Biology subjects

Aoi, M.

Publications and source records attributed to Aoi, M..

3 recordsLinked to original sources

bioRxiv2024

Accept-reject decision-making revealed via a quantitative and ethological study of C. elegans foraging

Decision-making is a ubiquitous component of animal behavior that is often studied in the context of foraging. Foragers make a series of decisions while locating food (food search), choosing between food types (diet or patch choice), and allocating time spent within patches of food (patch-leaving). Here, we introduce a framework for investigating foraging decisions using detailed analysis of individual behavior and quantitative modeling in the nematode Caenorhabditis elegans. We demonstrate that C. elegans make accept-reject patch choice decisions upon encounter with food. Specifically, we show that when foraging amongst small, dispersed, and dilute patches of bacteria, C. elegans initially reject several bacterial patches, opting to prioritize exploration of the environment, before switching to a more exploitatory foraging strategy during subsequent encounters. Observed across a range of bacterial patch densities, sizes, and distributions, we use a quantitative model to show that this decision to explore or exploit is guided by available sensory information, internal satiety signals, and learned environmental statistics related to the bacterial density of recently encountered and exploited patches. We behaviorally validated model predictions on animals that had been food-deprived, animals foraging in environments with multiple patch densities, and null mutants with defective sensory modalities. Broadly, we present a framework to study ecologically relevant foraging decisions that could guide future investigations into the cellular and molecular mechanisms underlying decision-making.

neuroscience↗

bioRxiv2023

The effective number of shared dimensions: A simple method for revealing shared structure between datasets

A number of recent studies have sought to understand the behavior of artificial and biological neural networks by comparing representations across layers, networks and brain areas. Simultaneously, there has been growing interest in using dimensionality of a dataset as a proxy for computational complexity. At the intersection of these topics, studies exploring the dimensionality of shared computational and representational subspaces have relied on model-based methods, but a standard, model-free measure is lacking. Here we present a candidate measure for shared dimensionality that we call the effective number of shared dimensions (ENSD). The ENSD can be applied to data matrices sharing at least one dimension, reduces to the well-known participation ratio when both data sets are equivalent and has a number of other robust and intuitive mathematical properties. Notably, the ENSD can be written as a similarity metric that is a re-scaled version of centered kernel alignment (CKA) but additionally describes the dimensionality of the aligned subspaces. Unlike methods like canonical correlation analysis (CCA), the ENSD is robust to cases where data is sparse or low rank. We demonstrate its utility and computational efficiency by a direct comparison of CKA and ENSD on across-layer similarities in convolutional neural networks as well as by recovering results from recent studies in neuroscience on communication subspaces between brain regions. Finally, we demonstrate how the ENSD and its constituent statistics allow us to perform a variety of multi-modal analyses of multivariate datasets. Specifically, we use connectomic data to probe the alignment of parallel pathways in the fly olfactory system, revealing novel results in the interaction between innate and learned olfactory representations. Altogether, we show that the ENSD is an interpretable and computationally efficient model-free measure of shared dimensionality and that it can be used to probe shared structure in a wide variety of data types.

neuroscience↗

bioRxiv2017

Scalable Bayesian inference for high-dimensional neural receptive fields

We examine the problem of rapidly and efficiently estimating a neurons linear receptive field (RF) from responses to high-dimensional stimuli. This problem poses important statistical and computational challenges. Statistical challenges arise from the need for strong regularization when using correlated stimuli in high-dimensional parameter spaces, while computational challenges arise from extensive time and memory costs associated with evidence-optimization and inference in high-dimensional settings. Here we focus on novel methods for scaling up automatic smoothness determination (ASD), an empirical Bayesian method for RF estimation, to high-dimensional settings. First, we show that using a zero-padded Fourier domain representation and a \"coarse-to-fine\" evidence optimization strategy gives substantial improvements in speed and memory, while maintaining exact numerical accuracy. We then introduce a suite of scalable approximate methods that exploit Kronecker and Toeplitz structure in the stimulus autocovariance, which can be related to the method of expected log-likelihoods [1]. When applied together, these methods reduce the cost of estimating an RF with tensor order D and d coefficients per tensor dimension from O(d3D) time and O(d2D) space for standard ASD to O(Dd log d) time and O(Dd) space. We show that evidence optimization for a linear RF with 160K coefficients using 5K samples of data can be carried out on a laptop in < 2s.

neuroscience↗