Neural dynamics in auditory cortex during implicit learning of complex sounds

Johns Hopkins University, Baltimore MD

Investigators

Abstract

In hearing, our ability to rapidly and effortlessly recognize newly presented auditory input in a complex sensory environment, referred to as implicit learning, is one of the key processes that enables efficient auditory perception. This process would require the encoding of new auditory patterns at the neural level. However, little is known about underlying neural mechanisms and circuits that enable implicit learning. To draw a comprehensive neural circuit for implicit learning, investigating neural mechanisms for auditory processing is required. This project focuses on investigating neural dynamics and mapping relevant neural circuits in the auditory cortex (AC) during auditory implicit learning. To study this, we first investigate neuronal responses of awake mice in AC. First, in the passive listening condition, we use two-photon Ca+2 imaging to identify neural dynamics of different cell types by manipulating neural activity on a subset of neurons at a single-cell resolution using holographic optogenetic stimulation. To study neural processes for implicit learning, we present a series of randomly generated tone sequences while a specific target sound re-occurs at random trials. We define distinct neuronal responses that are developed only for the re-occurring target sound as an index of implicit learning. We expect distinctive neuronal characteristics for implicit learning to emerge for each cell type of inhibitory interneurons. A specific cell type that directly modulates the activity of excitatory neurons will be further identified. Upon thorough investigation on the role of different cell types in the passive listening condition, we move on to the active listening condition. In the active listening condition where a listening task is given to trained animals, we expect similar neuronal characteristics to the passive condition from a different subgroup of cells. We further manipulate activities of selected neurons of different cell types, both excitatory and inhibitory neurons, and compute behavioral change between pre- and post-stimulation to map neural circuits during implicit learning. We expect to identify a causal link between neuronal characteristics and implicit learning and draw the complete neural circuit. Altogether, this project will provide a complete overview of neural mechanisms for implicit learning.