Biology subjects

Walker, E. Y.

Publications and source records attributed to Walker, E. Y..

15 recordsLinked to original sources

bioRxiv2026

Neural representations of beliefs in a multi-dimensional inference task

Adaptive behavior requires maintaining and updating probabilistic beliefs about the world, yet how distributed brain circuits implement such computations remains unknown. We recorded from over 1,400 neurons across six brain regions in monkeys performing a multi-dimensional inference task requiring them to infer hidden rules through trial-and-error learning. Behavior was well-described by models based on Bayesian updating of beliefs over rule features. Neural representations of both observable variables (stimuli, rewards) and latent beliefs (rule preferences, confidence) were broadly distributed across hippocampus, amygdala, prefrontal cortex, anterior cingulate, striatum, and inferior temporal cortex. Belief representations were present throughout all task periods but exhibited region- and epoch-specific dynamics. Critically, trial-to-trial changes in population activity reflected Bayesian belief updating: neural responses evolved according to the integration of prior beliefs with new evidence. Additionally, we identified confidence representations that were independent of specific beliefs and showed distinct temporal profiles. These results demonstrate that probabilistic inference emerges from coordinated dynamics across distributed brain systems, with different regions contributing flexibly according to computational demands at different states of learning and decision-making.

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bioRxiv2025

Dual-feature selectivity enables bidirectional coding in visual cortical neurons

Sensory neurons are traditionally viewed as feature detectors that respond with an increase in firing rate to preferred stimuli while remaining unresponsive to others. Here, we identify a dual-feature encoding strategy in macaque visual cortex, wherein many neurons in areas V1 and V4 are selectively tuned to two distinct visual features--one that enhances and one that suppresses activity--around an elevated baseline firing rate. By combining neuronal recordings with functional digital twin models--deep learning-based predictive models of biological neurons--we were able to systematically identify each neurons preferred and non-preferred features. These feature pairs served as anchors for a continuous, low-dimensional axis in natural image similarity space, along which neuronal activity varied approximately linearly. Within a single visual area, visual features that strongly or weakly activated individual neurons also had a high probability of modulating the activity of other neurons, suggesting a shared feature selectivity across the population that structures stimulus encoding. We show that this encoding strategy is conserved across species, present in both primary and lateral visual areas of mouse cortex. Dual-feature selectivity is consistent with recent anatomical evidence for feature-specific inhibitory connectivity, complementing the feature-detector principle through circuit mechanisms in which selective excitation and inhibition may together enhance the representational capacity of the neuronal population.

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bioRxiv2024

Data science and its future in large neuroscience collaborations.

The rise of large scientific collaborations in neuroscience requires systematic, scalable, and reliable data management. How this is best done in practice remains an open question. To address this, we conducted a data science survey among currently active U19 grants, funded through the NIHs BRAIN Initiative. The survey was answered by both data science liaisons and Principal Investigators, speaking for [~]500 researchers across 21 nation-wide collaborations. We describe the tools, technologies, and methods currently in use, and identify several shortcomings of current data science practice. Building on this survey, we develop plans and propose policies to improve data collection, use, publication, re-use and training in the neuroscience community.

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bioRxiv2021

A flow-based latent state generative model of neural population responses to natural images

We present a joint deep neural system identification model for two major sources of neural variability: stimulus-driven and stimulus-conditioned fluctuations. To this end, we combine (1) state-of-the-art deep networks for stimulus-driven activity and (2) a flexible, normalizing flow-based generative model to capture the stimulus-conditioned variability including noise correlations. This allows us to train the model end-to-end without the need for sophisticated probabilistic approximations associated with many latent state models for stimulus-conditioned fluctuations. We train the model on the responses of thousands of neurons from multiple areas of the mouse visual cortex to natural images. We show that our model outperforms previous state-of-the-art models in predicting the distribution of neural population responses to novel stimuli, including shared stimulus-conditioned variability. Furthermore, it successfully learns known latent factors of the population responses that are related to behavioral variables such as pupil dilation, and other factors that vary systematically with brain area or retinotopic location. Overall, our model accurately accounts for two critical sources of neural variability while avoiding several complexities associated with many existing latent state models. It thus provides a useful tool for uncovering the interplay between different factors that contribute to variability in neural activity.

