Modular Toolkit for Data Processing (MDP): A Python Data Processing Framework

Modular toolkit for Data Processing (MDP) is a data processing framework written in Python. From the user's perspective, MDP is a collection of supervised and unsupervised learning algorithms and other data processing units that can be combined into data processing sequences and more complex feed-forward network architectures. Computations are performed efficiently in terms of speed and memory requirements. From the scientific developer's perspective, MDP is a modular framework, which can easily be expanded. The implementation of new algorithms is easy and intuitive. The new implemented units are then automatically integrated with the rest of the library. MDP has been written in the context of theoretical research in neuroscience, but it has been designed to be helpful in any context where trainable data processing algorithms are used. Its simplicity on the user's side, the variety of readily available algorithms, and the reusability of the implemented units make it also a useful educational tool

Reinforcement Learning on Slow Features of High-Dimensional Input Streams

Humans and animals are able to learn complex behaviors based on a massive stream of sensory information from different modalities. Early animal studies have identified learning mechanisms that are based on reward and punishment such that animals tend to avoid actions that lead to punishment whereas rewarded actions are reinforced. However, most algorithms for reward-based learning are only applicable if the dimensionality of the state-space is sufficiently small or its structure is sufficiently simple. Therefore, the question arises how the problem of learning on high-dimensional data is solved in the brain. In this article, we propose a biologically plausible generic two-stage learning system that can directly be applied to raw high-dimensional input streams. The system is composed of a hierarchical slow feature analysis (SFA) network for preprocessing and a simple neural network on top that is trained based on rewards. We demonstrate by computer simulations that this generic architecture is able to learn quite demanding reinforcement learning tasks on high-dimensional visual input streams in a time that is comparable to the time needed when an explicit highly informative low-dimensional state-space representation is given instead of the high-dimensional visual input. The learning speed of the proposed architecture in a task similar to the Morris water maze task is comparable to that found in experimental studies with rats. This study thus supports the hypothesis that slowness learning is one important unsupervised learning principle utilized in the brain to form efficient state representations for behavioral learning

Comparison between measured tissue phantom ratio values and calculated from percent depth doses with and without peak scatter correction factor in a 6 MV beam

The purpose of this study is to examine the accuracy of calculated tissue phantom ratio (TPR) data with measured TPR values of a 6MV photon beam. TPR was calculated from the measured percent depth dose (PDD) values using 2 methods – with and without correcting for the differences in peak scatter fraction (PSF). Mean error less than 1% was observed between the measured and calculated TPR values with the PSF correction, for all clinically relevant field sizes and depths. When not accounting for the PSF correction, mean difference between the measured and calculated TPR values was larger than 1% for square field sizes ranging from 3 cm to 10 cm

Commissioning and cross-comparison of four scanning water tanks

Purpose: Water scanning systems are commonly used for data collection to characterize dosimetric properties of photon and electron beams, and the commissioning of such systems has been previously described. The aim in this study, however, was to investigate tank-specific dependencies as well as conduct a dosimetric comparison between four distinct water scanning systems.Methods: Four water scanning systems were studied including the PTW MP3-M Phantom Tank, the Standard Imaging DoseView 3D, the IBA Blue Phantom, and the Sun Nuclear 3D Scanner. Mechanical accuracy and reproducibility was investigated by driving the chamber holder to nominal positions relative to a zero point and using a leveled caliper with 30 cm range to measure the actual position. Dosimetric measurements were also performed not only to compare percent-depth-dose (PDD) curves and profiles between tanks but also to assess dependencies such as directionality, scanning speed, and reproducibility for each tank individually. A PTW Semiflex 31010 ionization chamber with a sensitive volume of 0.125 cc was used at a Varian Clinac 2300 linear accelerator.Results: Mechanical precision was ensured to within 0.1 mm with the standard deviation (SD) of reproducibility <0.1 mm for measurements made with calipers. Dependencies on scanning direction and speed are presented. 6 MV PDDs between tanks agreed to within 0.6% relative to an averaged PDD beyond dmax and within 2.5% in the build-up region. Specifically, the maximum difference was 1.0% between MP3-M and Blue Phantom at 6.1 cm depth. Lateral profiles agreed between tanks within 0.5% in the central 80% of the field. 6 MeV PDD maximum difference was 1.3% occurring at the steepest portion, where the R50 was nevertheless within 0.6 mm across tanks. Setup uncertainties estimated at ≤1 mm are presumed to have contributed some of the difference between water tank data.Conclusion: Modern water scanning systems have achieved high accuracy across vendors, but commissioning tests nevertheless reveal tank-specific dependencies. This study not only ensures confidence in the individual systems but also provides the medical physicist with an understanding of variation in water tank properties between vendors

