MR-guided stereotactic navigation

Functional magnetic resonance imaging allows precise localization of brain regions specialized for different perceptual and higher cognitive functions. However, targeting these deep brain structures for electrophysiology still remains a challenging task. Here, we propose a novel framework for MRI-stereotactic registration and chamber placement for precise electrode guidance to recording sites defined in MRI space. The proposed “floating frame” approach can be used without usage of ear bars, greatly reducing pain and discomfort common in standard stereotactic surgeries. Custom pre-surgery planning software was developed to automatically solve the registration problem and report the set of parameters needed to position a stereotactic manipulator to reach a recording site along arbitrary, non-vertical trajectories. Furthermore, the software can automatically identify blood vessels and assist in finding safe trajectories to targets. Our approach was validated by targeting different regions in macaque monkeys and rats. We expect that our method will facilitate recording in new brain areas and provide a valuable tool for electrophysiologists

A topological solution to object segmentation and tracking

The world is composed of objects, the ground, and the sky. Visual perception of objects requires solving two fundamental challenges: segmenting visual input into discrete units, and tracking identities of these units despite appearance changes due to object deformation, changing perspective, and dynamic occlusion. Current computer vision approaches to segmentation and tracking that approach human performance all require learning, raising the question: can objects be segmented and tracked without learning? Here, we show that the mathematical structure of light rays reflected from environment surfaces yields a natural representation of persistent surfaces, and this surface representation provides a solution to both the segmentation and tracking problems. We describe how to generate this surface representation from continuous visual input, and demonstrate that our approach can segment and invariantly track objects in cluttered synthetic video despite severe appearance changes, without requiring learning.Comment: 21 pages, 6 main figures, 3 supplemental figures, and supplementary material containing mathematical proof

The Macaque Face Patch System: A Window into Object Representation

The macaque brain contains a set of regions that show stronger fMRI activation to faces than other classes of object. This “face patch system” has provided a unique opportunity to gain insight into the organizing principles of IT cortex and to dissect the neural mechanisms underlying form perception, because the system is specialized to process one class of complex forms, and because its computational components are spatially segregated. Over the past 5 years, we have set out to exploit this system to clarify the nature of object representation in the brain through a multilevel approach combining electrophysiology, anatomy, and behavior. These experiments reveal (1) a remarkably precise connectivity of face patches to each other, (2) a functional hierarchy for representation of view-invariant identity comprising at least three distinct stages along the face patch system, and (3) the computational mechanisms used by cells in face patches to detect and recognize faces, including measurement of diagnostic local contrast features for detection and measurement of face feature values for recognition

Systems for Category-Selective Processing in the Macaque

fMRI studies in the mid and late 1990s described an area in the human brain that showed strongly increased blood flow in functional magnetic resonance imaging (fMRI) experiments when people viewed pictures of faces compared to pictures of objects (1). This seemed to offer an ideal potential preparation for tackling the problem of how the brain extracts global visual form: a small piece of brain specialized to encode a single visual form. Thus, 12 years ago, Winrich Freiwald and I began a journey into exploring the neural basis of face processing. We decided to look for a face-selective area in macaque monkeys, reasoning that it would not be unreasonable to find such a region in monkeys, since face recognition is also integral to macaques—and most importantly, if we did find such a region, then we could target an electrode to the region (something not possible in humans) and directly record from individual neurons to ask how they are encoding faces. In my talk, I will discuss the anatomical and functional organization of the macaque face processing system, as well as the more recently discovered macaque scene processing system. How are regions within these two systems system connected to each other and the rest of the brain? What representations are used in face and scene-selective regions? What is the contribution of different regions to behavior? What information is communicated between regions

Does finding a face cell tell us anything much at all?

There are two approaches to doing science. One is the “tractor” approach. You take a big, powerful piece of machinery and apply it in systematic fashion to a problem. Here, “you” is often a large group of people who share the same goal. A recent example is the International Brain Lab (Abbott et al., 2017), a consortium of labs across the world all performing the same experiment to understand visually-guided decision making in the rodent. The plan is for each lab to train mice on a common behavioral paradigm and then insert high channel count electrodes into different parts of the brain, like a fleet of tractors mowing a field. A very different, older approach is that of the lone hunter pursuing a question no one else cares about, guided by a vision in his or her own head. Auden captures the essence of this approach in his wonderful poem “History of Science.” The poem tells the tale of the Fourth Brother, who has been excised from the official fairy tale..

