How adaptation currents change threshold, gain and variability of neuronal spiking

Many types of neurons exhibit spike rate adaptation, mediated by intrinsic slow $\mathrm{K}^+$-currents, which effectively inhibit neuronal responses. How these adaptation currents change the relationship between in-vivo like fluctuating synaptic input, spike rate output and the spike train statistics, however, is not well understood. In this computational study we show that an adaptation current which primarily depends on the subthreshold membrane voltage changes the neuronal input-output relationship (I-O curve) subtractively, thereby increasing the response threshold. A spike-dependent adaptation current alters the I-O curve divisively, thus reducing the response gain. Both types of adaptation currents naturally increase the mean inter-spike interval (ISI), but they can affect ISI variability in opposite ways. A subthreshold current always causes an increase of variability while a spike-triggered current decreases high variability caused by fluctuation-dominated inputs and increases low variability when the average input is large. The effects on I-O curves match those caused by synaptic inhibition in networks with asynchronous irregular activity, for which we find subtractive and divisive changes caused by external and recurrent inhibition, respectively. Synaptic inhibition, however, always increases the ISI variability. We analytically derive expressions for the I-O curve and ISI variability, which demonstrate the robustness of our results. Furthermore, we show how the biophysical parameters of slow $\mathrm{K}^+$-conductances contribute to the two different types of adaptation currents and find that $\mathrm{Ca}^{2+}$-activated $\mathrm{K}^+$-currents are effectively captured by a simple spike-dependent description, while muscarine-sensitive or $\mathrm{Na}^+$-activated $\mathrm{K}^+$-currents show a dominant subthreshold component.Comment: 20 pages, 8 figures; Journal of Neurophysiology (in press

How adaptation currents and synaptic inhibition change threshold, gain and variability of neuronal spiking : From Twenty Second Annual Computational Neuroscience Meeting: CNS*2013 Paris, France. 13-18 July 2013

Published by BioMed Central Ladenbauer, Josef ; Augustin, Moritz ; Obermayer, Klaus : How adaptation currents and synaptic inhibition change threshold, gain and variability of neuronal spiking. - In: BMC Neuroscience. - ISSN 1471-2202 (online). - 14 (2013), suppl. 1, P299. - doi:10.1186/1471-2202-14-S1-P299

Low-dimensional spike rate models derived from networks of adaptive integrate-and-fire neurons : comparison and implementation

The spiking activity of single neurons can be well described by a nonlinear integrate-and-fire model that includes somatic adaptation. When exposed to fluctuating inputs sparsely coupled populations of these model neurons exhibit stochastic collective dynamics that can be effectively characterized using the Fokker-Planck equation. This approach, however, leads to a model with an infinite-dimensional state space and non-standard boundary conditions. Here we derive from that description four simple models for the spike rate dynamics in terms of low-dimensional ordinary differential equations using two different reduction techniques: one uses the spectral decomposition of the Fokker-Planck operator, the other is based on a cascade of two linear filters and a nonlinearity, which are determined from the Fokker-Planck equation and semi-analytically approximated. We evaluate the reduced models for a wide range of biologically plausible input statistics and find that both approximation approaches lead to spike rate models that accurately reproduce the spiking behavior of the underlying adaptive integrate-and-fire population. Particularly the cascade-based models are overall most accurate and robust, especially in the sensitive region of rapidly changing input. For the mean-driven regime, when input fluctuations are not too strong and fast, however, the best performing model is based on the spectral decomposition. The low-dimensional models also well reproduce stable oscillatory spike rate dynamics that are generated either by recurrent synaptic excitation and neuronal adaptation or through delayed inhibitory synaptic feedback. The computational demands of the reduced models are very low but the implementation complexity differs between the different model variants. Therefore we have made available implementations that allow to numerically integrate the low-dimensional spike rate models as well as the Fokker-Planck partial differential equation in efficient ways for arbitrary model parametrizations as open source software. The derived spike rate descriptions retain a direct link to the properties of single neurons, allow for convenient mathematical analyses of network states, and are well suited for application in neural mass/mean-field based brain network models.Characterizing the dynamics of biophysically modeled, large neuronal networks usually involves extensive numerical simulations. As an alternative to this expensive procedure we propose efficient models that describe the network activity in terms of a few ordinary differential equations. These systems are simple to solve and allow for convenient investigations of asynchronous, oscillatory or chaotic network states because linear stability analyses and powerful related methods are readily applicable. We build upon two research lines on which substantial efforts have been exerted in the last two decades: (i) the development of single neuron models of reduced complexity that can accurately reproduce a large repertoire of observed neuronal behavior, and (ii) different approaches to approximate the Fokker-Planck equation that represents the collective dynamics of large neuronal networks. We combine these advances and extend recent approximation methods of the latter kind to obtain spike rate models that surprisingly well reproduce the macroscopic dynamics of the underlying neuronal network. At the same time the microscopic properties are retained through the single neuron model parameters. To enable a fast adoption we have released an efficient Python implementation as open source software under a free license

