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Pas de résumé en français / It is generally maintained that one of cortex’ functions is the storage of a large number of memories. In this picture, the physical substrate of memories is thought to be realised in pattern and strengths of synaptic connections among cortical neurons. Memory recall is associated with neuronal activity that is shaped by this connectivity. In this framework, active memories are represented by attractors in the space of neural activity. Electrical activity in cortical neurones in vivo exhibits prominent temporal irregularity. A standard way to account for this phenomenon is to postulate that recurrent synaptic excitation and inhibition as well as external inputs are balanced. In the common view, however, these balanced networks do not easily support the coexistence of multiple attractors. This is problematic in view of memory function. Recently, theoretical studies showed that balanced networks with synapses that exhibit short-term plasticity (STP) are able to maintain multiple stable states. In order to investigate whether experimentally obtained synaptic parameters are consistent with model predictions, we developed a new methodology that is capable to quantify both response variability and STP at the same synapse in an integrated and statistically-principled way. This approach yields higher parameter precision than standard procedures and allows for the use of more efficient stimulation protocols. However, the findings with respect to STP parameters do not allow to make conclusive statements about the validity of synaptic theories of balanced working memory. In the second part of this thesis an alternative theory of cortical memory storage is developed. The theory is based on the assumptions that memories are stored in attractor networks, and that memories are not represented by network states differing in their average activity levels, but by micro-states sharing the same global statistics. Different memories differ with respect to their spatial distributions of firing rates. From this the main result is derived: the balanced state is a necessary condition for extensive memory storage. Furthermore, we analytically calculate memory storage capacities of rate neurone networks. Remarkably, it can be shown that crucial properties of neuronal activity and physiology that are consistent with experimental observations are directly predicted by the theory if optimal memory storage capacity is required.

In response to DNA damage during S phase, cells slow DNA replication. This slowing is orchestrated by the intra-S checkpoint and involves inhibition of origin firing and reduction of replication fork speed. Slowing of replication allows for tolerance of DNA damage and suppresses genomic instability. Although the mechanisms of origin inhibition by the intra-S checkpoint are understood, major questions remain about how the checkpoint regulates replication forks: Does the checkpoint regulate the rate of fork progression? Does the checkpoint affect all forks, or only those encountering damage? Does the checkpoint facilitate the replication of polymerase-blocking lesions? To address these questions, we have analyzed the checkpoint in the fission yeast Schizosaccharomyces pombe using a single-molecule DNA combing assay, which allows us to unambiguously separate the contribution of origin and fork regulation towards replication slowing, and allows us to investigate the behavior of individual forks. Moreover, we have interrogated the role of forks interacting with individual sites of damage by using three damaging agents—MMS, 4NQO and bleomycin—that cause similar levels of replication slowing with very different frequency of DNA lesions. We find that the checkpoint slows replication by inhibiting origin firing, but not by decreasing fork rates. However, the checkpoint appears to facilitate replication of damaged templates, allowing forks to more quickly pass lesions. Finally, using a novel analytic approach, we rigorously identify fork stalling events in our combing data and show that they play a previously unappreciated role in shaping replication kinetics in response to DNA damage.

