Information recovery from rank-order encoded images

The time to detection of a visual stimulus by the primate eye is recorded at 100 – 150ms. This near instantaneous recognition is in spite of the considerable processing required by the several stages of the visual pathway to recognise and react to a visual scene. How this is achieved is still a matter of speculation. Rank-order codes have been proposed as a means of encoding by the primate eye in the rapid transmission of the initial burst of information from the sensory neurons to the brain. We study the efficiency of rank-order codes in encoding perceptually-important information in an image. VanRullen and Thorpe built a model of the ganglion cell layers of the retina to simulate and study the viability of rank-order as a means of encoding by retinal neurons. We validate their model and quantify the information retrieved from rank-order encoded images in terms of the visually-important information recovered. Towards this goal, we apply the ‘perceptual information preservation algorithm’, proposed by Petrovic and Xydeas after slight modification. We observe a low information recovery due to losses suffered during the rank-order encoding and decoding processes. We propose to minimise these losses to recover maximum information in minimum time from rank-order encoded images. We first maximise information recovery by using the pseudo-inverse of the filter-bank matrix to minimise losses during rankorder decoding. We then apply the biological principle of lateral inhibition to minimise losses during rank-order encoding. In doing so, we propose the Filteroverlap Correction algorithm. To test the perfomance of rank-order codes in a biologically realistic model, we design and simulate a model of the foveal-pit ganglion cells of the retina keeping close to biological parameters. We use this as a rank-order encoder and analyse its performance relative to VanRullen and Thorpe’s retinal model

Fractals in the Nervous System: conceptual Implications for Theoretical Neuroscience

This essay is presented with two principal objectives in mind: first, to document the prevalence of fractals at all levels of the nervous system, giving credence to the notion of their functional relevance; and second, to draw attention to the as yet still unresolved issues of the detailed relationships among power law scaling, self-similarity, and self-organized criticality. As regards criticality, I will document that it has become a pivotal reference point in Neurodynamics. Furthermore, I will emphasize the not yet fully appreciated significance of allometric control processes. For dynamic fractals, I will assemble reasons for attributing to them the capacity to adapt task execution to contextual changes across a range of scales. The final Section consists of general reflections on the implications of the reviewed data, and identifies what appear to be issues of fundamental importance for future research in the rapidly evolving topic of this review

Neuronal computation on complex dendritic morphologies

When we think about neural cells, we immediately recall the wealth of electrical behaviour which, eventually, brings about consciousness. Hidden deep in the frequencies and timings of action potentials, in subthreshold oscillations, and in the cooperation of tens of billions of neurons, are synchronicities and emergent behaviours that result in high-level, system-wide properties such as thought and cognition. However, neurons are even more remarkable for their elaborate morphologies, unique among biological cells. The principal, and most striking, component of neuronal morphologies is the dendritic tree. Despite comprising the vast majority of the surface area and volume of a neuron, dendrites are often neglected in many neuron models, due to their sheer complexity. The vast array of dendritic geometries, combined with heterogeneous properties of the cell membrane, continue to challenge scientists in predicting neuronal input-output relationships, even in the case of subthreshold dendritic currents. In this thesis, we will explore the properties of neuronal dendritic trees, and how they alter and integrate the electrical signals that diffuse along them. After an introduction to neural cell biology and membrane biophysics, we will review Abbott's dendritic path integral in detail, and derive the theoretical convergence of its infinite sum solution. On certain symmetric structures, closed-form solutions will be found; for arbitrary geometries, we will propose algorithms using various heuristics for constructing the solution, and assess their computational convergences on real neuronal morphologies. We will demonstrate how generating terms for the path integral solution in an order that optimises convergence is non-trivial, and how a computationally-significant number of terms is required for reasonable accuracy. We will, however, derive a highly-efficient and accurate algorithm for application to discretised dendritic trees. Finally, a modular method for constructing a solution in the Laplace domain will be developed

G-CSC Report 2010

The present report gives a short summary of the research of the Goethe Center for Scientific Computing (G-CSC) of the Goethe University Frankfurt. G-CSC aims at developing and applying methods and tools for modelling and numerical simulation of problems from empirical science and technology. In particular, fast solvers for partial differential equations (i.e. pde) such as robust, parallel, and adaptive multigrid methods and numerical methods for stochastic differential equations are developed. These methods are highly adanvced and allow to solve complex problems.. The G-CSC is organised in departments and interdisciplinary research groups. Departments are localised directly at the G-CSC, while the task of interdisciplinary research groups is to bridge disciplines and to bring scientists form different departments together. Currently, G-CSC consists of the department Simulation and Modelling and the interdisciplinary research group Computational Finance

