How models of canonical microcircuits implement cognitive functions

Major cognitive functions such as language, memory, and decision-making are thought to rely on distributed networks of a large number of fundamental neural elements, called canonical microcircuits. A mechanistic understanding of the interaction of these canonical microcircuits promises a better comprehension of cognitive functions as well as their potential disorders and corresponding treatment techniques. This thesis establishes a generative modeling framework that rests on canonical microcircuits and employs it to investigate composite mechanisms of cognitive functions. A generic, biologically plausible neural mass model was derived to parsimoniously represent conceivable architectures of canonical microcircuits. Time domain simulations and bifurcation and stability analyses were used to evaluate the model’s capability for basic information processing operations in response to transient stimulations, namely signal flow gating and working memory. Analysis shows that these basic operations rest upon the bistable activity of a neural population and the selectivity for the stimulus’ intensity and temporal consistency and transiency. In the model’s state space, this selectivity is marked by the distance of the system’s working point to a saddle-node bifurcation and the existence of a Hopf separatrix. The local network balance, in regard of synaptic gains, is shown to modify the model’s state space and thus its operational repertoire. Among the investigated architectures, only a three-population model that separates input-receiving and output-emitting excitatory populations exhibits the necessary state space characteristics. It is thus specified as minimal canonical microcircuit. In this three-population model, facilitative feedback information modifies the retention of sensory feedforward information. Consequently, meta-circuits of two hierarchically interacting minimal canonical microcircuits feature a temporal processing history that enables state-dependent processing operations. The relevance of these composite operations is demonstrated for the neural operations of priming and structure-building. Structure-building, that is the sequential and selective activation of neural circuits, is identified as an essential mechanism in a neural network for syntax parsing. This insight into cognitive processing proves the modeling framework’s potential in neurocognitive research. This thesis substantiates the connectionist notion that higher processing operations emerge from the combination of minimal processing elements and advances the understanding how cognitive functions are implemented in the neocortical matter of the brain.Kognitive Fähigkeiten wie Sprache, Gedächtnis und Entscheidungsfindung resultieren vermutlich aus der Interaktion vieler fundamentaler neuronaler Elemente, sogenannter kanonischer Schaltkreise. Eine vertiefte Einsicht in das Zusammenwirken dieser kanonischen Schaltkreise verspricht ein besseres Verständnis kognitiver Fähigkeiten, möglicher Funktionsstörungen und Therapieansätze. Die vorliegende Dissertation untersucht ein generatives Modell kanonischer Schaltkreise und erforscht mit dessen Hilfe die Zusammensetzung kognitiver Fähigkeiten aus konstitutiven Mechanismen neuronaler Interaktion. Es wurde ein biologisch-plausibles neuronales Massenmodell erstellt, das mögliche Architekturen kanonischer Schaltkreise generisch berücksichtigt. Anhand von Simulationen sowie Bifurkations- und Stabilitätsanalysen wurde untersucht, inwiefern das Modell grundlegende Operationen der Informationsverarbeitung, nämlich Selektion und temporäre Speicherung einer transienten Stimulation, unterstützt. Die Untersuchung zeigt, dass eine bistabile Aktivität einer neuronalen Population und die Beurteilung der Salienz des Signals den grundlegenden Operationen zugrunde liegen. Die Beurteilung der Salienz beruht dabei hinsichtlich der Signalstärke auf dem Abstand des Arbeitspunktes zu einer Sattel-Knoten-Bifurkation und hinsichtlich der Signalkonsistenz und-–vergänglichkeit auf einer Hopf-Separatrix im Zustandsraum des Systems. Die Netzwerkbalance modifiziert diesen Zustandsraum und damit die Funktionsfähigkeit des Modells. Nur ein Drei-Populationenmodell mit getrennten erregenden Populationen für Signalempfang und -emission weist die notwendigen Bedingungen im Zustandsraum auf und genügt der Definition eines minimalen kanonischen Schaltkreises. In diesem Drei-Populationenmodell erleichtert ein Feedbacksignal die Speicherfähigkeit für sensorische Feedforwardsignale. Dementsprechend weisen hierarchisch interagierende minimale kanonische Schaltkreise ein zeitliches Verarbeitungsgedächtnis auf, das zustandsabhängige Verarbeitungsoperationen erlaubt. Die Bedeutung dieser konstitutiven Operationen wird für die neuronalen Operationen Priming und Strukturbildung verdeutlicht. Letztere wurde als wichtiger Mechanismus in einem Netzwerk zur Syntaxanalyse identifiziert und belegt das Potential des Modellansatzes für die neurokognitive Forschung. Diese Dissertation konkretisiert die konnektionistische Ansicht höhere Verarbeitungsoperationen als Ergebnis der Kombination minimaler Verarbeitungselemente zu verstehen und befördert das Verständnis für die Frage wie kognitive Fähigkeiten im Nervengewebe des Gehirns implementiert sind