Does finding a face cell tell us anything much at all?

There are two approaches to doing science. One is the “tractor” approach. You take a big, powerful piece of machinery and apply it in systematic fashion to a problem. Here, “you” is often a large group of people who share the same goal. A recent example is the International Brain Lab (Abbott et al., 2017), a consortium of labs across the world all performing the same experiment to understand visually-guided decision making in the rodent. The plan is for each lab to train mice on a common behavioral paradigm and then insert high channel count electrodes into different parts of the brain, like a fleet of tractors mowing a field. A very different, older approach is that of the lone hunter pursuing a question no one else cares about, guided by a vision in his or her own head. Auden captures the essence of this approach in his wonderful poem “History of Science.” The poem tells the tale of the Fourth Brother, who has been excised from the official fairy tale..

Lie Group Model Neuromorphic Geometric Engine for Real-time Terrain Reconstruction from Stereoscopic Aerial Photos

In the 1980's, neurobiologist suggested a simple mechanism in primate visual cortex for maintaining a stable and invariant representation of a moving object: The receptive field of visual neurons has real-time transforms in response to motion, to maintain a stable representation. When the visual stimulus is changed due to motion, the geometric transform of the stimulus triggers a dual transform of the receptive field. This dual transform in the receptive fields compensates geometric variation in the stimulus. This process can be modelled using a Lie group method. The massive array of affine parameter sensing circuits will function as a smart sensor tightly coupled to the passive imaging sensor (retina) . Neural geometric engine is a neuromorphic computing device simulating our Lie group model of spatial perception of primate's primal visual cortex. We have developed the computer simulation and experimented on realistic and synthetic image data, and performed a preliminary research of using analog VLSI technology for implementation of the neural geometric engine. We have benchmark tested on DMA's terrain data with their result and have built an analog integrated circuit to verify the computational structure of the engine. When fully implemented on ANALOG VLSI chip, we will be able to accurately reconstruct 3-D terrain surface in real-time from stereoscopic imagery

An Open Resource for Non-human Primate Imaging

Non-human primate neuroimaging is a rapidly growing area of research that promises to transform and scale translational and cross-species comparative neuroscience. Unfortunately, the technological and methodological advances of the past two decades have outpaced the accrual of data, which is particularly challenging given the relatively few centers that have the necessary facilities and capabilities. The PRIMatE Data Exchange (PRIME-DE) addresses this challenge by aggregating independently acquired non-human primate magnetic resonance imaging (MRI) datasets and openly sharing them via the International Neuroimaging Data-sharing Initiative (INDI). Here, we present the rationale, design, and procedures for the PRIME-DE consortium, as well as the initial release, consisting of 25 independent data collections aggregated across 22 sites (total = 217 non-human primates). We also outline the unique pitfalls and challenges that should be considered in the analysis of non-human primate MRI datasets, including providing automated quality assessment of the contributed datasets

Consistency of Border-Ownership Cells across Artificial Stimuli, Natural Stimuli, and Stimuli with Ambiguous Contours

Segmentation and recognition of objects in a visual scene are two problems that are hard to solve separately from each other. When segmenting an ambiguous scene, it is helpful to already know the present objects and their shapes. However, for recognizing an object in clutter, one would like to consider its isolated segment alone to avoid confounds from features of other objects. Border-ownership cells (Zhou et al., 2000) appear to play an important role in segmentation, as they signal the side-of-figure of artificial stimuli. The present work explores the role of border-ownership cells in dorsal macaque visual areas V2 and V3 in the segmentation of natural object stimuli and locally ambiguous stimuli. We report two major results. First, compared with previous estimates, we found a smaller percentage of cells that were consistent across artificial stimuli used previously. Second, we found that the average response of those neurons that did respond consistently to the side-of-figure of artificial stimuli also consistently signaled, as a population, the side-of-figure for borders of single faces, occluding faces and, with higher latencies, even stimuli with illusory contours, such as Mooney faces and natural faces completely missing local edge information. In contrast, the local edge or the outlines of the face alone could not always evoke a significant border-ownership signal. Our results underscore that border ownership is coded by a population of cells, and indicate that these cells integrate a variety of cues, including low-level features and global object context, to compute the segmentation of the scene