Biology subjects

Mohinta, S.

Publications and source records attributed to Mohinta, S..

3 recordsLinked to original sources

bioRxiv2025

Beyond Agreement: Standardizing Crowdsourced Synapse Annotations through Proofreading in EM Connectomics

AO_SCPLOWBSTRACTC_SCPLOWReliable synapse identification in volumetric EM is hampered by subtle, 3D cues that yield variable human judgments. We present a standardized proofreading protocol that pairs explicit, operational criteria with machine-learning candidate generation and a two-stage calibration of annotators. In two larval Drosophila melanogaster volumes imaged at 8x8x8 nm, five raters (expert + 4 calibrated annotators) reviewed model-proposed candidates using efficient node-based labels. Multi-rater judgments were aggregated with a probabilistic Dawid-Skene (DS) model to produce consensus labels with calibrated uncertainty. Post-calibration, individual annotator accuracy versus the expert improved (McNemar p < 0.05 for all raters), DS-expert agreement increased, and DS posterior entropy decreased for true positives/negatives, indicating more decisive consensus; gains were modest and dataset-dependent in chance-corrected agreement (Krippendorffs ). By making uncertainty explicit, this protocol converts noisy judgments into auditable supervision suitable for training and evaluation, while honestly communicating residual ambiguity essential for reliable and robust connectomics at scale.

neuroscience↗

bioRxiv2025

Mapping the nervous system of the Idiosepius hallami pygmy squid: insights from whole-animal X-ray nanotomography imaging.

The study of a nervous system as big as the cephalopods requires multimodal imaging approaches capable of capturing neural architecture across scales. Here, we present a whole-animal volume of the pygmy squid hatchling Idiosepius hallami, acquired using X-ray holographic nanotomography at the beamline ID16A of the European Synchrotron. The reconstructed 3D volume comprises 40 tiled scans acquired at a voxel size of 125 nm. While individual neurons are not resolved at this resolution, we segmented major body regions and mapped the large-scale connectivity by tracing afferent and efferent nerve bundles, including projections from the olfactory organs, chromatophore lobes, and arm ganglia to the brain. The acquisition of this dataset represents a significant milestone for X-ray nanotomography, being the largest whole animal volume imaged at this spatial resolution. The volume serves as a resource for comparative neuroscience and cephalopod biology.

neuroscience↗

bioRxiv2023

Hippocampal networks support reinforcement learning in partially observable environments

Mastering navigation in environments with limited visibility is crucial for survival. Although the hippocam-pus has been associated with goal-oriented navigation, its role in real-world behaviour remains unclear. To investigate this, we combined deep reinforcement learning (RL) modelling with behavioural and neural data analysis. First, we trained RL agents in partially observable environments using egocentric and allocentric tasks. We show that agents equipped with recurrent hippocampal circuitry, but not purely feedforward networks, learned the tasks in line with animal behaviour. Next, using dimensionality reduction, our agents predicted reward, strategy, and temporal rep-resentations, which we validated experimentally using hippocampal recordings. Moreover, hippocampal RL agents predicted state-specific trajectories, mirroring empirical findings. In contrast, agents trained in fully observable en-vironments failed to capture experimental observations. Finally, we show that hippocampal-like RL agents demon-strated improved generalisation across novel task conditions. In summary, our findings suggest an important role of hippocampal networks in facilitating reinforcement learning in naturalistic environments.

neuroscience↗