Dynamical principles in neuroscience

Dynamical modeling of neural systems and brain functions has a history of success over the last half century. This includes, for example, the explanation and prediction of some features of neural rhythmic behaviors. Many interesting dynamical models of learning and memory based on physiological experiments have been suggested over the last two decades. Dynamical models even of consciousness now exist. Usually these models and results are based on traditional approaches and paradigms of nonlinear dynamics including dynamical chaos. Neural systems are, however, an unusual subject for nonlinear dynamics for several reasons: (i) Even the simplest neural network, with only a few neurons and synaptic connections, has an enormous number of variables and control parameters. These make neural systems adaptive and flexible, and are critical to their biological function. (ii) In contrast to traditional physical systems described by well-known basic principles, first principles governing the dynamics of neural systems are unknown. (iii) Many different neural systems exhibit similar dynamics despite having different architectures and different levels of complexity. (iv) The network architecture and connection strengths are usually not known in detail and therefore the dynamical analysis must, in some sense, be probabilistic. (v) Since nervous systems are able to organize behavior based on sensory inputs, the dynamical modeling of these systems has to explain the transformation of temporal information into combinatorial or combinatorial-temporal codes, and vice versa, for memory and recognition. In this review these problems are discussed in the context of addressing the stimulating questions: What can neuroscience learn from nonlinear dynamics, and what can nonlinear dynamics learn from neuroscience?This work was supported by NSF Grant No. NSF/EIA-0130708, and Grant No. PHY 0414174; NIH Grant No. 1 R01 NS50945 and Grant No. NS40110; MEC BFI2003-07276, and Fundación BBVA

Stochastic dynamics and delta-band oscillations in clustered spiking networks

Following experimental measurements of clustered connectivity in the cortex, recent studies have found that clustering connections in simulated spiking networks causes transitions between high and low firing-rate states in subgroups of neurons. An open question is to what extent the sequence of transitions in such networks can be related to existing statistical and mechanical models of sequence generation. In this thesis we present several studies of the relationship between connection structure and network dynamics in balanced spiking networks. We investigate which qualities of the network connection matrix support the generation of state sequences, and which properties determine the specific structure of transitions between states. We find that adding densely overlapping clusters with equal levels of recurrent connectivity to a network with dense inhibition can produce sequential winner-takes-all dynamics in which high-activity states pass between correlated clusters. This activity is reflected in the power spectrum of spiking activity as a peak in the low-frequency delta range. We describe and verify sequence dynamics with a Markov chain framework, and compare them mechanically to “latching” models of sequence generation. Additionally we quantify the chaos of clustered networks and find that minimally separated states diverge in distinct stages. The results clarify the computational capabilities of clustered spiking networks and their relationship to experimental findings. We conclude that the results provide a supporting intermediate link between abstract models and biological instances of sequence generation

Prefrontal rhythms for cognitive control

Goal-directed behavior requires flexible selection among action plans and updating behavioral strategies when they fail to achieve desired goals. Lateral prefrontal cortex (LPFC) is implicated in the execution of behavior-guiding rule-based cognitive control while anterior cingulate cortex (ACC) is implicated in monitoring processes and updating rules. Rule-based cognitive control requires selective processing while process monitoring benefits from combinatorial processing. I used a combination of computational and experimental methods to investigate how network oscillations and neuronal heterogeneity contribute to cognitive control through their effects on selective versus combinatorial processing modes in LPFC and ACC. First, I adapted an existing LPFC model to explore input frequency- and coherence-based output selection mechanisms for flexible routing of rate-coded signals. I show that the oscillatory states of input encoding populations can exhibit a stronger influence over downstream competition than their activity levels. This enables an output driven by a weaker resonant input signal to suppress lower-frequency competing responses to stronger, less resonant (though possibly higher-frequency) input signals. While signals are encoded in population firing rates, output selection and signal routing can be governed independently by the frequency and coherence of oscillatory inputs and their correspondence with output resonant properties. Flexible response selection and gating can be achieved by oscillatory state control mechanisms operating on input encoding populations. These dynamic mechanisms enable experimentally-observed LPFC beta and gamma oscillations to flexibly govern the selection and gating of rate-coded signals for downstream read-out. Furthermore, I demonstrate how differential drives to distinct interneuron populations can switch working memory representations between asynchronous and oscillatory states that support rule-based selection. Next, I analyzed physiological data from the LeBeau laboratory and built a de novo model constrained by the biological data. Experimental data demonstrated that fast network oscillations at both the beta- and gamma frequency bands could be elicited in vitro in ACC and neurons exhibited a wide range of intrinsic properties. Computational modeling of the ACC network revealed that the frequency of network oscillation generated was dependent upon the time course of inhibition. Principal cell heterogeneity broadened the range of frequencies generated by the model network. In addition, with different frequency inputs to two neuronal assemblies, heterogeneity decreased competition and increased spike coherence between the networks thus conferring a combinatorial advantage to the network. These findings suggest that oscillating neuronal populations can support either response selection (routing), or combination, depending on the interplay between the kinetics of synaptic inhibition and the degree of heterogeneity of principal cell intrinsic conductances. Such differences may support functional differences between the roles of LPFC and ACC in cognitive control

