Adam Kohn - US grants

imaging /visualization /scanning; optics; membrane activity; somesthetic sensory cortex; brain electrical activity; long term potentiation; action potentials; neural information processing; conditioning; bioimaging /biomedical imaging; voltage /patch clamp; laboratory rat;

[unreadable] DESCRIPTION (provided by applicant): The brain is affected strongly by experience, both during development and in adulthood. Understanding precisely how experience alters the brain and its processing is a central question in neuroscience, from studies of learning and memory to those of cortical reorganization following injury. In the realm of sensory processing, we know that both perception and cortical neurons are strongly affected by adaptation--the sensory input of the preceding tens of milliseconds to many minutes. Because of its rapid time scale, this form of plasticity is likely to be a critical component of ongoing sensory processing. Our long-term goal is to understand the effects of adaptation and how they contribute to vision. Previous work has established that adaptation alters neuronal response properties throughout the visual system, sometimes in different ways at different stages of processing. The goal of this project is to determine, for the early stages of the visual motion processing pathway, how neurons adapt to arbitrary spatial and temporal patterns of visual input. In the first series of experiments, we will determine how the responsiveness and tuning of neurons in primary visual cortex and in extrastriate area MT are affected by individual visual stimuli of different spatial form, size and duration. In these experiments, we will make use of electrode arrays that allow us to sample many neurons simultaneously and to study interactions among them. Based on preliminary and published work, we hypothesize that the plasticity triggered by individual stimuli serves to maintain the balance of activity in a local cortical network, not to optimize the sensory encoding of individual cells as previously suggested. As a result, we propose that not all stimuli that are effective at driving visual neurons will induce plasticity. In our second series of experiments, we will evaluate how cortical neurons adjust to the statistics of an ensemble of inputs, presented in a dynamic, continuous sequence. Our hypothesis in these experiments is that neurons adjust to the range of inputs in such ensembles and that this plasticity is a rapid gain control that is distinct from the effects triggered by persistent stimuli. The knowledge we gain in this study will be important for understanding how the locus and nature of plasticity depends on the properties of sensory input. This, in turn, is important for allowing us to integrate information gained in psychophysical, neuroimaging, and neurophysiological studies that use adaptation as a tool to study the visual system. In addition, many of the questions that we address are common to studies of other forms of plasticity, such as cortical reorganization after injury. By studying how cortical circuits are affected by recent stimulus history, we hope to learn more generally about the capacity of these circuits to learn and reorganize. PUBLIC HEALTH RELEVANCE This project aims to determine how the visual system adapts to recent sensory input. Studying the rapid plasticity caused by sensory experience is likely to provide knowledge important for a number of clinical issues, including understanding how the brain reorganizes after central (e.g. stroke) or peripheral (e.g. limb amputation) injury and designing sensory devices (e.g. artificial cochlea or visual aids) that extract or insert signals into the brain. [unreadable] [unreadable] [unreadable]

DESCRIPTION (provided by applicant): Intellectual merit: A central question in neuroscience is understanding how cortical networks process complex natural stimuli. Neurophysiological studies and computational models have traditionally focused on simple stimuli, such as gratings and bars. While providing important insights, it is difficult to extrapolate from these studies to an understanding of the processing of more natural input. On the other hand, a main hurdle to making progress in the field is that natural scenes are complex and it is not clear what it is about a given scene that evokes a given neural response. To overcome this limitation and to push forward our understanding of cortical processing of natural inputs, we will make use of recent advances in understanding natural scene statistics to closely integrate theory and neurophysiological experiments. We posit that a key factor distinguishing natural images and movies from random scenes are joint statistical dependencies in space and time. Further, we hypothesize that visual neurons are sensitive to these dependencies. We will build a unified modeling framework of spatiotemporal contextual effects in neurons, which is determined by the statistical dependencies in scenes. Importantly, the predictions of the model will be used to guide neurophysiology experiments and to interpret the results. Using natural stimuli, we will measure effects of spatial, temporal, and spatiotemporal context in single neurons and in populations of cells, including determining how interactions between neurons contribute to contextual effects. We will record in primary visual cortex (V1) because it provides a solid background on which to base our experiments. We will conduct parallel recordings in extrastriate area V2 because previous work suggests that it may have different sensitivity to contextual information. The experimental results will validate and guide the modeling framework. Our approach will be a significant advance over previous scene statistics modeling work that has focused on explaining limited contextual physiology data for simple stimuli such as gratings, and will for the first time make full use of the power of scene statistics to answer a fundamental question. Most importantly, our work will make significant strides in elucidating how cortical circuits process natural scenes, within a theoretical framework that provides both predictive and explanatory power. Collaboration: The project will involve a collaborative effort between two young investigators with expertise in computational visual neuroscience and systems physiology; it combines state-ofthe- art algorithms from computational vision and technology for recording populations of neurons in early visual cortex. We will achieve our goal by closely integrating theory and model development with electrophysiological experiments, an approach fostered by the proximity of the two investigators. Broader Impacts: This proposal is expected to have broad impacts in five main areas. First, the work will have broad impact for basic, biomedical, and applied disciplines, including: studying other sensory systems under natural input; building superior visual aids; designing artificial systems; and advancing image and signal processing. Second, the data and stimuli will be made broadly available to the community through the CRCNS data sharing website. Third, the project will be used to train and mentor postdoctoral fellows to become independent research scientists. Fourth, the project will for the first time introduce students at Albert Einstein to the combination of theoretical and experimental approaches for solving fundamental questions in neuroscience. Finally, the project will be used as part of an outreach effort to expose local underrepresented high school students in the Bronx to exciting scientific research.

