David J. Freedman, Ph.D. - US grants

DESCRIPTION (provided by applicant): A great deal of evidence supports the notion that visual perception and visually guided behaviors are affected by our predictions about the non-random behavior and interdependencies of objects in the visual world. Previous studies have shown that posterior parietal cortex neurons in area LIP of the monkey can convey predictive signals about the upcoming direction of simple motion (in one dimension) when it was expected by the animal. However, critical issues remain unanswered concerning the mechanisms by which predictive signals develop during learning, the nature of predicted information (i.e. spatial vs. temporal) and how that information is used during visually guided behavior. We propose to address these issues by utilizing a novel behavioral paradigm in which monkeys learn to predict upcoming movements of a target that can follow complex two dimensional trajectories. By recording from neurons in LIP while monkeys learn to predict complex motion paths, we expect to gain substantial insights into the neuronal mechanisms that underlie visual prediction and learning and, further, a more detailed understanding of the nature of spatial information encoded in LIP.

DESCRIPTION (provided by applicant): Humans and other advanced animals have an impressive capacity to recognize the behavioral significance, or category membership, of a wide range of sensory stimuli. This ability, which is disrupted by a number of brain diseases and conditions such as Alzheimer's disease, schizophrenia, stroke, and attention deficit disorder, is critical because it allows us to respond appropriately to the continuous stream of stimuli and events that we encounter in our interactions with the environment. Of course, we are not born with a built in library of meaningful categories, such as tables and chairs, which we are preprogrammed to recognize. Instead, we learn to recognize the meaning of such stimuli through experience. The goal of the studies proposed here is to move towards a more detailed understanding of the brain mechanisms underlying the learning and recognition visual categories. Recently, we found evidence that the posterior parietal cortex plays a surprisingly direct role in encoding the category membership of visual stimuli. In these studies, we recorded from neurons in the parietal cortex during performance of a categorization task in which 360 degrees of motion directions were grouped into two arbitrary categories that were divided by a learned category boundary. These recordings revealed that parietal neurons robustly encoded stimuli according to their learned category membership, suggesting that parietal visual representations can reflect abstract information about the learned significance of visual stimuli. The goals of the proposed studies are to develop a mechanistic understanding of how visual feature representations in visual cortex are transformed into category encoding in parietal cortex, and to determine how neuronal category signals develop in real time during the category learning process. While much is known about how the brain processes simple sensory features (such as color, orientation, and direction of motion), less is known about how the brain learns and represents the meaning, or category, of stimuli. A greater understanding of visual learning and categorization is critical for addressing a number of brain diseases and conditions (e.g. stroke, Alzheimer's disease, attention deficit disorder, schizophrenia, and stroke) that leave patients impaired in everyday tasks that require visual learning, recognition and/or evaluating and responding appropriately to sensory information. The long-term goal of this project is to guide the next generation of treatments for these brain-based diseases and disorders by helping to develop a detailed understanding of the brain mechanisms that underlie learning, memory and recognition. These studies also have relevance for understanding and addressing learning disabilities, such as attention deficit disorder and dyslexia, which affect a substantial fraction f school age children and young adults. Thus, a more detailed understanding of the basic brain mechanisms underlying learning and attention will likely give important insights into the causes and potential treatments for disorders involving these cognitive faculties.

