12 papers · ranked by Valyu relevance
Jenkin Tsui, Naila Adam, Woongcheol Choi, Luna Y. Liu + 10 more
Imaging-based spatial transcriptomics technologies have opened new avenues for studying cellular organization and gene expression within intact tissues. However, the accuracy of downstream analyses depends critically on the decoding step that reconstructs barcodes from fluorescence patterns and maps them to gene…
Jenkin Tsui, Naila Adam, Woongcheol Choi, Luna Liu + 10 more
Imaging based spatial transcriptomics technologies such as MERFISH have opened new avenues for studying cellular organization and gene expression within intact tissues. However, the accuracy of downstream analyses depends critically on the decoding step that reconstructs barcodes from fluorescence patterns and maps…
Simon Geirnaert, Ruochen Ding, Alexander Bertrand
Selective auditory attention decoding (sAAD) enables neuro-steered hearing devices by identifying the attended speaker in a multi-speaker environment from neural activity recorded with electroencephalography (EEG). Despite algorithmic progress, practical deployment remains constrained by a lack of wearable…
Boqiao Lai, Melissa Englund, Ramit Bharanikumar, Isabel Nocedal + 3 more
Modeling recognition between T-cell receptors (TCRs) and peptide-MHC (pMHC) complexes is a fundamental challenge in computational immunology, constrained by sparse paired interaction data relative to abundant unpaired sequences. We introduce DecoderTCR, a masked language model framework that addresses this through two…
Ibrahim Nawaz, Parv Agarwal, Thomas Heinis
DNA storage is a developing field that uses DNA to archive digital data owing to its superior information density and stability. Although DNA storage has been performed on a significant scale, challenges arise from the synthesis and sequencing of data-encoded oligonucleotides. Synthesis of DNA introduces significant…
Lorenzo Posani
Neural decoding is a powerful approach for inferring which variables are represented in the activity of a population of neurons, with broad applications ranging from basic neuroscience to clinical settings such as brain-computer interfaces. More recently, decoding has also been used as a cross-validated tool for…
G. Bilodeau, A. Miao, G. Gagnon-Turcotte, C. Ethier + 1 more
Bidirectional interfaces combined with neural de-coding algorithms are essential for closed-loop (CL) neuromodulation, enabling simultaneous neural monitoring and responsive optogenetic stimulation. However, implementing these capabilities in compact wireless headstages for freely moving animals remains challenging, as…
Ramy Khabbaz, Jérémy Mateos, Marc Antonini, Serge Kas Hanna
The biochemical processes underlying DNA data storage, including synthesis, amplification, and sequencing, are inherently noisy. Consequently, base-level insertion, deletion, and substitution (IDS) errors, as well as sequence-level dropouts, occur and pose major challenges for reliable data retrieval. Here we introduce…
Melvin Vaupel, Valdemar Kargård Olsen, Sigurd Gaukstad, Erik Hermansen + 1 more
Neural recordings are usually analyzed by comparing neural spiketrains or comparing time bins (population vectors). If multiple variables drive the neural activity these comparisons will be affected by all of them. Our aim is to disentangle the different latent variables or covariates that drive neural activity and…
Théo Desbordes, Itsaso Olasagasti, Nicolas Piron, Sophie Schwartz + 1 more
Multivariate decoding analyses have become a cornerstone method in cognitive neuroscience. When applied to time-resolved brain imaging signals, they provide insights into the temporal dynamics of information processing in the brain. In particular, the temporal generalization (TG) method—where a decoder trained at one…
Alice Tor, Yuxin Wu, Stephen E Clarke, Lisa Yamada + 2 more
The complexity of neural data changes as the brain processes information during events. Universal lossless compression algorithms, which are broadly applicable and grounded in information theory, identify and exploit redundancies in data in order to compress it to essentially-optimal sizes regardless of underlying…
William Dorrell, Peter E. Latham, Timothy E. J. Behrens, James C. R. Whittington
The efficient coding hypothesis presents a compelling success story for theoretical and systems neuroscience. It marshals a unifying idea, that neural codes can be understood as efficient encodings of natural stimuli, to explain phenomena from across sensory systems, sometimes with exquisite precision. However, similar…