Dezhe Jin - US grants

Sequences of actions are fundamental to many animal and human behaviors, including locomotion, playing piano, language, and logical reasoning. Sequential behaviors often follow some action syntax, which defines how elementary actions can be strung together to form complex sequences, similar to following grammatical rules in the construction of sentences. How action syntax is generated and controlled in the nervous system is poorly understood. The Bengalese finch is an ideal animal model for studying this problem, because the songs of the Bengalese finch consist of several syllables arranged into sequences with complex syntactical structures. This project will test the hypothesis that the premotor brain area HVC encodes the syntactic structure of the Bengalese finch's song. It is hypothesized that the propagation of spiking neural activity along a chain-like network structure in the HVC is driving the song of the bird and generating the song syntax. The approach combines computational modeling and single unit recordings of neural activity in the song control system of Bengalese finches. Neural recordings will be combined with altering acoustic feedback and transient electrical stimulations of the song control system to elucidate the role of the HVC in song syntax. The results of this research will have a significant impact on understanding the neural mechanisms underlying the generation of sequences of motor actions, and may also shed light on the neural mechanisms of human speech synthesis. The research brings together interdisciplinary expertise drawn from physics, computational neuroscience, and electrophysiology, and involves a wide range of modern experimental and computational techniques. Consequently, it will provide ample opportunity for postdoctoral researchers, graduate and undergraduate students, and summer high school interns to gain expertise in electrophysiology, neural data analysis and modern methods in computational neuroscience.

Recognizing speech or other auditory objects in adverse environments -- e.g. with noise, reverberation, and multiple speakers -- is essential for human and animal communication. Current speech recognition technologies work well in high signal-to-noise conditions, but perform orders of magnitude below human performance in adverse conditions. Converging evidence from neuroscience suggests that auditory information is encoded in sparse and precisely timed spikes of sub-cortical neurons. However, the extent to which codes based on spike timing might underlie the robustness of human auditory object recognition has not yet been fully investigated. This project bridges this gap by devising a biologically inspired computational model of auditory processing at the cortical level and extracting computational principles that are essential for the model to achieve robust auditory object recognition.

The approach is to transform sounds into the spike sequences generated by feature-detecting thalamic auditory neurons, and to integrate these spikes spatially and temporally using the state-dependent dynamics of cortical neurons with active dendrites. In the proposed model, an auditory object first evokes sequential spiking of thalamic neurons that have been trained to detect useful features. Then, through feed-forward excitation and inhibition from the thalamus, and lateral excitation and inhibition from the cortical neurons, the state of the cortical network evolves, leading to temporal integration. Recognition of the auditory object is signaled when the cortical neurons reach a specific network state. The computational model is constrained by experimental results on the properties of cortical neurons, the organization principles of cortical networks, and the activity-dependent plasticity rules of the network structures. The project aims both to design feature detectors that can robustly represent auditory objects with spatiotemporal spike sequences, and to build a cortical network model that can recognize specific auditory objects using state transitions driven by the thalamic inputs, with neuron dynamics that can be compared with those observed in the auditory cortex. The recognition performance of the computational model will be evaluated and improved with auditory tasks designed to compare different approaches to speech recognition.

Skilled behaviors such as singing and playing the piano require precise timing. The primary goal of this project is to use theoretical and experimental approaches to understand the network properties of neurons that can produce the extremely precise activity necessary to enable these actions. Such networks are likely to be wired during the development of the brain, but the precise mechanisms involved remain a mystery. Previous computational models and experimental observations suggest that the wiring process is gradual. The investigators of this project will study how individual neurons are incorporated into the network. Of particular interest are postnatally born neurons, which have more immature properties compared with other neurons within the circuit, including a higher degree of spontaneous activity, which potentially facilitates their recruitment into the network. These ideas will be tested by experimentally tagging and manipulating immature neurons, as well as by constructing computational models and simulating the network growth process. The findings may shed light on how functional neuronal networks develop. The research may also help to formulate strategies of repairing dysfunctional or injured brain networks through manipulation of neuron maturity. This research will involve a wide range of innovative experimental and computational techniques and provide opportunities for students to gain expertise in electrophysiology, neural data analysis, and modern methods of computational neuroscience. The principal investigators will train postdoctoral researchers as well as graduate students, undergraduates, and summer high school interns.

The model system used in this project is the motor control circuitry of the zebra finch, a songbird whose adult courtship song consists of a highly repeatable sequence of vocal elements (or motif) sung with millisecond precision. The timing of song is controlled by a premotor forebrain region called HVC (proper name). Each premotor HVC neuron fires once per motif with sub-millisecond timing jitter across renditions. As a population, these neurons drive downstream song production circuits to produce specific acoustic patterns. During development, precise timing within HVC gradually emerges while the bird is learning to perform his song. Previous experimental observations suggest that neurons are gradually incorporated into the network generating song-relevant neural sequences, potentially from the newly born neurons that are robustly added to HVC during this period. This project aims to investigate the cellular and synaptic mechanisms underlying the development of the sequence generating network in HVC. The central hypothesis of this work is that these spontaneously active, newly born neurons are preferentially added to the leading edge of the growing timing network. This hypothesis will be tested with a combined experimental and computational modeling approach: (1) directly imaging the dynamics of network integration of newly born neurons in vivo through a targeted retroviral method; (2) constructing a computational model of HVC that is constrained by these observations and using the model to investigate the mechanisms of the network growth; and (3) measuring the cellular and synaptic properties of newly born neurons and their spontaneous activity as they mature.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.