Doctor of Philosophy

dissertationThe goal of this work is to construct a simulation toolset for studying and improving neuroprosthetic devices for restoring neural functionality to patients with neural disorders or diseases. This involves the construction and validation of coupled electromagnetic-neural computational models of retina and hippocampus, compiling knowledge from a broad multidisciplinary background into a single computational platform, with features specific to implant electronics, bulk tissue, cellular and neural network behavior, and diseased tissue. The application of a retina prosthetic device for restoring partial vision to patients blinded by degenerative diseases was first considered. This began with the conceptualization of the retina model, translating features of a connectome, implant electronics, and medical images into a computational model that was "degenerated." It was then applied to the design of novel electrode geometries towards increasing the resolution of induced visual percept, and of stimulation waveform shapes for increasing control of induced neural activity in diseased retina. Throughout this process, features of the simulation toolset itself were modified to increase the precision of the results, leading to a novel method for computing effective bulk resistivity for use in such multiscale modeling. This simulation strategy was then extended to the application of a hippocampus prosthetic device, which has been proposed to restore and/or enhance memory in patients with memory disorders such as Alzheimer's disease or dementia. Using this multiscale modeling approach, we are able to provide recommendations for electrode geometry, placement, and stimulation magnitude for increased safety and efficacy in future experimental trials. In attempt to model neural activity in dense hippocampal tissue, a simulation platform for considering the effects the electrical activity of neural networks have on the extracellular electric field, and therefore have on their neighboring cells, was constructed, further increasing the predictive ability of the proposed methodology for modeling electrical stimulation of neural tissue

General features of the retinal connectome determine the computation of motion anticipation

Motion anticipation allows the visual system to compensate for the slow speed of phototransduction so that a moving object can be accurately located. This correction is already present in the signal that ganglion cells send from the retina but the biophysical mechanisms underlying this computation are not known. Here we demonstrate that motion anticipation is computed autonomously within the dendritic tree of each ganglion cell and relies on feedforward inhibition. The passive and non-linear interaction of excitatory and inhibitory synapses enables the somatic voltage to encode the actual position of a moving object instead of its delayed representation. General rather than specific features of the retinal connectome govern this computation: an excess of inhibitory inputs over excitatory, with both being randomly distributed, allows tracking of all directions of motion, while the average distance between inputs determines the object velocities that can be compensated for

Simulating and optimizing electrical current flow and neuronal activation in retinal implants

© 2018 Dr Timothy Bede Esler[Abstract Withheld

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

Temporal Dynamics of Binocular Display Processing with Corticogeniculate Interactions

A neural model of binocular vision is developed to simulate psychophysical and neurobiological data concerning the dynamics of binocular disparity processing. The model shows how feedforward and feedback interactions among LGN ON and OFF cells and cortical simple, complex, and hypercomplex cells can simulate binocular summation, the Pulfrich effect, and the fusion of delayed anticorrelated stereograms. Model retinal ON and OFF cells are linked by an opponent process capable of generating antagonistic rebounds from OFF cells after offset of an ON cell input. Spatially displaced ON and OFF cells excite simple cells. Opposite polarity simple cells compete before their half-wave rectified outputs excite complex cells. Complex cells binocularly match like-polarity simple cell outputs before pooling half-wave rectified signals frorn opposite polarities. Competitive feedback among complex cells leads to sharpening of disparity selectivity and normalizes cell activity. Slow inhibitory interneurons help to reset complex cells after input offset. The Pulfrich effect occurs because the delayed input from the one eye fuses with the present input from the other eye to create a disparity. Binocular summation occurs for stimuli of brief duration or of low contrast because competitive normalization takes time, and cannot occur for very brief or weak stimuli. At brief SOAs, anticorrelatecd stereograms can be fused because the rebound mechanism ensures that the present image to one eye can fuse with the afterimage from a previous image to the other eye. Corticogeniculate feedback embodies a matching process that enhances the speed and temporal accuracy of complex cell disparity tuning. Model mechanisms interact to control the stable development of sharp disparity tuning.Air Force Office of Scientific Research (F19620-92-J-0499, F49620-92-J-0334, F49620-92-J-0225); Office of Naval Research (N00014-95-1-0409, N00014-95-l-0657, N00014-92-J-1015, N00014-91-J-4100

Retinal drug delivery: rethinking outcomes for the efficient replication of retinal behavior

