Biology subjects

Minni, S.

Publications and source records attributed to Minni, S..

3 recordsLinked to original sources

bioRxiv2025

A vector-based strategy for olfactory navigation in Drosophila

Odors serve as essential cues for navigation. Although tracking an odor plume has been modeled as a reflexive process, it remains unclear whether animals can use memories of their past odor encounters to infer the spatial structure of their chemical environment or their location within it. Here we developed a virtual-reality olfactory paradigm that allows head-fixed Drosophila to navigate structured chemical landscapes, offering insight into how memory mechanisms shape their navigational strategies. We found that flies track an appetitive odor corridor by following its boundary, alternating between rapid counterturns to exit the plume and directed returns to its edge. Using a combination of behavioral modeling, functional calcium imaging, and neural perturbations, we demonstrate that this edge-tracking strategy relies on vector-based computations within the Drosophila central complex in which flies store and dynamically update memories of the direction to return them to the plumes boundary. Consistent with this, we find that FC2 neurons within the fan-shaped body, which encode a flys navigational goal, signal the direction back to the odor boundary when flies are outside the plume. Together, our studies suggest that flies leverage the plumes boundary as a dynamic landmark to guide their navigation, analogous to the memory-based strategies other insects use for long-distance migration or homing to their nests. Plume tracking thus uses components of a conserved navigational toolkit, enabling flies to use memory mechanisms to navigate through a complex shifting chemical landscape.

neuroscience↗

bioRxiv2023

Predicting brain activity using Transformers

The Algonauts challenge [Gifford et al., 2023] called on the community to provide novel solutions for predicting brain activity of humans viewing natural scenes. This report provides an overview and technical details of our submitted solution. We use a general transformer encoder-decoder model to map images to fMRI responses. The encoder model is a vision transformer trained using self-supervised methods (DINOv2). The decoder uses queries corresponding to different brain regions of interests (ROI) in different hemispheres to gather relevant information from the encoder output for predicting neural activity in each ROI. The output tokens from the decoder are then linearly mapped to the fMRI activity. The predictive success (challenge score: 63.5229, rank 2) suggests that features from self-supervised transformers may deserve consideration as models of human visual brain representations and shows the effectiveness of transformer mechanisms (self and cross-attention) to learn the mapping from features to brain responses. Code is available in this github repository.

neuroscience↗

bioRxiv2019

Understanding the functional and structural differences across excitatory and inhibitory neurons

One of the most fundamental organizational principles of the brain is the separation of excitatory (E) and inhibitory (I) neurons. In addition to their opposing effects on post-synaptic neurons, E and I cells tend to differ in their selectivity and connectivity. Although many such differences have been characterized experimentally, it is not clear why they exist in the first place. We studied this question in an artificial neural network equipped with multiple E and I cell types. We found that a deep convolutional recurrent network trained to perform an object classification task was able to capture salient distinctions between E and I neurons. We explored the necessary conditions for the network to develop distinct selectivity and connectivity across cell types. We found that neurons that project to higher-order areas will have greater stimulus selectivity, regardless of whether they are excitatory or not. Sparser connectivity is required for higher selectivity, but only when the recurrent connections are excitatory. These findings demonstrate that the differences observed across E and I neurons are not independent, and can be explained using a smaller number of factors.

neuroscience↗