Short-term synaptic plasticity (STP) is a phenomenon that has been closely associated with how neurons communicate with each other. I study communication between neurons tied to synapses endowed with short-term plasticity (dynamic synapses). This is achieved by using mathematical models of neural phenomena that align with those found in real neurons. In addition to dynamic synapses, a model of static synapses is created, on which control experiments are performed. The response of postsynaptic neurons, to spiking-sequences from presynaptic neurons, is examined in order to infer how information is transmitted across cells. During these computational experiments, it was found that the range of firing rates to which postsynaptic neurons responded, depends heavily on certain parameters of STP-processes. These parameters are the time constants for short-term synaptic depression and facilitation: the two time-dependent processes that define STP. Some results confirm those of the existing literature, while this work places an added emphasis on the sensitivity of the propagation of rate codes to the aforementioned parameters of synapses. This is relevant because it has been found that real synapses display a wide range of time constants in the nervous system. Hence, understanding how this variation carries a significant impact on rate-coding schemes is vital when engaging in further studies of neural rate codes. / Synaptisk plasticitet på kort sikt (STP) är ett fenomen som har blivit nära förknippat med hur nervceller kommunicerar med varandra. Jag studerar kommunikation mellan nervceller som är kopplade till synapser som är försedda med kortsiktig synaptisk plasticitet (dynamiska synapser). Detta har åstadkommits genom matematisk modellering av fenomen i nervsystemet som är konsekventa med de som är funna i verkliga nervceller. Utöver dynamiska synapser, så skapas även en modell av statiska synapser på vilka kontrollexperiment utövas. Gensvaret av postsynaptiska nervceller, på sekvenser av nervimpulser från presynaptiska nervceller, kartläggs för att studera hur information transmitteras mellan celler. I dessa beräkningsmässiga experiment så var det funnet att spannet av frekvenser för vilka postsynaptiska nervceller visade gensvar, var kraftigt beroende på specifika parametrar för STP-processer. Dessa parametrar är tidskonstanterna för synaptisk depression på kort sikt samt synaptisk facilitering på kort sikt: de två tidsberoende processerna som definierar STP. Vissa resultat bekräftade de som återfinns i den befintliga literaturen, samtidigt som detta arbete placerar adderad tyngd på känsligheten som frekvensmässiga koder uppvisar för ovannämnda synaptiska parametrar. Detta är relevant eftersom det är känt att verkliga synapser uppvisar ett brett spann av tidskonstanter i nervsystemet. Följdaktigen är det centralt att förstå hur denna variation innehar signifikant påverkan på frekvenskoder vid fortsatta studier inom frekvensmässiga neurala koder.

In vielen Kommunikationssytemen wird Information durch die zeitliche Struktur von Signalen kodiert. Ein neuronales System, welches rhythmische Signale verarbeitet, sollte davon profitieren, seine inhärenten Filtereigenschaften den Frequenzcharakteristika dieser Signale anzupassen. Die Grille Gryllus bimaculatus stellt ein einfaches biologisches System dar, für welches nur wenige, spezifische Modulationsfrequenzen verhaltensrelevant sind. Ich habe einzelne Neuronen im peripheren und höheren auditorischen System der Grille hinsichtlich einer möglichen Anpassung auf diese Frequenzen untersucht. Hierfür habe ich extrazelluläre Elektrophysiologie mit verschiedenen Stimulationsparadigmen kombiniert, welche auf amplitudenmodulierten Tönen basierten. Die Analyse der experimentellen Daten ergab, dass bereits in der auditorischen Peripherie einige der untersuchten Neurone Bandpasseigenschaften aufwiesen, da sie verhaltensrelevante Modulationsfrequenzen mit den höchsten Feuerraten beantworteten. Anhand einfacher mathematischer Modelle demonstriere ich, wie weitverbreitete, zellintrinsische und netzwerkbasierte Mechanismen die beobachteten Feuerratenresonanzen erklären könnten. Diese Mechanismen umfassen unterschwellige Resonanz von Membranströmen, aktivitätsabhängige Adaptation, sowie das Zusammenwirken von Exzitation und Inhibition. Ich zeige, wie eine serielle Kombination solcher elementarer Filter die deutliche Selektivität im Verhalten der Grille erklären könnte, ohne dabei auf ein dediziertes Filterelement zurückzugreifen. Allgegenwärtige neuronale Mechanismen könnten demnach eine dezentralisierte Filterkaskade in einem hochspezialisierten und größenbeschränkten neuronalen System begründen. / In many communication systems, information is encoded in the temporal pattern of signals. For rhythmic signals that carry information in specific frequency bands, a neuronal system may profit from tuning its inherent filtering properties towards a peak sensitivity in the respective frequency range. The cricket Gryllus bimaculatus is a simple biological system for which only a narrow range of modulation frequencies is behaviorally relevant. I examined individual neurons in the peripheral and higher auditory system for tuning towards specific temporal parameters and modulation frequencies. To this end, I combined extracellular electrophysiology with different stimulation paradigms involving amplitude-modulated sounds. Analysis of the experimental data revealed that—even in the auditory periphery—some of the examined neurons acted as tuned band-pass filters, yielding highest firing-rates for behaviorally relevant modulation frequencies. Using simple computational models, I demonstrate how common, cell-intrinsic or network-based mechanisms could account for the experimentally observed firing-rate resonances. These mechanisms include subthreshold resonances, spike-triggered adaptation, as well as the interplay of excitation and inhibition. I present how a serial combination of such elementary filters could explain the strong selectivity evident in the cricket’s behavior—without the need for a dedicated filter element. Pervasive neuronal mechanisms could therefore constitute an efficient, distributed frequency filter in a highly specialized, size-constrained neuronal system.