Efficiency and timing performance of the MuPix7 high-voltage monolithic active pixel sensor

The MuPix7 is a prototype high voltage monolithic active pixel sensor with 103 times 80 um2 pixels thinned to 64 um and incorporating the complete read-out circuitry including a 1.25 Gbit/s differential data link. Using data taken at the DESY electron test beam, we demonstrate an efficiency of 99.3% and a time resolution of 14 ns. The efficiency and time resolution are studied with sub-pixel resolution and reproduced in simulations.Comment: 7 pages, 13 figures, submitted to Nucl.Instr.Meth.

Primary tumor–derived systemic nANGPTL4 inhibits metastasis

Primary tumors and distant site metastases form a bidirectionally communicating system. Yet, the molecular mechanisms of this crosstalk are poorly understood. Here, we identified the proteolytically cleaved fragments of angiopoietin-like 4 (ANGPTL4) as contextually active protumorigenic and antitumorigenic contributors in this communication ecosystem. Preclinical studies in multiple tumor models revealed that the C-terminal fragment (cANGPTL4) promoted tumor growth and metastasis. In contrast, the N-terminal fragment of ANGPTL4 (nANGPTL4) inhibited metastasis and enhanced overall survival in a postsurgical metastasis model by inhibiting WNT signaling and reducing vascularity at the metastatic site. Tracing ANGPTL4 and its fragments in tumor patients detected full-length ANGPTL4 primarily in tumor tissues, whereas nANGPTL4 predominated in systemic circulation and correlated inversely with disease progression. The study highlights the spatial context of the proteolytic cleavage-dependent pro- and antitumorigenic functions of ANGPTL4 and identifies and validates nANGPTL4 as a novel biomarker of tumor progression and antimetastatic therapeutic agent

MEDBERT.de: A Comprehensive German BERT Model for the Medical Domain

This paper presents medBERTde, a pre-trained German BERT model specifically designed for the German medical domain. The model has been trained on a large corpus of 4.7 Million German medical documents and has been shown to achieve new state-of-the-art performance on eight different medical benchmarks covering a wide range of disciplines and medical document types. In addition to evaluating the overall performance of the model, this paper also conducts a more in-depth analysis of its capabilities. We investigate the impact of data deduplication on the model's performance, as well as the potential benefits of using more efficient tokenization methods. Our results indicate that domain-specific models such as medBERTde are particularly useful for longer texts, and that deduplication of training data does not necessarily lead to improved performance. Furthermore, we found that efficient tokenization plays only a minor role in improving model performance, and attribute most of the improved performance to the large amount of training data. To encourage further research, the pre-trained model weights and new benchmarks based on radiological data are made publicly available for use by the scientific community.Comment: Keno K. Bressem and Jens-Michalis Papaioannou and Paul Grundmann contributed equall

International Consensus Conference for Advanced Breast Cancer, Lisbon 2019: ABC5 Consensus – Assessment by a German Group of Experts

The 5th International Consensus Conference for Advanced Breast Cancer (ABC5) took place on November 14–16, 2019, in Lisbon, Portugal. Its aim is to standardize the treatment of advanced breast cancer based on the available evidence and to ensure that all breast cancer patients worldwide receive adequate treatment and access to new therapies. This year, the conference focused on developments and study results in the treatment of patients with hormone receptor-positive/HER2-negative breast cancer as well as precision medicine. As in previous years, patient advocates from around the world were integrated into the ABC conference and had seats on the ABC consensus panel. In the present paper, a working group of German breast cancer experts comments on the results of the on-site ABC5 consensus votes by ABC panelists regarding their applicability for routine treatment in Germany. These comments take the recommendations of the Breast Committee of the Gynecological Oncology Working Group (Arbeitsgemeinschaft Gynäkologische Onkologie; AGO) into account. The report and assessment presented here pertain to the preliminary results of the ABC5 consensus. The final version of the statements will be published in Annals of Oncology and The Breast