The retina is a highly organized structure that is considered to be "an approachable part of the brain." It is attracting the interest of development scientists, as it provides a model neurovascular system. Over the last few years, we have been witnessing significant development in the knowledge of the mechanisms that induce the shape of the retinal vascular system, as well as knowledge of disease processes that lead to retina degeneration. Knowledge and understanding of how our vision works are crucial to creating a hardware-adaptive computational model that can replicate retinal behavior. The neuronal system is nonlinear and very intricate. It is thus instrumental to have a clear view of the neurophysiological and neuroanatomic processes and to take into account the underlying principles that govern the process of hardware transformation to produce an appropriate model that can be mapped to a physical device. The mechanistic and integrated computational models have enormous potential toward helping to understand disease mechanisms and to explain the associations identified in large model-free data sets. The approach used is modulated and based on different models of drug administration, including the geometry of the eye. This work aimed to review the recently used mathematical models to map a directed retinal network.The authors acknowledge the financial support received from the Portuguese Science and Technology Foundation (FCT/MCT) and the European Funds (PRODER/COMPETE) for the project UIDB/04469/2020 (strategic fund), co-financed by FEDER, under the Partnership Agreement PT2020. The authors also acknowledge FAPESP – São Paulo Research Foundation, for the financial support for the publication of the article.info:eu-repo/semantics/publishedVersio

Temporal Dynamics of Binocular Disparity Processing with Corticogeniculate Interactions

A neural model is developed to probe how corticogeniculate feedback may contribute to the dynamics of binocular vision. Feedforward and feedback interactions among retinal, lateral geniculate, and cortical simple and complex cells are used to simulate psychophysical and neurobiological data concerning the dynamics of binocular disparity processing, including correct registration of disparity in response to dynamically changing stimuli, binocular summation of weak stimuli, and fusion of anticorrelated stimuli when they are delayed, but not when they are simultaneous. The model exploits dynamic rebounds between opponent ON and OFF cells that are due to imbalances in habituative transmitter gates. It shows how corticogeniculate feedback can carry out a top-down matching process that inhibits incorrect disparity response and reduces persistence of previously correct responses to dynamically changing displays.Air Force Office of scientific Research (F49620-92-J-0499, F49620-92-J-0334, F49620-92-J-0225); Defense Advanced Research Projects Agency and the Office of Naval Research (N00014-95-1-0409, N00014-92-J-4015); Natioanl Science Foundation (IRI-97-20333); Office of Naval Research (N00014-95-0657

On the Origin of the Functional Architecture of the Cortex

The basic structure of receptive fields and functional maps in primary visual cortex is established without exposure to normal sensory experience and before the onset of the critical period. How the brain wires these circuits in the early stages of development remains unknown. Possible explanations include activity-dependent mechanisms driven by spontaneous activity in the retina and thalamus, and molecular guidance orchestrating thalamo-cortical connections on a fine spatial scale. Here I propose an alternative hypothesis: the blueprint for receptive fields, feature maps, and their inter-relationships may reside in the layout of the retinal ganglion cell mosaics along with a simple statistical connectivity scheme dictating the wiring between thalamus and cortex. The model is shown to account for a number of experimental findings, including the relationship between retinotopy, orientation maps, spatial frequency maps and cytochrome oxidase patches. The theory's simplicity, explanatory and predictive power makes it a serious candidate for the origin of the functional architecture of primary visual cortex

The Role of Non-Linearities in Visual Perception studied with a Computational Model of the Vertebrate Retina

Processing of visual stimuli in the vertebrate retina is complex and diverse. The retinal output to the higher centres of the nervous system, mediated by ganglion cells, consists of several different channels. Neurons in these channels can have very distinct response properties, which originate in different retinal pathways. In this work, the retinal origins and possible functional implications of the segregation of visual pathways will be investigated with a detailed, biologically realistic computational model of the retina. This investigation will focus on the two main retino-cortical pathways in the mammalian retina, the parvocellular and magnocellular systems, which are crucial for conscious visual perception. These pathways differ in two important aspects. The parvocellular system has a high spatial, but low temporal resolution. Conversely, the magnocellular system has a high temporal fidelity, spatial sampling however is less dense than for parvocellular cells. Additionally, the responses of magnocellular ganglion cells can show pronounced nonlinearities, while the parvocellular system is essentially linear. The origin of magnocellular nonlinearities is unknown and will be investigated in the first part of this work. As their main source, the results suggest specific properties of the photoreceptor response and a specialised amacrine cell circuit in the inner retina. The results further show that their effect combines in a multiplicative way. The model is then used to examine the influence of nonlinearities on the responses of ganglion cells in the presence of involuntary fixational eye movements. Two different stimulus conditions will be considered: visual hyperacuity and motion induced illusions. In both cases, it is possible to directly compare properties of the ganglion cell population response with psychophysical data, which allows for an analysis of the influence of different components of the retinal circuitry. The simulation results suggest an important role for nonlinearities in the magnocellular stream for visual perception in both cases. First, it will be shown how nonlinearities, triggered by fixational eye movements, can strongly enhance the spatial precision of magnocellular ganglion cells. As a result, their performance in a hyperacuity task can be equal to or even surpass that of the parvocellular system. Second, the simulations imply that the origin of some of the illusory percepts elicited by fixational eye movements could be traced back to the nonlinear properties of magnocellular ganglion cells. As these activity patterns strongly differ from those in the parvocellular system, it appears that the magnocellular system can strongly dominate visual perception in certain conditions. Taken together, the results of this theoretical study suggest that retinal nonlinearities may be important for and strongly influence visual perception. The model makes several experimentally verifiable predictions to further test and quantify these findings. Furthermore, models investigating higher visual processing stages may benefit from this work, which could provide the basis to produce realistic afferent input

