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Soto, N.

Publications and source records attributed to Soto, N..

2 recordsLinked to original sources

Subspace communication in the hippocampal-retrosplenial axis

The capacity and flexibility of hippocampal circuits for transforming inputs into downstream outputs is fundamental for navigation and memory, yet the circuit-level mechanisms that allow this operation to adapt across experiences remain unknown. We approach this problem by performing large-scale (up to 1024-channel) recordings across the hippocampal-retrosplenial cortex (RSC) circuit in behaving mice, enabling simultaneous access to spiking activity in dentate gyrus (DG), CA3, CA2, CA1, RSC. Based on a linear dimensionality reduction technique known as partial canonical correlation analysis, we identify low-dimensional communication subspaces1 between two regions while accounting for measured third-area influences. These subspaces captured distinct input-output transformations in CA1, linking upstream (DG, CA3, and CA2) hippocampal activity to downstream cortical targets (RSC). Iintrinsic firing properties and anatomical location constrained subspace memberships--members were mapped to deep sublayers of the CA3-CA1-RSC axis during both spatial and non-spatial tasks. These subspaces could recombine overlapping neuronal pools to support distinct interareal interactions across changing experiences and brain states. Reactivation patterns of CA1-CA3 subspaces, but not those of CA1-RSC, during post-experience sleep correlated with replay, reflecting a plasticity-stability balance of the input-output transformation in the hippocampal-retrosplenial axis. Our data suggest a model in which hippocampal-neocortical communication reconfigures predetermined circuit motifs to flexibly encode experiences.

neuroscience↗

Peptipedia v2.0: A peptide sequence database and user-friendly web platform. A major update

In recent years, peptides have gained significant relevance due to their therapeutic properties. The surge in peptide production and synthesis has generated vast amounts of data, enabling the creation of comprehensive databases and information repositories. Advances in sequencing techniques and artificial intelligence have further accelerated the design of tailor-made peptides. However, leveraging these techniques requires versatile and continuously updated storage systems, along with tools that facilitate peptide research and the implementation of machine learning for predictive systems. This work introduces Peptipedia v2.0, one of the most comprehensive public repositories of peptides, supporting biotechnological research by simplifying peptide study and annotation. Peptipedia v2.0 has expanded its collection by over 45% with peptide sequences that have reported biological activities. The functional biological activity tree has been revised and enhanced, incorporating new categories such as cosmetic and dermatological activities, molecular binding, and anti-ageing properties. Utilizing protein language models and machine learning, more than 90 binary classification models have been trained, validated, and incorporated into Peptipedia v2.0. These models exhibit average sensitivities and specificities of 0.877 {+/-} 0.0530 and 0.873 {+/-}0.054, respectively, facilitating the annotation of more than 3.6 million peptide sequences with unknown biological activities, also registered in Peptipedia v2.0. Additionally, Peptipedia v2.0 introduces description tools based on structural and ontological properties and user-friendly machinelearning tools to facilitate the application of machine-learning strategies to study peptide sequences. Peptipedia v2.0 is accessible under the Creative Commons CC BY-NC-ND 4.0 license at https://peptipedia.cl/.

bioinformatics↗