Biology subjects

Gurnani, H.

Publications and source records attributed to Gurnani, H..

4 recordsLinked to original sources

bioRxiv2026

A corticostriatal circuit updates subjective beliefs about latent task states

Beliefs about states of the world profoundly impact decision-making and learning, but little is known about how neural circuits represent and update beliefs. We performed projection-specific recordings and perturbations from neurons in the orbitofrontal cortex (OFC) projecting to the intermediate or rostral cau-date putamen (CPi/CPr) in rats performing a task with hidden reward states. Stimulating OFC[->]CPi neurons biased rats beliefs towards high reward states. Recordings from optogenetically-tagged OFC[->]CPi neurons showed that they encoded categorical evidence for high reward states, shaped by a saturating non-linearity in neural responses. Downstream neurons could, in principle, decode the full belief distribution over reward states as rats deliberated about deci-sions. Finally, projection-specific perturbations disrupted encoding of hidden states within OFC. These findings reveal the circuit implementation of a core cognitive computation, updating subjective beliefs about abstract latent states of the environment.

neuroscience↗

bioRxiv2024

Feedback control of recurrent dynamics constrains learning timescales during motor adaptation

Neural activity has been observed to lie on low-dimensional manifolds, constraining what new behaviors can be easily learned. We propose that beyond this geometric constraint, learning on fast timescales is limited by how neural activity can flow over time within these manifolds--i.e., by the systems underlying dynamics. In primary motor cortex (M1), these neural dynamics are shaped not only by internal recurrence but also by sensory feedback that can continually update cortical activity. Modeling recurrent neural networks with adaptive feedback controllers in a brain-computer interface (BCI) task, we show that feedback-driven dynamics determine not just the robustness but also the flexibility of motor output. Through a control-theoretic approach, we quantitatively link learning speed and success for individual BCI decoders to the structure of input-driven network dynamics. We show that rapid learning is fundamentally limited by the networks controllability--the ease with which inputs can steer neural activity along desired directions. Crucially, this dynamical systems perspective explains a continuous form of experimentally-observed learning variability across decoders with similar geometry, that has not been addressed previously. We also make a testable prediction that rapid adaptation to new BCI decoders depends on upstream input plasticity, such as remapping of sensory feedback, beyond local plasticity within M1. Overall, our work identifies potential network mechanisms for fast but limited motor learning, and clarifies how constraints on learning arise from both the geometry of neural activity and its underlying dynamical structure.

neuroscience↗

bioRxiv2023

The NeuroML ecosystem for standardized multi-scale modeling in neuroscience

Data-driven models of neurons and circuits are important for understanding how the properties of membrane conductances, synapses, dendrites and the anatomical connectivity between neurons generate the complex dynamical behaviors of brain circuits in health and disease. However, the inherent complexity of these biological processes make the construction and reuse of biologically detailed models challenging. A wide range of tools have been developed to aid their construction and simulation, but differences in design and internal representation act as technical barriers to those who wish to use data-driven models in their research workflows. NeuroML, a model description language for computational neuroscience, was developed to address this fragmentation in modeling tools. Since its inception, NeuroML has evolved into a mature community standard that encompasses a wide range of model types and approaches in computational neuroscience. It has enabled the development of a large ecosystem of interoperable open source software tools for the creation, visualization, validation and simulation of data-driven models. Here, we describe how the NeuroML ecosystem can be incorporated into research workflows to simplify the construction, testing and analysis of standardized models of neural systems, and supports the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles, thus promoting open, transparent and reproducible science.

neuroscience↗

bioRxiv2018

Dopaminergic and frontal signals for decisions guided by sensory evidence and reward value

Making efficient decisions requires combining present sensory evidence with previous reward values, and learning from the resulting outcome. To establish the underlying neural processes, we trained mice in a task that probed such decisions. Mouse choices conformed to a reinforcement learning model that estimates predicted value (reward value times sensory confidence) and prediction error (outcome minus predicted value). Predicted value was encoded in the pre-outcome activity of prelimbic frontal neurons and midbrain dopamine neurons. Prediction error was encoded in the post-outcome activity of dopamine neurons, which reflected not only reward value but also sensory confidence. Manipulations of these signals spared ongoing choices but profoundly affected subsequent learning. Learning depended on the pre-outcome activity of prelimbic neurons, but not dopamine neurons. Learning also depended on the post-outcome activity of dopamine neurons, but not prelimbic neurons. These results reveal the distinct roles of frontal and dopamine neurons in learning under uncertainty.

neuroscience↗