Biology subjects

K Namboodiri, V. M.

Publications and source records attributed to K Namboodiri, V. M..

4 recordsLinked to original sources

bioRxiv2023

Few-shot learning: temporal scaling in behavioral and dopaminergic learning

Learning the causes of rewards is necessary for survival. Thus, it is critical to understand the mechanisms of such a vital biological process. Cue-reward learning is controlled by mesolimbic dopamine and improves with spacing of cue-reward pairings. However, whether a mathematical rule governs such improvements in learning rate, and if so, whether a unifying mechanism captures this rule and dopamine dynamics during learning remain unknown. Here, we investigate the behavioral, algorithmic, and dopaminergic mechanisms governing cuereward learning rate. Across a range of conditions in mice, we show a strong, mathematically proportional relationship between both behavioral and dopaminergic learning rates and the duration between rewards. Due to this relationship, removing up to 19 out of 20 cue-reward pairings over a fixed duration has no influence on overall learning. These findings are explained by a dopamine-based model of retrospective learning, thereby providing a unified account of the biological mechanisms of learning.

neuroscience↗

bioRxiv2023

Sensory cortical ensembles exhibit differential coupling to ripples in distinct hippocampal subregions

Cortical neurons activated during recent experiences often reactivate with dorsal hippocampal CA1 sharp-wave ripples (SWRs) during subsequent rest. Less is known about cortical interactions with intermediate hippocampal CA1, whose connectivity, functions, and SWRs differ from those of dorsal CA1. We identified three clusters of visual cortical excitatory neurons that are excited together with either dorsal or intermediate CA1 SWRs, or suppressed before both SWRs. Neurons in each cluster were distributed across primary and higher visual cortices and co-active even in the absence of SWRs. These ensembles exhibited similar visual responses but different coupling to thalamus and pupil-indexed arousal. We observed a consistent activity sequence: (i) suppression of SWR-suppressed cortical neurons, (ii) thalamic silence, and (iii) activation of the cortical ensemble preceding and predicting intermediate CA1 SWRs. We propose that the coordinated dynamics of these ensembles relay visual experiences to distinct hippocampal subregions for incorporation into different cognitive maps.

neuroscience↗

bioRxiv2022

Hyperactivity of indirect pathway-projecting spiny projection neurons drives compulsive behavior

Compulsive behaviors are a hallmark symptom of obsessive compulsive disorder (OCD). Striatal hyperactivity has been linked to compulsive behavior generation in correlative studies in humans and causal studies in rodents. However, the contribution of the two distinct striatal output populations to the generation and treatment of compulsive behavior is unknown. These populations of direct and indirect pathway-projecting spiny projection neurons (SPNs) have classically been thought to promote or suppress actions, respectively, leading to a long-held hypothesis that increased output of direct relative to indirect pathway promotes compulsive behavior. Contrary to this hypothesis, here we find that indirect pathway hyperactivity drives compulsive grooming in the Sapap3-knockout mouse model of OCD-relevant behavior. Furthermore, we show that suppression of indirect pathway activity using optogenetics or treatment with the first-line OCD pharmacotherapy fluoxetine is associated with reduced compulsive behavior. Together, these findings highlight the striatal indirect pathway as a potential new treatment target for compulsive behavior.

neuroscience↗

bioRxiv2021

What is the state space of the world for real animals?

A key concept in reinforcement learning (RL) is that of a state space. A state space is an abstract representation of the world using which statistical relations in the world can be described. The simplest form of RL, model free RL, is widely applied to explain animal behavior in numerous neuroscientific studies. More complex RL versions assume that animals build and store an explicit model of the world in memory. To apply these approaches to explain animal behavior, typical neuroscientific RL models make assumptions about the underlying state space formed by animals, especially regarding the representation of time. Here, we explicitly list these assumptions and show that they have several problematic implications. We propose a solution for these problems by using a continuous time Markov renewal process model of the state space. We hope that our explicit treatment results in a serious consideration of these issues when applying RL models to real animals.

neuroscience↗