DESCRIPTION (provided by applicant): Objects move through the environment in three dimensions, and humans are clearly capable of perceiving such motion in depth. Visual neuroscientists do not, however, know how our nervous system encodes this fundamental aspect of the visual world. Despite large literatures motion and depth perception, there is a surprising lack of knowledge integrating these two visual features to directly characterize how the brain processes the three-dimensional direction of moving objects. The goal of this proposed research is to understand how neural circuits in the primate brain exploit binocular information to represent the direction of objects moving through a 3D environment. First, we will psychophysically characterize the binocular cues to 3D motion. Recent work suggests that the perception of motion through depth relies on two binocular cues, one based on changing disparities over time, and one based on an inter-ocular comparison of velocities. Our overarching hypothesis is that the velocity-based cue is of great importance for the perception of 3D motion. We will therefore perform psychophysical experiments that isolate eye-specific motion signals and characterize them relative to (and in interaction with) disparity-based signals. These experiments will unpack the psychophysical building blocks and signatures of this important perceptual information, and refine visual displays used for neuroimaging and electrophysiological studies. Second, we will use neuroimaging to identify the neural circuits that process 3D motion. Taking stimuli and insights from our psychophysical experiments, we will perform fMRI experiments to visualize this processing in the human brain. Direction-selective adaptation protocols will be used to characterize both the disparity-based and velocity-based cues and their interactions, and to understand how (or if) these cues are integrated into a single (cue-independent) representation of 3D motion. These experiments will also assess how directly psychophysical assays of the system map on to neural signals. Third, we will perform electrophysiology to specify the underlying neural computations. Guided by the psychophysics and neuroimaging, we will perform single-neuron recordings to characterize the binocular neural signals that encode 3D motion. Recordings in V1 will employ multi-electrode arrays;recordings in MT will employ both multiple-tetrode and single-electrode awake preparations. This work will test the hypothesis that eye-specific motion signals are represented at the level of V1 and are then integrated (by single neurons) in MT. This 3D motion pathway may be exposed at slower speeds, and thus may be multiplexed in the same circuitry known to extract 2D/frontoparallel direction when assessed using faster speeds. PUBLIC HEALTH RELEVANCE: Objects in the real world move through three dimensions, and humans rely on their ability to sense motion in depth in order to guide appropriate actions. A thorough understanding of the neural basis of the perception of 3D motion will prove valuable for the refinement of biomedical technologies involving the visually-guided control of robotic or prosthetic devices in a dynamic three-dimensional environment.

Project Summary Visual neurons are strongly influenced by sensory history or adaptation. Our long-term goal is to understand how such experience influences cortical processing and contributes to vision. Adaptation effects have been described throughout the visual system, from the retina to higher visual cortex. Almost without exception, these effects have been measured in individual neurons. This is problematic, as it is widely believed that sensory information is encoded and processed by neuronal populations distributed across multiple cortical areas. Population coding is not adequately described by the mean tuning functions of individual neurons; it can be strongly influence by the coordination of activity among cells. How adaptation affects population coding is poorly understood, hampering our understanding of how experience driven changes in cortical circuits contribute to visual experience. In this proposal, we make use of recent theoretical work in understanding population coding, to test how adaptation influences the encoding of stimulus orientation by populations of neurons distributed across multiple stages of the macaque visual system. In specific aim 1, we will determine how brief periods of adaptation affect neuronal tuning, response variability and response correlations, in the primary visual cortex (V1) and area V4 of awake monkeys performing a fixation task. In specific aim 2, we will relate changes in population responses in V1 and V4 to the animal's performance on a fine orientation discrimination task. We will test algorithms for estimating stimulus orientation from the measured population responses and we will compare these with algorithms that best predict the animals' perceptual decisions, in both control and adapted states. This will reveal how animals weight the sensory responses to make perceptual decisions, how this weighting corresponds to the theoretically-optimal for estimating orientation, and how that weighting is altered by experience-driven plasticity. In specific aim 3, we will conduct complementary experiments, with a more mechanistic focus. We will use a novel recording arrangement to measure simultaneously the activity of an output population in V1 and its downstream targets in V2 in anesthetized animals. We will determine how adaptation alters the functional coupling between these networks, and how coupling depends on the experience-driven changes in single neuronal properties and ensemble responses. Our project will provide the first comprehensive view of how population responses in visual cortex are affected by recent history, and how population responses are interpreted by downstream networks, to give rise to perceptual experience. The combination of recently-developed tools will allow us to bring unprecedented power to addressing these central and basic issues. We expect this project will contribute broadly to our understanding of population coding, corticocortical communication, and plasticity. The knowledge gained should be invaluable for developing more effective devices for interpreting brain activity for prosthetic devices, and for understanding the coordination of activity within and between cortical areas, in health and disease.

