Neural spiking for causal inference, bioRxiv, 2018-01-26
AbstractWhen a neuron is driven beyond its threshold it spikes, and the fact that it does not communicate its continuous membrane potential is usually seen as a computational liability. Here we show that this spiking mechanism allows neurons to produce an unbiased estimate of their causal influence, and a way of approximating gradient descent learning. Importantly, neither activity of upstream neurons, which act as confounders, nor downstream non-linearities bias the results. By introducing a local discontinuity with respect to their input drive, we show how spiking enables neurons to solve causal estimation and learning problems.
biorxiv neuroscience 100-200-users 2018Widespread and targeted gene expression by systemic AAV vectors Production, purification, and administration, bioRxiv, 2018-01-12
ABSTRACTWe recently developed novel AAV capsids for efficient and noninvasive gene transfer across the central and peripheral nervous systems. In this protocol, we describe how to produce and systemically administer AAV-PHP viruses to label andor genetically manipulate cells in the mouse nervous system and organs including the heart. The procedure comprises three separate stages AAV production, intravenous delivery, and evaluation of transgene expression. The protocol spans eight days, excluding the time required to assess gene expression, and can be readily adopted by laboratories with standard molecular and cell culture capabilities. We provide guidelines for experimental design and choosing the capsid, cargo, and viral dose appropriate for the experimental aims. The procedures outlined here are adaptable to diverse biomedical applications, from anatomical and functional mapping to gene expression, silencing, and editing.
biorxiv neuroscience 0-100-users 2018Graph theory approaches to functional network organization in brain disorders A critique for a brave new small-world, bioRxiv, 2018-01-06
AbstractOver the past two decades, resting-state functional connectivity (RSFC) methods have provided new insights into the network organization of the human brain. Studies of brain disorders such as Alzheimer’s disease or depression have adapted tools from graph theory to characterize differences between healthy and patient populations. Here, we conducted a review of clinical network neuroscience, summarizing methodological details from 106 RSFC studies. Although this approach is prevalent and promising, our review identified four challenges. First, the composition of networks varied remarkably in terms of region parcellation and edge definition, which are fundamental to graph analyses. Second, many studies equated the number of connections across graphs, but this is conceptually problematic in clinical populations and may induce spurious group differences. Third, few graph metrics were reported in common, precluding meta-analyses. Fourth, some studies tested hypotheses at one level of the graph without a clear neurobiological rationale or considering how findings at one level (e.g., global topology) are contextualized by another (e.g., modular structure). Based on these themes, we conducted network simulations to demonstrate the impact of specific methodological decisions on case-control comparisons. Finally, we offer suggestions for promoting convergence across clinical studies in order to facilitate progress in this important field.
biorxiv neuroscience 0-100-users 2018Acoustically Targeted Chemogenetics for Noninvasive Control of Neural Circuits, bioRxiv, 2018-01-02
ABSTRACTNeurological and psychiatric diseases often involve the dysfunction of specific neural circuits in particular regions of the brain. Existing treatments, including drugs and implantable brain stimulators, aim to modulate the activity of these circuits, but are typically not cell type-specific, lack spatial targeting or require invasive procedures. Here, we introduce an approach to modulating neural circuits noninvasively with spatial, cell-type and temporal specificity. This approach, called acoustically targeted chemogenetics, or ATAC, uses transient ultrasonic opening of the blood brain barrier to transduce neurons at specific locations in the brain with virally-encoded engineered G-protein-coupled receptors, which subsequently respond to systemically administered bio-inert compounds to activate or inhibit the activity of these neurons. We demonstrate this concept in mice by using ATAC to noninvasively modify and subsequently activate or inhibit excitatory neurons within the hippocampus, showing that this enables pharmacological control of memory formation. This technology allows a brief, noninvasive procedure to make one or more specific brain regions capable of being selectively modulated using orally bioavailable compounds, thereby overcoming some of the key limitations of conventional brain therapies.
biorxiv neuroscience 100-200-users 2018Confidence modulates exploration and exploitation in value-based learning, bioRxiv, 2017-12-29
AbstractUncertainty is ubiquitous in cognitive processing, which is why agents require a precise handle on how to deal with the noise inherent in their mental operations. Previous research suggests that people possess a remarkable ability to track and report uncertainty, often in the form of confidence judgments. Here, we argue that humans use uncertainty inherent in their representations of value beliefs to arbitrate between exploration and exploitation. Such uncertainty is reflected in explicit confidence judgments. Using a novel variant of a multi-armed bandit paradigm, we studied how beliefs were formed and how uncertainty in the encoding of these value beliefs (belief confidence) evolved over time. We found that people used uncertainty to arbitrate between exploration and exploitation, reflected in a higher tendency towards exploration when their confidence in their value representations was low. We furthermore found that value uncertainty can be linked to frameworks of metacognition in decision making in two ways. First, belief confidence drives decision confidence—that is people’s evaluation of their own choices. Second, individuals with higher metacognitive insight into their choices were also better at tracing the uncertainty in their environment. Together, these findings argue that such uncertainty representations play a key role in the context of cognitive control.
biorxiv neuroscience 100-200-users 2017Deep image reconstruction from human brain activity, bioRxiv, 2017-12-29
Machine learning-based analysis of human functional magnetic resonance imaging (fMRI) patterns has enabled the visualization of perceptual content. However, it has been limited to the reconstruction with low-level image bases or to the matching to exemplars. Recent work showed that visual cortical activity can be decoded (translated) into hierarchical features of a deep neural network (DNN) for the same input image, providing a way to make use of the information from hierarchical visual features. Here, we present a novel image reconstruction method, in which the pixel values of an image are optimized to make its DNN features similar to those decoded from human brain activity at multiple layers. We found that the generated images resembled the stimulus images (both natural images and artificial shapes) and the subjective visual content during imagery. While our model was solely trained with natural images, our method successfully generalized the reconstruction to artificial shapes, indicating that our model indeed reconstructs or generates images from brain activity, not simply matches to exemplars. A natural image prior introduced by another deep neural network effectively rendered semantically meaningful details to reconstructions by constraining reconstructed images to be similar to natural images. Furthermore, human judgment of reconstructions suggests the effectiveness of combining multiple DNN layers to enhance visual quality of generated images. The results suggest that hierarchical visual information in the brain can be effectively combined to reconstruct perceptual and subjective images.
biorxiv neuroscience 500+-users 2017