Optogenetics is a set of technologies that enable optically triggered gain or loss of function in genetically specified populations of cells. Optogenetic methods have revolutionized experimental neuroscience by allowing precise excitation or inhibition of firing in specified neuronal populations embedded within complex, heterogeneous tissue. Although optogenetic tools have greatly improved our ability manipulate neural activity, they do not offer control of neural firing in the face of ongoing changes in network activity, plasticity, or sensory input. In this thesis, I develop a feedback control technology that automatically adjusts optical stimulation in real-time to precisely control network activity levels. I describe hardware and software tools, modes of optogenetic stimulation, and control algorithms required to achieve robust neural control over timescales ranging from seconds to days. I then demonstrate the scientific utility of these technologies in several experimental contexts. First, I investigate the role of connectivity in shaping the network encoding process using continuously-varying optical stimulation. I show that synaptic connectivity linearizes the neuronal response, verifying previous theoretical predictions. Next, I use long-term optogenetic feedback control to show that reductions in excitatory neurotransmission directly trigger homeostatic increases in synaptic strength. This result opposes a large body of literature on the subject and has significant implications for memory formation and maintenance. The technology presented in this thesis greatly enhances the precision with which optical stimulation can control neural activity, and allows causally related variables within neural circuits to be studied independently.

Die Erhaltung der Gehirnfunktion trotz Veränderungen im Organismus und dessen Umwelt erfordert homöostatische Mechanismen. Inhibitorische Interneurone spielen eine Schlüsselrolle bei Berechnungen und Homöostase im Gehirn. Es ist jedoch unklar, welcher Mechanismus diese Eigenschaften erzeugen kann. Diese Arbeit hat das Ziel, die homöostatischen Fähigkeiten solcher Interneurone zu bestimmen und die daraus resultierenden funktionellen Konsequenzen mit analytischen und numerischen Techniken zu ergründen. Die zentrale Hypothese dieser Arbeit ist, dass Interneurone ihre Feuerraten modulieren, um langfristig die Aktivität exzitatorischer Neurone bei einem homöostatischen Sollwert zu halten. Wir beginnen mit einem normativen Ansatz und leiten eine Plastizitätsregel her, welche die Aktivität von Interneuronen regelt, um netzwerkweite Abweichungen vom Sollwert zu minimieren. Um die biologische Plausibilität zu erhöhen, liefern wir zwei Approximationen, bei denen jede Interneurone auf die exzitatorische Population reagiert, die sie inhibiert und zeigen, dass alle drei Varianten vergleichbare aber unterschiedliche homöostatische Fähigkeiten haben. Wir kontrastieren den normativen Ansatz mit Regeln, welche die Aktivität einer Interneurone verändern, wenn die Neuronen, die sie treiben, vom Sollwert abweichen. Diese Regeln erzeugen Konkurrenz zwischen Neuronen und führen daher zu zerstreuter Netzwerkaktivität. Im zweiten Teil dieser Arbeit untersuchen wir, wie eine der approximierten Regeln die funktionellen Eigenschaften des sensorischen Kortex beeinflusst. Wir zeigen, dass sie mehrere experimentell Beobachtungen erklären kann, inklusive des Ko-Tunings von exzitatorischen und inhibitorischen Strömen und der Entwicklung von Zellverbänden. Zusammenfassend liefert diese Arbeit neue Erkenntnisse darüber, wie die Regulierung der Interneuron-Aktivität für neuronale Netzwerke homöostatisch sein kann, und zeigt mögliche Auswirkungen auf die Entwicklung und Erhaltung der Gehirnfunktion auf. / Preserving brain function despite ongoing changes inside the organism, and out in the world, necessitates homeostatic mechanisms. Inhibitory interneurons play a key role in both computation and homeostasis within the brain. However, it remains unclear if there is a mechanism that can account for both of these properties. This thesis therefore aims to determine the homeostatic capabilities of such interneurons and elucidate the resulting computational consequences, using analytical and numerical techniques. The central hypothesis of this thesis is that some interneurons slowly modulate their firing rates to maintain the