Biology subjects

Diamantaki, M.

Publications and source records attributed to Diamantaki, M..

3 recordsLinked to original sources

bioRxiv2025

EthoPy: Reproducible Behavioral Neuroscience Made Simple

As brain activity is tightly coupled to behavior, an accurate understanding of neural function necessitates consideration of behavioral tasks that capture the complexity and variety animals encounter. Nevertheless, animal training for behavioral experiments is often labor-intensive, costly, and difficult to standardize. To overcome these challenges, we developed EthoPy, an open-source, Python-based behavioral control framework that integrates stimulus presentation, hardware management, and data logging. EthoPy supports diverse behavioral paradigms, stimulus modalities, and experimental systems, from homecage to head-fixed configurations, while operating on affordable hardware, such as Raspberry Pi. Its modular architecture and database integration enable scalable, high-throughput automatic behavioral training with minimal experimenter involvement while ensuring reproducibility through comprehensive metadata tracking. By automating training workflows, EthoPy makes it feasible to implement sophisticated behavioral paradigms that are traditionally difficult to achieve. EthoPy thus provides an accessible, extensible framework to study behavior and the underlying neural activity.

animal behavior and cognition↗

bioRxiv2025

Dual-feature selectivity enables bidirectional coding in visual cortical neurons

Sensory neurons are traditionally viewed as feature detectors that respond with an increase in firing rate to preferred stimuli while remaining unresponsive to others. Here, we identify a dual-feature encoding strategy in macaque visual cortex, wherein many neurons in areas V1 and V4 are selectively tuned to two distinct visual features--one that enhances and one that suppresses activity--around an elevated baseline firing rate. By combining neuronal recordings with functional digital twin models--deep learning-based predictive models of biological neurons--we were able to systematically identify each neurons preferred and non-preferred features. These feature pairs served as anchors for a continuous, low-dimensional axis in natural image similarity space, along which neuronal activity varied approximately linearly. Within a single visual area, visual features that strongly or weakly activated individual neurons also had a high probability of modulating the activity of other neurons, suggesting a shared feature selectivity across the population that structures stimulus encoding. We show that this encoding strategy is conserved across species, present in both primary and lateral visual areas of mouse cortex. Dual-feature selectivity is consistent with recent anatomical evidence for feature-specific inhibitory connectivity, complementing the feature-detector principle through circuit mechanisms in which selective excitation and inhibition may together enhance the representational capacity of the neuronal population.

neuroscience↗

bioRxiv2020

Object manifold geometry across the mouse cortical visual hierarchy

Despite variations in appearance, we recognize objects robustly. Neuronal populations responding to objects presented under varying transformations form object manifolds, and hierarchically organized visual areas are hypothesized to untangle pixel intensities into linearly decodable object representations. However, the associated changes in the geometry of the object manifolds along the cortex remain unknown. Using home cage training, we showed that mice are capable of invariant object recognition. We recorded the responses of thousands of neurons to measure the information about object identity across the visual cortex and found that the lateral areas LM, LI, and AL carry more linearly decodable object information compared to other visual areas. We applied the theory of linear separability of manifolds and found that the increase in classification capacity is associated with a decrease in the dimension and radius of the object manifold, identifying the key features in the geometry of the population neural code that are associated with invariant object coding.

neuroscience↗