A circuit for heading direction estimation in the zebrafish anterior hindbrain

To successfully navigate their environment, animals may generate an inter- nal representation of the environment that can be updated based on sensory cues or internally generated motor commands. Head-direction cells, neu- rons that fire when the animal faces a particular direction in space have been recorded in various areas of the vertebrate brain. The dynamics of heading direction circuits are well by described by ring attractor networks, where a ring attractor organizes the activity of the circuit and positions along the ring represent the heading direction. Although this model has found remarkable validation in the invertebrate central complex, the anatomical dissection of a ring attractor circuit has been elusive in the vertebrate brain. Here, I report experimental observations in the larval zebrafish that high- light a possible role of the interpenducular nucleus (IPN) and a connected area, the anterior hindbrain, in generating heading direction-related signals. The internal organization of the interpeduncular nucleus is poorly un- derstood. In the first part of this thesis, I will present anatomical recon- structions that provide crucial insights in the organization of this struc- ture in the larval zebrafish. I will show that 1) the internal circuitry of the ventral IPN is organized in a fix number of glomeruli, domains of neuropil that receive dense and segregated dendritic and axonal arborizations and exhaustively tile the ventral IPN; and that 2) neurons in the anterior hind- brain dorsal from the interpeduncular nucleus contribute many dendritic and axonal projections to the IPN neuropil. In the second part of the thesis, I will describe a population of r1π neu- rons in the anterior hindbrain that exhibit a highly constrained dynamics lying on a ring manifold in the phase space of the network. Intriguingly, clock- and counterclock-wise shifts along this manifold correspond to left and right movements of the fish, so that the network state can keep track of current heading direction. The dynamics of the network full-fills several criteria that define a head-direction network: 1) There is a sustained and unique bump of activity that translates across the network (uniqueness); 2) the activity shifts in opposite directions when the animal perform leftward and rightward movements (integration); 3) activation of the network is sta- ble over tens of seconds in the absence of motion (persistence). Finally, I will turn back to the anatomy of r1π neurons and show how they could connect with each other in the IPN according to their proximity in activity space, and I will conclude by proposing a mechanistic model for the organization of the ring network dynamics. Together, these data repre- sent the first observation of a head-direction network with an anatomical organization in the vertebrate brain

Oculomotor learning revisited: a model of reinforcement learning in the basal ganglia incorporating an efference copy of motor actions

In its simplest formulation, reinforcement learning is based on the idea that if an action taken in a particular context is followed by a favorable outcome, then, in the same context, the tendency to produce that action should be strengthened, or reinforced. While reinforcement learning forms the basis of many current theories of basal ganglia (BG) function, these models do not incorporate distinct computational roles for signals that convey context, and those that convey what action an animal takes. Recent experiments in the songbird suggest that vocal-related BG circuitry receives two functionally distinct excitatory inputs. One input is from a cortical region that carries context information about the current “time” in the motor sequence. The other is an efference copy of motor commands from a separate cortical brain region that generates vocal variability during learning. Based on these findings, I propose here a general model of vertebrate BG function that combines context information with a distinct motor efference copy signal. The signals are integrated by a learning rule in which efference copy inputs gate the potentiation of context inputs (but not efference copy inputs) onto medium spiny neurons in response to a rewarded action. The hypothesis is described in terms of a circuit that implements the learning of visually guided saccades. The model makes testable predictions about the anatomical and functional properties of hypothesized context and efference copy inputs to the striatum from both thalamic and cortical sources

Controllability of structural brain networks.

Cognitive function is driven by dynamic interactions between large-scale neural circuits or networks, enabling behaviour. However, fundamental principles constraining these dynamic network processes have remained elusive. Here we use tools from control and network theories to offer a mechanistic explanation for how the brain moves between cognitive states drawn from the network organization of white matter microstructure. Our results suggest that densely connected areas, particularly in the default mode system, facilitate the movement of the brain to many easily reachable states. Weakly connected areas, particularly in cognitive control systems, facilitate the movement of the brain to difficult-to-reach states. Areas located on the boundary between network communities, particularly in attentional control systems, facilitate the integration or segregation of diverse cognitive systems. Our results suggest that structural network differences between cognitive circuits dictate their distinct roles in controlling trajectories of brain network function

Slipping into Sleep: neurodynamics of alertness transitions in humans and fruit flies

