Biology subjects

Coen-Cagli, R.

Publications and source records attributed to Coen-Cagli, R..

11 recordsLinked to original sources

bioRxiv2026

Natural scene segmentation dynamics reveal iterative Bayesian inference

The visual system operates by segmenting visual inputs into distinct perceptual objects. Segmentation is dynamic, as revealed by the tempo of perceptual choices and neural activity in visual cortex. Dynamics for natural stimuli however, are poorly understood because natural scene segmentation is ambiguous and subjective. We measured subjective human segmentation maps for natural images using an innovative paradigm, and uncovered richer spatiotemporal dynamics than predicted by current theories of segmentation. To explain these dynamics, we introduced Iterative Bayesian Inference algorithms for segmentation that iteratively integrate visual inputs with the prior expectation that objects are spatially compact. When visual inputs were consistent with such a spatial prior, iterative inference was faster. This predicted relationship between spatial prior and inferential dynamics was evident in our data, and correctly reflected each individual participants spatial biases. We conclude that iterative Bayesian inference sets the tempo for a fundamental function of natural vision.

neuroscience↗

bioRxiv2025

A Multimodal Framework for Understanding Perceptual Segmentation of Natural Scenes In Autism

Understanding how individuals organize complex natural scenes into perceptual segments is essential for explaining both typical and atypical sensory processing. In Autism Spectrum Disorder (ASD), differences in visual segmentation are widely documented. However, existing paradigms often rely on simplified stimuli and do not capture the dynamic, naturalistic processes underlying real world scene segmentation. To overcome those limitations, in this work we present a novel, multimodal experimental framework. The framework combines precisely timed, trial-based perceptual measurements with electroencephalography (EEG) to examine how neural activity aligns temporally with segmentation decisions, and with eye-tracking to examine visual exploration strategies and attention allocation. To demonstrate the framework, we report basic characterization results from a pilot study. Neurotypical (NT) participants and participants with ASD aged 16 years and above viewed natural scenes and textures and indicated whether two cued image regions belonged to the same or different perceptual segments. Each trial used a different pair of cues, and responses across trials enabled the estimation of subjectively perceived segmentation maps and the associated uncertainty. Both cohorts performed the task successfully and produced interpretable segmentation maps. Analysis of reaction time distributions, gaze profiles, and trial-aligned neural responses give us a window into cohort differences and the opportunity to study individual heterogeneity. Together, these findings establish a reproducible method for studying the temporal and spatial components of natural scene segmentation and illustrate that meaningful behavioral and neural distinctions in ASD may be studied with this approach. This framework provides a methodological bridge between controlled experimental stimuli and real-world perception in both neurotypical and neurodivergent populations.

neuroscience↗

bioRxiv2024

Measuring Stimulus Information Transfer Between Neural Populations through the Communication Subspace

Sensory processing arises from the communication between neural populations across multiple brain areas. While the widespread presence of neural response variability shared throughout a neural population limits the amount of stimulus-related information those populations can accurately represent, how this variability affects the interareal communication of sensory information is unknown. We propose a mathematical framework to understand the impact of neural population response variability on sensory information transmission. We combine linear Fisher information, a metric connecting stimulus representation and variability, with the framework of communication subspaces, which suggests that functional mappings between cortical populations are low-dimensional relative to the space of population activity patterns. From this, we partition Fisher information depending on the alignment between the population covariance and the mean tuning direction projected onto the communication subspace or its orthogonal complement. We provide mathematical and numerical analyses of our proposed decomposition of Fisher information and examine theoretical scenarios that demonstrate how to leverage communication subspaces for flexible routing and gating of stimulus information. This work will provide researchers investigating interareal communication with a theoretical lens through which to understand sensory information transmission and guide experimental design.

neuroscience↗

bioRxiv2022

Adult neurogenesis improves spatial information encoding in the mouse hippocampus