Plasticity of Cell Migration in Vivo and in Silico

Cell migration results from stepwise mechanical and chemical interactions between cells and their extracellular environment. Mechanistic principles that determine single-cell and collective migration modes and their interconversions depend upon the polarization, adhesion, deformability, contractility, and proteolytic ability of cells. Cellular determinants of cell migration respond to extracellular cues, including tissue composition, topography, alignment, and tissue-associated growth factors and cytokines. Both cellular determinants and tissue determinants are interdependent; undergo reciprocal adjustment; and jointly impact cell decision making, navigation, and migration outcome in complex environments. We here review the variability, decision making, and adaptation of cell migration approached by live-cell, in vivo, and in silico strategies, with a focus on cell movements in morphogenesis, repair, immune surveillance, and cancer metastasi

Perspectives on adaptive dynamical systems

Adaptivity is a dynamical feature that is omnipresent in nature, socio-economics, and technology. For example, adaptive couplings appear in various real-world systems like the power grid, social, and neural networks, and they form the backbone of closed-loop control strategies and machine learning algorithms. In this article, we provide an interdisciplinary perspective on adaptive systems. We reflect on the notion and terminology of adaptivity in different disciplines and discuss which role adaptivity plays for various fields. We highlight common open challenges, and give perspectives on future research directions, looking to inspire interdisciplinary approaches.Comment: 46 pages, 9 figure

Functional dissection of a gene expression oscillator in C. elegans

Gene expression oscillations control diverse biological processes. One such example of gene expression oscillations, are those found for thousands of genes during C. elegans larval development. However, it remains unclear whether and how gene expression oscillations regulate development processes in C. elegans. In this work, I aimed to study the molecular architecture and the system properties of the C. elegans oscillator to provide insight into potential developmental functions and reveal features that are unique, as well as those that are shared among oscillators. Here, performing temporally highly resolved mRNA-sequencing across all larval stages (L1-L4) of C. elegans development, we identified 3,739 genes, whose transcripts revealed high-amplitude oscillations (>2-fold from peak to trough), peaking once every larval stage with stable amplitudes, but variable periods. Oscillations appeared tightly coupled to the molts, but were absent from freshly hatched larvae, developmentally arrested dauer larvae and adults. Quantitative characterization of transitions between oscillatory and stable states of the oscillator showed that the stable states are similar to a particular phase of the oscillator, which coincided with molt exit. Given that these transitions are sensitive to food, we postulate that feeding might impact the state of the oscillator. These features appear rather unique, and hence a better understanding may help to reveal general principles of gene expression oscillators. Our RNAPII ChIP-seq revealed rhythmic occupancy of RNAPII at the promoters of oscillating genes, suggesting that mRNA transcript oscillations arise from rhythmic transcription. Given that oscillations are coupled to the repetitive molts and that the molecular mechanisms that regulate molting are unknown, we aimed to find transcription factors important for molting and oscillations. Hence, we screened 92 transcription factors that oscillate on the mRNA level for their role in molting and identified grh-1, myrf1, blmp-1, bed-3, nhr-23, nhr-25 and ztf-6. We showed that oscillatory activity of GRH-1 is required for timely completion of the molt, to prevent cuticle rupturing, and for oscillatory expression of structural components of the cuticle and ‘ECM regulators’, among others, including grh-1 itself. Hence, we propose GRH-1 as a putative component of the (sub-)oscillator that regulates molting. We showed that loss of BLMP-1 increased the duration of molts, affected cuticle integrity, and changed the oscillatory dynamics of a subset of genes in diverse ways. We postulate that BLMP-1 acts as factor that couples gene expression oscillations, and potentially sub-oscillators or repetitive developmental processes. In conclusion, this work provides insight into the function of the oscillator, and its system properties. Moreover, we identified relevant factors, which we propose as a starting point to unravel the molecular wiring of the C. elegans oscillator and its functional relevance

Modelling human choices: MADeM and decision‑making

Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)