? DESCRIPTION (provided by applicant): In line with the strategic plan of the NEI, this project is focused on filling a profound gap in our understanding of neural mechanisms of visual perception. Specifically, we aim to understand how the adaptation of visual cortical circuits contributes to perception. Adaptation is a ubiquitous process by which neural processing and perception are dramatically influenced by recent visual inputs. However, the functional purpose of adaptation is poorly understood. Based on preliminary data, this project tests the hypothesis that visual adaptation instantiates a form of predictive coding, which is used to make unexpected events salient. We posit that cortical circuits learn the statistical structure of visua input in a manner that extends beyond previous fatigue- based descriptions of adaptation effects. This learning is used to discount expected features and signal novel ones. Our project will test this hypothesis through the collaborative effort of three investigators with expertise in human EEG, animal neurophysiology, and computational modeling. Aim 1 will assess the ability of cortical circuits to adapt to temporal sequences of input and to signal deviations from expected sequences. Aim 2 will evaluate the effect of stimulus uncertainty on adaptation and responses to novel events. Aim 3 will determine how adaptation dynamics and responses to novel stimuli are influenced by the temporal constancy of stimulus statistics. Each of these aims involves an experimental manipulation that yields distinct behavior from fatigue- based and predictive coding mechanisms. Thus, together our aims will provide a robust test of our core hypothesis, and provide a much richer understanding of the adaptive properties of cortical circuits. Results from our project will contribute to answering one of the continuing puzzles in visual research, which is to understand the functional purpose of adaptive mechanisms in visual perception.

PROJECT SUMMARY / ABSTRACT Neuroscience is highly specialized?even visual submodalities such as motion, depth, form and color processing are often studied in isolation. One disadvantage of this isolation is that results from each subfield are not brought together to constrain common underlying neural circuitry. Yet, to understand the cortical computations that support vision, it is important to unify our fragmentary models that capture isolated insights across visual submodalities so that all relevant experimental and theoretical efforts can benefit from the most powerful and robust models that can be achieved. This proposal aims to take the first concrete step in that direction by unifying models of direction selectivity, binocular disparity selectivity and 3D motion selectivity (also known as motion-in-depth) to reveal circuits and understand computations from V1 to area MT. Motion in 3D inherently bridges visual submodalities, necessitating the integration of motion and binocular processing, and we are motivated by two recent paradigm-breaking physiological studies that have shown that area MT has a robust representation of 3D motion. In Aim 1, we will create the first unified model and understanding of the relationship between pattern and 3D motion in MT. In Aim 2, we will construct the first unified model of motion and disparity processing in MT. In Aim 3, we will develop a large-scale biologically plausible model of these selectivities that represents realistic response distributions across an MT population. Having a population output that is complete enough to represent widely-used visual stimuli will amplify our ability to link to population read-out theories and to link to results from psychophysical studies of visual perception. Key elements of our approach are (1) an iterative loop between modeling and electrophysiological experiments; (2) building a set of shared models, stimuli, data and analysis tools in a cloud-based system that unifies efforts across labs, creating opportunities for deep collaboration between labs that specialize in relevant submodalities, and encouraging all interested scientists to contribute and benefit; (3) using model-driven experiments to answer open, inter-related questions that involve motion and binocular processing, including motion opponency, spatial integration, binocular integration and the timely problem of how 3D motion is represented in area MT; (4) unifying insights from filter-based models and conceptual, i.e., non-image- computable, models to generate the first large-scale spiking hierarchical circuits that predict and explain how correlated signals and noise are transformed across multiple cortical stages to carry out essential visual computations; and (5) carrying out novel simultaneous recordings across visual areas. This research also has potential long-term benefits in medicine and technology. It will build fundamental knowledge about functional cortical circuitry that someday may be useful for interpreting dysfunctions of the cortex or for helping biomedical engineers construct devices to interface to the brain. Insights gained from the visual cortex may also help to advance computer vision technology.

