Biology subjects

Tolooshams, B.

Publications and source records attributed to Tolooshams, B..

3 recordsLinked to original sources

bioRxiv2025

VARS-fUSI: Variable Sampling for Fast and Efficient Functional Ultrasound Imaging using Neural Operators

Functional ultrasound imaging (fUSI) is a promising neuroimaging method that infers neural activity by detecting cerebral blood volume changes. It offers high sensitivity and spatial resolution relative to fMRI and is an epidural alternative to electrophysiology for medical and neuroscience applications, including brain-computer interfaces. However, current fUSI methods require hundreds of compounded images and ultrasound pulse emissions, leading to high computational costs, memory demands, and potential probe heating. We propose VARiable Sampling fUSI (VARS-fUSI), the first deep learning fUSI method to allow for different sampling durations and rates during training and inference by using neural operators. VARS-fUSI reconstructs high-quality fUSI images using 10 - 15% of the time or sampling rate needed per image while preserving decodable behavior-correlated signals. Additionally, VARS-fUSI offers efficient finetuning for generalization to new animals and humans. Demonstrated across mouse, monkey, and human data, VARS-fUSI achieves state-of-the-art performance, enhancing imaging efficiency by significantly reducing storage and processing needs.

bioengineering↗

bioRxiv2025

Perception and neural representation of intermittent odor stimuli in mice

Odor cues in nature are sparse and highly fluctuating due to turbulent transport. To investigate how animals perceive these intermittent cues, we developed a behavioral task in which head-restrained mice made binary decisions based on the total number of discrete odor pulses presented stochastically over several seconds. Mice readily learned this task, and their performance was well-described by widely used decision models. Logistic regression of binary choices against the timing of odor pulses within the respiratory cycle revealed that mice placed higher perceptual weight to stimuli arriving during inhalation than exhalation, a phase dependency that strongly correlated with the magnitude of responses in olfactory sensory neurons. The population response of anterior piriform cortex (APCx) neurons to odor pulses was also modulated by respiration phase, although individual neurons displayed varying levels of phase-dependence. Single APCx neurons responded stochastically and transiently to odor pulses, leading to a representation that carries signatures of sensory evidence, but not its accumulation. Our study reveals that mice can integrate intermittent odor signals across dozens of breaths, but respiratory modulation of sensory inputs imposes limits on information acquisition that cortical circuits cannot overcome to improve behavior.

neuroscience↗

bioRxiv2024

Interpretable deep learning for deconvolutional analysis of neural signals

The widespread adoption of deep learning to build models that capture the dynamics of neural populations is typically based on "black-box" approaches that lack an interpretable link between neural activity and network parameters. Here, we propose to apply algorithm unrolling, a method for interpretable deep learning, to design the architecture of sparse deconvolutional neural networks and obtain a direct interpretation of network weights in relation to stimulus-driven single-neuron activity through a generative model. We characterize our method, referred to as deconvolutional unrolled neural learning (DUNL), and show its versatility by applying it to deconvolve single-trial local signals across multiple brain areas and recording modalities. To exemplify use cases of our decomposition method, we uncover multiplexed salience and reward prediction error signals from midbrain dopamine neurons in an unbiased manner, perform simultaneous event detection and characterization in somatosensory thalamus recordings, and characterize the heterogeneity of neural responses in the piriform cortex and in the striatum during unstructured, naturalistic experiments. Our work leverages the advances in interpretable deep learning to gain a mechanistic understanding of neural activity.

neuroscience↗