Two-photon imaging and analysis of neural network dynamics

The glow of a starry night sky, the smell of a freshly brewed cup of coffee or the sound of ocean waves breaking on the beach are representations of the physical world that have been created by the dynamic interactions of thousands of neurons in our brains. How the brain mediates perceptions, creates thoughts, stores memories and initiates actions remains one of the most profound puzzles in biology, if not all of science. A key to a mechanistic understanding of how the nervous system works is the ability to analyze the dynamics of neuronal networks in the living organism in the context of sensory stimulation and behaviour. Dynamic brain properties have been fairly well characterized on the microscopic level of individual neurons and on the macroscopic level of whole brain areas largely with the help of various electrophysiological techniques. However, our understanding of the mesoscopic level comprising local populations of hundreds to thousands of neurons (so called 'microcircuits') remains comparably poor. In large parts, this has been due to the technical difficulties involved in recording from large networks of neurons with single-cell spatial resolution and near- millisecond temporal resolution in the brain of living animals. In recent years, two-photon microscopy has emerged as a technique which meets many of these requirements and thus has become the method of choice for the interrogation of local neural circuits. Here, we review the state-of-research in the field of two-photon imaging of neuronal populations, covering the topics of microscope technology, suitable fluorescent indicator dyes, staining techniques, and in particular analysis techniques for extracting relevant information from the fluorescence data. We expect that functional analysis of neural networks using two-photon imaging will help to decipher fundamental operational principles of neural microcircuits.Comment: 36 pages, 4 figures, accepted for publication in Reports on Progress in Physic

Neurofly 2008 abstracts : the 12th European Drosophila neurobiology conference 6-10 September 2008 Wuerzburg, Germany

This volume consists of a collection of conference abstracts

Evolution of swimming behaviors in nudibranch molluscs: A comparative analysis of neural circuitry

Behaviors are a product of underlying neural circuits, yet there is a paucity of mechanistic information about how nervous systems contribute to the repeated evolution of similar behaviors. Theoretical studies have predicted that the same behavioral output can be generated by neural circuits with different properties. Here, we test the theory in biological circuits by comparing the central pattern generator (CPG) circuits underlying swimming behaviors in nudibranchs (Mollusca, Gastropoda, Euthyneura, Nudipleura). In comparative studies of neural circuits, neurotransmitter content can serve as landmarks or molecular markers for neuron types. Here, we created a comprehensive map of GABA-immunoreactive neurons in six Nudipleura species. None of the known swim CPG neurons were GABA-ir, but they were located next to identifiable GABA-ir neurons/clusters. Despite strong conservation of the GABA-ergic system, there were differences, particularly in the buccal ganglia, which may represent adaptive changes. We applied our knowledge of neurotransmitter distribution along with morphological traits to identify the neuron type Si1 in Flabellina, a species that swims via whole body left-right (LR) flexions and in Tritonia, a dorsal-ventral (DV) swimming species. Si1 is a CPG member of the LR species Melibe, whereas its homologue in the LR species Dendronotus is not. In Flabellina, Si1 was part of the LR CPG and despite having similar synaptic connections as Flabellina and Melibe, Si1 in Tritonia was not part of its DV swim CPG. Side by side circuit comparison of Flabellina, Melibe and Dendronotus revealed different combinations of circuit architecture and modulation resulting in different circuit configurations for LR swimming. This includes differences in the role and activity pattern of Si1, sensitivity to curare and the effect of homologues of C2, a DV CPG neuron, on the LR motor pattern. These results collectively reveal three different circuit variations for generating the same behavior. It suggests that the neural substrate from which behaviors arise is phylogenetically constrained. While this neural substrate can be configured in multiple different ways to generate the same outcome, the possibilities are finite and, as seen here, similar structural and functional neural motifs are used in the evolution of these circuits

Intracellular transport driven by cytoskeletal motors: General mechanisms and defects

Cells are strongly out-of-equilibrium systems driven by continuous energy supply. They carry out many vital functions requiring active transport of various ingredients and organelles, some being small, others being large. The cytoskeleton, composed of three types of filaments, determines the shape of the cell and plays a role in cell motion. It also serves as a road network for the so-called cytoskeletal motors. These molecules can attach to a cytoskeletal filament, perform directed motion, possibly carrying along some cargo, and then detach. It is a central issue to understand how intracellular transport driven by molecular motors is regulated, in particular because its breakdown is one of the signatures of some neuronal diseases like the Alzheimer. We give a survey of the current knowledge on microtubule based intracellular transport. We first review some biological facts obtained from experiments, and present some modeling attempts based on cellular automata. We start with background knowledge on the original and variants of the TASEP (Totally Asymmetric Simple Exclusion Process), before turning to more application oriented models. After addressing microtubule based transport in general, with a focus on in vitro experiments, and on cooperative effects in the transportation of large cargos by multiple motors, we concentrate on axonal transport, because of its relevance for neuronal diseases. It is a challenge to understand how this transport is organized, given that it takes place in a confined environment and that several types of motors moving in opposite directions are involved. We review several features that could contribute to the efficiency of this transport, including the role of motor-motor interactions and of the dynamics of the underlying microtubule network. Finally, we discuss some still open questions.Comment: 74 pages, 43 figure

