Biology subjects

Doudlah, R.

Publications and source records attributed to Doudlah, R..

7 recordsLinked to original sources

bioRxiv2026

Manifolds for the neural representation of 3D spatial experience

The precision with which animals navigate and interact with objects depends in part on how their brains represent position and orientation in three dimensions. A central challenge is to explain within a common framework how neural systems ranging from insects to primates represent three-dimensional (3D) pose. Here we develop a framework based on the hypothesis that neural representations of pose lie on manifolds derived from the Special Euclidean Group, SE(3), which describes the full space of rigid-body positions and orientations in three dimensions. The framework shows how movement constraints, object geometry, and visual features jointly determine the topology of the resulting pose manifold, yielding a taxonomy that guides experimental tests. We apply the framework to derive low-dimensional manifolds for neural representations of 3D object orientation in macaque parietal cortex and head direction in the limbic systems of fruit bats, mice, and monkeys. This analysis also guides the construction of neuronal tuning functions that respect the topology determined by experimental conditions. Together, these results provide a common approach for linking the geometry of 3D pose to neural coding across species and brain systems.

neuroscience↗

bioRxiv2026

Reassessing Choice Probability: What 59 Macaque Studies Tell Us About Decision-Related Activity in Visual Cortex

Choice probability (CP) quantifies the trial-by-trial covariation between a sensory neurons response and perceptual reports. Despite extensive interest in CP as a window into how perception is linked to the activity of sensory neurons, the usefulness of CP remains debated. On one hand, reported CP magnitudes vary widely across seemingly equivalent studies, questioning its utility as a metric. On the other hand, the absence of clear patterns in CP variability has made it difficult to use it to adjudicate between competing models of visual perception. Here, we performed a meta-analysis of 150 CP estimates from 59 macaque neurophysiology studies to identify factors that systematically influence CP. We confirmed the positive relationship between CP and neuronal sensitivity both across and within individual studies. When controlled for sensitivity, we found remarkable consistency in CP across varying tasks and brain regions with two notable exceptions. First, CPs were higher in tasks involving bistable percepts, reinforcing the link between CP magnitude and subjective perception. Second, CPs were smaller in area V1, supporting prior suggestions about V1s special role in visual processing. We further found a significant effect of stimulus duration on CP, providing evidence against strictly feedforward models and favoring models with substantial feedback and recurrent processing. Finally, we offer recommendations for future studies to enhance the cross-study comparability and theoretical utility of choice signals as the field transitions to large-scale population recordings. More broadly, our findings demonstrate the benefits of meta-studies that expose patterns across many different tasks and animals - yielding insights that complement large-scale population recordings.

neuroscience↗

bioRxiv2025

A Semi-Automated MEA Spike sorting (SAMS) method for high throughput assessment of cultured neurons

Neurons derived from human pluripotent stem cells (hPSCs) are valuable models for studying brain development and developing therapies for brain disorders. Evaluating human-derived neurons requires assessing their electrical activity, which can be achieved using multi-electrode arrays (MEAs) for extracellular recordings. Because each electrode channel generally detects activity from multiple neurons, resolving the activity of single neurons requires a process called spike sorting. However, currently available spike sorting methods are not optimized for the analysis of hPSC-derived neurons, and require complex workflows and time-consuming manual intervention. Here, we introduce a Semi-Automated MEA Spike sorting software (SAMS) designed specifically for low-density MEA recordings of cultured neurons. SAMS outperforms commercially available automated spike sorting algorithms in terms of accuracy and greatly reduces computational and human processing time. By providing an accessible, efficient, and integrated platform for spike sorting, SAMS enhances the resolution and utility of MEA in disease modeling and drug development using human-derived neurons. HighlightsO_LISAMS is designed and optimized for high throughput analysis of hPSC-derived neurons. C_LIO_LISAMS is more efficient and accurate compared to recommended spike-sorting software. C_LIO_LISAMS resolves phenotypic differences previously not observed without spike sorting. C_LIO_LISAMS is an open-source software. C_LI

neuroscience↗

bioRxiv2022

Parallel processing, hierarchical transformations, and sensorimotor associations along the 'where pathway

Visually guided behaviors require the brain to transform ambiguous retinal images into object-level spatial representations and implement sensorimotor transformations. These processes are supported by the dorsal where pathway. However, the specific functional contributions of areas along this pathway remain elusive due in part to methodological differences across studies. We previously showed that macaque caudal intraparietal (CIP) area neurons possess robust three-dimensional (3D) visual representations, carry choice-related and presaccadic activity, and exhibit experience-dependent sensorimotor associations (Chang et al., 2020b). Here, we used a common experimental design to reveal parallel processing, hierarchical transformations, and the formation of sensorimotor associations along the where pathway by extending the investigation to V3A, a major feedforward input to CIP. Higher-level 3D representations and choice-related activity were more prevalent in CIP than V3A. Both areas contained presaccadic activity that predicted the direction/timing of eye movements. Intriguingly, the time-course of presaccadic activity in CIP aligned with the temporally integrated V3A output. Sensorimotor associations between 3D orientation and saccade direction preferences were strongest in CIP and moderated by choice signals in both areas. Together, the results explicate parallel representations, hierarchical transformations, and functional associations of visual and presaccadic signals at a key juncture in the where pathway.

