Variational learning of quantum ground states on spiking neuromorphic hardware.

Recent research has demonstrated the usefulness of neural networks as variational ansatz functions for quantum many-body states. However, high-dimensional sampling spaces and transient autocorrelations confront these approaches with a challenging computational bottleneck. Compared to conventional neural networks, physical model devices offer a fast, efficient and inherently parallel substrate capable of related forms of Markov chain Monte Carlo sampling. Here, we demonstrate the ability of a neuromorphic chip to represent the ground states of quantum spin models by variational energy minimization. We develop a training algorithm and apply it to the transverse field Ising model, showing good performance at moderate system sizes ( ). A systematic hyperparameter study shows that performance depends on sample quality, which is limited by temporal parameter variations on the analog neuromorphic chip. Our work thus provides an important step towards harnessing the capabilities of neuromorphic hardware for tackling the curse of dimensionality in quantum many-body problems

Stochasticity from function -- why the Bayesian brain may need no noise

An increasing body of evidence suggests that the trial-to-trial variability of spiking activity in the brain is not mere noise, but rather the reflection of a sampling-based encoding scheme for probabilistic computing. Since the precise statistical properties of neural activity are important in this context, many models assume an ad-hoc source of well-behaved, explicit noise, either on the input or on the output side of single neuron dynamics, most often assuming an independent Poisson process in either case. However, these assumptions are somewhat problematic: neighboring neurons tend to share receptive fields, rendering both their input and their output correlated; at the same time, neurons are known to behave largely deterministically, as a function of their membrane potential and conductance. We suggest that spiking neural networks may, in fact, have no need for noise to perform sampling-based Bayesian inference. We study analytically the effect of auto- and cross-correlations in functionally Bayesian spiking networks and demonstrate how their effect translates to synaptic interaction strengths, rendering them controllable through synaptic plasticity. This allows even small ensembles of interconnected deterministic spiking networks to simultaneously and co-dependently shape their output activity through learning, enabling them to perform complex Bayesian computation without any need for noise, which we demonstrate in silico, both in classical simulation and in neuromorphic emulation. These results close a gap between the abstract models and the biology of functionally Bayesian spiking networks, effectively reducing the architectural constraints imposed on physical neural substrates required to perform probabilistic computing, be they biological or artificial

CoryneRegNet: An ontology-based data warehouse of corynebacterial transcription factors and regulatory networks

Baumbach J, Brinkrolf K, Czaja LF, Rahmann S, Tauch A. CoryneRegNet: An ontology-based data warehouse of corynebacterial transcription factors and regulatory networks. BMC Genomics. 2006;7(1): 24

Pre-hypertension: another ‘pseudodisease’?

Hypertension is one of the most important and common cardiovascular risk factors. Defining the level at which blood pressure starts causing end-organ damage is challenging, and is not easily answered. The threshold of blood pressure defining hypertension has progressively been reduced over time, from systolic >160 mmHg to >150 mmHg, then to >140 mmHg; and now even blood pressures above 130 to 120 mmHg are labeled as ‘pre-hypertension’ by some expert committees. Are interest groups creating another ‘pseudodisease’ or is this trend scientifically justified? A recent meta-analysis published in BMC Medicine by Huang et al. clearly indicates that pre-hypertension (120 to 140/80 to 90 mmHg) is a significant marker of increased cardiovascular risk. This raises the question as to whether we now need to lower the threshold of ‘hypertension’ (as opposed to ‘pre-hypertension’) to >120/80 mmHg, redefining a significant proportion of currently healthy people as ‘patients’ with an established disease. These data need to be interpreted with some caution. It is controversial whether pre-hypertension is an independent risk factor or just a risk marker and even more controversial whether treatment of pre-hypertension will lower cardiovascular risk

Almanac 2013: acute coronary syndromes

Nestabilni plak u koronarnim arterijama je najčešći uzrok akutnog koronarnog sindroma (AKS) koji se može manifestirati kao nestabilna angina, infarkt miokarda bez elevacije ST-segmenta i infarkt miokarda s elevacijom ST-segmenta (STEMI), ali se također može manifestirati i kao iznenadni srčani zastoj zbog ishemijom izazvane tahiaritmije. Smrtnost AKS je značajno smanjena u posljednjih nekoliko godina, posebice od njegovih najtežih manifestacija, STEMI i srčanog zastoja. Ovaj trend će se najvjerojatnije nastaviti zbog terapijskog napretka novijeg datuma koji uključuje i nove antitrombocitne lijekove kao što prasugrel, tikagrelor i kangrelor.Unstable coronary artery plaque is the most common underlying cause of acute coronary syndromes (ACS) and can manifest as unstable angina, non-ST segment elevation infarction, and ST elevation myocardial infarction (STEMI), but can also manifest as sudden cardiac arrest due to ischaemia induced tachyarrhythmias. ACS mortality has decreased significantly over the last few years, especially from the more extreme manifestations of ACS, STEMI, and cardiac arrest. This trend is likely to continue based on recent therapeutic progress which includes novel antiplatelet agents such as prasugrel, ticagrelor, and cangrelor

Reliable transfer of transcriptional gene regulatory networks between taxonomically related organisms

Baumbach J, Rahmann S, Tauch A. Reliable transfer of transcriptional gene regulatory networks between taxonomically related organisms. BMC Systems Biology. 2009;3(1):8.Background: Transcriptional regulation of gene activity is essential for any living organism. Transcription factors therefore recognize specific binding sites within the DNA to regulate the expression of particular target genes. The genome-scale reconstruction of the emerging regulatory networks is important for biotechnology and human medicine but cost-intensive, time-consuming, and impossible to perform for any species separately. By using bioinformatics methods one can partially transfer networks from well-studied model organisms to closely related species. However, the prediction quality is limited by the low level of evolutionary conservation of the transcription factor binding sites, even within organisms of the same genus. Results: Here we present an integrated bioinformatics workflow that assures the reliability of transferred gene regulatory networks. Our approach combines three methods that can be applied on a large-scale: re-assessment of annotated binding sites, subsequent binding site prediction, and homology detection. A gene regulatory interaction is considered to be conserved if (1) the transcription factor, (2) the adjusted binding site, and (3) the target gene are conserved. The power of the approach is demonstrated by transferring gene regulations from the model organism Corynebacterium glutamicum to the human pathogens C. diphtheriae, C. jeikeium, and the biotechnologically relevant C. efficiens. For these three organisms we identified reliable transcriptional regulations for similar to 40% of the common transcription factors, compared to similar to 5% for which knowledge was available before. Conclusion: Our results suggest that trustworthy genome-scale transfer of gene regulatory networks between organisms is feasible in general but still limited by the level of evolutionary conservation