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

Internal structure of the fly elementary motion detector

Flies use visual motion information for flight control, stabilization and object tracking. However, information about local motion such as direction and velocity is not explicitly represented at the level of the retina but must be computed by subsequent motion detection circuitry. The output of these circuits can be recorded in large, direction-selective lobula plate tangential cells, that integrate over hundreds of elementary motion detectors. The computational structure of these detectors is best described by the Reichardt model, where the signals from two neighboring photoreceptors become multiplied after one of them has been delayed. However, the neural correlate of the Reichardt Detector, i.e. the identity, physiology and connectivity of the constituting cells, has escaped further characterization due to technical difficulties in recording from these small neurons. In this thesis, I investigated the internal structure of the fly motion detection circuit by a combination of electrophysiology, computer simulations and mathematical modeling. First, I studied the effect of the mean luminance on motion detection. I found that the response strength of lobula plate tangential cells strongly depends on stimulus contrast but barely changes as a function of mean luminance. Adaptation to a new mean luminance follows an exponential decay with a time constant of several hundred milliseconds. I next investigated the structural consequences of splitting the visual input into ON and OFF components, as recently discovered in the fruit fly. The original Reichardt Detector can be refined by incorporating these findings, giving rise to two alternative structures. The 4-Quadrant-Detector consists of four independent subunits of the Reichardt type, correlating ON with ON, OFF with OFF, ON with OFF and OFF with ON signals. In contrast, the 2-Quadrant-Detector consists of two subunits only, that correlate ON with ON and OFF with OFF signals. In order to distinguish between these two models, I first stimulated flies with apparent motion stimuli consisting of a sequence of two brightness steps at neighboring locations, while recording the motion detector output in lobula plate tangential cells of the blow fly. I found strongly direction-selective responses to ON-ON and OFF-OFF sequences, but also to ON-OFF and OFF-ON sequences. At first sight, these results seem to support the 4-Quadrant-Detector. However, I showed with simulations and an analytical treatment that the 2-Quadrant-Detector, when equipped with an appropriate preprocessing stage, is capable of reproducing such responses as well. Based on predictions from model simulations, I designed a new stimulus protocol consisting of a sequence of short brightness pulses instead of steps. For such stimuli, the 2-Quadrant-Detector does not produce significant responses to ON-OFF and OFF-ON sequences, in contrast to the 4-Quadrant-Detector. The corresponding recordings cannot be reconciled with the 4-Quadrant-Detector but are in good agreement with the 2-Quadrant-Detector. I therefore conclude that the internal structure of the y elementary motion detector consists of two non-interacting subunits for detecting ON and OFF motion, respectively. These results mark an important step in the ongoing dissection of the insect motion detection circuit by providing an updated model that better matches the structure and physiology of the corresponding neural hardware

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

Internal structure of the fly elementary motion detector

Flies use visual motion information for flight control, stabilization and object tracking. However, information about local motion such as direction and velocity is not explicitly represented at the level of the retina but must be computed by subsequent motion detection circuitry. The output of these circuits can be recorded in large, direction-selective lobula plate tangential cells, that integrate over hundreds of elementary motion detectors. The computational structure of these detectors is best described by the Reichardt model, where the signals from two neighboring photoreceptors become multiplied after one of them has been delayed. However, the neural correlate of the Reichardt Detector, i.e. the identity, physiology and connectivity of the constituting cells, has escaped further characterization due to technical difficulties in recording from these small neurons. In this thesis, I investigated the internal structure of the fly motion detection circuit by a combination of electrophysiology, computer simulations and mathematical modeling. First, I studied the effect of the mean luminance on motion detection. I found that the response strength of lobula plate tangential cells strongly depends on stimulus contrast but barely changes as a function of mean luminance. Adaptation to a new mean luminance follows an exponential decay with a time constant of several hundred milliseconds. I next investigated the structural consequences of splitting the visual input into ON and OFF components, as recently discovered in the fruit fly. The original Reichardt Detector can be refined by incorporating these findings, giving rise to two alternative structures. The 4-Quadrant-Detector consists of four independent subunits of the Reichardt type, correlating ON with ON, OFF with OFF, ON with OFF and OFF with ON signals. In contrast, the 2-Quadrant-Detector consists of two subunits only, that correlate ON with ON and OFF with OFF signals. In order to distinguish between these two models, I first stimulated flies with apparent motion stimuli consisting of a sequence of two brightness steps at neighboring locations, while recording the motion detector output in lobula plate tangential cells of the blow fly. I found strongly direction-selective responses to ON-ON and OFF-OFF sequences, but also to ON-OFF and OFF-ON sequences. At first sight, these results seem to support the 4-Quadrant-Detector. However, I showed with simulations and an analytical treatment that the 2-Quadrant-Detector, when equipped with an appropriate preprocessing stage, is capable of reproducing such responses as well. Based on predictions from model simulations, I designed a new stimulus protocol consisting of a sequence of short brightness pulses instead of steps. For such stimuli, the 2-Quadrant-Detector does not produce significant responses to ON-OFF and OFF-ON sequences, in contrast to the 4-Quadrant-Detector. The corresponding recordings cannot be reconciled with the 4-Quadrant-Detector but are in good agreement with the 2-Quadrant-Detector. I therefore conclude that the internal structure of the y elementary motion detector consists of two non-interacting subunits for detecting ON and OFF motion, respectively. These results mark an important step in the ongoing dissection of the insect motion detection circuit by providing an updated model that better matches the structure and physiology of the corresponding neural hardware

Effective influences in neuronal networks : attentional modulation of effective influences underlying flexible processing and how to measure them

Selective routing of information between brain areas is a key prerequisite for flexible adaptive behaviour. It allows to focus on relevant information and to ignore potentially distracting influences. Selective attention is a psychological process which controls this preferential processing of relevant information. The neuronal network structures and dynamics, and the attentional mechanisms by which this routing is enabled are not fully clarified. Based on previous experimental findings and theories, a network model is proposed which reproduces a range of results from the attention literature. It depends on shifting of phase relations between oscillating neuronal populations to modulate the effective influence of synapses. This network model might serve as a generic routing motif throughout the brain. The attentional modifications of activity in this network are investigated experimentally and found to employ two distinct channels to influence processing: facilitation of relevant information and independent suppression of distracting information. These findings are in agreement with the model and previously unreported on the level of neuronal populations. Furthermore, effective influence in dynamical systems is investigated more closely. Due to a lack of a theoretical underpinning for measurements of influence in non-linear dynamical systems such as neuronal networks, often unsuited measures are used for experimental data that can lead to erroneous conclusions. Based on a central theorem in dynamical systems, a novel theory of effective influence is developed. Measures derived from this theory are demonstrated to capture the time dependent effective influence and the asymmetry of influences in model systems and experimental data. This new theory holds the potential to uncover previously concealed interactions in generic non-linear systems studied in a range of disciplines, such as neuroscience, ecology, economy and climatology