Aug 2026· Neuroinformatics· Vol 24· 0 citations· 28 references
Medicine
TL;DR
An attention-based BiLSTM that deliberately diverges from the sparse-PCA baseline by operating directly on the pre-sPCA family-vector tensor rather than on the 44 sparse-PC representation, providing feature-family-level interpretability via learned attention weights supports the utility of the Gouwens feature-family organization.
Whole-brain transcriptomic atlases are now widely available, yet computational neural models are almost exclusively parameterized from rodent data and used to infer human brain function, an extrapolation whose cost remains unquantified. To address this, we constructed a biophysically detailed, conductance-based Hodgkin...
It is found that basic cell type descriptions often predicted electrical properties better than more complex gene-based methods alone, and when linking data is in the hundreds, simpler representations outperform large general-purpose AI models in isolation, but the two approaches may be complementary rather than compet...
Harshil Sharma, Xiao-Ping Liu, Thomas Chartrand et al.· bioRxiv· 0 citations
HIPPIE is introduced, a deep-learning model that classifies neuron types from electrophysiological recordings across mice, rats and macaques, and can generate and translate signals between species or recording technologies.
Jesus Gonzalez-Ferrer, Julian Lehrer, Bruno Alvarez-Esteban et al.· Nature Communications· 0 citations
The cerebral cortex depends on a diverse repertoire of inhibitory neurons, yet how this diversity emerges during development remains unclear. Rare inhibitory subtypes are often underrepresented in single-cell RNA-sequencing datasets, limiting resolution of their developmental trajectories. Here we developed a computati...
We present a reusable dataset of neuronal population activity from multiple cortical areas, recorded from layers 2/3 of the mouse cortex at single-cell resolution during wakefulness, natural sleep (including NREM and REM sleep), and isoflurane anesthesia. Using wide-field two-photon microscopy, we recorded approximatel...
This study leverages transcriptomic profiling of spinal V1 interneurons to understand how they contribute to movement, and decouple locomotor speed and limb flexion phenotypes, suggesting these key elements of motor control may be governed by different subsets of V1 interneurons.
Alexandra J. Trevisan, Ka-Tie Han, Phillip Chapman et al.· Nature Communications· 0 citations
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