Awakened oscillations in coupled consumer-resource pairs

The paper concerns two interacting consumer-resource pairs based on chemostat-like equations under the assumption that the dynamics of the resource is considerably slower than that of the consumer. The presence of two different time scales enables to carry out a fairly complete analysis of the problem. This is done by treating consumers and resources in the coupled system as fast-scale and slow-scale variables respectively and subsequently considering developments in phase planes of these variables, fast and slow, as if they are independent. When uncoupled, each pair has unique asymptotically stable steady state and no self-sustained oscillatory behavior (although damped oscillations about the equilibrium are admitted). When the consumer-resource pairs are weakly coupled through direct reciprocal inhibition of consumers, the whole system exhibits self-sustained relaxation oscillations with a period that can be significantly longer than intrinsic relaxation time of either pair. It is shown that the model equations adequately describe locally linked consumer-resource systems of quite different nature: living populations under interspecific interference competition and lasers coupled via their cavity losses.Comment: 31 pages, 8 figures 2 tables, 48 reference

Ongoing Spontaneous Activity Controls Access to Consciousness: A Neuronal Model for Inattentional Blindness

Even in the absence of sensory inputs, cortical and thalamic neurons can show structured patterns of ongoing spontaneous activity, whose origins and functional significance are not well understood. We use computer simulations to explore the conditions under which spontaneous activity emerges from a simplified model of multiple interconnected thalamocortical columns linked by long-range, top-down excitatory axons, and to examine its interactions with stimulus-induced activation. Simulations help characterize two main states of activity. First, spontaneous gamma-band oscillations emerge at a precise threshold controlled by ascending neuromodulator systems. Second, within a spontaneously active network, we observe the sudden “ignition” of one out of many possible coherent states of high-level activity amidst cortical neurons with long-distance projections. During such an ignited state, spontaneous activity can block external sensory processing. We relate those properties to experimental observations on the neural bases of endogenous states of consciousness, and particularly the blocking of access to consciousness that occurs in the psychophysical phenomenon of “inattentional blindness,” in which normal subjects intensely engaged in mental activity fail to notice salient but irrelevant sensory stimuli. Although highly simplified, the generic properties of a minimal network may help clarify some of the basic cerebral phenomena underlying the autonomy of consciousness