Meeting at the Membrane – Confined Water at Cationic Lipids & Neuronal Growth on Fluid Lipid Bilayers: Meeting at the Membrane – Confined Water at Cationic Lipids &Neuronal Growth on Fluid Lipid Bilayers

Die Zellmembran dient der Zelle nicht nur als äußere Hülle, sondern ist auch an einer Vielzahl von lebenswichtigen Prozessen wie Signaltransduktion oder Zelladhäsion beteiligt. Wasser als integraler Bestandteil von Zellen und der extrazellulären Matrix hat sowohl einen großen Einfluss auf die Struktur von Biomolekülen, als auch selbst besondere Merkmale in eingschränkter Geometrie. Im Rahmen dieser Arbeit wurden zwei Effekte an Modellmembranen untersucht: Erstens der Einfluss des Gegenions an kationischen Lipiden (DODAX, X = F, Cl, Br, I) auf die Eigenschaften des Grenzflächenwassers und zweitens das Vermögen durch Viskositätsänderungen das Wachstum von Nervenzellen anzuregen sowie die einzelnen Stadien der Bildung von neuronalen Netzwerken und deren Optimierung zu charakterisieren. Lipidmultischichten und darin adsorbiertes Grenzflächenwasser wurden mittels Infrarotspektroskopie mit abgeschwächter Totalreflexion untersucht. Nach Charakterisierung von Phasenverhalten und Wasserkapazität der Lipide wurden die Eigenschaften des Wassers durch kontrollierte Hydratisierung bei einem Wassergehalt von einem Wassermolekül pro Lipid verglichen. Durch die geringe Wasserkapazität können in diesem besonderen System direkte Wechselwirkungen zwischen Lipiden und Wasser aus der ersten Hydratationsschale beobachtet werden. Bemerkenswert strukturierte OH-Streckschwingungsbanden in Abhängigkeit des Anions und niedrige IR-Ordnungsparameter zeigen, dass stark geordnete, in ihrer Mobilität eingeschränkte Wassermoleküle an DODAX in verschiedenen Populationen mit unterschiedlich starken Wasserstoffbrückenbindungen existieren und sich vermutlich in kleinen Clustern anordnen. Die zweite Fragestellung hatte zum Ziel, das Wachstum von Nervenzellen auf Membranen zu beleuchten. Auf der Ebene einzelner Zellen wurde untersucht, ob sich in Analogie zu den bisher verwendeten elastischen Substraten, die Viskosität von Membranen als neuartiger physikalischer Stimulus dafür eignet, das mechanosensitive Verhalten von Neuronen zu modulieren. Das Wachstum der Neuronen wurde auf substrat- und polymergestützten Lipiddoppelschichten mittels Phasenkontrastmikroskopie beobachtet. Die Quantifizierung der Neuritenlängen, -auswuchsgeschwindigkeiten und -verzweigungen zeigten kaum signifikante Unterschiede. Diffusionsmessungen (FRAP) ergaben, dass entgegen der Erwartungen, die Substrate sehr ähnliche Fluiditäten aufweisen. Die Betrachtung der zeitlichen Entwicklung des kollektiven Neuronenwachstums, also der Bildung von komplexen Netzwerken, offenbarte robuste „Kleine-Welt“-Eigenschaften und darüber hinaus unterschiedliche Stadien. Diese wurden durch graphentheoretische Analyse beschrieben, um anhand typischer Größen wie dem Clusterkoeffizienten und der kürzesten Pfadlänge zu zeigen, wie sich die Neuronen in einem frühen Stadium vernetzen, im Verlauf eine maximale Komplexität erreichen und letztlich das Netzwerk durch effiziente Umstrukturierung hinsichtlich kurzer Pfadlängen optimiert wird