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 and stroke, 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 of the behavioral relevance, or category membership, of visual stimuli. Recently, we found evidence that the posterior parietal cortex plays a 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: 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 LIP category representations and to compare the roles of LIP and other brain areas (e.g. prefrontal cortex) that are involved in learning and encoding the behavioral significance of visual stimuli. 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, and schizophrenia) 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 Dr. Freedman's research is to help 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 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.
Our ability to recognize the behavioral significance, or meaning, of visual stimuli is essential for planning and carrying out successful behaviors in response to our surroundings. Thus, the long term goal of our research is to provide a detailed and mechanistic understanding about the brain processes that underlie the learning and recognition of the behavioral significance of visual stimuli. A detailed understanding of these brain mechanisms is critical for understanding and ultimately addressing the profound deficits of learning, memory and recognition that frequently accompany brain diseases and conditions such as stroke, schizophrenia and Alzheimer's disease.
|Ibos, Guilhem; Freedman, David J (2016) Interaction between Spatial and Feature Attention in Posterior Parietal Cortex. Neuron 91:931-43|
|Sarma, Arup; Masse, Nicolas Y; Wang, Xiao-Jing et al. (2016) Task-specific versus generalized mnemonic representations in parietal and prefrontal cortices. Nat Neurosci 19:143-9|
|Freedman, David J; Assad, John A (2016) Neuronal Mechanisms of Visual Categorization: An Abstract View on Decision Making. Annu Rev Neurosci 39:129-47|
|Engel, Tatiana A; Chaisangmongkon, Warasinee; Freedman, David J et al. (2015) Choice-correlated activity fluctuations underlie learning of neuronal category representation. Nat Commun 6:6454|
|Lim, Sukbin; McKee, Jillian L; Woloszyn, Luke et al. (2015) Inferring learning rules from distributions of firing rates in cortical neurons. Nat Neurosci 18:1804-10|
|McKee, Jillian L; Riesenhuber, Maximilian; Miller, Earl K et al. (2014) Task dependence of visual and category representations in prefrontal and inferior temporal cortices. J Neurosci 34:16065-75|
|Ibos, Guilhem; Freedman, David J (2014) Dynamic integration of task-relevant visual features in posterior parietal cortex. Neuron 83:1468-80|
|Murray, John D; Bernacchia, Alberto; Freedman, David J et al. (2014) A hierarchy of intrinsic timescales across primate cortex. Nat Neurosci 17:1661-3|
|Rishel, Chris A; Huang, Gang; Freedman, David J (2013) Independent category and spatial encoding in parietal cortex. Neuron 77:969-79|
|Asaad, Wael F; Santhanam, Navaneethan; McClellan, Steven et al. (2013) High-performance execution of psychophysical tasks with complex visual stimuli in MATLAB. J Neurophysiol 109:249-60|
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