10 papers · ranked by Valyu relevance
Thach V. Bui
Neural coding is an important tool to discover the inner workings of mind. In this work, we propose and consider a simple but novel self-decoding model for neural coding based on the principle that the neuron body represents ongoing stimulus while dendrites are used to store that stimulus as a memory. In particular…
Jia Lu, Ryan Tsoi, Nan Luo, Yuanchi Ha + 8 more
Dynamical systems often generate distinct outputs according to different initial conditions, and one can infer the corresponding input configuration given an output. This property captures the essence of information encoding and decoding. Here, we demonstrate the use of self-organized patterns, combined with machine…
Maria Beatriz Ramos, José Diogo Marques dos Santos, Bruno Direito, Luís Paulo Reis + 1 more
Brain decoding from fMRI data using artificial neural networks traditionally operates at the regional level, identifying which brain areas activate during tasks but ignoring how these regions interact through structural networks. While Graph Neural Networks can capture connectivity, they require prohibitively large…
Jingcheng Zhang, Lei Chen, Jinlin Sun, Shumin Li + 5 more
DNA has emerged as a compelling archival storage medium, offering unprecedented information density and millennia-scale durability. Despite its promise, DNA-based data storage faces critical challenges due to error-prone processes during DNA synthesis, storage, and sequencing. In this study, we introduce Gungnir, a…
Jiao Cao, Zhong Zheng, Di Sun, Xin Chen + 7 more
Spatially resolved transcriptomic technologies show promise in revealing complex pathophysiological processes, but developing sensitive, high-resolution, and cost-effective methodology is challenging. Here, we report a dendrimeric DNA coordinate barcoding design for spatial RNA sequencing (Decoder-seq). This technology…
Inbal Preuss, Ben Galili, Zohar Yakhini, Leon Anavy
This study introduces a novel model for analyzing and determining the required sequencing coverage in DNA-based data storage, focusing on combinatorial DNA encoding. We explore the application of the coupon collector model for combinatorial-letter reconstruction, post-sequencing, which ensure efficient data retrieval…
Kevin D. Volkel, Paul W. Hook, Albert Keung, Winston Timp + 1 more
As nanopore technology reaches ever higher throughput and accuracy, it becomes an increasingly viable candidate for reading out DNA data storage. Nanopore sequencing offers considerable flexibility by allowing long reads, real-time signal analysis, and the ability to read both DNA and RNA. We need flexible and…
Maximilian Gehri, Lukas Stelzl, Heinz Koeppl
Biochemical systems process signals through stochastic reaction dynamics that are inherently continuous in time and often exhibit memory, feedback, and nonequilibrium driving. At the same time, they are frequently modeled by effective reactions, e.g., multi-step processes such as transcription are treated as single…
Ganchao Wei, Zeinab Tajik Mansouri, Xiaojing Wang, Ian H. Stevenson
Accurately decoding external variables from observations of neural activity is a major challenge in systems neuroscience. Bayesian decoders, that provide probabilistic estimates, are some of the most widely used. Here we show how, in many common settings, the probabilistic predictions made by traditional Bayesian…
Lucy Ham, Megan A. Coomer, Kaan Öcal, Ramon Grima + 1 more
Changes in cell state are driven by key molecular events whose timing can often be measured experimentally. Of particular interest is the time taken for the levels of RNA or protein molecules to reach a critical threshold defining the triggering of a cellular event. While this mean trigger time can be estimated by…