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bioRxiv2021

Functional connectomics spanning multiple areas of mouse visual cortex

To understand the brain we must relate neurons functional responses to the circuit architecture that shapes them. Here, we present a large functional connectomics dataset with dense calcium imaging of a millimeter scale volume. We recorded activity from approximately 75,000 neurons in primary visual cortex (VISp) and three higher visual areas (VISrl, VISal and VISlm) in an awake mouse viewing natural movies and synthetic stimuli. The functional data were co-registered with a volumetric electron microscopy (EM) reconstruction containing more than 200,000 cells and 0.5 billion synapses. Subsequent proofreading of a subset of neurons in this volume yielded reconstructions that include complete dendritic trees as well the local and inter-areal axonal projections that map up to thousands of cell-to-cell connections per neuron. Here, we release this dataset as an open-access resource to the scientific community including a set of tools that facilitate data retrieval and downstream analysis. In accompanying papers we describe our findings using the dataset to provide a comprehensive structural characterization of cortical cell types1-3 and the most detailed synaptic level connectivity diagram of a cortical column to date2, uncovering unique cell-type specific inhibitory motifs that can be linked to gene expression data4. Functionally, we identify new computational principles of how information is integrated across visual space5, characterize novel types of neuronal invariances6 and bring structure and function together to decipher a general principle that wires excitatory neurons within and across areas7, 8.

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bioRxiv2021

DataJoint Elements: Data Workflows for Neurophysiology

A new resource--DataJoint Elements--provides modular designs for assembling complete workflow solutions to organize data and computations for common neurophysiology experiments. The designs are derived from working solutions developed in leading research groups using the open-source DataJoint framework to integrate data collection and analysis in collaborative workflows.

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bioRxiv2020

Generalization in data-driven models of primary visual cortex

AO_SCPLOWBSTRACTC_SCPLOWDeep neural networks (DNN) have set new standards at predicting responses of neural populations to visual input. Most such DNNs consist of a convolutional network (core) shared across all neurons which learns a representation of neural computation in visual cortex and a neuron-specific readout that linearly combines the relevant features in this representation. The goal of this paper is to test whether such a representation is indeed generally characteristic for visual cortex, i.e. gener-alizes between animals of a species, and what factors contribute to obtaining such a generalizing core. To push all non-linear computations into the core where the generalizing cortical features should be learned, we devise a novel readout that reduces the number of parameters per neuron in the readout by up to two orders of magnitude compared to the previous state-of-the-art. It does so by taking advantage of retinotopy and learns a Gaussian distribution over the neurons receptive field po-sition. With this new readout we train our network on neural responses from mouse primary visual cortex (V1) and obtain a gain in performance of 7% compared to the previous state-of-the-art network. We then investigate whether the convolutional core indeed captures general cortical features by using the core in transfer learning to a different animal. When transferring a core trained on thousands of neurons from various animals and scans we exceed the performance of training directly on that animal by 12%, and outperform a commonly used VGG16 core pre-trained on imagenet by 33%. In addition, transfer learning with our data-driven core is more data-efficient than direct training, achieving the same performance with only 40% of the data. Our model with its novel readout thus sets a new state-of-the-art for neural response prediction in mouse visual cortex from natural images, generalizes between animals, and captures better characteristic cortical features than current task-driven pre-training approaches such as VGG16.

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bioRxiv2020

Object manifold geometry across the mouse cortical visual hierarchy

Despite variations in appearance, we recognize objects robustly. Neuronal populations responding to objects presented under varying transformations form object manifolds, and hierarchically organized visual areas are hypothesized to untangle pixel intensities into linearly decodable object representations. However, the associated changes in the geometry of the object manifolds along the cortex remain unknown. Using home cage training, we showed that mice are capable of invariant object recognition. We recorded the responses of thousands of neurons to measure the information about object identity across the visual cortex and found that the lateral areas LM, LI, and AL carry more linearly decodable object information compared to other visual areas. We applied the theory of linear separability of manifolds and found that the increase in classification capacity is associated with a decrease in the dimension and radius of the object manifold, identifying the key features in the geometry of the population neural code that are associated with invariant object coding.

