Biology subjects

Tarokh, V.

Publications and source records attributed to Tarokh, V..

4 recordsLinked to original sources

bioRxiv2024

Neuron synchronization analyzed through spatial-temporal attention

Across diverse organisms, the temporal dynamics of spiking responses between neurons, the neural synchrony, is crucial for encoding different stimuli. Neural synchrony is especially important in the insect antennal (olfactory) lobe (AL). Previous studies on synchronization, however, rely on pair-wise synchronization metrics including the cross-correlogram and cos-similarity between kernelized spikes train. These pair-wise analyses overlook an important aspect of synchronization which is the interaction at the population neuron level. There are also limited modeling techniques that incorporate the synchronization between neurons in modeling population spike trains. Inspired by recent advancements in machine learning, we leverage a modern attention mechanism to learn a generative normalizing flow that captures neuron population synchronization. Our method not only reveals the spiking mechanism of neurons in the AL region but also produces semi-interpretable attention weights that characterize neuron interactions over time. These automatically learned attention weights allow us to elucidate the known principles of neuron synchronization and further shed light on the functional roles of different cell types (the local interneurons (LNs), and projection neurons (PNs)) in the dynamic neural network in the AL. By varying the balance of excitation and inhibition in this neural circuit, our method further uncovers the pattern between the strength of synchronization and the ratio of an odorant in the mixture. Author SummaryThe olfactory system can accurately compute the mixture of volatile compounds emitted from distant sources, enabling the foraging species to exhibit fast and effective decisions. However, altering ratios of one of the compounds in the mixture could be perceived as a different odor. Leveraging the current understanding of neural synchronization on sensory neural regions of insects, we construct a spatial-temporal attention normalizing flow, which partially replicates the AL regions functionality by learning the spiking mechanics of neurons. Beyond providing insights of the spiking mechanism of neurons in the AL region, our method also produces semi-interpretable attention weights that characterize neuron interaction over time. These automatically learned attention weights allow us to dissect out the principles of neuron synchronization and interaction mechanisms between projection neurons (PNs) and local neurons (LNs). Utilizing our accurate model of these AL functionality, we show evidence that the behavioral relevant compounds are closely clustered together while varying the intensities of one of the behavioral compounds in the mixture could attenuate the synchronization

neuroscience↗

bioRxiv2024

Body size and light environment modulate flight speed and saccadic behavior in free flying Drosophila melanogaster

For flying insects, visual control relies on acquiring adequate light, but many circumstances limit this, such as dim environments, high image speeds, or eyes of modest light gathering power. To determine these effects on vinegar flies, we limited light by either placing them in dim conditions, or generating individuals with developmentally smaller eyes, then examined activity levels and three-dimensional flight paths. When simulating dawn and dusk light periods, walking flies increase activity, reflecting their crepuscular nature, and this is stronger for flies with larger eyes. When light switches abruptly, similar to many lab settings, activity associated with crepuscular periods diminishes, as does activity associated with greater facet size. During free flight, we find flight speed decreases similarly in both dim light and small eye conditions, but excess light induces smaller individuals to restore their flight speed. Through a machine learning approach, we confirmed that two features, translational speed and saccade distance, are sufficient to classify treatment groups by light niche, size, and age. Together, these imply that flight changes in smaller individuals result from visual deficits, rather than other elements of body structure.

animal behavior and cognition↗

bioRxiv2023

Cross-subject Mapping of Neural Activity with Restricted Boltzmann Machines

Subject-to-subject variability is a common challenge in generalizing neural data models across subjects. While many methods exist that map one subject to another, it remains challenging to combine many subjects in a computationally efficient manner, especially with features that are highly non-linear such as when considering populations of spiking neurons or motor units. Our objective is to transfer data from one or more target subjects to the data space of one or more source subject(s) such that the neural decoder of the source subject can directly decode the target data when the source(s) is not available during test time. We propose to use the Gaussian-Bernoulli Restricted Boltzmann Machine (RBM); once trained over the entire set of subjects, the RBM allows the mapping of target features on source feature spaces using Gibbs sampling. We also consider a novel computationally efficient training technique for RBMs based on the minimization of the Fisher divergence, which allows the gradients of the RBM to be computed in closed form, in contrast to the more traditional contrastive divergence. We apply our methods to decode turning behaviors from a comprehensive spike-resolved motor program - neuromuscular recordings of spike trains from the ten muscles that control wing motion in an agile flying Manduca sexta. The dataset consists of the comprehensive motor program recorded from nine subjects driven by six discrete visual stimuli. The evaluations show that the target features can be decoded using the source classifier with an accuracy of up to 95% when mapped using an RBM trained by Fisher divergence. Significant StatementIn this study, we address the variability of neural data across subjects, which is a significant obstacle in developing models that can generalize across subjects. Our objective is to create a task-specific representation of the target subject signal in the feature space of the source subject. Our proposed RBM architectures achieve highly flexible and accurate cross-subject mapping with few assumptions. Our Fisher RBM improved the previous state of the art method by 300%. Our methods show promise in generalizing features of complex neural datasets across individuals, tuning neural interfaces to subject-specific features, and leveraging data across multiple subjects when experiments are limited in time or completeness.

neuroscience↗

bioRxiv2021

Consistent coordination patterns provide near perfect behavior decoding in a comprehensive motor program for insect flight

Patterns of motor activity can be used to decode behavior state. Precise spike timing encoding is present in many motor systems, but is not frequently utilized to decode behavior or to examine how coordination is achieved across many motor units. Testing whether the same coordinated sets of muscles control different movements is difficult without a complete motor representation at the level of the currency of control - action potentials. Here, we demonstrate nearly perfect decoding of six hawk moth flight behaviors elicited in response to wide-field drifting visual stimuli about the flight axes - pitch, roll, and yaw - using a comprehensive, spike-resolved motor program and a simple linear decoding pipeline. A complex decoding scheme is not necessary, even if the functional patterns of control are nonlinear. We show that muscle covariation present in one pair of visual stimulus conditions can be used to decode behavior in a different pair of visual stimulus conditions, indicating the presence of conserved muscle coordination patterns at the level of motor neuronal timings in functionally distinct behaviors. We also demonstrate that as few as half the muscles can be used to retain decoding performance, linking coordination to redundancy in encoding, if not function, across the entire moth flight motor program.

neuroscience↗