Project Summary Perception and cognition rely on processing performed in a distributed network of cortical areas. These areas communicate through feedforward, lateral, and feedback corticocortical connections. Our long-term goal is to understand the function of corticocortical feedback circuitry, which has been proposed to underlie a number of functions but remains poorly understood. Previous work has attempted to elucidate the function of feedback by reversibly inactivating higher cortex with either pharmacological or cooling methods, and measuring effects in single neurons in lower cortex. While important, this previous work suffers from critical limitations. Inactivation has limited specificity, usually involving large swaths of cortex and thus neurons with different functional properties. Measuring the consequences on lower cortex using only single neuron firing rates would fail to capture any effects of feedback on network coordination, which much recent work suggests may be central to cortical function. Previous studies have also often neglected to determine the laminar location of their recordings: feedback connections are laminar-specific, thus their effects may vary through the targeted cortical columns. In this study we will use optogenetic methods, combined with multielectrode neurophysiology, to elucidate the function of corticocortical feedback in the macaque visual cortex. Guided by preliminary results, our working hypothesis is that these connections are functionally-specific and alter the coordination among targeted neurons. This coordination may in turn enhance the coupling to downstream target networks. In specific aim 1, we will test how optogenetic recruitment and silencing of small populations of neurons in cortical area V2 affects neuronal responsivity and tuning across layers of primary visual cortex (V1). Using the local field potential and small ensembles of simultaneously-recorded neurons, we will also measure how network coordination is affected by manipulating feedback. We will measure effects both in the presence and absence of visual drive, to determine how the effects of feedback depend on network state. Finally, we will evaluate how the effects of feedback depend on the functional and retinotopic alignment of the stimulation and recording site. In specific aim 2, we will use planar electrode arrays to explore in detail the effects of feedback on the coordination of large neuronal ensembles in the superficial layers of V1, the primary output population projecting to higher visual cortex. Our experiments will provide an unprecedented view of how feedback modulates V1, how these effects depend on the relationship between the functional properties of the source and target neurons, and how feedback interacts with bottom-up sensory information. This work will form a solid basis for future work in which we will use optogenetic methods to manipulate circuits in a perceptual decision making paradigm. Disrupted corticocortical signaling has been implicating in a number of disorders, including schizophrenia and autism. By furthering our understanding of the signals provided from higher to lower cortex, our results will contribute to improved diagnosis and treatment of such disorders.  

Summary Crowding is a perceptual phenomenon in which a clearly discernible stimulus becomes unrecognizable because of nearby `distractor' stimuli. Crowding affects core aspects of visual processing, including feature integration, scene perception and reading. Because crowding is stronger in non-foveal vision, it strongly affects individuals with central vision loss (e.g. macular degeneration), with substantial consequences for their quality of life. Crowding has been explored extensively in human psychophysical studies. Functional imaging and electrophysiological studies in humans have provided some neural correlates of crowding in early and midlevel visual cortex, but the neural underpinnings of crowding remain largely unexplored and poorly understood. This project will determine how crowded displays affect the representation of sensory information by neuronal populations in low and midlevel cortex (encoding), how crowding affects the manner in which this sensory information is used to make perceptual judgments (decoding), and whether the effects of crowding can be mitigated by brief periods of sensory experience. In Specific Aim 1, we will record from neuronal populations in primary visual cortex (V1) and V4 of macaque monkeys. We will test the hypothesis that crowding corrupts sensory representations in these areas. We will determine how crowded displays affect neuronal responsivity, tuning, and variability, and interneuronal noise correlations. Making use of recent advances in understanding population codes, we will assess how chances in these response features combine to affect encoding of visual information with crowding. In Specific Aim 2, we will train animals to perform a fine orientation discrimination task, for target stimuli presented in isolation and with distractors. We will use a stimulus paradigm that allows us to compute ?psychophysical kernels??a method for assessing perceptual strategy by which different elements of a visual display are combined to make decisions. We will pair these behavioral measures with recordings of neuronal population in V1 and V4, to infer the read-out strategy used by the animal to relate sensory responses to perceptual decisions. These experiments will test the hypothesis that crowding arises in part from suboptimal read out of sensory information. In Specific Aim 3, we will test whether brief periods of visual adaptation can mitigate crowding. We hypothesize that adaptation can be used to improve the salience of novel stimuli, with heightened salience resulting in improved perceptual performance. We will test our hypothesis by measuring the population information for target stimuli in isolation or with distractors, under control conditions and different adaptation states. We will also test whether adaptation alters the read-out strategy used by animals in our orientation discrimination task. These experiments will reveal which aspects of crowding are most plastic, and test for a novel functional benefit of sensory adaptation. Together our aims will provide a comprehensive investigation of the neural basis of crowding, providing knowledge needed to develop therapeutic and behavioral strategies to alleviate the quality of life issues caused by crowding.