Single cell and population coding principles in the songbird auditory cortex

The present thesis is divided in two parts. In the first part I discuss two modeling efforts to analyze extracellularly recorded spiking activity of auditory neurons. First, in Chapter 2 I introduce a receptive field estimation method based on a generalized linear model with a sparse prior (L1-GLM). I apply this method to the estimation of spectro-temporal receptive fields (STRFs) of songbird auditory midbrain neurons from natural and synthetic stimuli, and show that the L1-GLM outperforms a traditionally used STRF estimation method by reducing estimation biases and increasing predictive power. Second, in Chapter 3 I describe a computationally efficient approach to the spike sorting problem that can automatically track non-stationarities in electrophysiological recordings. In the second part of this thesis I describe a series of electrophysiological experiments and computational tools for characterizing several information coding properties of single cells and ensembles of cells in the songbird primary auditory cortex (A1). In Chapter 4 I demonstrate that, despite the absence of a laminar structure, the avian A1 displays the same information coding principles that define the canonical cortical microcircuit in mammals, at the single neuron, cell types and pairwise interaction levels. Lastly, in Chapter 5, I study the emergence of song selectivity in the songbird A1 and demonstrate that vocalization selectivity is a network-level effect rather than a single cell property. I show that increased firing of single neurons to songs occurs jointly with a decrease in trial to trial variability in song responses that is shared across neurons in the population. Using a probabilistic model of population responses I characterize the spatial and temporal structure of shared response variability, providing insight into the potential mechanisms underlying vocalization selectivity in the songbird primary auditory cortex. The results presented in this chapter are the product of a collaborative effort between myself and Lars Buesing (Columbia University).