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bioRxiv2019

Data architecture and visualization for a large-scale neuroscience collaboration

Effective data management is a major challenge for neuroscience labs, and even greater for collaborative projects. In the International Brain laboratory (IBL), ten experimental labs spanning 7 geographically distributed sites measure neural activity across the brains of mice making perceptual decisions. Here, we report a novel, modular architecture that allows users to contribute, access, and analyze data across this collaboration. Users contribute data using a web-based electronic lab notebook (Alyx), which automatically registers recorded data files and uploads them to a central server. Users access data with a lightweight interface, the Open Neurophysiology Environment (ONE), which searches data from all labs and loads it into MATLAB or Python. To analyze data, we have developed pipelines based on DataJoint, which automatically populate a website displaying a graphical summary of results to date. This architecture provides a new framework to contribute, access and analyze data, surmounting many challenges currently faced by neuroscientists.

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bioRxiv2019

Learning Divisive Normalization in Primary Visual Cortex

Divisive normalization (DN) is a prominent computational building block in the brain that has been proposed as a canonical cortical operation. Numerous experimental studies have verified its importance for capturing nonlinear neural response properties to simple, artificial stimuli, and computational studies suggest that DN is also an important component for processing natural stimuli. However, we lack quantitative models of DN that are directly informed by measurements of spiking responses in the brain and applicable to arbitrary stimuli. Here, we propose a DN model that is applicable to arbitrary input images. We test its ability to predict how neurons in macaque primary visual cortex (V1) respond to natural images, with a focus on nonlinear response properties within the classical receptive field. Our model consists of one layer of subunits followed by learned orientation-specific DN. It outperforms linear-nonlinear and wavelet-based feature representations and makes a significant step towards the performance of state-of-the-art convolutional neural network (CNN) models. Unlike deep CNNs, our compact DN model offers a direct interpretation of the nature of normalization. By inspecting the learned normalization pool of our model, we gained insights into a long-standing question about the tuning properties of DN that update the current textbook description: we found that within the receptive field oriented features were normalized preferentially by features with similar orientation rather than non-specifically as currently assumed. Author summaryDivisive normalization (DN) is a computational building block throughout sensory processing in the brain. We currently lack an understanding of what role this normalization mechanism plays when processing complex stimuli like natural images. Here, we use modern machine learning methods to build a general DN model that is directly informed by data from primary visual cortex (V1). Contrary to high-predictive deep learning models, our DN-based models parameters offer a straightforward interpretation of the nature of normalization. Within the receptive field, we found that neurons responding strongly to a specific orientation are preferentially normalized by other neurons that are highly active for similar orientations, rather than being normalized by all neurons as it is currently assumed by textbook models.

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bioRxiv2019

A global map of orientation tuning in mouse visual cortex

In primates and most carnivores, neurons in primary visual cortex are spatially organized by their functional properties. For example, neurons with similar orientation preferences are grouped together in iso-orientation domains that smoothly vary over the cortical sheet. In rodents, on the other hand, neurons with different orientation preferences are thought to be spatially intermingled, a feature which has been termed \"salt-and-pepper\" organization. The apparent absence of any systematic structure in orientation tuning has been considered a defining feature of the rodent visual system for more than a decade, with broad implications for brain development, visual processing, and comparative neurophysiology. Here, we revisited this question using new techniques for wide-field two-photon calcium imaging that enabled us to collect nearly complete population tuning preferences in layers 2-4 across a large fraction of the mouse visual hierarchy. Examining the orientation tuning of these hundreds of thousands of neurons, we found a global map spanning multiple visual cortical areas in which orientation bias was organized around a single pinwheel centered in V1. This pattern was consistent across animals and cortical depth. The existence of this global organization in rodents has implications for our understanding of visual processing and the principles governing the ontogeny and phylogeny of the visual cortex of mammals.

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bioRxiv2018

Inception in visual cortex: in vivo-silico loops reveal most exciting images

Much of our knowledge about sensory processing in the brain is based on quasi-linear models and the stimuli that optimally drive them. However, sensory information processing is nonlinear, even in primary sensory areas, and optimizing sensory input is difficult due to the high-dimensional input space. We developed inception loops, a closed-loop experimental paradigm that combines in vivo recordings with in silico nonlinear response modeling to identify the Most Exciting Images (MEIs) for neurons in mouse V1. When presented back to the brain, MEIs indeed drove their target cells significantly better than the best stimuli identified by linear models. The MEIs exhibited complex spatial features that deviated from the textbook ideal of V1 as a bank of Gabor filters. Inception loops represent a widely applicable new approach to dissect the neural mechanisms of sensation.

