Biology subjects

Reddy, C. B.

Publications and source records attributed to Reddy, C. B..

3 recordsLinked to original sources

bioRxiv2025

Suite3D: Volumetric cell detection for two-photon microscopy

In two-photon imaging of neuronal activity it is increasingly common to acquire 3-dimensional volumes. However, these volumes are typically processed plane by plane, leading to uncorrected axial movement, duplicated cells across planes, reduced signal-to-noise ratio per cell, and missed cells. To overcome these limitations, we introduce Suite3D, a volumetric cell detection pipeline. Suite3D corrects for rigid and non-rigid 3D brain motion, both lateral and axial. It detects neurons using 3D correlation, improving detectability. Finally, it performs 3D segmentation, identifying cells across imaging planes. We validated Suite3D with data from conventional multi-plane microscopes and advanced volumetric microscopes, at multiple resolutions and in multiple brain regions, and with ground truth anatomical labelling. Suite3D successfully detected cells appearing on multiple imaging planes, improving cell detectability and signal quality, avoiding duplications, and running faster than a prior volumetric pipeline. Suite3D offers a powerful solution for analyzing volumetric two-photon data.

neuroscience↗

bioRxiv2024

Adaptation shapes the representational geometry in mouse V1 to efficiently encode the environment

Sensory adaptation dynamically changes neural responses as a function of previous stimuli, profoundly impacting perception. The response changes induced by adaptation have been characterized in detail in individual neurons and at the population level after averaging across trials. However, it is not clear how adaptation modifies the aspects of the representations that relate more directly to the ability to perceive stimuli, such as their geometry and the noise structure in individual trials. To address this question, we recorded from a population of neurons in the mouse visual cortex and presented one stimulus (an oriented grating) more frequently than the others. We then analyzed these data in terms of representational geometry and studied the ability of a linear decoder to discriminate between similar visual stimuli based on the single-trial population responses. Surprisingly, the discriminability of stimuli near the adaptor increased, even though the responses of individual neurons to these stimuli decreased. This improvement arose mainly from how adaptation reshaped the geometry of the mean population responses, with changes in the noise structure playing a minor role. Locomotion modulated the population responses along a direction largely independent of adaptation, allowing the adaptation-induced increase in discriminability to persist during locomotion. Finally, artificial neural networks trained to reconstruct the visual stimulus under metabolic constraints reproduced the increase in discriminability and the decrease in responses. The paradoxical effects of adaptation are thus consistent with the efficient coding framework, allowing the brain to improve the representation of frequent stimuli while limiting the associated metabolic cost.

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↗