Neural network mechanisms underlying the combination-sensitivity property in the auditory cortex of songbirds

Abstract

Temporal order of information processing in the brain is an important code in many acoustic signals including speech, music, and animal vocalizations. Despite its significance, very little is known about its underlying cellular and network mechanisms. Songbirds, among many other species are able to integrate continuous temporal information through a specialized set of neurons, known as the combination-sensitive neurons. These neurons respond in a facilitatory or inhibitory manner to patterns of distinct spectral elements in a signal, as long as they occur in a precise temporal order. The HVC (used as a proper name) nucleus of songbirds in particular, is the hub of many response-specific cells exhibiting the combination-sensitivity property. Although combination-sensitive neurons in the HVC are known to play a critical role in temporal auditory processing, enabling precise response selectivity to auditory signals, the underlying mechanisms of this property in the HVC are unknown. In this work, we develop conductance-based neural network models connecting the different classes of HVC neurons via different network architecture patterns with the aim of unveiling the intrinsic and synaptic mechanisms that orchestrate their combination sensitivity properties, as well as replicating their in vivo firing patterns observed when various auditory stimuli are presented. The model neurons in each class are designed to express pharmacologically identified ionic currents (Daou, Ross et al. 2013) and the neurons are connected via pharmacologically identified synaptic currents (Mooney and Prather 2005), rendering our network biologically plausible. We present for the first time five possible realistic scenarios in which the different types of HVC neurons can interact to produce this behavior. The result is an improved characterization of the HVC network responsible for auditory processing in the songbird system and a step forward into better understanding of temporal signal integration in the brain.