Oscillatory architecture of memory circuits

The coordinated activity between remote brain regions underlies cognition and memory function. Although neuronal oscillations have been proposed as a mechanistic substrate for the coordination of information transfer and memory consolidation during sleep, little is known about the mechanisms that support the widespread synchronization of brain regions and the relationship of neuronal dynamics with other bodily rhythms, such as breathing. During exploratory behavior, the hippocampus and the prefrontal cortex are organized by theta oscillations, known to support memory encoding and retrieval, while during sleep the same structures are dominated by slow oscillations that are believed to underlie the consolidation of recent experiences. The expression of conditioned fear and extinction memories relies on the coordinated activity between the mPFC and the basolateral amygdala (BLA), a neuronal structure encoding associative fear memories. However, to date, the mechanisms allowing this long-range network synchronization of neuronal activity between the mPFC and BLA during fear behavior remain virtually unknown. Using a combination of extracellular recordings and open- and closed-loop optogenetic manipulations, we investigated the oscillatory and coding mechanisms mediating the organization and coupling of the limbic circuit in the awake and asleep brain, as well as during memory encoding and retrieval. We found that freezing, a behavioral expression of fear, is tightly associated with an internally generated brain state that manifests in sustained 4Hz oscillatory dynamics in prefrontal-amygdala circuits. 4Hz oscillations accurately predict the onset and termination of the freezing state. These oscillations synchronize prefrontal-amygdala circuits and entrain neuronal activity to dynamically regulate the development of neuronal ensembles. This enables the precise timing of information transfer between the two structures and the expression of fear responses. Optogenetic induction of prefrontal 4Hz oscillations promotes freezing behavior and the formation of long-lasting fear memory, while closed-loop phase specific manipulations bidirectionally modulate fear expression. Our results unravel a physiological signature of fear memory and identify a novel internally generated brain state, characterized by 4Hz oscillations. This oscillation enables the temporal coordination and information transfer in the prefrontal-amygdala circuit via a phase-specific coding mechanism, facilitating the encoding and expression of fear memory. In the search for the origin of this oscillation, we focused our attention on breathing, the most fundamental and ubiquitous rhythmic activity in life. Using large-scale extracellular recordings from a number of structures, including the medial prefrontal cortex, hippocampus, thalamus, amygdala and nucleus accumbens in mice we identified and characterized the entrainment by breathing of a host of network dynamics across the limbic circuit. We established that fear-related 4Hz oscillations are a state-specific manifestation of this cortical entrainment by the respiratory rhythm. We characterized the translaminar and transregional profile of this entrainment and demonstrated a causal role of breathing in synchronizing neuronal activity and network dynamics between these structures in a variety of behavioral scenarios in the awake and sleep state. We further revealed a dual mechanism of respiratory entrainment, in the form of an intracerebral corollary discharge that acts jointly with an olfactory reafference to coordinate limbic network dynamics, such as hippocampal ripples and cortical UP and DOWN states, involved in memory consolidation. Respiration provides a perennial stream of rhythmic input to the brain. In addition to its role as the condicio sine qua non for life, here we provide evidence that breathing rhythm acts as a global pacemaker for the brain, providing a reference signal that enables the integration of exteroceptive and interoceptive inputs with the internally generated dynamics of the hippocampus and the neocortex. Our results highlight breathing, a perennial rhythmic input to the brain, as an oscillatory scaffold for the functional coordination of the limbic circuit, enabling the segregation and integration of information flow across neuronal networks

Breathing coordinates cortico-hippocampal dynamics in mice during offline states

Network dynamics have been proposed as a mechanistic substrate for the information transfer across cortical and hippocampal circuits. However, little is known about the mechanisms that synchronize and coordinate these processes across widespread brain regions during offline states. Here we address the hypothesis that breathing acts as an oscillatory pacemaker, persistently coupling distributed brain circuit dynamics. Using large-scale recordings from a number of cortical and subcortical brain regions in behaving mice, we uncover the presence of an intracerebral respiratory corollary discharge, that modulates neural activity across these circuits. During offline states, the respiratory modulation underlies the coupling of hippocampal sharp-wave ripples and cortical DOWN/UP state transitions, which mediates systems memory consolidation. These results highlight breathing, a perennial brain rhythm, as an oscillatory scaffold for the functional coordination of the limbic circuit that~supports the segregation and integration of information flow across neuronal networks during offline states

The “conscious pilot”—dendritic synchrony moves through the brain to mediate consciousness

Cognitive brain functions including sensory processing and control of behavior are understood as “neurocomputation” in axonal–dendritic synaptic networks of “integrate-and-fire” neurons. Cognitive neurocomputation with consciousness is accompanied by 30- to 90-Hz gamma synchrony electroencephalography (EEG), and non-conscious neurocomputation is not. Gamma synchrony EEG derives largely from neuronal groups linked by dendritic–dendritic gap junctions, forming transient syncytia (“dendritic webs”) in input/integration layers oriented sideways to axonal–dendritic neurocomputational flow. As gap junctions open and close, a gamma-synchronized dendritic web can rapidly change topology and move through the brain as a spatiotemporal envelope performing collective integration and volitional choices correlating with consciousness. The “conscious pilot” is a metaphorical description for a mobile gamma-synchronized dendritic web as vehicle for a conscious agent/pilot which experiences and assumes control of otherwise non-conscious auto-pilot neurocomputation

