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.
Our ability to recognize the category, or behavioral significance, 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 visual categories. 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.
|Masse, Nicolas Y; Grant, Gregory D; Freedman, David J (2018) Alleviating catastrophic forgetting using context-dependent gating and synaptic stabilization. Proc Natl Acad Sci U S A 115:E10467-E10475|
|Freedman, David J; Ibos, Guilhem (2018) An Integrative Framework for Sensory, Motor, and Cognitive Functions of the Posterior Parietal Cortex. Neuron 97:1219-1234|
|Chaisangmongkon, Warasinee; Swaminathan, Sruthi K; Freedman, David J et al. (2017) Computing by Robust Transience: How the Fronto-Parietal Network Performs Sequential, Category-Based Decisions. Neuron 93:1504-1517.e4|
|Masse, Nicolas Y; Hodnefield, Jonathan M; Freedman, David J (2017) Mnemonic Encoding and Cortical Organization in Parietal and Prefrontal Cortices. J Neurosci 37:6098-6112|
|Ibos, Guilhem; Freedman, David J (2017) Sequential sensory and decision processing in posterior parietal cortex. Elife 6:|
|Pesaran, Bijan; Freedman, David J (2016) Where Are Perceptual Decisions Made in the Brain? Trends Neurosci 39:642-644|
|Freedman, David J; Assad, John A (2016) Neuronal Mechanisms of Visual Categorization: An Abstract View on Decision Making. Annu Rev Neurosci 39:129-47|
|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|
|Ibos, Guilhem; Freedman, David J (2016) Interaction between Spatial and Feature Attention in Posterior Parietal Cortex. Neuron 91:931-943|
|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|
Showing the most recent 10 out of 18 publications