DESCRIPTION (provided by applicant): Systems and cognitive neuroscience depend on carefully designed and precisely implemented behavioral tasks to investigate neural phenomena. In most neuroscience laboratories, such tasks are implemented on a personal computer (PC) using software that is usually custom written by that lab for their specific needs, or by modifying preexisting software. Because many laboratories have developed separate solutions for their specific experiments, a modern, full-featured and flexible software solution is not currently available. The most commonly used software packages for controlling cognitive neuroscience experiments have become outdated due to their age, limited in their capabilities and flexibility, and difficult to use largely because of the requirement to construct behavioral tasks using low- level programming languages (e.g. C and C++) that are difficult to learn. To address these issues, we have developed a software system, called "Monkeylogic" (http://www.monkeylogic.net), that allows for straightforward coding and temporally-reliable execution of these tasks in Matlab, the most commonly used programming environment for data analysis in the neuroscience community. We find that Monkeylogic is capable of millisecond accuracy for presenting stimuli and measuring behavioral responses, and can control a wide range of behavioral tasks. Developing behavioral tasks is fast and easy compared to existing software solutions, and it runs on modern and inexpensive hardware and operating systems. Here, we propose to continue development of Monkeylogic in order to add the features necessary to make it the most comprehensive, flexible, and easy to use software solution for neurophysiological, psychophysical, and functional imaging studies of perception, cognition and behavior. Following the completion of this project, we intend to provide this software (along with the source code) free of charge to the scientific community with the hope that it will be a useful tool for a wide range of researchers. This software fills an important niche and satisfies and ever-growing demand for flexible software tools for controlling behavioral and sensory experiments. Thus, Monkeylogic will have a large positive impact on the ability of researchers to easily design and conduct studies aimed at understanding the brain mechanisms underlying perception, action and cognition. Due to its flexibility and large set of features, this software could be used by thousands of researchers across a wide range of behavioral and neuroscience disciplines. Furthermore, our intent to provide this software to the research community free of charge and with adequate documentation and support will maximize the impact and appeal of our software. PUBLIC HEALTH RELEVANCE: Systems and cognitive neuroscience depend on carefully designed and precisely implemented behavioral tasks to elicit the neural phenomena of interest. To facilitate this process, we have developed a software system that allows for the straightforward coding and temporally-reliable execution of these tasks in Matlab, a high-level programming language that is powerful, easy to use, and widely used in the behavioral and brain sciences. Our software has several advantages over existing systems including ease of use, compatibility with modern computer hardware and operating systems, low cost, and the ability to rapidly develop new behavioral paradigms. The goal of this project is to complete the development of this software system by adding critical features that are necessary for designing and controlling a wide range of behavioral, psychophysical and neurophysiological experiments.

DESCRIPTION (provided by applicant): The proposed research will investigate the cortical circuit mechanisms of visual categorization, the process of learning to classify visual stimuli into groups of objects that are equivalent in terms of their behavioral significance. Previous work revealed that individual neurons in the prefrontal cortex (PFC) and the lateral interparietal (LIP) area encode the category membership of stimuli during visual categorization tasks. Built on these findings, we will combine biophysically-realistic neural modeling and single-unit recording from behaving monkeys, to elucidate the mechanistic questions concerning category learning and category-based behavior. First, we will develop a spiking network model of the reciprocally interacting sensory circuit and parieto-prefrontal circuit, to elucidate the cortical basis of key neural computations underlying a delayed match-to-category (DMC) task (do the attributes of a sample and a test stimulus belong to the same category?) versus delayed match-to-sample (DMS) task (are the attributes of the sample and test identical?). Second, we will examine how categories are learnt through discrete training stages, from identity-based match-to-sample to fine category discrimination with stimuli near an arbitrary category boundary. This will be done using models endowed with reward-dependent synaptic learning, monkey behavioral assessment and single-unit recordings from monkeys at different stages of training. Third, we will examine task switching, on a trial-by-trial basis, between the identity-based DMS versus category-based DMC, to clarify the differential neural coding of stimulus identity and category, as well as task-rule representation in visual categorization, in the LIP and PFC. Together, these studies will shed important insights and yield a computational framework for understanding how the brain encodes the learned significance, or category membership, of visual stimuli. Intellectual Merits: Without the ability to classify or categorize stimuli, it would be difficult to perceive and comprehend the world; concepts and language would seem impossible. Therefore, elucidating the neural mechanisms of categorization is a crucial step in our quest for a neurobiological understanding of higher cognition. While much is known about how the brain processes sensory attributes (such as orientation and direction of motion), much less is known about how the brain achieves more abstract knowledge acquisition such as how attributes are grouped into categories through learning, and what are the computational advantages of category-based behavior. A mechanistic understanding of these issues, at the neural circuit level, necessitates a concerted computational and experimental effort. Thus, the results of our proposed research program are likely to represent a significant advance in this area, with broad implications. Our highly promising preliminary computational, behavioral and neuronal studies have validated our approach, and have ensured that all aspects of this project have a high likelihood of success. Broader Impacts and Integration of Education and Research Activities: Both PIs are actively involved with teaching. Dr. Wang teaches for the Interdepartmental Neuroscience graduate program and for the new Physics/Engineering/Biology (PEB) integrated graduate program at Yale. Dr Freedman is preparing new workshop course called Methods in neuronal data analysis to both graduate and undergraduate students. Lessons and exercises will revolve around computational and statistical analysis of real data collected in his laboratory during the experiments proposed here. Dr Wang is a member of the Oversight Committee for Description Standards in Neural Network Modeling, International Neuroinformatics Coordinating Facility (INCF). Models developed in his lab will be made available to the computational community. Broaden Participation of under-represented groups-Both PI have a strong track record of recruiting and mentoring students from under-represented groups. At this time, Dr. Wang has a female graduate student and a female postdoctoral fellow (Dr Tatiana Engel who will spearhead the proposed research in his laboratory). Over the past two years four graduate students in Dr. Freedman's laboratory are from underrepresented groups (one is African American and the others are women). Outreach to general public- Both PIs have been active in outreach. Dr Wang has given lectures on the brain at the Hopkins School in New Haven; Dr Freedman has been involved in the Science and Technology Outreach and Mentoring Program, The Young Scientist Training Program, and the student science fair at Kenwood Academy public school, in Chicago. Our work focuses on the brain mechanisms of learning and memory, a topic which is both accessible and of great interest to the general public. For our outreach and mentorship efforts, we will use data generated during the proposed work to produce educational demonstrations of how the brain learns and processes visual information that will be accessible to a lay audience. These demonstrations will be used in K-12 classroom presentations and also available online.

