Neural representation of auditory temporal structure

Neurons in the songbird forebrain nucleus HVc are highly sensitive to auditory temporal context and have some of the most complex auditory tuning properties yet discovered. HVc is crucial for learning, perceiving, and producing song, thus it is important to understand the neural circuitry and mechanisms that give rise to these remarkable auditory response properties. This thesis investigates these issues experimentally and computationally.

Extracellular studies reported here compare the auditory context sensitivity of neurons in HV c with neurons in the afferent areas of field L. These demonstrate that there is a substantial increase in the auditory temporal context sensitivity from the areas of field L to HVc. Whole-cell recordings of HVc neurons from acute brain slices are described which show that excitatory synaptic transmission between HVc neurons involve the release of glutamate and the activation of both AMPA/kainate and NMDA-type glutamate receptors. Additionally, widespread inhibitory interactions exist between HVc neurons that are mediated by postsynaptic GABA_A receptors. Intracellular recordings of HVc auditory neurons in vivo provides evidence that HV c neurons encode information about temporal structure using a variety of cellular and synaptic mechanisms including syllable-specific inhibition, excitatory post-synaptic potentials with a range of different time courses, and burst-firing, and song-specific hyperpolarization.

The final part of this thesis presents two computational approaches for representing and learning temporal structure. The first method utilizes comput ational elements that are analogous to temporal combination sensitive neurons in HVc. A network of these elements can learn using local information and lateral inhibition. The second method presents a more general framework which allows a network to discover mixtures of temporal features in a continuous stream of input.

Responses to auditory stimuli in macaque lateral intraparietal area

The lateral intraparietal area (LIP) of macaque posterior parietal cortex participates in the sensorimotor transformations underlying visually guided eye movements. Area LIP has long been considered unresponsive to auditory stimulation. However, recent studies have shown that neurons in LIP respond to auditory stimuli during an auditory-saccade task, suggesting possible involvement of this area in auditory-to-oculomotor as well as visual-to-oculomotor processing. This dissertation describes investigations which clarify the role of area LIP in auditory-to-oculomotor processing.

Extracellular recordings were obtained from a total of 332 LIP neurons in two macaque monkeys, while the animals performed fixation and saccade tasks involving auditory and visual stimuli. No auditory activity was observed in area LIP before animals were trained to make saccades to auditory stimuli, but responses to auditory stimuli did emerge after auditory-saccade training. Auditory responses in area LIP after auditory-saccade training were significantly stronger in the context of an auditory-saccade task than in the context of a fixation task. Compared to visual responses, auditory responses were also significantly more predictive of movement-related activity in the saccade task. Moreover, while visual responses often had a fast transient component, responses to auditory stimuli in area LIP tended to be gradual in onset and relatively prolonged in duration.

Overall, the analyses demonstrate that responses to auditory stimuli in area LIP are dependent on auditory-saccade training, modulated by behavioral context, and characterized by slow-onset, sustained response profiles. These findings suggest that responses to auditory stimuli are best interpreted as supramodal (cognitive or motor) responses, rather than as modality-specific sensory responses. Auditory responses in area LIP seem to reflect the significance of auditory stimuli as potential targets for eye movements, and may differ from most visual responses in the extent to which they arc abstracted from the sensory parameters of the stimulus.

Robust near-threshold QDI circuit analysis and design

The two most important digital-system design goals today are to reduce power consumption and to increase reliability. Reductions in power consumption improve battery life in the mobile space and reductions in energy lower operating costs in the datacenter. Increased robustness and reliability shorten down time, improve yield, and are invaluable in the context of safety-critical systems. While optimizing towards these two goals is important at all design levels, optimizations at the circuit level have the furthest reaching effects; they apply to all digital systems. This dissertation presents a study of robust minimum-energy digital circuit design and analysis. It introduces new device models, metrics, and methods of calculation—all necessary first steps towards building better systems—and demonstrates how to apply these techniques. It analyzes a fabricated chip (a full-custom QDI microcontroller designed at Caltech and taped-out in 40-nm silicon) by calculating the minimum energy operating point and quantifying the chip’s robustness in the face of both timing and functional failures.