Computing the local field potential (LFP) from integrate-and-fire network models

Leaky integrate-and-fire (LIF) network models are commonly used to study how the spiking dynamics of neural networks changes with stimuli, tasks or dynamic network states. However, neurophysiological studies in vivo often rather measure the mass activity of neuronal microcircuits with the local field potential (LFP). Given that LFPs are generated by spatially separated currents across the neuronal membrane, they cannot be computed directly from quantities defined in models of point-like LIF neurons. Here, we explore the best approximation for predicting the LFP based on standard output from point-neuron LIF networks. To search for this best "LFP proxy", we compared LFP predictions from candidate proxies based on LIF network output (e.g, firing rates, membrane potentials, synaptic currents) with "ground-truth" LFP obtained when the LIF network synaptic input currents were injected into an analogous three-dimensional (3D) network model of multi-compartmental neurons with realistic morphology, spatial distributions of somata and synapses. We found that a specific fixed linear combination of the LIF synaptic currents provided an accurate LFP proxy, accounting for most of the variance of the LFP time course observed in the 3D network for all recording locations. This proxy performed well over a broad set of conditions, including substantial variations of the neuronal morphologies. Our results provide a simple formula for estimating the time course of the LFP from LIF network simulations in cases where a single pyramidal population dominates the LFP generation, and thereby facilitate quantitative comparison between computational models and experimental LFP recordings in vivo

Studies on the function of PRG2/PLPPR3 in neuron morphogenesis

Neuron development follows a multifaceted sequence of cell migration, polarisation, neurite elongation, branching, tiling, and pruning. The implementation of this sequence differs between neuronal cell types and even in individual neurons between sub-compartments such as dendrites and axons. Membrane proteins are at a prime position in neurons to couple extrinsic morphogenetic signals with their intrinsic responses to orchestrate this defined morphological progression. The Phospholipid phosphatase-related / Plasticity-related gene (PLPPR/PRG)-family comprises five neuron-enriched and developmentally regulated membrane proteins with functions in cellular morphogenesis. At the start of this project, no publication had characterised the function of PLPPR3/PRG2 during neuron development. The presented work describes PLPPR3 as an axon-enriched protein localising to the plasma membrane and internal membrane compartments of neurons. Mutagenesis studies in cell lines establish the plasma membrane localisation of PLPPR3 as a regulator of its function to increase filopodia density (Chapter 2). Furthermore, the generation of a Plppr3-/- mouse line using CRISPR/Cas9 genome editing techniques (Chapter 3) enabled characterising endogenous phenotypes of PLPPR3 in neurons. In primary neuronal cultures, PLPPR3 was found to specifically control branch formation in a pathway with the phosphatase PTEN, without altering the overall growth capacity of neurons (Chapter 4). Loss of PLPPR3 specifically reduced branches forming from filopodia without affecting the stability of branches. This precise characterisation of PLPPR3 function unravelled the existence of parallel, independent programs for branching morphogenesis that are utilised and implemented differentially in developing axons and dendrites (Chapter 5). Furthermore, this thesis establishes multiple tools to study PLPPR3, the membrane lipid phosphatidylinositol-trisphosphate, and neuron morphogenesis by providing molecular tools, protocols, and semi-automated and automated image analysis pipelines (Appendix Chapter 7) and discusses experiments to test, refine and extend models of PLPPR3 function (Chapter 6). In summary, this thesis generated and utilised several tools and a Plppr3-/- mouse model to characterise PLPPR3 as a specific regulator of neuron branching morphogenesis. This precise characterisation refined and expanded the understanding of axon-specific branching morphogenesis.Nervenzellen entwickeln ihre komplexe Morphologie durch das Zusammenwirken diverser molekularer Entwicklungs-Programme der Zellkörper-Migration, der Polarisierung und der Morphogenese durch Wachstum, Verzweigung, Stabilisierung und Koordinierung ihrer Neuriten. Dabei unterscheidet sich die exakte Implementierung zwischen Nervenzell-Typen und selbst innerhalb einzelner Zellen zwischen Axonen und Dendriten. Diese unterschiedliche Morphogenese wird dabei speziell durch Membranproteine stark beeinflusst, die durch ihre Präsenz an der Plasmamembran Zell-extrinsische Signale mit den Zell-intrinsischen Morphogeneseprogrammen verbinden und beeinflussen. Die Familie der Phospholipid phosphatase-related / Plasticity-related gene (PLPPR/PRG) Proteine umfasst fünf Nervenzell-spezifische Membranproteine mit Effekten auf die Morphologie von Zellen. Zu Beginn dieses Projektes hatte noch keine Studie die Funktion des Familienmitglieds PLPPR3/PRG2 in Nervenzellen untersucht. Diese Dissertation beschreibt die Lokalisation von PLPPR3 an der Plasmamembran und in Zell-internen Membranstrukturen von Nervenzellen. Experimente in Zellkultur zeigen eine erhöhte Filopodien-Dichte nach Überexpression von PLPPR3, Mutagenese-Studien deuten eine strikte Kontrolle der Plasmamembran-Lokalisation an (Kapitel 2). Die Generierung einer Plppr3 Knockout Mauslinie mittels CRISPR/Cas9 Genom-Modifizierung (Kapitel 3) erlaubte eine Charakterisierung der endogenen Funktion von PLPPR3 in Nervenzellen. In Primärzellkultur von Nervenzellen des murinen Hippocampus zeigte sich, dass PLPPR3 im Zusammenspiel mit der Phosphatase PTEN spezifisch die Verzweigung von Nervenzellen kontrolliert, ohne deren Wachstumspotential global zu verändern (Kapitel 4). Dadurch kann PLPPR3 als ein Schalter zwischen Verzweigung und Verlängerung eines Nervenzell-Fortsatzes agieren. Der Verlust von PLPPR3 verursachte reduzierte spezifisch die Anzahl an Verzweigungen, die aus Filopodien entstanden, ohne dabei die Stabilität dieser Verzweigungen zu beeinflussen. Die präzise Charakterisierung dieser Funktion von PLPPR3 deckte auf, dass Verzweigungen von Nervenzell-Fortsätzen durch voneinander unabhängige Entwicklungsprogramme ausgebildet und stabilisiert werden können (Kapitel 5). Diese Programme werden von Axonen und Dendriten in unterschiedlicher Weise eingesetzt. Zusätzlich etabliert diese Arbeit sowohl diverse molekulare Werkzeuge und Visualisierungs-Protokolle zur Analyse von PLPPR3 und dem Membranlipid Phosphatidylinositol-Trisphosphat, als auch automatisierte Quantifizierungssoftware zur Studie der Nervenzellmorphologie (Appendix-Kapitel 7). Abschließend entwickelt und verfeinert die Dissertation mögliche Modelle zur PLPPR3-Funktion und zeigt experimentelle Strategien auf, um diese Modelle besser charakterisieren zu können (Kapitel 6). Zusammenfassend wurden in dieser Promotionsarbeit diverse Experimental- und Analyse-Strategien und eine Plppr3-/- Mauslinie entwickelt und genutzt, um PLPPR3 als einen spezifischen Regulator der Nervenzell-Morphogenese zu etablieren. Diese präzise Charakterisierung des PLPPR3 Phänotyps erlaubte zusätzlich eine Verfeinerung und Erweiterung der Erkenntnisse zur Axon-spezifischen Entwicklung von Verzweigungen

