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–Huxley spiking microcircuit of a five-population prefrontal network, where every ion-channel, receptor, and gap-junction conductance was scaled by cell-type-specific gene expression. We parameterized the identical circuit using single-nucleus RNA-seq from mouse mPFC and human DLPFC, alongside a literature-derived baseline, and compared their high-frequency-oscillation (HFO) outputs across seven physiological and pathological states. While population firing rates differed only modestly between the two refinements (∼20% for pyramidal and PV cells), the oscillatory dynamics diverged dramatically. The human-refined circuit generated strongly synchronized PV activity and robust ripple- and fast-ripple-band power (e.g., healthy-wake ripple power, in arbitrary units: 322 vs. 24 and 22), whereas the mouse-refined and literature arms remained asynchronous (interneuron synchrony: 0.21 vs. 0.02). This human ≫mouse ≈ original hierarchy was statistically consistent across all seven states (significant arm differences in 75/77 comparisons). Mechanistically, the human transcriptome drove markedly stronger PV–PV electrical coupling (gap-junction scale: 1.78 vs. 0.96) paired with stronger recurrent pyramidal excitation, which collectively synchronized the fast-spiking PV population into a coherent rhythm that perisomatic inhibition then imposed on the local field potential. Critically, these results are model-dependent; the gene-to-conductance mapping is phenomenological, and mRNA expression does not linearly translate to functional conductance. Nonetheless, under this mapping the divergence localizes PV-mediated coupling and excitation–inhibition balance as the parameters most in need of human-specific recalibration. More broadly, this work establishes transcriptome-informed spiking simulation as a powerful strategy for uncovering species-specific computational principles and for building mechanistically grounded, human-relevant models of prefrontal circuit dysfunction, an approach that moves beyond generic rodent defaults to enable targeted, species-appropriate modeling of neurological and psychiatric disorders.
Single-cell RNA sequencing (scRNA-seq) captures detailed gene expression profiles at scale, while patch-clamp recordings measure intrinsic neuronal electrophysiological properties. Modeling the relations between these two modalities remains a challenge. Here, we compare how well electrophysiological features can be predicted by traditional transcriptomic cell type classification, representations derived from a foundational model (scGPT) pretrained on large-scale scRNA-seq datasets, ion channel-coding genes, and highly variable genes. Using paired transcriptomic and electrophysiological patch-sequencing data from 495 human neurons from neurosurgical tissue, we find that cluster-level cell type representations consistently outperform highly variable gene selection, ion channel gene selection, and context-enriched scGPT embeddings. Notably, performance varies across model architectures and initializations, and the best results are obtained by combining the outputs of separate cell type and scGPT-based models. Together, these findings suggest that traditional discrete cellular classification is highly effective in predicting physiological features. For maximum performance it can be complemented by pretrained transformer models. Author summary Understanding how a neuron’s genes relate to its electrical properties is a major goal in neuroscience. New technologies now make it possible to measure gene expression in individual cells and, simultaneously, to record how those same cells respond to electrical signals. However, relating these two types of information at the single cell level remains difficult. In this study, we tested whether a modern artificial intelligence model trained on large collections of gene expression data could help connect gene activity to electrical behavior in human brain cells. We compared this approach with simpler strategies, such as using selected sets of genes or grouping cells by their known types. We found that basic cell type descriptions often predicted electrical properties better than more complex gene-based methods alone. The strongest results were achieved by combining cell type knowledge with information from the artificial intelligence model and training them together. These findings suggest that 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 competing.
Neuronal classification from extracellular electrophysiological recordings is challenging due to intrinsic waveform variability, noise, and technical differences across experiments, technologies, and species. We introduce HIPPIE (High-dimensional Interpretation of Physiological Patterns In Intercellular Electrophysiology), a deep learning framework that combines self-supervised pretraining on unlabeled datasets with supervised fine-tuning to classify neurons from extracellular recordings. Using conditional convolutional joint autoencoders, HIPPIE learns technology-adjusted representations of waveforms and spiking dynamics. Here we show, across mouse, rat, and macaque recordings, that HIPPIE classifies cell types competitively with existing methods while additionally supporting generative analyses that discriminative models cannot perform, including counterfactual decoding of electrophysiological signals under changed experimental conditioning, cross-species latent interpolation, and a cross-modal analysis revealing that spike-timing modalities and waveform morphology encode largely independent dimensions of neuronal identity. HIPPIE is available as both a Python package and a coding-free web application, providing a unified framework for multimodal neuronal classification across technologies, experimental conditions, and species.
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 computational pipeline to enrich and integrate rare cell types across datasets and applied it to somatostatin-expressing (SST+) inhibitory neurons, the most diverse inhibitory class in cortex. We generated Dev-SST-v1 and Dev-SST-v2, transcriptomic reference maps comprising more than 55,000 mouse SST+ neurons. These maps identify three major SST+ inhibitory neuron groups—Martinotti cells (MCs), non-Martinotti cells (nMCs) and long-range projecting (LRP) neurons—each defined by a distinct developmental strategy. MCs commit early, whereas nMCs diversify progressively. LRPs follow a contracting trajectory, with one transient subtype eliminated by programmed cell death. Together, these findings establish three distinct modes of SST+ inhibitory neuron diversification, including a previously unrecognized contracting mode. Researchers discover a transient inhibitory cell type, and reveal that the developing cortex builds inhibitory diversity through three routes—early commitment, gradual diversification and selective elimination.
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 approximately 4,000 to 10,000 neurons per session at 7.65 Hz and provided the spatial coordinates of individual neurons. The repository provides both processed datasets and the corresponding raw imaging movies (TIFF) and electrophysiological recordings (MATLAB format). Processed data are distributed in MATLAB format and include Δ F/F fluorescence signals, deconvolved spike estimates, Gaussian-smoothed spike estimates, behavioral state annotations, and metadata. This dataset supports reuse in studies of cortical population dynamics, brain-state-dependent activity, and spatially distributed neuronal organization. It should also be useful for method development, benchmarking, and comparative analyses of large-scale neuronal activity across physiological and pharmacological brain states.
Neural circuits in the spinal cord are composed of diverse populations of interneurons that play crucial roles in shaping motor output. However, the extent of interneuron heterogeneity and how this diversity relates to functional aspects of movement remain unclear. Here, through a focus on mouse spinal V1 interneurons, we show that loss of the V1 transcription factor En1 selectively disrupts the frequency of rhythmic locomotor output but does not disrupt flexion/extension limb movement, thereby decoupling two key functional roles ascribed to this neuronal population. To investigate the cellular basis of these deficits, we generated a single-nucleus transcriptomic atlas of V1 interneurons across postnatal development. Our analysis reveals age-dependent transcriptional changes while also demonstrating that their core molecular taxonomy perdures into adulthood. Notably, En1 deficiency selectively perturbed a single subset of V1
Pou6f2
interneurons, thereby identifying a possible cellular substrate for influencing locomotor speed. Beyond serving as a molecular resource, our study highlights how deep neuronal profiling provides an entry point for understanding the multifunctional nature of heterogeneous interneuron populations.
Alexandra J. Trevisan, Katie Han, Phillip Chapman et al.· Nature Communications· 0 citations
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