The ability to react to events in the external world determines the fate of every living organism, this general state of readiness is called as ’alertness’. What happens to neurodynamics in the brain when alertness fades away as we fall asleep? How is behaviour affected? These questions will help us understand the organizing principles in the brain and functions of sleep itself. Here I use two distant animal models, the richness in behaviour and complexity of the human brain to understand how alertness transitions affects attention; and the experimental flexibility of the fruit fly, to understand its effect over longer time intervals. I first develop an objective method to track alertness using Electroencephalography (EEG). Then, I investigate the behavioural dynamics using an auditory spatial attention task while participants fall asleep. By using multilevel modelling and psychophysics, I show that participants systematically misclassify tones from the left side when drowsy, and further with a hierarchical drift diffusion model (HDDM) show how drift-rate (evidence accumulation) explains errors. Then, I show convergent evidence in the neural dynamics using multivariate pattern analysis (MVPA). Next, I probe the effect of handedness on the same task. Handedness affects behaviour only under drowsy condition and I show how neural dynamics are affected by a combination of handedness and alertness. To approach alertness transitions in a system with reduced neural complexity, I explore those dynamics in the fruit fly (Drosophila melanogaster), using both single and multichannel local field potential (LFP) data to show how alertness transitions and sleep modulate different regions of the fly brain. Further, I validate the results by converging evidence from causal manipulations. Finally, I discuss how the mapping of alertness transitions -under natural conditions- can help us understand fundamental questions in neuroscience such as the functions of sleep or the mechanisms of general anaesthesia.Gates Cambridg

Representations of Reward and Movement in Drosophila Dopaminergic Neurons

The neuromodulator dopamine is known to influence both immediate and future behavior, motivating and invigorating an animal’s ongoing movement but also serving as a reinforcement signal to instruct learning. Yet it remains unclear whether this dual role of dopamine involves the same dopaminergic pathways. Although reward-responsive dopaminergic neurons display movement-related activity, debate continues as to what features of an individual’s experience these motor-correlates correspond and how they influence concurrent behavior. The mushroom body, a prominent neuropil in the brain of the fruit fly Drosophila melanogaster, is richly innervated by dopaminergic neurons that play an essential role in the formation of olfactory associations. While dopaminergic neurons respond to reward and punishment to drive associative learning, they have also been implicated in a number of adaptive behaviors and their activity correlates with the behavioral state of an animal and its coarse motor actions. Here, we take advantage of the concise circuit architecture of the Drosophila mushroom body to investigate the nature of motor-related signals in dopaminergic neurons that drive associative learning. In vivo functional imaging during naturalistic tethered locomotion reveals that the activity of different subsets of mushroom body dopaminergic neurons reflects distinct aspects of movement. To gain insight into what facets of an animal’s experience are represented by these movement-related signals, we employed a closed loop virtual reality paradigm to monitor neural activity as animals track an olfactory stimulus and are actively engaged in a goal-directed and sensory-motivated behavior. We discover that odor responses in dopaminergic neurons correlate with the extent to which an animal tracks upwind towards the fictive odor source. In different experimental contexts where distinct motor actions were required to track the odor, dopaminergic neurons become emergently linked to the behavioral metric most relevant for effective olfactory navigation. Subsets of dopaminergic neurons were correlated with the strength of upwind tracking regardless of the identity of the odor and remained so even after the satiety state of an animal was altered. We proceed to demonstrate that transient inhibition of dopaminergic neurons that are positively correlated with upwind tracking significantly diminishes the normal approach responses to an appetitive olfactory cue. Accordingly, activation of those same dopaminergic neurons enhances approach to an odor and even drives upwind tracking in clean air alone. Together, these results reveal that the same dopaminergic pathways that convey reinforcements to instruct learning also carry representations of an animal’s moment-by-moment movements and actively influence behavior. The complex activity patterns of mushroom body dopaminergic neurons therefore represent neither purely sensory nor motor variables but rather reflect the goal or motivation underlying an animal’s movements. Our data suggest a fundamental coupling between reinforcement signals and motivation-related locomotor representations within dopaminergic circuitry, drawing a striking parallel between the mushroom body dopaminergic neurons described here and the emerging understanding of mammalian dopaminergic pathways. The apparent conservation in dopaminergic neuromodulatory networks between mammals and insects suggests a shared logic for how neural circuits assign meaning to both sensory stimuli and motor actions to generate flexible and adaptive behavior

Functional mechanisms of stimulus-specific adaptation and deviance detection in the auditory pathway

Tesis por compendio de publicaciones[ES]En resumen, esta Tesis Doctoral demuestra que la SSA es un mecanismo presente en el cerebro del mamífero y que no se trata de un artefacto generado por la anestesia. Muestra además que la SSA es un mecanismo que puede explicarse perfectamente, a nivel subcortical, por el modelo de los canales de frecuencia. La existencia de controles de ganancia consecutivos ejercidos por el sistema GABAérgico sugiere también la presencia de varios niveles jerárquicos de procesamiento que ayudan a refinar y reducir la información redundante. En conjunto, la SSA parece ser un mecanismo que actúa como filtro preatentivo reduciendo las señales sensoriales irrelevantes, ayudando a los animales a presentar respuestas adecuadas para facilitar su supervivencia

A tweaking principle for executive control: neuronal circuit mechanism for rule-based task switching and conflict resolution