Electrotonic signal processing in AII amacrine cells: compartmental models and passive membrane properties for a gap junction-coupled retinal neuron

Under embargo until: 14.06.2019Amacrine cells are critical for processing of visual signals, but little is known about their electrotonic structure and passive membrane properties. AII amacrine cells are multifunctional interneurons in the mammalian retina and essential for both rod- and cone-mediated vision. Their dendrites are the site of both input and output chemical synapses and gap junctions that form electrically coupled networks. This electrical coupling is a challenge for developing realistic computer models of single neurons. Here, we combined multiphoton microscopy and electrophysiological recording from dye-filled AII amacrine cells in rat retinal slices to develop morphologically accurate compartmental models. Passive cable properties were estimated by directly fitting the current responses of the models evoked by voltage pulses to the physiologically recorded responses, obtained after blocking electrical coupling. The average best-fit parameters (obtained at − 60 mV and ~ 25 °C) were 0.91 µF cm−2 for specific membrane capacitance, 198 Ω cm for cytoplasmic resistivity, and 30 kΩ cm2 for specific membrane resistance. We examined the passive signal transmission between the cell body and the dendrites by the electrotonic transform and quantified the frequency-dependent voltage attenuation in response to sinusoidal current stimuli. There was significant frequency-dependent attenuation, most pronounced for signals generated at the arboreal dendrites and propagating towards the soma and lobular dendrites. In addition, we explored the consequences of the electrotonic structure for interpreting currents in somatic, whole-cell voltage-clamp recordings. The results indicate that AII amacrines cannot be characterized as electrotonically compact and suggest that their morphology and passive properties can contribute significantly to signal integration and processing.acceptedVersio