Eine einzigartige experimentelle Beobachtung, welche die Basis für eine ganzheitliche, neurowissentschafliche Theorie für Gedächtnis darstellen könnte, sind sharp wave-ripples (SWRs). SWRs werden in lokalen Neuronennetzwerken erzeugt und sind wichtig für Gedächtniskonsolidierung; SWRs sind charakteristische Ereignisse der lokalen Feldpotentiale im Hippocampus des Säugetiers, die in Phasen von Schlaf und Ruhe vorkommen. Eine SWR besteht aus einer sharp wave, einer ≈ 100 ms langen Auslenkung des Feldpotentials, welche mit ripples, 110–250 Hz Oszillationen, überlagert ist. Jüngste Experimente bekräftigen die Theorie, dass ripples in Netzwerken inhibitorischer Interneurone (INT-INT) erzeugt werden, die aus parvalbumin-positive basket cells (PV+BCs) bestehen. PV+BCs sind untereinander über rekurrente inhibitorische Synapsen und Gap Junctions (GJs) gekoppelt. In dieser Arbeit untersuche ich die spezifische Funktion von interneuronalen Gap Junctions in ripples. Im Hauptteil dieser Arbeit demonstriere ich, dass GJs in INT-INT Netzwerken die neuronale Synchronität und die Feuerrate während ripples erhöhen, die ripple-Frequenz sich hingegen nur leicht verändert. Zusätzlich zeige ich, dass diese rippleunterstützenden Effekte nur dann auftreten, wenn die GJ-Transmission schnell genug ist (≈< 0.5 ms), was wiederum somanahe Kopplung voraussetzt (≈< 100 µm). Darüber hinaus zeige ich, dass GJs die oszillatorische Stärke der ripples erhöhen und so die minimale für ripples notwendige Netzwerkgröße verringern. Abschließend zeige ich, dass ausschließlich mit Gap Junctions gekoppelte INT-INT Netzwerke zwar mit ripple Frequenz oszillieren können, aber wahrscheinlich nicht der Erzeuger von experimentell beobachteten ripple-artigen Oszillationen sind. Zusammengenommen zeigen meine Resultate, dass schnelle Gap Junction-Kopplung von Interneuronen die Entstehung von ripples begünstigt und somit SWRs unterstützt, welche einen wichtigen Beitrag zur Bildung unserers Gedächtnisses leisten. / A unique experimental observation that opens ways for a holistic, bottom-up theory for memory generation are sharp-wave ripples (SWRs). SWRs are generated in local neuronal networks and are important for memory consolidation. SWRs are prominent features of the extracellular field potentials in the mammalian hippocampus that occur during rest and sleep; they are characterized by sharp waves, ≈ 100 ms long voltage deflections, that are accompanied by ripples, i.e., 110–250 Hz oscillations. Recent experiments support the view that ripples are clocked by recurrent networks of inhibitory interneurons (INT-INT), which are likely constituted by networks of parvalbumin-positive basket cells (PV+BCs). PV+BCs are not only recurrently coupled by inhibition but also by gap junctions (GJs). In this thesis, I investigate the specific function of interneuronal GJs in hippocampal ripples. Consequently, I simulate INT-INT networks and demonstrate that gap junctions increase the neuronal synchrony and firing rates during ripple oscillations, while the ripple frequency is only affected mildly. I further show that GJs only have these supporting effects on ripples when they are sufficiently fast (≈< 0.5 ms), which requires proximal GJ coupling (≈< 100 µm). Additionally, I find that gap junctions increase the oscillatory power of ripple oscillations and by this means reduce the minimal network size required for INT-INT networks to generate ripple oscillations. Finally, I demonstrate that exclusively GJ-coupled INT-INT networks can oscillate at ripple frequency, however, are unlikely the generator of experimentally observed ripple-like oscillations. In sum, my results show that fast interneuronal gap junction coupling promotes the emergence of ripples and hereby supports SWRs, which are important for the formation of memory.