Adult neurogenesis is a unique form of neuronal plasticity in which newly generated neurons are integrated into the adult dentate gyrus in a process that is modulated by environmental stimuli. Adult-born neurons can contribute to spatial memory but it is unknown whether they alter neural representations of space in the hippocampus. Using in vivo two-photon calcium imaging, we found that mice that were previously housed in an enriched environment, which triggers an increase in neurogenesis, had increased spatial information encoding in the hippocampal dentate gyrus during novel context exposure. Ablating adult neurogenesis by prior focal irradiation of the hippocampus blocked the effect of enrichment and lowered spatial information content, as did the chemogenetic silencing of adult-born neurons. Both ablating neurogenesis and silencing adult-born neurons decreased the calcium activity rates of dentate gyrus neurons, resulting in a decreased amplitude of place-specific responses. These findings are in contrast to previous studies that suggested a predominantly inhibitory action for adult-born neurons. We propose that adult neurogenesis improves neural representations of space by increasing the gain of dentate gyrus neurons and thereby improving their ability to tune to spatial features. This mechanism may mediate the beneficial effects of environmental enrichment on spatial learning and memory.

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bioRxiv2022

Modeling the Diverse Effects of Divisive Normalization on Noise Correlations

Divisive normalization, a prominent descriptive model of neural activity, is employed by theories of neural coding across many different brain areas. Yet, the relationship between normalization and the statistics of neural responses beyond single neurons remains largely unexplored. Here we focus on noise correlations, a widely studied pairwise statistic, because its stimulus and state dependence plays a central role in neural coding. Existing models of covariability typically ignore normalization despite empirical evidence suggesting it affects correlation structure in neural populations. We therefore propose a pairwise stochastic divisive normalization model that accounts for the effects of normalization and other factors on covariability. We first show that normalization modulates noise correlations in qualitatively different ways depending on whether normalization is shared between neurons, and we discuss how to infer when normalization signals are shared. We then apply our model to calcium imaging data from mouse primary visual cortex (V1), and find that it accurately fits the data, often outperforming a popular alternative model of correlations. Our analysis indicates that normalization signals are often shared between V1 neurons in this dataset. Our model will enable quantifying the relation between normalization and covariability in a broad range of neural systems, which could provide new constraints on circuit mechanisms of normalization and their role in information transmission and representation. Author SummaryCortical responses are often variable across identical experimental conditions, and this variability is shared between neurons (noise correlations). These noise correlations have been studied extensively to understand how they impact neural coding and what mechanisms determine their properties. Here we show how correlations relate to divisive normalization, a mathematical operation widely adopted to describe how the activity of a neuron is modulated by other neurons via divisive gain control. We introduce the first statistical model of this relation. We extensively validate the model and investigate parameter inference in synthetic data. We find that our model, when applied to data from mouse visual cortex, outperforms a popular model of noise correlations that does not include normalization, and it reveals diverse influences of normalization on correlations. Our work demonstrates a framework to measure the relation between noise correlations and the parameters of the normalization model, which could become an indispensable tool for quantitative investigations of noise correlations in the wide range of neural systems that exhibit normalization.

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bioRxiv2021

Modeling Neural Variability in Deep Networks with Dropout

Computational neuroscience studies have shown that the structure of neural variability to an unchanged stimulus affects the amount of information encoded. Some artificial deep neural networks, e.g. those with Monte Carlo dropout layers, also have variable responses when the input is fixed. However, the structure of the trial-by-trial neural co-variance in neural networks with dropout has not been studied and its role in decoding accuracy is unknown. We studied the above questions in a convolutional neural network model with dropout in both the training and testing phase. We found that trial-by-trial correlation between neurons, i.e. noise correlation, is positive and low-dimensional. Neurons that are close in a feature map have larger noise correlation. These properties are surprisingly similar to the findings in the visual cortex. We further analyzed the alignment of the main axes of the covariance matrix. We found that different images share a common trial-by-trial noise covariance subspace, and they are aligned with the global signal covariance. The above evidence that the noise covariance is aligned with signal covariance suggests that noise covariance in dropout neural networks reduces network accuracy, which we further verified directly with a trial-shuffling procedure commonly used in neuroscience. These findings highlight a previously overlooked as-pect of dropout layers that can affect network performance. Such dropout networks could also potentially be a computational model of neural variability.

