NEURONAL CIRCUITS FOR CONTEXT-DRIVEN BIAS IN AUDITORY CATEGORIZATION In everyday life, because both sensory signals and neuronal responses are noisy, important cognitive tasks, such as auditory categorization, are based on uncertain information. To overcome this limitation, listeners incorporate other types of signals, such as the statistics of sounds over short and long time scales and signals from other sensory modalities into their categorization decision processes. At the behavioral level, such contextual signals bias categorization by shifting the listener's psychometric curve. At the neuronal level, categorization requires a transformation of sensory representation into a representation of category membership that is modulated by these contextual signals. While categorical representations have been found in the cortex, the cell types and neuronal mechanisms supporting the emergence of these representations remains unknown. Furthermore, the mechanisms by which neuronal categorical representations are modulated by contextual signals, giving rise to a behavioral bias, have not been explored. Our goal is to identify the contribution of specific cell types to categorization and to understand the neuronal mechanisms for how contextual signals bias auditory categorization. Multiple studies have demonstrated that neurons in auditory cortex (AC) and the posterior parietal cortex (PPC) are involved in auditory categorization. Based on the well-described circuit architecture of the AC, recent studies, and our preliminary data, we propose a series of hypotheses that delineate the role of excitatory-inhibitory circuits within AC in creating and biasing categorical stimulus representations and for the role of PPC-AC projections in driving the source for the bias signal. To test these hypotheses, we train mice in a two-alternative-forced choice task in which mice categorize the task, associations). frequency of a ?target? sound into one of two overlapping categories (?low? or ?high?). While mice participate in this we systematically manipulate three bias signals (short-term and long-term stimulus statistics, and cross-modal Thisdesign allows us to frame the cognitive task within a Bayesian framework, which generates formal computational models for the function of specific neuronal cell types that are tested experimentally. behavioral activity. category. in auditory We will combine this and computational framework with electrophysiological recordings and optogenetic manipulations of neuronal First, we will test whether distinct neuronal cell types in AC differentially encode information about stimulus Second, we will test whether and how specific inhibitory neuronal cell types in AC mediate context dependence auditory categorization. Third, we will test whether and how cortico-cortical feedback mediates context dependence in categorization. Aligned with the goals of the BRAIN initiative, our project will deliver a mechanistic framework for a cortical circuit supporting a complex behavior. These results will quantitatively address an important open question to what extent the same or distinct neuronal populations integrate information across multiple temporal scales and across sensory modalities, generalizing or specializing the representation of the bias in categorization.

Public Health Relevance

The goal of this project is to develop a novel computational framework and identify the circuit that supports auditory categorization under uncertainty. The data obtained from the auditory and posterior parietal cortex will provide important insights into hearing dysfunction, perception and cognition. This understanding is crucial for guiding the development of more effective clinical approaches to alleviating hearing difficulties in complex acoustic environments ? difficulties which are common in the elderly, the hearing impaired, and in individuals with cochlear implants or certain developmental disorders. In addition, patients with autism and with some forms of psychiatric disorders, such as schizophrenia, experience deficits in cognitive tasks, including categorization. Insights into neuronal circuit and interactions that we will identify will help develop a new series of therapies for these vast groups of patients.

National Institute of Health (NIH)
National Institute of Neurological Disorders and Stroke (NINDS)
Research Project (R01)
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Special Emphasis Panel (ZRG1)
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David, Karen Kate
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University of Pennsylvania
Schools of Medicine
United States
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