Biology subjects

Alreja, A.

Publications and source records attributed to Alreja, A..

4 recordsLinked to original sources

bioRxiv2026

Face-selective responses correlate with deep networks that learn from environment feedback

AO_SCPLOWBSTRACTC_SCPLOWDeep neural networks have shown high accuracy in modeling neural responses in the visual system, but most models rely on supervised learning, which requires training on ground-truth labels that are typically unavailable in real-world settings. While unsupervised models can address this limitation, they miss another key aspect: visual representations are shaped by feedback from the environment. We introduce a reinforcement learning (RL) model of face perception that incorporates both input stimuli and feedback from the environment. Inspired by human interactions, we train the model to approach faces yielding positive interactions and avoid faces yielding negative interactions. Using intracortical electroencephalography (iEEG) data and Representational Dissimilarity Matrices (RDMs), we evaluate the models ability to account for neural responses. Our RL model performs at the same level as supervised and unsupervised models, capturing neural responses to complex visual stimuli. The findings suggest that RL models are a promising approach for understanding perception. Significance StatementUnderstanding how the brain encodes faces is central to vision science. Existing models rely on supervised learning, which requires ground-truth labels that are often unavailable in real-world settings, or on unsupervised learning, which ignores the role of environmental-feedback in shaping visual representations. We introduce a reinforcement learning (RL) model that learns through environmental feedback, simulating human interactions by associating approaching faces with positive interactions and avoiding faces with negative interactions. Using intracortical electroencephalography (iEEG) data from face-selective regions, we show that an RL model with a variational DenseNet encoder accounts for neural representations comparably to supervised and unsupervised models. Task and architecture jointly shaped representational geometry, highlighting the importance of both learning objective and encoder design. These findings suggest the potential of RL-based approaches to understand neural representations of naturalistic faces.

neuroscience↗

bioRxiv2025

Respiratory exposure to inert particulate matter during pregnancy leads to oxidative stress and cardiac proteome remodeling

Withdrawal StatementThe authors have withdrawn this manuscript because upon peer review major revisions have been identified as needed to ensure that the conclusions are in par with the presented data. Current studies are ongoing to provide a more complete version of the manuscript for peer review publication. Therefore, the authors do not wish this work to be cited as reference for the project in its current form. If you have any questions, please contact the corresponding author.

molecular biology↗

bioRxiv2021

A Multimodal Approach to Investigate the Neural Mechanisms of Real World Social Vision

Eye tracking and other behavioral measurements collected from patient-participants in their hospital rooms afford a unique opportunity to study immersive natural behavior for basic and clinical translational research. We describe an immersive social and behavioral paradigm implemented in patients undergoing evaluation for surgical treatment of epilepsy, with electrodes implanted in the brain to determine the source of their seizures. Our studies entail collecting eye tracking with other behavioral and psychophysiological measurements from patient-participants during unscripted behavior, including social interactions with clinical staff, friends and family, in the hospital room. This approach affords a unique opportunity to study the neurobiology of natural social behavior, though it requires carefully addressing distinct logistical, technical, and ethical challenges. Collecting neurophysiological data synchronized to behavioral and psychophysiological measures helps us to study the relationship between behavior and physiology. Combining across these rich data sources while participants eat, read, converse with friends and family, etc., enables clinical-translational research aimed at understanding the participants disorders and clinician-patient interactions, as well as basic research into natural, real-world behavior. We discuss data acquisition, quality control, annotation, and analysis pipelines that are required for our studies. We also discuss the clinical, logistical, and ethical and privacy considerations critical to working in the hospital setting.

neuroscience↗

bioRxiv2020

Constrained brain volume in an efficient coding model explains the fraction of excitatory and inhibitory neurons in sensory cortices

The number of neurons in mammalian cortex varies by multiple orders of magnitude across different species. In contrast, the ratio of excitatory to inhibitory neurons (E:I ratio) varies in a much smaller range, from 3:1 to 9:1 and remains roughly constant for different sensory areas within a species. Despite this structure being important for understanding the function of neural circuits, the reason for this consistency is not yet understood. While recent models of vision based on the efficient coding hypothesis show that increasing the number of both excitatory and inhibitory cells improves stimulus representation, the two cannot increase simultaneously due to constraints on brain volume. In this work, we implement an efficient coding model of vision under a volume (i.e., total number of neurons) constraint while varying the E:I ratio. We show that the performance of the model is optimal at biologically observed E:I ratios under several metrics. We argue that this happens due to trade-offs between the computational accuracy and the representation capacity for natural stimuli. Further, we make experimentally testable predictions that 1) the optimal E:I ratio should be higher for species with a higher sparsity in the neural activity and 2) the character of inhibitory synaptic distributions and firing rates should change depending on E:I ratio. Our findings, which are supported by our new preliminary analyses of publicly available data, provide the first quantitative and testable hypothesis based on optimal coding models for the distribution of neural types in the mammalian sensory cortices.

neuroscience↗