Biophysics-based modeling and data analysis of local field potential signal

Understanding the neurophysiological mechanisms of information processing within and across brain regions has always been a fundamental and challenging topic in neuroscience. Considerable works in the brain connectome and transcriptome have laid a profound foundation for understanding brain function by its structure. At the same time, the recent advance in recording techniques allows us to probe the nonstationary brain activity from various spatial and temporal scales. However, how to effectively build the dialogue between the anatomical structure and the dynamical brain signal still needs to be solved. To tackle the problem, we explore interpreting electrophysiology signals with mechanistic models. In chapter 2 we first segregate high-coherent brain signals into different pathways and then connect their dynamics to synaptic properties. Based on a state space model of LFP generation, we explore several preprocessing methods to bias the signal to the synaptic inputs and enhance the separatability of pathway-specific contributions. The separated sources are more reliable with the preprocessing methods, especially in highly coherent states, e.g., awake running. With reliably separated pathways, we further studied their synaptic properties and explored the local directional connections in the hippocampus. The estimated synaptic time constant and pathway connection agrees with well-established anatomical studies. In chapter 3 we explore establishing a simple model to capture the impulse response of passive neurons with detailed dendritic morphology. We validate Green’s function methods based on compartmentalized models by comparing them to numerical simulations and analytical solutions on continuous neuron membrane potentials. A parameterized model based on laminar Green’s function is further developed and helps to infer the anatomical properties, like the input current distribution and cell position, from their spatiotemporal response patterns. The effect of cell position and template are examed. Based on the model of chapter 3, we use the biophysical possible impulse response profile to regularize the source separation in the frequency domain in chapter 4. The components from different frequencies are clustered according to the same latent input distributions. The source separation in better-separated frequency bins from the same pathway helps separation in other highly contaminated frequencies. The optimization is formulated as a probabilistic model to maximize the negentropy as well as spatial likelihood. Similar to dipole approximation for EEG signals, Green’s function method provides an effective approximation to capture biologically possible spatiotemporal patterns and helps to guide the separation. We validated the method on real data with optogenetic stimulation. In chapter 5 we further separate the far-field signals from the local pathway activities according to their physiological properties. We propose a pipeline to reliably separate and automatically detect far-field signal components. Based on this, a toolbox is provided to remove the EMG artifacts and assess the cleaning performance. In the free-running animals, we show that EMG artifacts shadow the high-frequency oscillatory events detection, and EMG cleaning rescues this effect. Overall, this thesis explored multiple possibilities to incorporate neurophysiology knowledge to understand and model the electrical field potential signals.Das Verständnis der neurophysiologischen Mechanismen der Informationsverarbeitung innerhalb und zwischen Gehirnregionen war schon immer ein grundlegendes und herausforderndes Thema in den Neurowissenschaften. Weitreichende Arbeiten zum Konnektom und Transkriptom des Gehirns haben eine Grundlage für das Verständnis der Gehirnfunktion gelegt. Des Weiteren ermöglicht uns der derzeitige Fortschritt in der Aufnahmetechnik, die nicht stationäre Gehirnaktivität auf verschiedenen räumlichen und zeitlichen Skalen zu untersuchen. Wie jedoch die anatomischen Strukturen und die dynamischen Gehirnsignal effektiv zusammen wirken können, muss jedoch noch gelöst werden. Um dieses Problem anzugehen, untersuchen wir die Interpretation elektrophysiologischer Signale mit mechanistischen Modellen. In Kapitel 2 trennen wir zunächst die hochkohärenten Gehirnsignale in verschiedene Leitungsbahnen und verbinden dann die Dynamik mit synaptischen Eigenschaften. Basierend auf einem Zustandsraummodell zur Erzeugung lokaler Feldpotentiale (LFP) untersuchen wir verschiedene Vorverarbeitungsmethoden, die die Signale bestmöglich auf die synaptischen Eingangsströme ausrichten und die Trennbarkeit von leitungsbahnspezifischen Beiträgen verbessert. Die Trennung der Signalquellen ist durch das Vorverarbeitungsverfahren insbesondere während hochkohärenter Verhaltenszustände (z. B. laufen im Wachzustand) zuverlässiger. Mit zuverlässig getrennten Leitungsbahnen konnten wir die entsprechenden synaptischen Eigenschaften weiter untersuchen und die lokalen gerichteten Verbindungen im Hippocampus untersuchen. Die geschätzte synaptische Zeitkonstante und die Verbindungen der Leitungsbahnen stimmen mit etablierten anatomischen Studien überein. In Kapitel 3 untersuchen wir die Erstellung eines einfachen Modells zur Beschreibung der Impulsantwort passiver Neuronen mit detaillierter dendritischer Morphologie. Wir validieren Greensche Funktionsmethoden basierend auf kompartimentierten Modellen, indem wir sie mit numerischen Simulationen und analytischen Lösungen des kontinuierlichen Membranpotentials von Neuronen vergleichen. Ein parametrisiertes Modell, das auf der laminaren Greenschen Funktion basiert, wird weiterentwickelt. Es hilft dabei, die anatomischen Eigenschaften - die Verteilung des Eingangsstroms und die Zellposition - aus ihren raumzeitlichen Reaktionsmustern abzuleiten. Die Auswirkung der Zellposition und des Templates werden untersucht. Basierend auf dem Modell aus Kapitel 3 verwenden wir in Kapitel 4 das biophysikalisch mögliche Profil der Impulsantwort, um die Quellentrennung im Frequenzbereich festzulegen. Die Komponenten verschiedener Frequenzen werden nach derselben latenten Eingangsverteilungen geclustert. Die Quellentrennung in besser getrennten Frequenzbereichen derselben Leitungsbahn hilft bei der Quelltrennung in anderen stark kontaminierten Frequenzbereichen. Die Optimierung wird als probabilistisches Modell formuliert, um sowohl die Negentropie als auch die räumliche Wahrscheinlichkeit zu maximieren. Ähnlich wie die Dipolnäherungen für EEG-Signale bietet die Greensche Funktionsmethode eine effektive Annäherung, um biologisch mögliche raumzeitliche Muster zu erfassen, und hilft, die Quellen zu trennen. Wir haben die Methode an realen Daten mit optogenetischer Stimulation validiert. Im Kapitel 5 trennen wir weiter die Fernfeldsignale von den Signalen der lokalen Leitungsbahnen nach ihren physiologischen Eigenschaften. Wir schlagen eine Methode vor, die es erlaubt, Fernfeld-Signalkomponenten zuverlässig von lokaler Aktivitaet zu trennen und automatisch zu erkennen. Es wird eine Toolbox bereitgestellt, die EMG-Artefakte entfernt und die bereinigten Signale bewertet. In Ableitungen von freilaufenden Tieren zeigen wir, dass EMG-Artefakte die Erkennung von hochfrequenten Oszillationen beeintraechtigt, aber nach der Bereinigung des EMG-Signals erkannt werden kann. Insgesamt untersucht diese Dissertation mehrere Möglichkeiten die elektrischen Feldpotentiale neuronaler Aktivität unter Einbeziehung neurophysiologischen Wissens zu modellieren und zu verstehen

