UC San Diego

Relating the collective activities of neural populations to external sensory stimuli or to motor output is essential to understanding how nervous systems support behavior. Equally important is examining these processes in the context of ethological signals, which are typically high-dimensional with a wide range of co-varying features at multiple timescales. The synaptic and network mechanisms for encoding these complex signals are largely unknown, due in part to limitations inherent in experimental design practices leveraging dimensionality reduction rather than embracing the full suite of stimulus complexity. Additional limitations arise because most studies to date examine spiking statistics in randomly-selected populations without consideration for the functional relevance of sub-network groupings. We do not know how spiking responses of individual neurons are pooled, functionally, by downstream neurons – a mechanism that could significantly alter population coding. Stimulus and behavior likely modulate the functional selection of sub-populations, which then likely exhibit different spiking statistics than the population at large.

Using a combination of intracellular and extracellular electrophysiology techniques in the caudal mesopallium (CM) and the caudal nidopallium (NCM) of the common European Starling, I examine synaptic and spiking activity driven by previously-recorded conspecific vocalizations, maintaining the full ethologically-relevant complexity contained in each signal. Uniquely, I feature the synaptic response as an independent variable in the examination of population spiking activity. The main implication of the collective results presented in this study is that, rather than a model of hierarchical processing in which stimulus-specific information is restricted to parallel circuits within each region, sensory integration and processing are supported by a system in which information about even the most complex stimuli is likely massively redundant and shared among the population at large. This scaffolds a sensory processing model in which flexible network re-organization - on short timescales and in a stimulus-specific way - support the complexities of spiking output observed in sensory cortex in response to learning, adaptation, attention, and context to meet the demands of an ever-changing environment.