long-term activity of excitatory neurons at a homeostatic set-point. Thus we begin with a normative approach, deriving a plasticity rule that regulates the activity of interneurons to minimise network-wide deviations from that set-point. In the interest of biological plausibility we also provide two approximations, both of which make each interneuron responsive to the excitatory population it inhibits, and show that all three variants exhibit comparable though distinct homeostatic capabilities. We contrast this normative approach by characterising the homeostatic properties of rules which instead alter the activity of an interneuron when the neurons that drive it deviate from the set-point. Those rules induce a competition between neurons, causing network activity to become sparse. In the second part of this thesis, we investigate how one of the approximate rules affects computational properties of sensory cortex. We show that it can account for several experimentally reported results, including co-tuning of excitatory and inhibitory currents, and the development of excitatory-inhibitory cell assemblies. In summation, this thesis provides new insight into how regulating interneuron activity can be homeostatic for neuronal networks, and reveals potential implications for development and preservation of brain function.

Neurone sind anregbare Zellen, die mit Hilfe von elektrischen Signalen miteinander kommunizieren. Im allgemeinen werden eingehende Signale von den Nervenzellen in einer nichtlinearen Art und Weise verarbeitet. Wie diese Verarbeitung in einer umfassenden und exakten Art und Weise mathematisch beschrieben werden kann, ist bis heute nicht geklärt und ist Gegenstand aktueller Forschung. In dieser Arbeit untersuchen wir die nichtlineare Übertragung und Verarbeitung von Signalen durch stochastische Nervenzellen und wenden dabei zwei unterschiedliche Herangehensweisen an. Im ersten Teil der Arbeit befassen wir uns mit der Frage, auf welche Art und Weise ein Signal mit einer bekannten Zeitabhängigkeit die Rate der neuronalen Aktivität beeinflusst. Im zweiten Teil der Arbeit widmen wir uns der Rekonstruktion eingehender Signale aus der durch sie hervorgerufenen neuronalen Aktivität und beschäftigen uns mit der Abschätzung der übertragenen Informationsmenge. Die Ergebnisse dieser Arbeit demonstrieren, wie die etablierten linearen Theorien, die die Modellierung der neuronalen Aktivitätsrate bzw. die Rekonstruktion von Signalen beschreiben, um Beiträge höherer Ordnung erweitert werden können. Einen wichtigen Beitrag dieser Arbeit stellt allerdings auch die Darstellung der Signifikanz der nichtlinearen Theorien dar. Die nichtlinearen Beiträge erweisen sich nicht nur als schwache Korrekturen zu den etablierten linearen Theorien, sondern beschreiben neuartige Effekte, die durch die linearen Theorien nicht erfasst werden können. Zu diesen Effekten gehört zum Beispiel die Anregung von harmonischen Oszillationen der neuronalen Aktivitätsrate und die Kodierung von Signalen in der signalabhängigen Varianz einer Antwortvariablen. / Neurons are excitable cells which communicate with each other via electrical signals. In general, these signals are processed by the Neurons in a nonlinear fashion, the exact mathematical description of which is still an open problem in neuroscience. In this thesis, the broad topic of nonlinear signal processing is approached from two directions. The first part of the thesis is devoted to the question how input signals modulate the neural response. The second part of the thesis is concerned with the nonlinear reconstruction of input signals from the neural output and with the estimation of the amount of the transmitted information. The results of this thesis demonstrate how existing linear theories can be extended to capture nonlinear contributions of the signal to the neural response or to incorporate nonlinear correlations into the estimation of the transmitted information. More importantly, however, our analysis demonstrates that these extensions do not merely provide small corrections to the existing linear theories but can account for qualitatively novel effects which are completely missed by the linear theories. These effects include, for example, the excitation of harmonic oscillations in the neural firing rate or the estimation of information for systems with a signal-dependent output variance.