[EN] A hallmark of executive control is the brain's agility to shift between different tasks depending on the behavioral rule currently in play. In this work, we propose a "tweaking hypothesis" for task switching: a weak rule signal provides a small bias that is dramatically amplified by reverberating attractor dynamics in neural circuits for stimulus categorization and action selection, leading to an all-or-none reconfiguration of sensory-motor mapping. Based on this principle, we developed a biologically realistic model with multiple modules for task switching. We found that the model quantitatively accounts for complex task switching behavior: switch cost, congruency effect, and task-response interaction; as well as monkey's single-neuron activity associated with task switching. The model yields several testable predictions, in particular, that category-selective neurons play a key role in resolving sensory-motor conflict. This work represents a neural circuit model for task switching and sheds insights in the brain mechanism of a fundamental cognitive capability.This work was supported by the Office of Naval Research Grant N00014-13-1-0297, The Swartz Foundation Fellowship (S.A.), and John Simon Guggenheim Foundation Fellowship (X.-J.W.). We thank T.A. Engel for fruitful discussions, and A. Compte, J.B. Morton, W. Wei, and T. Womelsdorf for comments on a previous version of the paper. We also thank the reviewers for their thoughtful comments and suggestions.Ardid-Ramírez, JS.; Wang, X. (2013). A tweaking principle for executive control: neuronal circuit mechanism for rule-based task switching and conflict resolution. Journal of Neuroscience. 33(50):19504-19517. https://doi.org/10.1523/JNEUROSCI.1356-13.2013S1950419517335

Statistics of Neuronal Identification with Open- and Closed-Loop Measures of Intrinsic Excitability

In complex nervous systems patterns of neuronal activity and measures of intrinsic neuronal excitability are often used as criteria for identifying and/or classifying neurons. We asked how well identification of neurons by conventional measures of intrinsic excitability compares with a measure of neuronal excitability derived from a neuron’s behavior in a dynamic clamp constructed two-cell network. We used four cell types from the crab stomatogastric ganglion: the pyloric dilator, lateral pyloric, gastric mill, and dorsal gastric neurons. Each neuron was evaluated for six conventional measures of intrinsic excitability (intrinsic properties, IPs). Additionally, each neuron was coupled by reciprocal inhibitory synapses made with the dynamic clamp to a Morris–Lecar model neuron and the resulting network was assayed for four measures of network activity (network activity properties, NAPs). We searched for linear combinations of IPs that correlated with each NAP, and combinations of NAPs that correlated with each IP. In the process we developed a method to correct for multiple correlations while searching for correlating features. When properly controlled for multiple correlations, four of the IPs were correlated with NAPs, and all four NAPs were correlated with IPs. Neurons were classified into cell types by training a linear classifier on sets of properties, or using k-medoids clustering. The IPs were modestly successful in classifying the neurons, and the NAPs were more successful. Combining the two measures did better than either measure alone, but not well enough to classify neurons with perfect accuracy, thus reiterating that electrophysiological measures of single-cell properties alone are not sufficient for reliable cell identification

A combined experimental and computational approach to investigate emergent network dynamics based on large-scale neuronal recordings

Sviluppo di un approccio integrato computazionale-sperimentale per lo studio di reti neuronali mediante registrazioni elettrofisiologich

Identifying Changes of Functional Brain Networks using Graph Theory

This thesis gives an overview on how to estimate changes in functional brain networks using graph theoretical measures. It explains the assessment and definition of functional brain networks derived from fMRI data. More explicitly, this thesis provides examples and newly developed methods on the measurement and visualization of changes due to pathology, external electrical stimulation or ongoing internal thought processes. These changes can occur on long as well as on short time scales and might be a key to understanding brain pathologies and their development. Furthermore, this thesis describes new methods to investigate and visualize these changes on both time scales and provides a more complete picture of the brain as a dynamic and constantly changing network.:1 Introduction 1.1 General Introduction 1.2 Functional Magnetic Resonance Imaging 1.3 Resting-state fMRI 1.4 Brain Networks and Graph Theory 1.5 White-Matter Lesions and Small Vessel Disease 1.6 Transcranial Direct Current Stimulation 1.7 Dynamic Functional Connectivity 2 Publications 2.1 Resting developments: a review of fMRI post-processing methodologies for spontaneous brain activity 2.2 Early small vessel disease affects fronto-parietal and cerebellar hubs in close correlation with clinical symptoms - A resting-state fMRI study 2.3 Dynamic modulation of intrinsic functional connectivity by transcranial direct current stimulation 2.4 Three-dimensional mean-shift edge bundling for the visualization of functional connectivity in the brain 2.5 Dynamic network participation of functional connectivity hubs assessed by resting-state fMRI 3 Summary 4 Bibliography 5. Appendix 5.1 Erklärung über die eigenständige Abfassung der Arbeit 5.2 Curriculum vitae 5.3 Publications 5.4 Acknowledgement