Commissioning and cross-comparison of four scanning water tanks

Purpose: Water scanning systems are commonly used for data collection to characterize dosimetric properties of photon and electron beams, and the commissioning of such systems has been previously described. The aim in this study, however, was to investigate tank-specific dependencies as well as conduct a dosimetric comparison between four distinct water scanning systems.Methods: Four water scanning systems were studied including the PTW MP3-M Phantom Tank, the Standard Imaging DoseView 3D, the IBA Blue Phantom, and the Sun Nuclear 3D Scanner. Mechanical accuracy and reproducibility was investigated by driving the chamber holder to nominal positions relative to a zero point and using a leveled caliper with 30 cm range to measure the actual position. Dosimetric measurements were also performed not only to compare percent-depth-dose (PDD) curves and profiles between tanks but also to assess dependencies such as directionality, scanning speed, and reproducibility for each tank individually. A PTW Semiflex 31010 ionization chamber with a sensitive volume of 0.125 cc was used at a Varian Clinac 2300 linear accelerator.Results: Mechanical precision was ensured to within 0.1 mm with the standard deviation (SD) of reproducibility &lt;0.1 mm for measurements made with calipers. Dependencies on scanning direction and speed are presented. 6 MV PDDs between tanks agreed to within 0.6% relative to an averaged PDD beyond dmax and within 2.5% in the build-up region. Specifically, the maximum difference was 1.0% between MP3-M and Blue Phantom at 6.1 cm depth. Lateral profiles agreed between tanks within 0.5% in the central 80% of the field. 6 MeV PDD maximum difference was 1.3% occurring at the steepest portion, where the R50 was nevertheless within 0.6 mm across tanks. Setup uncertainties estimated at ≤1 mm are presumed to have contributed some of the difference between water tank data.Conclusion: Modern water scanning systems have achieved high accuracy across vendors, but commissioning tests nevertheless reveal tank-specific dependencies. This study not only ensures confidence in the individual systems but also provides the medical physicist with an understanding of variation in water tank properties between vendors.</p

Hierarchical Slow Feature Analysis on visual stimuli and top-down reconstruction

In dieser Dissertation wird ein Modell des visuellen Systems untersucht, basierend auf dem Prinzip des unüberwachten Langsamkeitslernens und des SFA-Algorithmus (Slow Feature Analysis). Dieses Modell wird hier für die invariante Objekterkennung und verwandte Probleme eingesetzt. Das Modell kann dabei sowohl die zu Grunde liegenden diskreten Variablen der Stimuli extrahieren (z.B. die Identität des gezeigten Objektes) als auch kontinuierliche Variablen (z.B. Position und Rotationswinkel). Dabei ist es in der Lage, mit komplizierten Transformationen umzugehen, wie beispielsweise Tiefenrotation. Die Leistungsfähigkeit des Modells wird zunächst mit Hilfe von überwachten Methoden zur Datenanalyse untersucht. Anschließend wird gezeigt, dass auch die biologisch fundierte Methode des Verstärkenden Lernens (reinforcement learning) die Ausgabedaten unseres Modells erfolgreich verwenden kann. Dies erlaubt die Anwendung des Verstärkenden Lernens auf hochdimensionale visuelle Stimuli. Im zweiten Teil der Arbeit wird versucht, das hierarchische Modell mit Top-down Prozessen zu erweitern, speziell für die Rekonstruktion von visuellen Stimuli. Dabei setzen wir die Methode der Vektorquantisierung ein und verbinden diese mit einem Verfahren zum Gradientenabstieg. Die wesentlichen Komponenten der für unsere Simulationen entwickelten Software wurden in eine quelloffene Programmbibliothek integriert, in das ``Modular toolkit for Data Processing'''' (MDP). Diese Programmkomponenten werden im letzten Teil der Dissertation vorgestellt.This thesis examines a model of the visual system, which is based on the principle of unsupervised slowness learning and using Slow Feature Analysis (SFA). We apply this model to the task of invariant object recognition and several related problems. The model not only learns to extract the underlying discrete variables of the stimuli (e.g., identity of the shown object) but also to extract continuous variables (e.g., position and rotational angles). It is shown to be capable of dealing with complex transformations like in-depth rotation. The performance of the model is first measured with the help of supervised post-processing methods. We then show that biologically motivated methods like reinforcement learning are also capable of processing the high-level output from the model. This enables reinforcement learning to deal with high-dimensional visual stimuli. In the second part of this thesis we try to extend the model with top-down processes, centered around the task of reconstructing visual stimuli. We utilize the method of vector quantization and combine it with gradient descent. The key components of our simulation software have been integrated into an open-source software library, the Modular toolkit for Data Processing (MDP). These components are presented in the last part of the thesis

Comparison between measured tissue phantom ratio values and calculated from percent depth doses with and without peak scatter correction factor in a 6 MV beam

The purpose of this study is to examine the accuracy of calculated tissue phantom ratio (TPR) data with measured TPR values of a 6MV photon beam. TPR was calculated from the measured percent depth dose (PDD) values using 2 methods – with and without correcting for the differences in peak scatter fraction (PSF). Mean error less than 1% was observed between the measured and calculated TPR values with the PSF correction, for all clinically relevant field sizes and depths. When not accounting for the PSF correction, mean difference between the measured and calculated TPR values was larger than 1% for square field sizes ranging from 3 cm to 10 cm

Slow Feature Analysis on Retinal Waves Leads to V1 Complex Cells

<div><p>The developing visual system of many mammalian species is partially structured and organized even before the onset of vision. Spontaneous neural activity, which spreads in waves across the retina, has been suggested to play a major role in these prenatal structuring processes. Recently, it has been shown that when employing an efficient coding strategy, such as sparse coding, these retinal activity patterns lead to basis functions that resemble optimal stimuli of simple cells in primary visual cortex (V1). Here we present the results of applying a coding strategy that optimizes for temporal slowness, namely Slow Feature Analysis (SFA), to a biologically plausible model of retinal waves. Previously, SFA has been successfully applied to model parts of the visual system, most notably in reproducing a rich set of complex-cell features by training SFA with quasi-natural image sequences. In the present work, we obtain SFA units that share a number of properties with cortical complex-cells by training on simulated retinal waves. The emergence of two distinct properties of the SFA units (phase invariance and orientation tuning) is thoroughly investigated via control experiments and mathematical analysis of the input-output functions found by SFA. The results support the idea that retinal waves share relevant temporal and spatial properties with natural visual input. Hence, retinal waves seem suitable training stimuli to learn invariances and thereby shape the developing early visual system such that it is best prepared for coding input from the natural world.</p></div