ABSTRACT
We describe an architecture for organizing, integrating and sharing neurophysiology data within a single laboratory or across a group of collaborators. It comprises a database linking data files to metadata and electronic laboratory notes; a module collecting data from multiple laboratories into one location; a protocol for searching and sharing data and a module for automatic analyses that populates a website. These modules can be used together or individually, by single laboratories or worldwide collaborations.
Subject(s)
Laboratories , Neurophysiology , Databases, FactualABSTRACT
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.
Subject(s)
Learning , Visual Cortex/physiology , Animals , Macaca mulatta , Male , Neural Networks, Computer , Neurons/physiology , Photic Stimulation , Visual Cortex/chemistry , Wavelet AnalysisABSTRACT
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 emerged for modeling these nonlinear computations: transfer learning from artificial neural networks trained on object recognition and data-driven convolutional neural network models trained end-to-end on large populations of neurons. Here, we test the ability of both approaches to predict spiking activity in response to natural images in V1 of awake monkeys. 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.
Subject(s)
Models, Neurological , Neural Networks, Computer , Visual Cortex/physiology , Visual Perception/physiology , Algorithms , Animals , Computational Biology , Macaca mulatta/physiology , Male , Neurons/physiologyABSTRACT
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 NIH's 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, reuse and training in the neuroscience community.
ABSTRACT
The study of the brain's representations of uncertainty is a central topic in neuroscience. Unlike most quantities of which the neural representation is studied, uncertainty is a property of an observer's beliefs about the world, which poses specific methodological challenges. We analyze how the literature on the neural representations of uncertainty addresses those challenges and distinguish between 'code-driven' and 'correlational' approaches. Code-driven approaches make assumptions about the neural code for representing world states and the associated uncertainty. By contrast, correlational approaches search for relationships between uncertainty and neural activity without constraints on the neural representation of the world state that this uncertainty accompanies. To compare these two approaches, we apply several criteria for neural representations: sensitivity, specificity, invariance and functionality. Our analysis reveals that the two approaches lead to different but complementary findings, shaping new research questions and guiding future experiments.
Subject(s)
Neurosciences , UncertaintyABSTRACT
We are now in the era of millimeter-scale electron microscopy (EM) volumes collected at nanometer resolution (Shapson-Coe et al., 2021; Consortium et al., 2021). Dense reconstruction of cellular compartments in these EM volumes has been enabled by recent advances in Machine Learning (ML) (Lee et al., 2017; Wu et al., 2021; Lu et al., 2021; Macrina et al., 2021). Automated segmentation methods can now yield exceptionally accurate reconstructions of cells, but despite this accuracy, laborious post-hoc proofreading is still required to generate large connectomes free of merge and split errors. The elaborate 3-D meshes of neurons produced by these segmentations contain detailed morphological information, from the diameter, shape, and branching patterns of axons and dendrites, down to the fine-scale structure of dendritic spines. However, extracting information about these features can require substantial effort to piece together existing tools into custom workflows. Building on existing open-source software for mesh manipulation, here we present "NEURD", a software package that decomposes each meshed neuron into a compact and extensively-annotated graph representation. With these feature-rich graphs, we implement workflows for state of the art automated post-hoc proofreading of merge errors, cell classification, spine detection, axon-dendritic proximities, and other features that can enable many downstream analyses of neural morphology and connectivity. NEURD can make these new massive and complex datasets more accessible to neuroscience researchers focused on a variety of scientific questions.
ABSTRACT
Sensory data about most natural task-relevant variables are entangled with task-irrelevant nuisance variables. The neurons that encode these relevant signals typically constitute a nonlinear population code. Here we present a theoretical framework for quantifying how the brain uses or decodes its nonlinear information. Our theory obeys fundamental mathematical limitations on information content inherited from the sensory periphery, describing redundant codes when there are many more cortical neurons than primary sensory neurons. The theory predicts that if the brain uses its nonlinear population codes optimally, then more informative patterns should be more correlated with choices. More specifically, the theory predicts a simple, easily computed quantitative relationship between fluctuating neural activity and behavioral choices that reveals the decoding efficiency. This relationship holds for optimal feedforward networks of modest complexity, when experiments are performed under natural nuisance variation. We analyze recordings from primary visual cortex of monkeys discriminating the distribution from which oriented stimuli were drawn, and find these data are consistent with the hypothesis of near-optimal nonlinear decoding.
