sont équipés de capteurs intelligents envoyant des informations en temps réel sur l'état de divers sous-systèmes. Ces informations se présentent sous la forme de longues séquences temporelles constituées d'une succession d'événements. Le développement d'outils d'analyse automatique de ces séquences permettra d'identifier des associations significatives entre événements dans un but de prédiction d'événement signant l'apparition de défaillance grave. Cette thèse aborde la problématique de la fouille de séquences temporelles pour la prédiction d'événements rares et s'inscrit dans un contexte global de développement d'outils d'aide à la décision. Nous visons à étudier et développer diverses méthodes pour découvrir les règles d'association entre événements d'une part et à construire des modèles de classification d'autre part. Ces règles et/ou ces classifieurs peuvent ensuite être exploités pour analyser en ligne un flux d'événements entrants dans le but de prédire l'apparition d'événements cibles correspondant à des défaillances. Deux méthodologies sont considérées dans ce travail de thèse: La première est basée sur la recherche des règles d'association, qui est une approche temporelle et une approche à base de reconnaissance de formes. Les principaux défis auxquels est confronté ce travail sont principalement liés à la rareté des événements cibles à prédire, la redondance importante de certains événements et à la présence très fréquente de "bursts". Les résultats obtenus sur des données réelles recueillies par des capteurs embarqués sur une flotte de trains commerciaux permettent de mettre en évidence l'efficacité des approches proposée