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bioRxiv2018

Stimulus domain transfer in recurrent models for large scale cortical population prediction on video

To better understand the representations in visual cortex, we need to generate better predictions of neural activity in awake animals presented with their ecological input: natural video. Despite recent advances in models for static images, models for predicting responses to natural video are scarce and standard linear-nonlinear models perform poorly. We developed a new deep recurrent network architecture that predicts inferred spiking activity of thousands of mouse V1 neurons simulta-neously recorded with two-photon microscopy, while accounting for confounding factors such as the animals gaze position and brain state changes related to running state and pupil dilation. Powerful system identification models provide an opportunity to gain insight into cortical functions through in silico experiments that can subsequently be tested in the brain. However, in many cases this approach requires that the model is able to generalize to stimulus statistics that it was not trained on, such as band-limited noise and other parameterized stimuli. We investigated these domain transfer properties in our model and find that our model trained on natural images is able to correctly predict the orientation tuning of neurons in responses to artificial noise stimuli. Finally, we show that we can fully generalize from movies to noise and maintain high predictive performance on both stimulus domains by fine-tuning only the final layers weights on a network otherwise trained on natural movies. The converse, however, is not true.

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bioRxiv2018

A neural basis of probabilistic computation in visual cortex

Bayesian models of behavior suggest that organisms represent uncertainty associated with sensory variables. However, the neural code of uncertainty remains elusive. A central hypothesis is that uncertainty is encoded in the population activity of cortical neurons in the form of likelihood functions. We studied the neural code of uncertainty by simultaneously recording population activity from the primate visual cortex during a visual categorization task in which trial-to-trial uncertainty about stimulus orientation was relevant for the decision. We decoded the likelihood function from the trial-to-trial population activity and found that it predicted decisions better than a point estimate of orientation. This remained true when we conditioned on the true orientation, suggesting that internal fluctuations in neural activity drive behaviorally meaningful variations in the likelihood function. Our results establish the role of population-encoded likelihood functions in mediating behavior, and provide a neural underpinning for Bayesian models of perception.

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bioRxiv2017

Deep convolutional models improve predictions of macaque V1 responses to natural images

Despite great efforts over several decades, our best models of primary visual cortex (V1) still predict spiking activity quite poorly when probed with natural stimuli, highlighting our limited understanding of the nonlinear computations in V1. Recently, two approaches based on deep learning have been successfully applied to neural data: On the one hand, transfer learning from networks trained on object recognition worked remarkably well for predicting neural responses in higher areas of the primate ventral stream, but has not yet been used to model spiking activity in early stages such as V1. On the other hand, data-driven models have been used to predict neural responses in the early visual system (retina and V1) of mice, but not primates. Here, we test the ability of both approaches to predict spiking activity in response to natural images in V1 of awake monkeys. Even though V1 is rather at an early to intermediate stage of the visual system, we found that the transfer learning approach performed similarly well to the data-driven approach and both outperformed classical linear-nonlinear and wavelet-based feature representations that build on existing theories of V1. Notably, transfer learning using a pre-trained feature space required substantially less experimental time to achieve the same performance. In conclusion, multi-layer convolutional neural networks (CNNs) set the new state of the art for predicting neural responses to natural images in primate V1 and deep features learned for object recognition are better explanations for V1 computation than all previous filter bank theories. This finding strengthens the necessity of V1 models that are multiple nonlinearities away from the image domain and it supports the idea of explaining early visual cortex based on high-level functional goals.\n\nAuthor summaryPredicting the responses of sensory neurons to arbitrary natural stimuli is of major importance for understanding their function. Arguably the most studied cortical area is primary visual cortex (V1), where many models have been developed to explain its function. However, the most successful models built on neurophysiologists intuitions still fail to account for spiking responses to natural images. Here, we model spiking activity in primary visual cortex (V1) of monkeys using deep convolutional neural networks (CNNs), which have been successful in computer vision. We both trained CNNs directly to fit the data, and used CNNs trained to solve a high-level task (object categorization). With these approaches, we are able to outperform previous models and improve the state of the art in predicting the responses of early visual neurons to natural images. Our results have two important implications. First, since V1 is the result of several nonlinear stages, it should be modeled as such. Second, functional models of entire visual pathways, of which V1 is an early stage, do not only account for higher areas of such pathways, but also provide useful representations for V1 predictions.

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