State Transitions Within The Cortex Are Strongly Influenced By Local Interactions Under General Anesthesia

General anesthetics are a class of drugs with diverse molecular mechanisms that cause a state of unconsciousness. Generally, anesthetics are thought to exert this effect by co- opting endogenous sleep pathways within the brain, and activity patterns recorded during anesthesia resemble those recorded during natural sleep. Monitors of anesthetic depth take advantage of the relationship between brain activity patterns and anesthetic concentration to define a depth of exposure. Recovery from anesthetic-induced unconsciousness is typically assumed to be a passive, linear process that relies upon elimination of drug from the body. However, it has been shown that activity patterns undergo discrete transitions between several distinct brain states under anesthesia. Furthermore, the brain exhibits a resistance to recovery of consciousness during emergence from anesthesia. Together, these results show that emergence cannot be explained by drug elimination alone. In this dissertation, we present evidence to suggest that stochastic fluctuations between distinct brain states account for this resistance to emergence. Furthermore, we show evidence to suggest that local cortical interactions are the principal organizing mechanism that gives rise to the brain states and state transitions recorded under general anesthesia. This mechanism is distinct from those known to drive state transitions during natural sleep. During sleep, broadly projecting modulatory pathways engage neurons throughout the thalamocortical network in coherent activity patterns and state transitions. Here, we demonstrate local heterogeneity in activity patterns and transition times within the cortex. Furthermore, our results indicate that, despite there being only weak coupling between activity patterns and transition times between different cortical regions, this coupling is sufficient to give rise to global brain states. Altogether, the work presented in this dissertation indicates that the nature of oscillations within the cortex is strongly influenced by local interactions. This finding suggests that the mechanisms thought to give rise to state transitions during sleep are not the same as those that give rise to transitions under anesthesia. This finding that local interactions are potentially a stronger organizing mechanism for cortical activity than previously appreciated has important implications for anesthetic monitoring, clinical sleep disorders, and our basic understanding of thalamocortical activity patterns