neuroscience↗

bioRxiv2020

Choice activity stabilizes sensory representations and mediates sensorimotor associations in parietal cortex

Selecting actions which achieve desired goals often requires three-dimensional (3D) representations of the environment. Because the sensory epithelia cannot directly encode the worlds 3D spatial features, sensory signals must be converted into 3D representations. Here we investigated the relationships between the quality of 3D visual representations, choice-related activity, and motor-related activity in the parietal cortex of macaque monkeys using an eight-alternative 3D orientation discrimination task, visually guided saccade task, and laminar probe recordings. We found that choice activity was preferentially carried by caudal intraparietal area neurons with more robust 3D representations. Choice activity further stabilized the 3D representations, rather than attenuating information not directly relevant to the behavioral task (nuisance variables). An experience-dependent, sensorimotor association additionally aligned sensory and saccade direction preferences, particularly for neurons with choice activity. These findings reveal novel roles for choice activity in improving the fidelity of ecologically relevant object representations and mediating sensorimotor associations.

neuroscience↗

bioRxiv2019

Optimized but not maximized cue integration for 3D visual perception

Reconstructing three-dimensional (3D) scenes from two-dimensional (2D) retinal images is an ill-posed problem. Despite this, our 3D perception of the world based on 2D retinal images is seemingly accurate and precise. The integration of distinct visual cues is essential for robust 3D perception in humans, but it is unclear if this mechanism is conserved in non-human primates, and how the underlying neural architecture constrains 3D perception. Here we assess 3D perception in macaque monkeys using a surface orientation discrimination task. We find that perception is generally accurate, but precision depends on the spatial pose of the surface and available cues. The results indicate that robust perception is achieved by dynamically reweighting the integration of stereoscopic and perspective cues according to their pose-dependent reliabilities. They further suggest that 3D perception is influenced by a prior for the 3D orientation statistics of natural scenes. We compare the data to simulations based on the responses of 3D orientation selective neurons. The results are explained by a model in which two independent neuronal populations representing stereoscopic and perspective cues (with perspective signals from the two eyes combined using nonlinear canonical computations) are optimally integrated through linear summation. Perception of combined-cue stimuli is optimal given this architecture. However, an alternative architecture in which stereoscopic cues and perspective cues detected by each eye are represented by three independent populations yields two times greater precision than observed. This implies that, due to canonical computations, cue integration for 3D perception is optimized but not maximized.\n\nAuthor summaryOur eyes only sense two-dimensional projections of the world (like a movie on a screen), yet we perceive the world in three dimensions. To create reliable 3D percepts, the human visual system integrates distinct visual signals according to their reliabilities, which depend on conditions such as how far away an object is located and how it is oriented. Here we find that non-human primates similarly integrate different 3D visual signals, and that their perception is influenced by the 3D orientation statistics of natural scenes. Cue integration is thus a conserved mechanism for creating robust 3D percepts by the primate brain. Using simulations of neural population activity, based on neuronal recordings from the same animals, we show that some computations which occur widely in the brain facilitate 3D perception, while others hinder perception. This work addresses key questions about how neural systems solve the difficult problem of generating 3D percepts, identifies a plausible neural architecture for implementing robust 3D vision, and reveals how neural computation can simultaneously optimize and curb perception.

neuroscience↗

bioRxiv2018

Real-time experimental control using network-based parallel processing

Modern neuroscience research often requires the coordination of multiple processes such as stimulus generation, real-time experimental control, as well as behavioral and neural measurements. The technical demands required to simultaneously manage these processes with high temporal fidelity limits the number of labs capable of performing such work. Here we present an open-source network-based parallel processing framework that eliminates these barriers. The Real-Time Experimental Control with Graphical User Interface (REC-GUI) framework offers multiple advantages: (i) a modular design agnostic to coding language(s) and operating system(s) that maximizes experimental flexibility and minimizes researcher effort, (ii) simple interfacing to connect measurement and recording devices, (iii) high temporal fidelity by dividing task demands across CPUs, and (iv) real-time control using a fully customizable and intuitive GUI. Testing results demonstrate that the REC-GUI framework facilitates technically demanding, behavior-contingent neuroscience research. Sample code and hardware configurations are downloadable, and future developments will be regularly released.

neuroscience↗