Integration of Leaky-Integrate-and-Fire-Neurons in Deep Learning Architectures

Up to now, modern Machine Learning is mainly based on fitting high dimensional functions to enormous data sets, taking advantage of huge hardware resources. We show that biologically inspired neuron models such as the Leaky-Integrate-and-Fire (LIF) neurons provide novel and efficient ways of information encoding. They can be integrated in Machine Learning models, and are a potential target to improve Machine Learning performance. Thus, we derived simple update-rules for the LIF units from the differential equations, which are easy to numerically integrate. We apply a novel approach to train the LIF units supervisedly via backpropagation, by assigning a constant value to the derivative of the neuron activation function exclusively for the backpropagation step. This simple mathematical trick helps to distribute the error between the neurons of the pre-connected layer. We apply our method to the IRIS blossoms image data set and show that the training technique can be used to train LIF neurons on image classification tasks. Furthermore, we show how to integrate our method in the KERAS (tensorflow) framework and efficiently run it on GPUs. To generate a deeper understanding of the mechanisms during training we developed interactive illustrations, which we provide online. With this study we want to contribute to the current efforts to enhance Machine Intelligence by integrating principles from biology

Sparsity through evolutionary pruning prevents neuronal networks from overfitting

Modern Machine learning techniques take advantage of the exponentially rising calculation power in new generation processor units. Thus, the number of parameters which are trained to resolve complex tasks was highly increased over the last decades. However, still the networks fail - in contrast to our brain - to develop general intelligence in the sense of being able to solve several complex tasks with only one network architecture. This could be the case because the brain is not a randomly initialized neural network, which has to be trained by simply investing a lot of calculation power, but has from birth some fixed hierarchical structure. To make progress in decoding the structural basis of biological neural networks we here chose a bottom-up approach, where we evolutionarily trained small neural networks in performing a maze task. This simple maze task requires dynamical decision making with delayed rewards. We were able to show that during the evolutionary optimization random severance of connections lead to better generalization performance of the networks compared to fully connected networks. We conclude that sparsity is a central property of neural networks and should be considered for modern Machine learning approaches

An Interactive Framework for Teaching Viscoelastic Modeling

Rheologic models consisting of combinations of linear elements, such as springs and dashpots, are widely used in biophysics to describe the mechanical and, in particular, the viscoelastic behavior of proteins, cells, tissue, and soft matter. Even simple arrangements with few elements often suffice to recapitulate the experimental data and to provide biophysical insights, making them an ideal subject for educational purposes. To provide students with an intuitive understanding of the mechanical behavior of spring and dashpot models, we describe a computer simulation tool, elastic viscous system simulator (ElViS), written in the JavaScript programming language for designing viscoelastic models via a graphical user interface and simulating the mechanical response to various inputs. As an example application, we designed a virtual laboratory course using ElViS that teaches the basic principles of viscoelastic modeling in a gamelike manner. We then surveyed 50 undergraduate students of a 1-semester course in biophysics who participated in the virtual laboratory course. Students felt that the course was a helpful addition to the lecture and that it improved learning success

Breast cancer cells adapt contractile forces to overcome steric hindrance

Cell migration through the extracellular matrix is governed by the interplay between cell-generated propulsion forces, adhesion forces, and resisting forces arising from the steric hindrance of the matrix. Steric hindrance in turn depends on matrix porosity, matrix deformability, cell size, and cell deformability. In this study, we investigate how cells respond to changes in steric hindrance that arise from altered cell mechanical properties. Specifically, we measure traction forces, cell morphology, and invasiveness of MDA-MB 231 breast cancer cells in three-dimensional collagen gels. To modulate cell mechanical properties, we either decrease nuclear deformability by twofold overexpression of the nuclear protein lamin A or we introduce into the cells stiff polystyrene beads with a diameter larger than the average matrix pore size. Despite this increase of steric hindrance, we find that cell invasion is only marginally inhibited, as measured by the fraction of motile cells and the mean invasion depth. To compensate for increased steric hindrance, cells employ two alternative strategies. Cells with higher nuclear stiffness increase their force polarity, whereas cells with large beads increase their net contractility. Under both conditions, the collagen matrix surrounding the cells stiffens dramatically and carries increased strain energy, suggesting that increased force polarity and increased net contractility are functionally equivalent strategies for overcoming an increased steric hindrance