Modelling emergent rhythmic activity in the cerebal cortex

A la portada consta: IDIBAPS Institut d'Investigacions Biomèdiques August Pi i SunyerThe brain, a natural adaptive system, can generate a rich dynamic repertoire of spontaneous activity even in the absence of stimulation. The spatiotemporal pattern of this spontaneous activity is determined by the brain state, which can range from highly synchronized to desynchronized states. During slow wave sleep, for example, the cortex operates in synchrony, defined by low-frequency fluctuations, known as slow oscillations (<1Hz). Conversely, during wakefulness, the cortex is characterized mainly by desynchronized activity, where low-frequency fluctuations are suppressed. Thus, an inherent property of the cerebral cortex is to transit between different states characterized by distinct spatiotemporal complexity patterns, varying in a large spectrum between synchronized and desynchronized activity. All these complex emergent patterns are the product of the interaction between tens of billions of neurons endowed with diverse ionic channels with complex biophysical properties. Nevertheless, what are the mechanisms behind these transitions? In this thesis, we sought to understand the mechanisms and properties behind slow oscillations, their modulation and their transitions towards wakefulness by employing experimental data analysis and computational models. We reveal the relevance of specific ionic channels and synaptic properties to maintaining the cortical state and also get out of it, and its spatiotemporal dynamics. Using a mean-field model, we also propose bridging neuronal spiking dynamics to a population description.El cerebro, un sistema adaptativo natural, es capaz de generar un amplio repertorio dinámico de actividad espontánea, incluso en ausencia de estímulos. La patrón espacio-temporal de esta actividad espontánea viene determinada por el estado cerebral, el cual puede variar de estados altamente sincronizados hasta estados muy desincronizados. Cuando en el sueño se entra en la fase de ondas lentas, por ejemplo, la corteza opera en sincronía, cuya actividad es definida por fluctuaciones de baja frecuencia, conocidas como oscilaciones lentas (<1Hz). En cambio, durante la vigilia, el córtex se caracteriza principalmente por tener una actividad desincronizada, donde las fluctuaciones de baja frecuencia desaparecen. Por lo tanto, una propiedad inherente de la corteza cerebral es transitar entre diferentes estados caracterizados por distintos patrones de complejidad espacio-temporal, los cuales se sitúan dentro del amplio espectro marcado por la actividad sincronizada y la desincronizada. Estos patrones emergentes son el producto de la interacción entre decenas de miles de millones de neuronas dotadas de múltiples y distintos canales iónicos con complejas propiedades biofísicas. Sin embargo, ¿cuáles son los mecanismos que regulan estas transiciones? En esta tesis tratamos de entender los mecanismos, propiedades y sus transiciones hacia la vigilia, que están detrás de las oscilaciones lentas a través del uso y análisis de datos experimentales y modelos computacionales. En ella describimos la importancia de los canales iónicos específicos y sus propiedades sinápticas tanto para mantener el estado cortical como para salir de él, estudiando así su dinámica espacio-temporal. Además, mediante el uso de un modelo de campo medio, proponemos establecer un puente que pueda describir la dinámica de disparos neuronales con una descripción general de la población neuronal.Postprint (published version