Mehrdad Jazayeri, Ph.D. - US grants

DESCRIPTION (provided by applicant): The brain's capacity to sense and produce time intervals flexibly is an essential building block of many cognitive functions and sensorimotor skills. Yet, the neural mechanisms for measuring and producing an interval of time are unknown. We will take advantage of the foundational work on the anatomy and physiology of the primate oculomotor system to assess the mechanisms of interval timing in the oculomotor circuits of the brain. First, we will focus on the lateral intraparietal area (LIP) of the associaton cortex, where correlates of elapsed time have been reported previously. We will record from LIP neurons in an oculomotor time reproduction task to test the hypothesis that LIP response dynamics track the animal's estimate of elapsed time during both measurement and production of time intervals. Second, we will use optogenetics to excite and suppress neural activity in LIP at high temporal resolution to ask whether and how LIP plays a causal role in the perception and production of time intervals. Third, we will examine the neural signals at the output node of the lateral cerebellum (i.e., the dentate nucleus, DN), which is thought to play an important role in timing and temporal coordination. Based on previous work suggesting a role for the lateral cerebellum in non-motor timing, we will test the hypothesis that DN signals encode elapsed time during measurement of time intervals. These experiments combine multiple innovations including a novel behavioral paradigm and cutting-edge technology and have the potential to make a significant contribution to our understanding of the neural mechanisms of interval timing in the primate brain.

DESCRIPTION (provided by applicant): The links between neural activity, perception and cognition are poorly understood. This proposal advances color as a model system to fill these gaps in knowledge. Color is an essential feature of visual experience, and much is known about how cone signals from the eye are encoded and transmitted to the cortex. But surprisingly little is known about the mechanisms that decode these signals to bring about perceived colors and guide perceptual decisions. Two competing decoding schemes have been proposed: an interval code, which requires a population of cells with sharp chromatic tuning that together encompass all color space, coupled with a winner- take-all rule; and a population code, which needs at minimum two groups of color-tuned neurons, coupled with a weighted-average rule. It is unclear which groups of neurons within the cerebral cortex are involved. One hint comes from lesions of inferior temporal cortex (IT) in rhesus monkeys, which cause profound color blindness similar to the achromatopsia that accompanies certain cerebral strokes in humans. IT is an expansive region of tissue implicated in many aspects of object coding, and the functional organization of IT is poorly understood. Without this information, it is almost impossible to know which neurons are the most likely to be contributing to color processing. Possible organizational schemes include a modular model comprising uniquely specialized areas; a distributed-processing model; or a hybrid model, consisting of a series of hierarchical stages, each comprising a full complement of functional subregions. Aim 1 calls for a battery of functional magnetic resonance imaging (fMRI) experiments in alert monkey that will determine the distribution of color-coding regions in IT, their functional connectivity and relationship to other functionally defined regions to test which model accounts for the organization of IT. Aim 2 outlines fMRI-guided microelectrode recordings of IT color regions paired with microstimulation while monkeys perform color tasks, to test the causal link between neural activity and perceived color, and to determine which of the two decoding schemes, interval or population, is implemented in IT. The research will uncover principles by which perception and cognition emerge from the activity of neural circuits. This information is required to understand the etiology, diagnosis, and treatment of mental illnesses and strokes that impair cognition and perception. Moreover, the work will establish the relationship of higher-order areas between humans and monkeys, which is necessary in order to use monkeys as models of human vision and disease.

Recurrent interactions between neurons generate dynamic patterns of activity that serve as a substrate for behaviorally relevant computations. However, we do not yet have a principled framework for relating neural dynamics to neural computations. We have recently synthesized a theory that explains how low-rank recurrent neural networks may serve as a building block for computations. Our overarching goal is to integrate insights from this theory with behavior and electrophysiology in awake, behaving monkeys to establish a principled framework relating neural dynamics to neural computations. The project will start with reverse engineering low-rank network models that capture cortical dynamics in simple timing tasks. We then move systematically toward progressively higher rank network models that can perform timing tasks with progressively more sophisticated computational demands such as probabilistic inference of time intervals. We aim to create models that simultaneously succeed in performing task-relevant computations (i.e., behavior) and emulate cortical dynamics recorded in monkeys performing those tasks. We will use this iterative process to establish a principled framework relating neural dynamics to neural computations underlying inference. Finally, we will put this framework to test using a novel task that demands an unprecedented level of computational flexibility. RELEVANCE (See instructions): It has become increasingly apparent that the neurobiology of behavior in health and disease has to be probed at the level of populations of neurons. However, we do not yet have a rigorous and quantitative language for linking population neural activity to behavior. Our work combines primate electrophysiology with neural network modeling and aims to develop such a language through the mathematics of dynamical systems. The results hold promise for future translational research to diagnose behavioral symptoms of brain dysfunction in terms of their computational modules and the dynamic patterns of activity that support those modules.