Humans and other advanced animals have an impressive capacity to recognize the behavioral significance, or category membership, of a wide range of sensory stimuli. This ability is critical, because it allows us to respond appropriately to the continuous stream of stimuli and events that we encounter in our interactions with the environment. Of course, we are not born with a built-in library of meaningful categories, such as 'tables' and 'chairs,' that we are pre-programmed to recognize. Instead, we learn to recognize the meaning of such stimuli through experience. With National Science Foundation Funding, Dr. David J. Freedman is carrying out studies whose goal is to understand how visual-feature encoding in early visual processing areas is transformed into more meaningful representations at more advanced neuronal processing stages in the brain. The goals of the proposed studies are to compare neuronal representations of visual-motion processing stages across a network of interconnected brain areas in and around the parietal lobe during visual motion categorization tasks. Specifically, one series of experiments compares neuronal responses in two distinct interconnected regions of parietal cortex, the lateral and medial interparietal areas, which are known to be more involved in visual and somatosensory or motor processing, respectively. Activity in these two areas is examined during a categorization task that requires motor decisions to be executed in response to visual stimuli, allowing the relative roles of the two areas in the decision making process to be determined. A second series of experiments is comparing cortical activity in the lateral intraparietal and prefrontal cortices during a novel visual categorization task in which subjects learn multiple independent category rules and apply those rules flexibly and dynamically to incoming visual stimuli. This study gives critical insights into the contributions of frontal and parietal cortex to flexible rule-based categorization. Together, these studies can yield important insights into how learning influences the encoding of visual information and into the roles of interconnected networks of parietal and frontal cortices in visual recognition and decision making.

While much is known about how the brain processes simple sensory features (such as color, orientation, and direction of motion), less is known about how the brain learns and represents the meanings or category of stimuli. A greater understanding of visual learning and categorization is critical for addressing a number of brain diseases and conditions (e.g., stroke, Alzheimer's disease, attention deficit disorder, and schizophrenia) that leave patients impaired in everyday tasks that require visual learning, recognition, and/or evaluating and responding appropriately to sensory information. Dr. Freedman's research is helping to guide the next generation of treatments for these brain-based diseases and disorders by helping to develop a detailed basic understanding of the brain mechanisms that underlie learning, memory and recognition. These studies also have relevance for understanding and addressing learning disabilities, such as attention deficit disorder and dyslexia, which affect a substantial fraction of school age children and young adults. A more detailed understanding of the basic brain mechanisms underlying learning, memory and attention will likely give important insights into the causes and potential treatments for disorders involving these cognitive faculties.

Categorization is the brain's ability to recognize the meaning of objects and events in our environment, and is an essential cognitive process underlying decision making. Categorical decisions are often flexible, and depend on the demands on the task at hand. The current project aims to understand the brain mechanisms which underlie flexible categorical decision making, as well as computational algorithms for making such decisions my artificially intelligent systems. Experiments will record from ensembles of cortical neurons during flexible categorization tasks. Computational modeling work will train recurrent neural networks to perform the same flexible categorization tasks used in the experiments, with parameters of the model inspired by the experimental data. This will result in a greater understanding of the neural mechanisms underlying categorization and decision making, as well as improvements in computational algorithms for flexible categorization by artificially intelligent systems. The broader impacts of the project include substantial training opportunities for undergraduates, Ph.D. students, and postdoctoral researchers in both experimental and computational approaches to flexible decision making. The project will also generate new experimental data and computational tools that will be shared with the broader scientific community.