Computational Mechanisms of Face Perception

The intertwined history of artificial intelligence and neuroscience has significantly impacted their development, with AI arising from and evolving alongside neuroscience. The remarkable performance of deep learning has inspired neuroscientists to investigate and utilize artificial neural networks as computational models to address biological issues. Studying the brain and its operational mechanisms can greatly enhance our understanding of neural networks, which has crucial implications for developing efficient AI algorithms. Many of the advanced perceptual and cognitive skills of biological systems are now possible to achieve through artificial intelligence systems, which is transforming our knowledge of brain function. Thus, the need for collaboration between the two disciplines demands emphasis. It\u27s both intriguing and challenging to study the brain using computer science approaches, and this dissertation centers on exploring computational mechanisms related to face perception. Face recognition, being the most active artificial intelligence research area, offers a wealth of data resources as well as a mature algorithm framework. From the perspective of neuroscience, face recognition is an important indicator of social cognitive formation and neural development. The ability to recognize faces is one of the most important cognitive functions. We first discuss the problem of how the brain encodes different face identities. By using DNNs to extract features from complex natural face images and project them into the feature space constructed by dimension reduction, we reveal a new face code in the human medial temporal lobe (MTL), where neurons encode visually similar identities. On this basis, we discover a subset of DNN units that are selective for facial identity. These identity-selective units exhibit a general ability to discriminate novel faces. By establishing coding similarities with real primate neurons, our study provides an important approach to understanding primate facial coding. Lastly, we discuss the impact of face learning during the critical period. We identify a critical period during DNN training and systematically discuss the use of facial information by the neural network both inside and outside the critical period. We further provide a computational explanation for the critical period influencing face learning through learning rate changes. In addition, we show an alternative method to partially recover the model outside the critical period by knowledge refinement and attention shifting. Our current research not only highlights the importance of training orientation and visual experience in shaping neural responses to face features and reveals potential mechanisms for face recognition but also provides a practical set of ideas to test hypotheses and reconcile previous findings in neuroscience using computer methods

Genetic determination and layout rules of visual cortical architecture

The functional architecture of the primary visual cortex is set up by neurons that preferentially respond to visual stimuli with contours of a specific orientation in visual space. In primates and placental carnivores, orientation preference is arranged into continuous and roughly repetitive (iso-) orientation domains. Exceptions are pinwheels that are surrounded by all orientation preferences. The configuration of pinwheels adheres to quantitative species-invariant statistics, the common design. This common design most likely evolved independently at least twice in the course of the past 65 million years, which might indicate a functionally advantageous trait. The possible acquisition of environment-dependent functional traits by genes, the Baldwin effect, makes it conceivable that visual cortical architecture is partially or redundantly encoded by genetic information. In this conception, genetic mechanisms support the emergence of visual cortical architecture or even establish it under unfavorable environments. In this dissertation, I examine the capability of genetic mechanisms for encoding visual cortical architecture and mathematically dissect the pinwheel configuration under measurement noise as well as in different geometries. First, I theoretically explore possible roles of genetic mechanisms in visual cortical development that were previously excluded from theoretical research, mostly because the information capacity of the genome appeared too small to contain a blueprint for wiring up the cortex. For the first time, I provide a biologically plausible scheme for quantitatively encoding functional visual cortical architecture by genetic information that circumvents the alleged information bottleneck. Key ingredients for this mechanism are active transport and trans-neuronal signaling as well as joined dynamics of morphogens and connectome. This theory provides predictions for experimental tests and thus may help to clarify the relative importance of genes and environments on complex human traits. Second, I disentangle the link between orientation domain ensembles and the species-invariant pinwheel statistics of the common design. This examination highlights informative measures of pinwheel configurations for model benchmarking. Third, I mathematically investigate the susceptibility of the pinwheel configuration to measurement noise. The results give rise to an extrapolation method of pinwheel densities to the zero noise limit and provide an approximated analytical expression for confidence regions of pinwheel centers. Thus, the work facilitates high-precision measurements and enhances benchmarking for devising more accurate models of visual cortical development. Finally, I shed light on genuine three-dimensional properties of functional visual cortical architectures. I devise maximum entropy models of three-dimensional functional visual cortical architectures in different geometries. This theory enables the examination of possible evolutionary transitions between different functional architectures for which intermediate organizations might still exist

For a "nanomusic" - Sound nanomachines and elastic space-time: A transversal approach to music composition

This thesis describes my transversal approach to composition and underlines how structuralist psychoanalys is, Deleuze and Guattari’s philosophy, and areas of thinking in the nanosciences played a central role on my conceptions of space and time. My most original contribution lies in the endeavour to elaborate a malleable and evanescent musical matter in which micro-fluctuations exert their forces on macro-entities creating a musical space-time as elastic and mobile as that of the psychic unconscious. The pieces are conceived as cartographies of flux, fleeting constellations of sound particles whose migrations either interweave filaments in elastic and heterochronic textures or converge on an object. Small modular pendulums are the elementary figures of this ramified network. They connect to one another and permeate the musical matter. Time is multiple and pluridirectional. Jumps and rebounds, temporal gaps, annunciative anticipation and retroactive reverberation (Green), keep combining fragmentary elements which echo with one another and which allow the listener to perceive the work of time as a "compound of splinters", an assemblage of "quanta of memory", or a pure flow. Repetition has a paradoxical function, both favouring the identification of abstract gestures and their step-by-step mutation. Sound trajectories are envisaged as "pulsounds" and chains of "sonifiers", in reference to concepts of pulsional phenomena (Freud) or signifiers (Lacan). This intersection between psychic topology and sound topography also investigates the structural potential of relationships between humans and urban environments. The addition of electronics, the use of both instrumental and mechanical or urban sounds, materialize a transitional space (Winnicott) with ambiguous boundaries between inside and outside, a hybrid sound territory, a zone of indiscernibility (Deleuze) between mind and machine, scream and noise, the organic and the mechanical. These evolving sound diagrams refer to nanotechnology’s atom-by-atom engineering with its increasing crossing-over of animate and inanimate matters. Sound processes operate as quasi microscopic units grouped in transitory configurations under the action of forces,and with this perspective I have coined the term "nanomusic" to describe my musical project