In this thesis we use computational neural network models to examine the dynamics and functionality of the CA3 region of the mammalian hippocampus. The emphasis of the project is to investigate how the dynamic control structures provided by inhibitory circuitry and cellular modification may effect the CA3 region during the recall of previously stored information. The CA3 region is commonly thought to work as a recurrent auto-associative neural network due to the neurophysiological characteristics found, such as, recurrent collaterals, strong and sparse synapses from external inputs and plasticity between coactive cells. Associative memory models have been developed using various configurations of mathematical artificial neural networks which were first developed over 40 years ago. Within these models we can store information via changes in the strength of connections between simplified model neurons (two-state). These memories can be recalled when a cue (noisy or partial) is instantiated upon the net. The type of information they can store is quite limited due to restrictions caused by the simplicity of the hard-limiting nodes which are commonly associated with a binary activation threshold. We build a much more biologically plausible model with complex spiking cell models and with realistic synaptic properties between cells. This model is based upon some of the many details we now know of the neuronal circuitry of the CA3 region. We implemented the model in computer software using Neuron and Matlab and tested it by running simulations of storage and recall in the network. By building this model we gain new insights into how different types of neurons, and the complex circuits they form, actually work. The mammalian brain consists of complex resistive-capacative electrical circuitry which is formed by the interconnection of large numbers of neurons. A principal cell type is the pyramidal cell within the cortex, which is the main information processor in our neural networks. Pyramidal cells are surrounded by diverse populations of interneurons which have proportionally smaller numbers compared to the pyramidal cells and these form connections with pyramidal cells and other inhibitory cells. By building detailed computational models of recurrent neural circuitry we explore how these microcircuits of interneurons control the flow of information through pyramidal cells and regulate the efficacy of the network. We also explore the effect of cellular modification due to neuronal activity and the effect of incorporating spatially dependent connectivity on the network during recall of previously stored information. In particular we implement a spiking neural network proposed by Sommer and Wennekers (2001). We consider methods for improving associative memory recall using methods inspired by the work by Graham and Willshaw (1995) where they apply mathematical transforms to an artificial neural network to improve the recall quality within the network. The networks tested contain either 100 or 1000 pyramidal cells with 10% connectivity applied and a partial cue instantiated, and with a global pseudo-inhibition.We investigate three methods. Firstly, applying localised disynaptic inhibition which will proportionalise the excitatory post synaptic potentials and provide a fast acting reversal potential which should help to reduce the variability in signal propagation between cells and provide further inhibition to help synchronise the network activity. Secondly, implementing a persistent sodium channel to the cell body which will act to non-linearise the activation threshold where after a given membrane potential the amplitude of the excitatory postsynaptic potential (EPSP) is boosted to push cells which receive slightly more excitation (most likely high units) over the firing threshold. Finally, implementing spatial characteristics of the dendritic tree will allow a greater probability of a modified synapse existing after 10% random connectivity has been applied throughout the network. We apply spatial characteristics by scaling the conductance weights of excitatory synapses which simulate the loss in potential in synapses found in the outer dendritic regions due to increased resistance. To further increase the biological plausibility of the network we remove the pseudo-inhibition and apply realistic basket cell models with differing configurations for a global inhibitory circuit. The networks are configured with; 1 single basket cell providing feedback inhibition, 10% basket cells providing feedback inhibition where 10 pyramidal cells connect to each basket cell and finally, 100% basket cells providing feedback inhibition. These networks are compared and contrasted for efficacy on recall quality and the effect on the network behaviour. We have found promising results from applying biologically plausible recall strategies and network configurations which suggests the role of inhibition and cellular dynamics are pivotal in learning and memory.

Les interneurones de la couche moléculaire du cervelet (ICM: cellules en panier et cellules étoilées) sont connectés par des synapses électriques fréquentes et puissantes chez les jeunes rats et souris autour de la fin de la deuxième semaine postnatale. Les courants capacitifs des ICM montrent une composante lente qui reflète la charge des interneurones couplés électriquement. Leur analyse permet de quantifier le nombre de cellules directement couplées à une cellule et le nombre équivalent de cellules couplées (Alcami et Marty, soumis), et d'établir une difference de couplage entre les cellules en panier et les cellules étoilées pendant le développement postnatal. Elle a mené à proposer une topologie de réseau des cellules en panier. La force du couplage peut être modulée par les courants intrinsèques, dont Ih dans le domaine hyperpolarisant. Les synapses électriques modifient la propagation et les patrons d'activité dans le réseau des ICM en réponse à une excitation du réseau.L'étude de la connectivité des ICM par des synapses chimiques GABAergiques nous a mené à réexaminer les sources d'erreur des mesures d'activité électrique en configuration cellule attachée (Alcami et coll., 2012). Les mesures en cellule attachée peuvent modifier l'activité électrique des ICM en introduisant un couplage conductif entre la pipette d'enregistrement et l'intérieur cellulaire, résultant d'une combinaison de mécanismes de couplage passifs et actifs. / Molecular layer interneurons of the cerebellum (MLIs: basket cells and stellate cells) are connected by frequent and strong electrical synapses in young rats and mice around the end of the second postnatal week. Capacitive currents of MLIs show a slow component that reflects the charge of electrically-coupled MLIs. The analysis of capacitive currents makes it possible to quantify the number of directly connected cells and the equivalent number of coupled cells (Alcami and Marty, submitted). They were used to show a difference in coupling between basket and stellate cells and propose a model of the basket cell coupled network. Electrical coupling strength can be modulated by intrinsic currents, like the h current in the hyperpolarizing range. Electrical synapses modify the propagation and the patterns of activity in the MLI network, when the network is excited.The study of connectivity of MLIs by chemical GABAergic synapses led us to reevaluate the sources of error of cell-attached recordings (Alcami et al., 2012). Cell-attached measurements can modify cellular electrical activity of MLIs, by introducing a conductif coupling between the recording pipette and the cell interior, resulting from a combination of passive and active coupling.