Neuronal representations underlying probabilistic sequence discrimination
- Author(s): Kiggins, Justin
- Advisor(s): Gentner, Timothy
- et al.
How language and music are processed by the biological connection in our heads is one of the most significant challenges to neuroscience. A critical aspect of this challenge is that the signals are necessarily sequential and the brain must process events at multiple hierarchical levels simultaneously. Aspects of this are not unique to humans, however, but shared with many animals who rely on vocal communication. In this work, we focus on characterizing the capacity of European starlings to discriminate between sequences and understanding how individual neurons and populations of neurons support sequence discrimination. We train starlings on novel behavioral protocols wherein subjects must discriminate between probabilistically generated sequences composed of vocal elements using only the sequential relationships between elements. When faced with uncertainty, subjects discriminate sequences in a way which is consistent with weighing the evidence afforded by the sequence. By recording the spiking activity of neurons in anesthetized subjects, we found that neurons in the caudomedial nidopallium (NCM) are sensitive to sequences and that their capacity to encode sequences is higher than their capacity to encode elements. Neuronal responses to sequences are shaped by a combination of reward-association and behavioral demands. Recordings of population activity in awake behaving subjects revealed that neurons in caudolateral mesopallium (CLM) exhibit mixed selectivity for elements and position, supporting robust population representations of element identity. Element encoding fidelity in CLM is state-dependent—accurate decoding of element identity from either fast-spiking or regular-spiking populations improves the reliability and encoding fidelity of the complementary population. These results emphasize the importance of behavioral goals in understanding how sequences are processed by neuronal populations.