Maria Neimark Geffen - US grants

DESCRIPTION (provided by applicant): Hearing perception relies on our ability to tell apart the spectral content of different sounds, and to learn to use this difference to distinguish behaviorally relevant (such as dangerous and safe) sounds. The primary auditory cortex (A1) has been shown to play an important modulatory role in frequency discrimination and auditory discriminative emotional learning. However, despite decades of research that have carefully mapped the auditory response properties of neurons in A1, the neuronal circuits that underlie this modulation are currently unknown. The goal of the proposal is to test the role of such candidate neuronal circuit. In this proposal, we test the hypothesis that the activity of the most common class of interneurons, parvalbumin- positive (PVs), modulates selectivity to tones at different frequencies of excitatory neurons in A1, and that PV activity affects the behavioral performance in frequency discrimination and precision of discriminative auditory emotional learning. To measure the effect of PV activity we activate or inhibit PV interneurons selectively and temporally precisely using a recently developed optogenetic system, in awake behaving mice, and compare the neurometric and behavioral performance during activation or inactivation of PVs to baseline condition.

CIRCUIT MECHANISMS OF SOUND PROCESSING AND DETECTION IN THE AUDITORY PATHWAY Auditory perception relies on predicting statistics of incoming signals, be it identifying the speech of a conversation partner in a crowded room or recognizing the sound of a bubbling brook in a forest. The human brain detects statistical regularities in sounds as a fundamental aspect of prediction, evidenced by reduced responses to repeated sound patterns and enhanced responses to unexpected sounds. Multiple studies demonstrate that the neuronal responses to regular signals are reduced through adaptation, which can contribute to prediction. However, adaptation alone is not sufficient to account for prediction and studies at cellular and neuronal population level in animals thus far lend onto partial support to existing theories of predictive coding. As such, the circuit level mechanisms for the prediction of statistical regularities beyond tone frequency in sounds, and their role in behavior, remain unknown. Our goal is to close this gap in knowledge and to determine the circuits that predict signals and detect statistical regularity and its violation in auditory behavior. To identify feedforward and feedback components of prediction of statistical regularities in sounds in the auditory system, we combine optogenetic selective perturbation and large-scale imaging and electrophysiology with behavioral methods in awake mice. First, we test whether and how excitatory-inhibitory interactions within the auditory cortex (AC) establish predictive code for sound patterns, detect statistical regularities, and contribute to enhanced responses for unexpected sounds. Second, we test whether and how detection of statistical regularities at the neuronal level contributes to behavioral detection of change in sound regularity. Third, we test whether and how feedback from higher cortical areas provides information about regularity and violation. Our results will identify the neuronal circuits for encoding statistical regularity and its violation in sound and establish their role in auditory behavior.

Neuronal circuits supporting learning-driven changes in auditory perception. Everyday auditory behavior depends critically on learning-driven changes in auditory perception that rely on neuronal plasticity within the auditory pathway. Discriminative auditory fear conditioning (DAFC), an important form of associative auditory learning, affects the fundamental auditory task of frequency discrimination acuity. While the auditory cortex (AC) is thought to be required for this modulation, how learning shapes frequency discrimination remains unknown. Previous work has largely suggested that the feedforward connections leading up to the AC are the site of this learning-induced plasticity. However, recent research suggests that frequency tuning within the AC itself is shaped by inhibitory-excitatory networks that include multiple morphologically and likely functionally distinct inhibitory interneuron types. Furthermore, the extensive feedback the AC sends to sub-cortical structures, including the inferior colliculus (IC) in the auditory midbrain, may also affect behavioral frequency discrimination. Thus, to dissect the functions of intra-cortical and sub-cortical circuits in auditory learning, we will determine (1) if DAFC affects tone response properties of different neuronal cell types in AC, and how these changes affect frequency discriminability at the neuronal population level in AC; (2) if feedback from AC to the auditory midbrain causally drives learning-mediated changes in auditory behavior. By combining state-of-the-art optogenetic, electrophysiological, behavioral and computational approaches, we are uniquely able to test function of specific circuit elements in awake behaving subjects. The proposed research will, for the first time, identify (1) the effect of auditory learning on specific inhibitory and excitatory neuronal cell types in AC; (2) the role of excitatory-inhibitory circuits in driving changes in frequency discrimination behavior; and (3) the role of cortico-collicular feedback in driving learning-driven changes in auditory frequency discrimination acuity. These important insights into the function of feedback circuits in auditory processing will inform future design of hearing aids and cochlear implants by configuring their outputs for optimal stimulation of these circuits.

Neuronal circuits supporting learning-driven changes in auditory perception. Everyday auditory behavior depends critically on learning-driven changes in auditory perception that rely on neuronal plasticity within the auditory pathway. Discriminative auditory fear conditioning (DAFC), an important form of associative auditory learning, affects the fundamental auditory task of frequency discrimination acuity. While the auditory cortex (AC) is thought to be required for this modulation, how learning shapes frequency discrimination remains unknown. Previous work has largely suggested that the feedforward connections leading up to the AC are the site of this learning-induced plasticity. However, recent research suggests that frequency tuning within the AC itself is shaped by inhibitory-excitatory networks that include multiple morphologically and likely functionally distinct inhibitory interneuron types. Furthermore, the extensive feedback the AC sends to sub-cortical structures, including the inferior colliculus (IC) in the auditory midbrain, may also affect behavioral frequency discrimination. Thus, to dissect the functions of intra-cortical and sub-cortical circuits in auditory learning, we will determine (1) if DAFC affects tone response properties of different neuronal cell types in AC, and how these changes affect frequency discriminability at the neuronal population level in AC; (2) if feedback from AC to the auditory midbrain causally drives learning-mediated changes in auditory behavior. By combining state-of-the-art optogenetic, electrophysiological, behavioral and computational approaches, we are uniquely able to test function of specific circuit elements in awake behaving subjects. The proposed research will, for the first time, identify (1) the effect of auditory learning on specific inhibitory and excitatory neuronal cell types in AC; (2) the role of excitatory-inhibitory circuits in driving changes in frequency discrimination behavior; and (3) the role of cortico-collicular feedback in driving learning-driven changes in auditory frequency discrimination acuity. These important insights into the function of feedback circuits in auditory processing will inform future design of hearing aids and cochlear implants by configuring their outputs for optimal stimulation of these circuits.

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.