Biology subjects

Gabel, J.

Publications and source records attributed to Gabel, J..

4 recordsLinked to original sources

bioRxiv2026

OpenDrive: An Open-Source Microdrive for Large-Scale Neurophysiology

Neuropixels-NHP probes have ushered in a new era of high-density neurophysiology recording in large animal models. Here, we present OpenDrive, an open-source microdrive system designed specifically for acute insertion of Neuropixels-NHPs. OpenDrive combines a custom motorized microdrive, six-drive control console, and software platform. Compared with commercially available options, OpenDrive offers (1) low cost, (2) precise, accurate, and stable probe movement, (3) scalability to many-probe recording, and (4) open-source hardware designs and software. In our experiments, OpenDrive has already supported recordings from thousands of neurons across a variety of cortical and subcortical structures. By lowering technical barriers and enabling community-driven development with OpenDrive, we aim to accelerate Neuropixels recording in large animal models.

neuroscience↗

bioRxiv2026

Physical sensorimotor contingencies facilitate learning in monkeys

Animal studies in neuroscience routinely use virtual tasks that deviate significantly from the animals' natural behavioral repertoire. This choice is often necessary for conducting careful and controlled experimentation. However, this approach raises a fundamental question: do virtual tasks expose neural computations and mechanisms associated with naturalistic tasks? In this study, we explore how naturalistic settings influence learning timescale and generalization behavior in rhesus monkeys. By training animals in a maze-solving task and comparing their behavior across two environments, we observe significant differences. Specifically, we found that a realistic setup using physical objects enabled learning that was orders of magnitude faster than the learning timescale observed in a comparable computerized virtual environment. Furthermore, we noted limited transfer of performance between the physical and virtual settings. These findings underscore the need for understanding how task environments shape learning and generalization, with potential implications for the design of future neuroscience and AI experiments.

neuroscience↗

bioRxiv2026

A Relational Code for Objects in Primate Frontal Cortex

When we remember a scene, we recall both the locations of individual objects and their spatial arrangement. Yet, how neurons encode this arrangement alongside individual locations is unknown. In this work, we recorded large-scale neural activity from the frontal and supplementary eye fields of monkeys performing a two-object spatial working-memory task. Using a continuum of two object configurations, we tested whether neurons encoded individual object locations or the spatial relationship between them. Many neurons had well-defined position tuning when a single object was presented. With two objects, responses spanned a continuum from retinotopic to relational tuning. Surprisingly, most units were better explained by relational tuning than retinotopic tuning. Relational and retinotopic units encoded complementary dimensions of the stimulus, and their observed mixture supported near-optimal population decoding of object locations. Relational tuning was systematically aligned with single-object receptive fields, enabling a readout that produced repulsive biases resembling those observed in behavior. Standard multi-object models such as averaging and normalization failed to reproduce the observed continuum, whereas a model integrating receptive fields over potential saccades captured it. These findings show that the frontal oculomotor cortex represents not only where objects are, but also how they are arranged with respect to one another.

neuroscience↗

bioRxiv2026

Working Memory of Multi-Object Scenes in Primate Frontal Cortex

Working memory allows primates to reason about complex scenes, yet how the brain maintains multiple objects in memory simultaneously remains unclear. Competing theories propose that objects are stored in discrete slots1,2, represented dynamically through switching3-6, or encoded by weighted combinations of single-object representations7-11. We formalized these hypotheses in terms of their quantitative predictions at the level of single neurons and tested them against densely recorded neural data from the dorsomedial frontal cortex and frontal eye field of monkeys trained to perform a novel multi-object working-memory task. Across cross-validated neural data, a Gain model, where population activity reflects weighted compositions of individual object responses, consistently outperformed Slot and Switching models. Trial-specific gain estimates predicted behavioral errors and reaction times, indicating that these latent weights capture meaningful fluctuations in memory fidelity. All results replicated in an independent dataset with different spatial configurations. Together, our work provides a rigorous framework to adjudicate a longstanding debate about how the frontal cortex retains multiple objects, identifying a weighted-sum representation as the format that best explains the neural data.

neuroscience↗