ELECTRONICS CORE: Project Summary The onsite Electronics Core service module has proven critical to the research productivity of the NEI Core Investigators, and it continues to evolve to tackle the next generation of NEI Investigatory needs in this area. Specifically, it has evolved from a primary focus on analog electronic circuits, to a hardware/software design and integration hub. Broadly, the Electronics Core designs, builds, and repairs advanced electronic hardware systems, which cannot be readily obtained as off-the-shelf commercial sources. This allows our researchers to continually develop state-of-the-art electronic devices and interfaces such as our recent advances in wireless electrophysiology recording. Often these devices require high-end and user-friendly software packages to control and/or integrate with either custom hardware or new applications for purchased hardware. Our Electronics Core engineer is an expert in both hardware and software, and provides engineering consultation throughout the development of projects and direct training for personnel in regard to the functionality of new and modified electronic systems. The Electronics Core, in collaboration with the Administrative Core personnel, disseminates novel apparatus designs and other work products to the local research community to help the research personnel access, re-use, and modify existing design files, and make contacts with expert peers across the research community.

Humans make rich inferences about the relationships between entities in the world from scarce information. For example, we can find a novel destination after seeing a few street numbers, or find a page in a dictionary by glancing at a few words in other pages. Theoretical considerations suggest that the brain makes such inferences by constructing internal models of the relationships in the environment (relationships between actions and states of the world), and by mentally simulating those models. However, the neural substrates and mechanisms of mental simulation are not understood. Our overarching goal is to integrate insights from theory and modeling with behavior and electrophysiology in awake, behaving monkeys to understand how mental simulation of internal models support relational inference. We will develop a behavioral task for monkeys in which they have ?navigate? mentally from one stimulus to another along a one-dimensional abstract space of discrete stimuli (i.e., a sequence of images). We will assess whether animals? behavioral characteristics exhibit hallmarks of mental simulation. We will then create a large library of neural network models to generate hypotheses for alternative computational strategies (including mental simulations) that the brain might employ for navigating abstract spaces. Next, we will record from candidate brain areas in the parietal and entorhinal cortex of monkeys, and analyze the data at single cell and population levels looking for signatures of mental simulation. Finally, we will adopt an iterative approach involving model-based data analyses and data-driven model revision with the ultimate goal of creating models that simultaneously succeed in performing task-relevant computations (i.e., behavior) and account for observed neural responses. Finally, we will validate our framework by evaluating the predictions of our models for both behavior and electrophysiology in new behavioral tasks.

Abstract Extensive research spanning theory, psychophysics, and physiology has investigated how we rely on statistical regularities in the environment to improve our sensorimotor behavior: (1) Bayesian theory has provided an understanding of how one should take advantage of statistical regularities, (2) psychophysical experiments have documented the impact of such regularities on behavior, and (3) electrophysiology experiments have identified neural signals that reflect those regularities. An important consideration is that statistical properties of the environment are rarely stable. Therefore, a most pressing and unresolved question at the frontier of this interdisciplinary body of work is how malleable brain signals, through experience, gradually acquire information about new environmental statistics. Here, we will tackle this problem by developing a sensorimotor behavioral paradigm in the non-human primate model that demands adaptive statistical learning (Aim 1). We will use this paradigm to test specific computationally-motivated hypotheses regarding how the structure and dynamics of neural activity in candidate regions of the frontal cortex change throughout learning (Aim 2). Finally, we will use a dynamical systems approach to analyze the laminar organization of learning signals in the frontal cortex to tease apart functional sub-circuits with distinct input-output properties that support sensorimotor learning (Aim 3).