Research

Social organization requires communication, and the complexity of a society is often reflected in the complexity of its signals. My research investigates how auditory communication signals are processed and learned, focusing on three major questions:

1. How does the brain process complex sounds to extract interesting signals?
2. How is the brain changed when new signals are learned?
3. How are these processes influenced by social structure?

I take a neuroethological approach to these questions, combining behavioral and neurophysiological experiments to develop an integrative understanding of complex biological processes. I also rely on advanced statistical modelling and signal processing methods.

Auditory Perceptual Learning

My primary work is with European starlings, an extremely successful species from the Eurasian branch of the Sturnidae. European starlings live in very large groups, with a complex fission-fusion social structure and a particularly rich vocal behavior. Their songs consist of long sequences of temporally discrete segments called motifs, which are about a second long. Individual birds have unique repertoires of 10 to 90 motifs, and starlings can learn to recognize the songs of specific individuals.

What happens when a bird learns the song of a conspecific? An association forms between the song stimulus and a response, but in order to make that association the stimulus may need to be discriminated from similar stimuli that have different meanings. The process in which new stimuli come to be perceived differently is called perceptual learning, and it involves changes to circuits in the brain that process sensory input.

One way of studying perceptual learning is to look at the bird's behavior. Does it respond to stimuli differently based on its past experience? We can test this in the lab using operant conditioning. Birds can be trained to listen to a stimulus and then manipulate an apparatus in response to get a food reward. As a research associate in the lab of Daniel Margoliash at the University of Chicago, I use operant conditioning to teach birds specific songs, and then measure how well they can discriminate sounds they've learned from each other. By changing the way the stimuli are presented during the learning and testing phase I can probe how the memory is formed and later accessed.

I also investigate perceptual learning from a physiological perspective, focusing on the circuits that process auditory information. How does their response depend on the acoustic features of auditory stimuli, and how do they change during learning? I make recordings from single units in the starling auditory system, specifically the part of the brain that corresponds to mammalian auditory cortex. I use advanced signal processing and statistical methods to measure the selectivity and tuning of these neurons, to infer the pathways that auditory information follows and the effects of learning.

Social Influences on Vocal Communication

In addition to the proximate mechanisms of auditory recognition described above, I am also interested in functional and comparative questions. Specifically, how are vocal signals used by social animals? What features of calls and songs carry information about group membership or social status? And how is the social structure of a species reflected in the way it communicates?

In collaboration with Dustin Rubenstein, I am studying the vocal behavior of the superb starling (Lamprotornis superbus), a coopertively breeding African starling. We are recording the songs and calls of a marked population of starlings, and examining how call structure differs as a function of genetic and social relatedness, and how the vocal behavior of individuals and groups changes as a result of immigration and intergroup interactions. Part of my contribution to this work has been to develop new methods for calculating the pitch of bioacoustic signals and for measuring the similarity of different birds' calls.

Biophysical Models of Neuronal Channel Dynamics

I am also involved in a collaborative project with the lab of Henry Abarbanel, at UC San Diego, in which we are developing biophysical models for neurons in the HVC nucleus of songbirds. This nucleus is responsible for premotor control during singing, and it contains at least four different classes of neurons. The circuits within HVC express rhythmic patterns of activity that specify the sequence of syllables and notes in a bird's song. The way these neurons respond to synaptic input and form networks with the capacity to generate patterned activity has implications not only for a better understanding of the song system but also for the study of complex motor behaviors.

Selected Publications

Meliza C.D. Effects of auditory recognition learning on the perception of vocal features in European starlings (Sturnus vulgaris). J Acoust Soc Am 2011, 130 3115-3123. [link]

Toth B.A., Kostuk M., Meliza C.D., Margoliash D., Abarbanel H.D.I. Dynamical estimation of neuron and network properties I: Variational methods. Biol Cybern 2011, 105 217-237. [link]

Meliza C.D., Chi Z., and Margoliash D. Representations of Conspecific Song by Starling Secondary Forebrain Auditory Neurons: Toward a Hierarchical Framework. J Neurophys 2010, 103 1195-1208. [link]

Meliza C.D. and Dan Y. Receptive-Field Modification in Rat Visual Cortex Induced by Paired Visual Stimulation and Single-Cell Spiking. Neuron 2006, 49, 183-189. [pdf] [link]

This work has been supported by a Ruth L Kirschstein National Research Service Award grant from the National Institutes for Deafness and Communicative Disorders (National Institutes of Health), and a Collaborative Research in Computational Neuroscience grant from the National Science Foundation