A study of dependency features of spike trains through copulas

Simultaneous recordings from many neurons hide important information and the connections characterizing the network remain generally undiscovered despite the progresses of statistical and machine learning techniques. Discerning the presence of direct links between neuron from data is still a not completely solved problem. To enlarge the number of tools for detecting the underlying network structure, we propose here the use of copulas, pursuing on a research direction we started in [1]. Here, we adapt their use to distinguish different types of connections on a very simple network. Our proposal consists in choosing suitable random intervals in pairs of spike trains determining the shapes of their copulas. We show that this approach allows to detect different types of dependencies. We illustrate the features of the proposed method on synthetic data from suitably connected networks of two or three formal neurons directly connected or influenced by the surrounding network. We show how a smart choice of pairs of random times together with the use of empirical copulas allows to discern between direct and un-direct interactions

Contour Integration via Cortical Interactions in Visual Cortex

The visual system possesses a remarkable ability to group fragmented line segments into coherent contours and to segregate them from background. This process, known as contour integration, is critical to identifying object boundaries in complex visual scenes, and thus particularly important for performing shape discrimination, image segmentation and ultimately object recognition. Current evidence supports the idea that long-range horizontal connections in early visual cortex contribute to the process of contour integration, but the underling cortical circuitry, particularly the top-down feedback influence from higher visual areas, is not fully understood. Throughout the thesis, we took computational approaches to systematically examine how contour information is represented across the network of cortical areas and the circuitry by which this information is encoded. Three closely related projects, each having new methods development and hypothesis testing, were performed to analyze and interpret a very large set of neural data. The data set consists of recently acquired multi-electrode multi-unit spikes and local field potentials (LFPs) simultaneously recorded in visual areas V1 and V4 of monkeys performing a visual contour detection task. In the first project, well-established Granger causality measure was extended to the analysis of spiking trains data, which enabled us to quantify the causal interactions within and between areas V1 and V4. Our findings provided clear evidence that there is a top-down V4 feedback influence upon early visual area V1 during contour integration. In the second project, we investigated whether the contour signals in V1 are derived from feedback inputs alone, or whether they are mediated by an intimate interaction between feedback and horizontal connections within V1. Conditional causality measure was developed to dissect the respective contributions of V1 horizontal connections and V4 feedback to contour grouping. Our results suggest that feedback and lateral connections closely interact to mediate the contour integration process. In the third project, a novel Granger causality measure was proposed for the analysis of mixed neural data of spikes and LFP. Spikes and LFP are generated by separate sources with distinct signal characteristics. A joint analysis of spikes and LFP was performed to address the fundamental question about how contour regulates cortical communication between individual neurons and local network activity. The results conform to the general input-output relationship between LFP and spikes within an area. Importantly, we found that contour-related causality is only observed from spikes to LFP, but not in the opposite direction. These findings suggest that Granger causality from spikes to LFP, rather than that from LFP to spikes, carries contour-related information. Taken together, these results indicate that cortical interactions underlie contour integration, thus contribute to a better understanding of the cortical circuitry for parsing visual images and for sensory processing in general. Given the increasing use of multi-electrode recordings in multiple cortical areas, the methodology developed in this thesis should also have a broad impact.Ph.D., Biomedical Engineering -- Drexel University, 201