Subject(s)
Primary Visual Cortex/metabolism , Algorithms , Animals , Brain/metabolism , Models, Neurological , Models, Theoretical , Neurons/metabolismABSTRACT
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 tested this hypothesis by simultaneously recording population activity from 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.
Subject(s)
Models, Neurological , Neurons/physiology , Uncertainty , Visual Cortex/physiology , Animals , Decision Making/physiology , Macaca mulatta , Male , Orientation/physiology , Visual Perception/physiologyABSTRACT
Finding sensory stimuli that drive neurons optimally is central to understanding information processing in the brain. However, optimizing sensory input is difficult due to the predominantly nonlinear nature of sensory processing and high dimensionality of the input. We developed 'inception loops', a closed-loop experimental paradigm combining in vivo recordings from thousands of neurons with in silico nonlinear response modeling. Our end-to-end trained, deep-learning-based model predicted thousands of neuronal responses to arbitrary, new natural input with high accuracy and was used to synthesize optimal stimuli-most exciting inputs (MEIs). For mouse primary visual cortex (V1), MEIs exhibited complex spatial features that occurred frequently in natural scenes but deviated strikingly from the common notion that Gabor-like stimuli are optimal for V1. When presented back to the same neurons in vivo, MEIs drove responses significantly better than control stimuli. Inception loops represent a widely applicable technique for dissecting the neural mechanisms of sensation.
Subject(s)
Models, Neurological , Neurons/physiology , Visual Cortex/physiology , Animals , Computer Simulation , Eye Movements/physiology , Female , Male , Mice , Mice, Transgenic , Nonlinear Dynamics , Photic Stimulation/methods , Visual Perception/physiologyABSTRACT
The critique of Barth et al centers on three points: (i) the completeness of our study is overstated; (ii) the connectivity matrix we describe is biased by technical limitations of our brain-slicing and multipatching methods; and (iii) our cell classification scheme is arbitrary and we have simply renamed previously identified interneuron types. We address these criticisms in our Response.
Subject(s)
Interneurons , Neocortex , Adult , HumansABSTRACT
The McGurk effect is an illusion in which visual speech information dramatically alters the perception of auditory speech. However, there is a high degree of individual variability in how frequently the illusion is perceived: some individuals almost always perceive the McGurk effect, while others rarely do. Another axis of individual variability is the pattern of eye movements make while viewing a talking face: some individuals often fixate the mouth of the talker, while others rarely do. Since the talker's mouth carries the visual speech necessary information to induce the McGurk effect, we hypothesized that individuals who frequently perceive the McGurk effect should spend more time fixating the talker's mouth. We used infrared eye tracking to study eye movements as 40 participants viewed audiovisual speech. Frequent perceivers of the McGurk effect were more likely to fixate the mouth of the talker, and there was a significant correlation between McGurk frequency and mouth looking time. The noisy encoding of disparity model of McGurk perception showed that individuals who frequently fixated the mouth had lower sensory noise and higher disparity thresholds than those who rarely fixated the mouth. Differences in eye movements when viewing the talker's face may be an important contributor to interindividual differences in multisensory speech perception.
Subject(s)
Fixation, Ocular/physiology , Illusions/physiology , Individuality , Speech Perception/physiology , Visual Perception/physiology , Adult , Female , Humans , Male , MouthABSTRACT
One promising neurorehabilitation therapy involves presenting neurotrophins directly into the brain to induce growth of new neural connections. The precise control of neurotrophin concentration gradients deep within neural tissue that would be necessary for such a therapy is not currently possible, however. Here we evaluate the theoretical potential of a novel method of drug delivery, discrete controlled release (DCR), to control effective neurotrophin concentration gradients in an isotropic region of neocortex. We do so by constructing computational models of neurotrophin concentration profiles resulting from discrete release locations into the cortex and then optimizing their design for uniform concentration gradients. The resulting model indicates that by rationally selecting initial neurotrophin concentrations for drug-releasing electrode coatings in a square 16-electrode array, nearly uniform concentration gradients (i.e. planar concentration profiles) from one edge of the electrode array to the other should be obtainable. DCR therefore represents a promising new method of precisely directing neuronal growth in vivo over a wider spatial profile than would be possible with single release points.