The origin of traveling waves in an emperor penguin huddle

Abstract Emperor penguins breed during the Antarctic winter and have to endure temperatures as low as −50 °C and wind speeds of up to 200 km h−1. To conserve energy, they form densely packed huddles with a triangular lattice structure. Video recordings from previous studies revealed coordinated movements in regular wave-like patterns within these huddles. It is thought that these waves are triggered by individual penguins that locally disturb the huddle structure, and that the traveling wave serves to remove the lattice defects and restore order. The mechanisms that govern wave propagation are currently unknown, however. Moreover, it is unknown if the waves are always triggered by the same penguin in a huddle. Here, we present a model in which the observed wave patterns emerge from simple rules involving only the interactions between directly neighboring individuals, similar to the interaction rules found in other jammed systems, e.g. between cars in a traffic jam. Our model predicts that a traveling wave can be triggered by a forward step of any individual penguin located within a densely packed huddle. This prediction is confirmed by optical flow velocimetry of the video recordings of emperor penguins in their natural habitat

HDR Brachytherapy and SBRT as Bridging Therapy to Liver Transplantation in HCC Patients: A Single-Center Experience

Background: In the treatment of patients with HCC awaiting liver transplantation (LT), local ablative treatments (LAT) are available either for downstaging or as bridging treatment. We present our clinical experience with both available radiation-based techniques, brachytherapy (BT), and stereotactic body radiotherapy (SBRT). Methods: All patients diagnosed with HCC and who were treated with BT or SBRT at our institution between 2011 and 2018 were retrospectively reviewed. The current analysis included all patients who subsequently underwent LT. Results: A total of 14 patients (male=9; female=5) were evaluated. Seven underwent BT for bridging before LT, and seven were treated with SBRT. BT was performed with a prescribed dose of 1 × 15 Gy, while SBRT was applied with 37 Gy (65%-iso) in three fractions in six patients, and one patient was treated with 54 Gy (100%-iso) in nine fractions. The treatment was generally well tolerated. One case of grade 3 bleeding was reported after BT, and one case of liver failure occurred following SBRT. All patients underwent LT after a median time interval of 152 days (range 47–311) after BT and 202 days (range 44–775) following SBRT. In eight cases, no viable tumor was found in the explanted liver, while four liver specimens showed vital tumor. The median follow-up after SBRT was 41 months and 17 months following BT. Overall, no hepatic HCC recurrence occurred following LT. Conclusion: Both SBRT and BT are feasible and well tolerated as bridging to LT when applied with caution in patients with impaired liver function. Radiation-based treatments can close the gap for patients not suitable for other locally ablative treatment options

Aspectos socioeconômicos.

A cultura do mamão possui grande importância na fruticultura nacional, e o Brasil se destaca como segundo produtor mundial da fruta, com uma produção de 1,06 milhão de toneladas em uma área colhida de 26 mil hectares (FAO, 2017). [...]

The SBRT database initiative of the German Society for Radiation Oncology (DEGRO): patterns of care and outcome analysis of stereotactic body radiotherapy (SBRT) for liver oligometastases in 474 patients with 623 metastases

Background: The intent of this pooled analysis as part of the German society for radiation oncology (DEGRO)stereotactic body radiotherapy (SBRT) initiative was to analyze the patterns of care of SBRT for liver oligometastases and to derive factors influencing treated metastases control and overall survival in a large patient cohort. Methods: From 17 German and Swiss centers, data on all patients treated for liver oligometastases with SBRT since its introduction in 1997 has been collected and entered into a centralized database. In addition to patient and tumor characteristics, data on immobilization, image guidance and motion management as well as dose prescription and fractionation has been gathered. Besides dose response and survival statistics, time trends of the aforementioned variables have been investigated. Results: In total, 474 patients with 623 liver oligometastases (median 1 lesion/patient; range 1–4) have been collected from 1997 until 2015. Predominant histologies were colorectal cancer (n= 213 pts.; 300 lesions) and breast cancer (n= 57; 81 lesions). All centers employed an SBRT specific setup. Initially, stereotactic coordinates and CT simulation were used for treatment set-up (55%), but eventually were replaced by CBCT guidance (28%) or more recently robotic tracking (17%). High variance in fraction (fx) number (median 1 fx; range 1–13) and dose per fraction (median: 18.5 Gy; range 3–37.5 Gy) was observed, although median BED remained consistently high after an initial learning curve. Median follow-up time was 15 months; median overall survival after SBRT was 24 months. One- and 2-year treated metastases control rate of treated lesions was 77% and 64%; if maximum isocenter biological equivalent dose (BED) was greater than 150 Gy EQD2Gy, it increased to 83% and 70%, respectively. Besides radiation dose colorectal and breast histology and motion management methods were associated with improved treated metastases control