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Found 27 papers, 11 papers with code
Available observation time regulates optimal balance between sensitivity and confidence
Tasks that require information about the world imply a trade-off between the time spent on observation and the variance of the response.
Infomorphic networks: Locally learning neural networks derived from partial information decomposition
Understanding the intricate cooperation among individual neurons in performing complex tasks remains a challenge to this date.
Signatures of hierarchical temporal processing in the mouse visual system
A core challenge for the brain is to process information across various timescales.
Model-based assessment of sampling protocols for infectious disease genomic surveillance
Consequently, it is convenient for countries with comparatively few resources to operate at lower sequencing rates, thereby profiting the most from adaptive sampling.
Impact of the Euro 2020 championship on the spread of COVID-19
Large-scale events like the UEFA Euro~2020 football (soccer) championship offer a unique opportunity to quantify the impact of gatherings on the spread of COVID-19, as the number and dates of matches played by participating countries resembles a randomized study.
SIR-Model for Households
In this setting, the basic reproduction number as well as prevalence and the peak of an infection wave in a population with given households size distribution can be computed analytically.
Evaluating vaccine allocation strategies using simulation-assisted causal modelling
Early on during a pandemic, vaccine availability is limited, requiring prioritisation of different population groups.
A Measure of the Complexity of Neural Representations based on Partial Information Decomposition
However, the specific way in which this mutual information about the classification label is distributed among the individual neurons is not well understood: While parts of it may only be obtainable from specific single neurons, other parts are carried redundantly or synergistically by multiple neurons.
Tackling the subsampling problem to infer collective properties from limited data
However, despite the development of large-scale data-acquisition techniques, experimental observations are often limited to a tiny fraction of the system.
Autocorrelations from emergent bistability in homeostatic spiking neural networks on neuromorphic hardware
The extent of memory about past inputs is commonly quantified by the autocorrelation time of collective dynamics.
Modular architecture facilitates noise-driven control of synchrony in neuronal networks
Brain functions require both segregated processing of information in specialized circuits, as well as integration across circuits to perform high-level information processing.
Intrinsic timescales of spiking activity in humans during wakefulness and sleep
Information processing in the brain requires integration of information over time.
Dendritic predictive coding: A theory of cortical computation with spiking neurons
However, experimental evidence for error units, which are central to the theory, is inconclusive, and it remains unclear how hPC can be implemented with spiking neurons.
Information-theoretic analyses of neural data to minimize the effect of researchers' assumptions in predictive coding studies
We demonstrate our approach by investigating two opposing accounts of predictive coding-like processing strategies, where we quantify the building blocks of predictive coding, namely predictability of inputs and transfer of information, by local active information storage and local transfer entropy.
Interplay between risk perception, behaviour, and COVID-19 spread
They are complemented by voluntary health-protective behaviour, building a complex interplay between risk perception, behaviour, and disease spread.
Nachhaltige Strategien gegen die COVID-19-Pandemie in Deutschland im Winter 2021/2022
In this position paper, a large group of interdisciplinary experts outlines response strategies against the spread of SARS-CoV-2 in the winter of 2021/2022 in Germany.
The winter dilemma
Opinions, in turn, depend on information, e. g., about vaccine safety or current infection levels.
Mutational signatures and transmissibility of SARS-CoV-2 Gamma and Lambda variants
The emergence of SARS-CoV-2 variants of concern endangers the long-term control of COVID-19, especially in countries with limited genomic surveillance.
A look into the future of the COVID-19 pandemic in Europe: an expert consultation
A moderate, adaptive level of NPIs will thus remain necessary.
Relaxing restrictions at the pace of vaccination increases freedom and guards against further COVID-19 waves
However, as vaccination progresses, demands to lift restrictions increase, despite most of the population remaining susceptible.
Low case numbers enable long-term stable pandemic control without lockdowns
The traditional long-term solutions for epidemic control involve eradication or population immunity.
Local dendritic balance enables learning of efficient representations in networks of spiking neurons
How can neural networks learn to efficiently represent complex and high-dimensional inputs via local plasticity mechanisms?
Assessing criticality in pre-seizure single-neuron activity of human epileptic cortex
Epileptic seizures are characterized by abnormal and excessive neural activity, where cortical network dynamics seem to become unstable.
Inferring COVID-19 spreading rates and potential change points for case number forecasts
As COVID-19 is rapidly spreading across the globe, short-term modeling forecasts provide time-critical information for decisions on containment and mitigation strategies.
Sampling effects and measurement overlap can bias the inference of neuronal avalanches
To date, it is still impossible to sample the entire mammalian brain with single-neuron precision.
Description of spreading dynamics by microscopic network models and macroscopic branching processes can differ due to coalescence
Spreading processes are conventionally monitored on a macroscopic level by counting the number of incidences over time.
Neurons and Cognition Disordered Systems and Neural Networks Physics and Society
Tailored ensembles of neural networks optimize sensitivity to stimulus statistics
The dynamic range of stimulus processing in living organisms is much larger than a single neural network can explain.
Disordered Systems and Neural Networks Neurons and Cognition