This project combines multi-channel neurophysiological recordings and neural circuit modeling to investigate the neural circuit mechanisms of flexibility and generalization in visual categorization. The project leverages a collaboration by the researchers that has proven fruitful in our previous joint research on category learning. The focus of the present project is on flexible task switching between discrimination and categorization, and between categorization rules, in the behavioral, experimental, and computational work. The task paradigms will also directly test the 'exemplar model' of categorization from cognitive psychology, linking behavioral models to neural circuit processes. The project will develop a novel modeling framework, based on training recurrent neural networks to learn to perform multiple tasks. This approach offers a potentially powerful data analysis tool and conceptualization of neural circuit computation in terms of neural population trajectories in a high-dimensional state space, and this perspective is urgently needed to analyze simultaneous recording from many single neurons during performance of complex cognitive tasks, a major thread of modern Data-Intensive Neuroscience and Cognitive Science.

This proposal aims to integrate two streams of research on learning and memory in an attempt to strengthen the links between theory and experiment, build models that explain experimental observations and use model predictions to guide new experiments. The experimental stream will record neuronal population activity in inferior temporal, perirhinal and prefrontal cortices during performance of delayed matching tasks which require maintenance of visual information in short term memory, using visual stimuli with various degrees of familiarity (from entirely novel to highly familiar). The modeling stream will investigate learning and memory in network models that include learning rules inferred from data, using a combination of mean field analysis and simulation. Models will generate predictions on patterns of delay period activity that will be tested using experimental data. The goals of this combined experimental and theoretical project will be to answer the following questions: · How do changes in synaptic connectivity induced by learning due to repeated presentation of a particular stimulus affect the distributions of visual responses of neurons? In other words, how do neuronal representations change in cortex as a novel stimulus becomes familiar? Can we infer the learning rule in cortical circuits from experimentally observed changes in distributions of neuronal responses as the stimuli become familiar? · Do changes in synaptic connectivity induced by learning rules that are consistent with the statistics of visual responses lead to delay period activity in a task such as the OMS task? Is delay period activity already present upon the first presentation of a stimulus, or does it develop over time? If it is not present during the initial presentations, how is sample information maintained in memory during the delayed match to sample task? see attached continuation RELEVANCE (See instructions): See attached

Summary and Relevance of Proposed Research Humans and other advanced animals have a remarkable capacity to rapidly acquire knowledge about our environment and to learn a wide array of complex tasks. Recent work has shown the importance of mental ?schema? in generalizing knowledge learned from simpler tasks and concepts, allowing rapid learning of new tasks and knowledge built upon the cognitive scaffold provided by earlier learning sets. For example, learning a simple card game like ?war? or ?old maid? facilitates learning of more complex games such as ?spades? or ?bridge?, which build on knowledge or schema from simpler games. Although neurophysiological studies of hippocampal-cortical interactions during complex behavior and learning have been conducted extensively in rodents, there is a surprising lack of knowledge about the patterns of hippocampal neuronal activity or cortical- hippocampal interactions which underlie rapid learning and the development of mental schema in humans and other advanced animals. This project will take advantage of a newly available large-scale semi-chronic neurophysiological approach to understand the interactions between hippocampus, parietal cortex, and prefrontal cortex, which underlie both the development of schema and use of schema for rapid visual associative and abstract category learning. While much is known about how the brain processes simple sensory features (such as color, orientation, and direction of motion), less is known about how the brain learns and represents the meaning, or category, of stimuli, and how categorical knowledge is generalized to learn new tasks and concepts. A greater understanding of learning and categorization is critical for addressing a number of brain diseases, conditions, and mental illnesses (e.g. stroke, Alzheimer?s disease, attention deficit disorder, schizophrenia, and stroke) that leave patients impaired in everyday tasks that require visual learning, recognition and/or evaluating and responding appropriately to sensory information. The long-term goal of this project is to guide the next generation of treatments for these brain-based diseases and disorders by helping to develop a detailed understanding of the brain mechanisms that underlie learning, memory and decision making. These studies also have relevance for understanding and addressing learning disabilities, such as attention deficit disorder and dyslexia, which affect a substantial fraction of school age children and young adults. Thus, a more detailed understanding of the basic brain mechanisms underlying learning and attention will likely give important insights into the causes and potential treatments for disorders involving these cognitive faculties.