Biology subjects

Nayak, A. K.

Publications and source records attributed to Nayak, A. K..

3 recordsLinked to original sources

bioRxiv2025

Decision revisions modulated by synaptic inhibition in the olfactory bulb facilitate learning

Behavioral responses are preceded by decisions based on perceived sensory evidence. In real life, sensory inputs are often noisy and change incessantly, raising the question of when and how accurate decisions are made. Cognitive flexibility allows us to revise the choices in case of perceptual conflicts, leading to response revisions. This involves switching between available choices or rapidly stopping misconstrued responses. Here, we quantified these erroneous behavioral actions and studied the underlying neural mechanisms. Mice were trained on an olfactory decision-making task wherein they had to distinguish between a rewarded and an unrewarded stimulus by responding with a lick or withholding it. While the animals respond by licking for both rewarded and unrewarded odor stimuli at the initial stages of learning, the responses almost disappear for the unrewarded ones in the learned stage. However, animals tend to initiate lick responses and stop within a few milliseconds for a few unrewarded trials, even when accuracy levels are high. We describe this phenomenon as decision revisions (DR), and observed it in 5-25% of trials among high-performance blocks. These revisions were mostly observed within a few hundred milliseconds of stimulation. We observed a significantly higher number of revision trials for binary odor mixture discriminations compared to monomolecular ones. Further, by enhancing inhibitory synaptic signaling in the olfactory bulb through photoactivation of ChR2-expressing GAD65-positive GABAergic interneurons, we observed faster odor discrimination and fewer revision trials. Thus, our findings confirm decision revisions that are stimulus complexity-dependent and the pre-cortical control over such a complex cognitive activity. Highlights of the studyO_LIMice revise context-inappropriate responses in a Go/No-go task. C_LIO_LIMice correct errors as they learn. C_LIO_LIDecision revisions are shaped by stimulus complexity. C_LIO_LIOptogenetic activation of the olfactory bulb inhibitory interneurons modulates decision revisions. C_LI

neuroscience↗

bioRxiv2025

Enhanced Formulation of Precision Probiotics through Active Machine Learning

The human gut microbiome is crucial to health, with dysbiosis increasingly linked to disease. Precision probiotics offer a promising approach to restoring microbial balance, but ensuring probiotic viability through gastrointestinal transit remains a challenge. This study applies an advanced active machine learning (ML) approach to predict how excipients affect the growth of Lactobacillus plantarum, a commonly used probiotic. State-of-the-art experiments were carried out to complement the ML study. Starting with five known excipient- probiotic interactions, we apply active ML over three rounds to predict the effects of 116 excipients, iteratively refining model certainty and accuracy. Five ML models--Neural Networks, Gradient Boosting, Logistic Regression, Random Forest, and Support Vector Machines--were trained and evaluated, with the final model achieving certainty levels close to 90%. Unlike previous methods, which retrained new models per iteration, our approach continuously optimized a single model, enhancing prediction stability and reducing error. These results highlight the potential of active ML to support accurate excipient selection in probiotic formulations.

bioinformatics↗

bioRxiv2023

Human Intelligence and the Connectome are Driven by Structural Brain Network Control

Research in network neuroscience demonstrates that human intelligence is shaped by the structural brain connectome, which enables a globally coordinated and dynamic architecture for general intelligence. Building on this perspective, the network neuroscience theory proposes that intelligence arises from system-wide network dynamics and the capacity to flexibly transition between network states. According to this view, network flexibility is made possible by network controllers that move the system into specific network states, enabling solutions to familiar problems by accessing nearby, easy-to-reach network states and adapting to novel situations by engaging distant, difficult-to-reach network states. Although this framework predicts that general intelligence depends on network controllability, the specific cortical regions that serve as network controllers and the nature of their control operations remain to be established. We therefore conducted a comprehensive investigation of the relationship between regional measures of network controllability and general intelligence within a sample of 275 healthy young adults using structural and diffusion-weighted MRI data. Our findings revealed significant associations between intelligence and network controllers located within the frontal, temporal and parietal cortex. Furthermore, we discovered that these controllers collectively enable access to both easy- and difficult-to-reach network states, aligning with the predictions made by the network neuroscience framework. Additionally, our research demonstrated that the identified network controllers are primarily localized within the left hemisphere and do not reside within regions or connections that possess the highest capacity for structural control in general. This discovery suggests that the identified regions may facilitate specialized control operations and motivates further exploration of the network topology and dynamics underlying intelligence in the human brain. SummaryThis study examines the relationship between regional measures of network controllability and general intelligence within a sample of 275 healthy young adults using structural and diffusion-weighted MRI data. We report that individual differences in general intelligence are associated average and modal controllability in specific left-hemisphere cortical regions, and further show that controller regions associated with intelligence are distinct from regions with the highest, centrality, controllability, or communication. These findings reveal a significant structural role for individual regions in controlling the trajectory of the connectome, advancing our understanding of the nature and mechanisms of network controllability in general intelligence.

neuroscience↗