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bioRxiv2021

Redundancy between spectral and higher-order texture statistics for natural image segmentation

Visual texture, defined by local image statistics, provides important information to the human visual system for perceptual segmentation. Second-order or spectral statistics (equivalent to the Fourier power spectrum) are a well-studied segmentation cue. However, the role of higher-order statistics (HOS) in segmentation remains unclear, particularly for natural images. Recent experiments indicate that, in peripheral vision, the HOS of the widely adopted Portilla-Simoncelli texture model are a weak segmentation cue compared to spectral statistics, despite the fact that both are necessary to explain other perceptual phenomena and to support high-quality texture synthesis. Here we test whether this discrepancy reflects a property of natural image statistics. First, we observe that differences in spectral statistics across segments of natural images are redundant with differences in HOS. Second, using linear and nonlinear classifiers, we show that each set of statistics individually affords high performance in natural scenes and texture segmentation tasks, but combining spectral statistics and HOS produces relatively small improvements. Third, we find that HOS improve segmentation for a subset of images, although these images are difficult to identify. We also find that different subsets of HOS improve segmentation to a different extent, in agreement with previous physiological and perceptual work. These results show that the HOS add modestly to spectral statistics for natural image segmentation. We speculate that tuning to natural image statistics under resource constraints could explain the weak contribution of HOS to perceptual segmentation in human peripheral vision.

neuroscience↗

bioRxiv2020

Modelling the neural code in large populations of correlated neurons

Neurons respond selectively to stimuli, and thereby define a code that associates stimuli with population response patterns. Certain correlations within population responses (noise correlations) significantly impact the information content of the code, especially in large populations. Understanding the neural code thus necessitates response models that quantify the coding properties of modelled populations, while fitting large-scale neural recordings and capturing noise correlations. In this paper we propose a class of response model based on mixture models and exponential families. We show how to fit our models with expectation-maximization, and that they capture diverse variability and covariability in recordings of macaque primary visual cortex. We also show how they facilitate accurate Bayesian decoding, provide a closed-form expression for the Fisher information, and are compatible with theories of probabilistic population coding. Our framework could allow researchers to quantitatively validate the predictions of neural coding theories against both large-scale neural recordings and cognitive performance.

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bioRxiv2020

Neuronal variability reflects probabilistic inference tuned to natural image statistics

Neuronal activity in sensory cortex fluctuates over time and across repetitions of the same input. This variability is often considered detrimental to neural coding. The theory of neural sampling proposes instead that variability encodes the uncertainty of perceptual inferences. In primary visual cortex (V1), modulation of variability by sensory and non-sensory factors supports this view. However, it is unknown whether V1 variability reflects the statistical structure of visual inputs, as would be required for inferences correctly tuned to the statistics of the natural environment. Here we combine analysis of image statistics and recordings in macaque V1 to show that probabilistic inference tuned to natural image statistics explains the widely observed dependence between spike-count variance and mean, and the modulation of V1 activity and variability by spatial context in images. Our results show that the properties of a basic aspect of cortical responses -- their variability -- can be explained by a probabilistic representation tuned to naturalistic inputs.

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bioRxiv2020

Flexible contextual modulation of naturalistic texture perception in peripheral vision

Peripheral vision comprises most of our visual field, and is essential in guiding visual behavior. Its characteristic capabilities and limitations, which distinguish it from foveal vision, have been explained by the most influential theory of peripheral vision as the product of representing the visual input using summary-statistics. Despite its success, this account may provide a limited understanding of peripheral vision, because it neglects processes of perceptual grouping and segmentation. To test this hypothesis, we studied how contextual modulation, namely the modulation of the perception of a stimulus by its surrounds, interacts with segmentation in human peripheral vision. We used naturalistic textures, which are directly related to summary-statistics representations. We show that segmentation cues affect contextual modulation, and that this is not captured by our implementation of the summary-statistics model. We then characterize the effects of different texture statistics on contextual modulation, providing guidance for extending the model, as well as for probing neural mechanisms of peripheral vision.

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bioRxiv2018

Neural response variability and divisive normalization

Cortical responses to repeated presentations of a stimulus are variable. This variability is sensitive to experimental manipulations that are also known to engage divisive normalization: a widespread description of neural activity as the ratio of a numerator (the excitatory stimulus drive) and denominator (the normalization signal). Yet, we lack a framework to quantify the effects of normalization on response variability. We extended the standard normalization model, treating the numerator and denominator as stochastic quantities, and derived a method to infer the single-trial normalization strength, which cannot be measured directly. The model revealed a general reduction of response variability in macaque primary visual cortex for neurons that were more strongly normalized, and during trials in which normalization was inferred to be strong. This framework could enable a direct quantification of the impact of single-trial normalization on perceptual judgments, and can readily be applied to other sensory and non-sensory factors.

neuroscience↗