Biology subjects

Haydaroglu, A.

Publications and source records attributed to Haydaroglu, A..

4 recordsLinked to original sources

bioRxiv2025

Characterizing neuronal population geometry with AI equation discovery

The activity of visual cortical neurons forms a population code representing image stimuli. There is, however, a discrepancy between our understanding of this code at the single-cell and population levels: direct measurements indicate the population code is high-dimensional, but established models of single-cell tuning give rise to low-dimensional codes. We reconciled this discrepancy by developing an AI science system to find a new parsimonious, interpretable equation for visual cortical orientation tuning. Candidate equations were expressed as short computer programs and evolved by Large Language Models (LLMs) using graphical diagnostics. The resulting equation not only improved single-cell fits, but also accurately modelled the population codes high-dimensional geometry. A novel parameter of the AI-discovered equation, which controls single-cell tuning smoothness, gives rise to high-dimensional population codes. The same parameter drives high-dimensional coding in head-direction cells, suggesting a common coding strategy across brain regions. We used this equation to hypothesize a circuit mechanism generating high-dimensional population codes, and to demonstrate the advantages of these codes in a simulated hyperacuity task. These results show that tuning smoothness has a key role in controlling population code geometry, and demonstrate how AI equation discovery can deliver interpretable models accelerating scientific understanding in neuroscience and beyond.

neuroscience↗

bioRxiv2025

High-dimensional neuronal activity from low-dimensional latent dynamics: a solvable model

Computation in recurrent networks of neurons has been hypothesized to occur at the level of low-dimensional latent dynamics, both in artificial systems and in the brain. This hypothesis seems at odds with evidence from large-scale neuronal recordings in mice showing that neuronal population activity is high-dimensional. To demonstrate that low-dimensional latent dynamics and high-dimensional activity can be two sides of the same coin, we present an analytically solvable recurrent neural network (RNN) model whose dynamics can be exactly reduced to a low-dimensional dynamical system, but generates an activity manifold that has a high linear embedding dimension. This raises the question: Do low-dimensional latents explain the high-dimensional activity observed in mouse visual cortex? Spectral theory tells us that the covariance eigenspectrum alone does not allow us to recover the dimensionality of the latents, which can be low or high, when neurons are nonlinear. To address this indeterminacy, we develop Neural Cross-Encoder (NCE), an interpretable, nonlinear latent variable modeling method for neuronal recordings, and find that high-dimensional neuronal responses to drifting gratings and spontaneous activity in visual cortex can be reduced to low-dimensional latents, while the responses to natural images cannot. We conclude that the high-dimensional activity measured in certain conditions, such as in the absence of a stimulus, is explained by low-dimensional latents that are nonlinearly processed by individual neurons.

neuroscience↗

bioRxiv2025

Suite3D: Volumetric cell detection for two-photon microscopy

In two-photon imaging of neuronal activity it is increasingly common to acquire 3-dimensional volumes. However, these volumes are typically processed plane by plane, leading to uncorrected axial movement, duplicated cells across planes, reduced signal-to-noise ratio per cell, and missed cells. To overcome these limitations, we introduce Suite3D, a volumetric cell detection pipeline. Suite3D corrects for rigid and non-rigid 3D brain motion, both lateral and axial. It detects neurons using 3D correlation, improving detectability. Finally, it performs 3D segmentation, identifying cells across imaging planes. We validated Suite3D with data from conventional multi-plane microscopes and advanced volumetric microscopes, at multiple resolutions and in multiple brain regions, and with ground truth anatomical labelling. Suite3D successfully detected cells appearing on multiple imaging planes, improving cell detectability and signal quality, avoiding duplications, and running faster than a prior volumetric pipeline. Suite3D offers a powerful solution for analyzing volumetric two-photon data.

neuroscience↗

bioRxiv2019

Shake-it-off: A simple ultrasonic cryo-EM specimen preparation device

Although microscopes and image analysis software for electron cryomicroscopy (cryo-EM) have improved dramatically in recent years, specimen preparation methods have lagged behind. Most strategies still rely on blotting microscope grids with paper to produce a thin film of solution suitable for vitrification. This approach loses more than 99.9% of the applied sample and requires several seconds, leading to problematic air-water interface interactions for macromolecules in the resulting thin film of solution and complicating time-resolved studies. Recently developed self-wicking EM grids allow use of small volumes of sample, with nanowires on the grid bars removing excess solution to produce a thin film within tens of milliseconds from sample application to freezing. Here we present a simple cryo-EM specimen preparation device that uses components from an ultrasonic humidifier to transfer protein solution onto a self-wicking EM grid. The device is controlled by a Raspberry Pi single board computer and all components are either widely available or can be manufactured by online services, allowing the device to be constructed in laboratories that specialize in cryo-EM, rather than instrument design. The simple open-source design permits straightforward customization of the instrument for specialized experiments.\n\nSynopsisA method is presented for high-speed low-volume cryo-EM specimen preparation with a device constructed from readily available components.

biophysics↗