neuroscience papers | Gist.Science

Neuroscience explores the intricate machinery of the brain and nervous system, seeking to understand how we think, feel, and move. From the microscopic dance of individual neurons to the complex networks that shape our memories and behaviors, this field peels back the layers of our biological selves to reveal the origins of consciousness and disease.

At Gist.Science, we bring these discoveries directly from bioRxiv, the leading preprint server for biological sciences, to a broader audience. We process every new neuroscience preprint as it is uploaded, transforming dense academic manuscripts into clear, plain-language explanations alongside detailed technical summaries. This ensures that both curious readers and specialists can stay current with the latest breakthroughs before they are formally published.

Below are the latest neuroscience papers we have processed from bioRxiv, offering fresh insights into the workings of the mind.

🧠 neuroscience

Automated detection of blink reflexes evoked by optogenetic stimulation of TRPV1-expressing corneal nociceptors in transgenic mice

This study presents a novel, automated behavioral paradigm that utilizes cell-type-specific optogenetic stimulation of TRPV1-expressing corneal nociceptors combined with DeepLabCut-based machine learning to achieve high-accuracy, observer-independent detection of blink reflexes in transgenic mice, offering a precise alternative to conventional air puff methods for studying corneal pain.

Jeong, K.-S., McPheeters, M. T., Chandrasekharan, A., Beeck, I., Veerubhotla, A., Roy, A., Lu, E. Y., Ghosn, S., Jenkins (…)2026-06-23

🧠 neuroscience

Power-Law Adaptation Stabilizes Primary Sensory Encoding of Natural Variance

This paper demonstrates that a multi-timescale sensory model utilizing a fractional power-law adaptation mechanism ( $t^{-0.5}$ ) effectively stabilizes primary sensory encoding against natural environmental fluctuations by preventing refractory saturation and maintaining homeostatic firing rates, a function that can be efficiently approximated by a simple three-pole system.

Bleeck, S.2026-06-23

🧠 neuroscience

Ventral pallidal GABAergic neurons control hedonic feeding and obesity

This study identifies ventral pallidal GABAergic neurons as a distinct neural population that selectively drives hedonic feeding and diet-induced obesity in mice while remaining largely insensitive to homeostatic hunger signals, offering a potential therapeutic target to block overeating without affecting normal caloric intake.

Wang, J. G., Xu, C. S., Murrell, C. L., Barrett, M. R., Basu, G. C., Fang, L. Z., Chen, Y., Schoukroun, F., Topilko, T. (…)2026-06-23

🧠 neuroscience

ComCat: Combating Covariate Effects in Brain Analysis

ComCat is a novel neuroimaging harmonization framework that extends the ComBat method to simultaneously remove unwanted variability from both categorical site indicators and continuous nuisance variables (such as image quality and motion) using B-spline basis expansion, thereby improving prediction accuracy and preserving biologically relevant signals across diverse multi-site datasets.

Gaser, C., Dahnke, R., Ganjgahi, H., Nichols, T.2026-06-23

🧠 neuroscience

Multisensory integration of stimulus-driven and goal-driven signals during urgent saccadic choices

This study demonstrates that the efficacy of multisensory integration during urgent saccadic choices is dynamically determined by the rapid interaction between stimulus-driven features and goal-driven rules, which can either enhance performance or lead to crossmodal capture depending on the alignment of cue location and task requirements.

Paro, A. N., Sheikh, B. I., Stanford, T. R., Salinas, E.2026-06-23

🧠 neuroscience

Mapping Lifespan Trajectories of Cognitive Flexibility with a Continuous Probabilistic Reversal Learning Measure

This study utilized a novel continuous probabilistic reversal learning task to demonstrate that reward-based learning peaks around age 18 while rule-based learning peaks around age 20, revealing distinct lifespan trajectories for cognitive flexibility components that align with the differential maturation of underlying brain circuits.

Jowkar, M., Makhsous, M., Rezayat, E.2026-06-23

🧠 neuroscience

Intravenous midazolam alters short-interval paired-pulse TMS responses differently in younger and older adults

This study demonstrates that intravenous midazolam reduces corticospinal excitability in both younger and older adults but differentially alters short-interval paired-pulse TMS responses by eliminating baseline inhibition in younger adults while producing minimal changes in older adults, suggesting that aging modifies the motor cortex's response to benzodiazepines.

McGregor, K. M., Safavynia, S., Novak, T., Weber, A., Wang, J., Nocera, J., Woodbury, A., Crosson, B., Garcia, P. S.2026-06-23

🧠 neuroscience

Next-Generation Neural Mass Models Reproduce Features of Speech Processing

This study demonstrates that a biophysical next-generation neural mass model successfully reproduces key features of neural speech tracking by revealing that thresholded phase-resetting triggered by sharp acoustic onsets provides a mechanistic explanation for how cortical populations generate rhythmic speech representations.

Shannon, A. J., Barton, D. A. W., Homer, M., Houghton, C. J.2026-06-22

🧠 neuroscience

How stimulation waveform shape affects collective oscillations in the brain networks

Using a whole-brain computational model, this study demonstrates that the shape of transcranial alternating current stimulation (tACS) waveforms critically determines their impact on brain network dynamics, with sinusoidal and pulsed waveforms enhancing global synchrony and reducing metastability, whereas square, triangular, and sawtooth waveforms produce weaker, fragmented modulation.

Sharma, V., Tiesinga, P. H. E., Cabral, J.2026-06-22

🧠 neuroscience

VFB-MCP: Natural-Language Access to Drosophila Neuroscience Grounded by an Expert-Curated Ontology-Led Knowledgebase

This paper introduces VFB-MCP, a Model Context Protocol implementation that connects Large Language Models to the expert-curated Virtual Fly Brain ontology, significantly outperforming standard and web-search-assisted LLMs in providing precise, verifiable, and quantified answers to Drosophila neuroscience queries.

McLachlan, A. D., Court, R., Pilgrim, C., Longden, K., Brown, N. H. D., Osumi-Sutherland, D., Jefferis, G. S. X. E., Arm (…)2026-06-21

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