Biology subjects

Watters, N.

Publications and source records attributed to Watters, N..

5 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

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↗

bioRxiv2024

MEDiCINe: Motion Correction for Neural Electrophysiology Recordings

Electrophysiology recordings from the brain using laminar multielectrode arrays allow researchers to measure the activity of many neurons simultaneously. However, laminar microelectrode arrays move relative to their surrounding neural tissue for a variety of reasons, such as pulsation, changes in intracranial pressure, and decompression of neural tissue after insertion. Inferring and correcting for this motion stabilizes the recording and is critical to identify and track single neurons across time. Such motion correction is a preprocessing step of standard spike sorting methods. However, estimating motion robustly and accurately in electrophysiology recordings is challenging due to the stochasticity of the neural data. To tackle this problem, we introduce MEDiCINe (Motion Estimation by Distributional Contrastive Inference for Neurophysiology), a novel motion estimation method. We show that MEDiCINe outperforms existing motion estimation methods on an extensive suite of simulated neurophysiology recordings and leads to more accurate spike sorting. We also show that MEDiCINe correctly estimates the motion in primate electrophysiology recordings with a variety of motion and stability statistics. We open-source MEDiCINe, usage instructions, examples integrating MEDiCINe with common tools for spike-sorting, and data and code for reproducing our results. This open software will enable other researchers to use MEDiCINe to improve spike sorting results and get the most out of their electrophysiology datasets.

neuroscience↗

bioRxiv2024

Computational basis of hierarchical and counterfactual information processing

Cognitive theories attribute humans unparalleled capacity in solving complex multistage decision problems to distinctive hierarchical and counterfactual reasoning strategies. Here, we used a combination of human psychophysics and behaviorally-constrained neural network modeling to understand the computational basis of these cognitive strategies. We first developed a multi-stage decision-making task that humans solve using a combination of hierarchical and counterfactual processing. We then used a series of hypothesis-driven behavioral experiments to systematically dissect the potential computational constraints that underlie these strategies. One experiment revealed that humans have limited capacity for parallel processing. Another indicated that counterfactuals do not fully compensate for this limitation because of working memory limits. A third experiment revealed that the degree to which humans use counterfactuals depends on the fidelity of their working memory. Next, we asked whether the strategies humans adopt are computationally rational; i.e., optimal under these constraints. To do so, we analyzed the behavior of a battery of task-optimized recurrent neural networks (RNNs) that were subjected to one or more of these constraints. Remarkably, only RNNs that were subjected to all these constraints behaved similarly to humans. Further analysis of the RNNs revealed that what cognitive theories posit as distinctive strategies such as hierarchical and counterfactual are subdivisions in a continuum of computationally rational solutions that includes optimal, counterfactual, postdictive, and hierarchical.

neuroscience↗