Faculty Bibliography

The globus pallidus interna (GPi) is the main output nucleus of the basal ganglia, the neural circuit involved in motor and cognitive performance which is impacted by Parkinson's Disease (PD). Although deep brain stimulation (DBS) of the GPi is an effective treatment for the motor symptoms of PD in humans, the link between the stimulation signal space and the therapeutic benefits of DBS is not well understood. The rodent model of PD is useful for characterization of ameliorative DBS, though prior work focuses on the rodent model for DBS of the subthalamic nucleus (STN). This work investigates GPi-DBS in the rat model of PD under the framework of an amphetamine-induced rotational behavior. This work elucidates the relationship between stimulation current intensity and the motor effects of the dopaminergic lesion. Our results show that rotational behavior is modulated by the current intensity and validates GPi-DBS as a beneficial treatment of PD.

Hippocampal information processing is often described as two-state, with a place cell state during movement and a reactivation state during stillness. Relatively little is known about how the network transitions between these different patterns of activity during exploration. Here we show that hippocampal network changes quickly and continuously as animals explore and become familiar with initially novel places. We measured the relationship between moment-by-moment changes in behavior and information flow through hippocampal output area CA1 in rats. We examined local field potential (LFP) patterns, evoked potentials and ensemble spiking and found evidence suggestive of a smooth transition from strong CA3 drive of CA1 activity at low speeds to entorhinal cortical drive of CA1 activity at higher speeds. These changes occurred with changes in behavior on a timescale of less than a second, suggesting a continuous modulation of information processing in the hippocampal circuit as a function of behavioral state.

The hippocampus is critical for spatial learning and memory. Hippocampal neurons in awake animals exhibit place field activity that encodes current location, as well as sharp-wave ripple (SWR) activity during which representations based on past experiences are often replayed. The relationship between these patterns of activity and the memory functions of the hippocampus is poorly understood. We interrupted awake SWRs in animals learning a spatial alternation task. We observed a specific learning and performance deficit that persisted throughout training. This deficit was associated with awake SWR activity, as SWR interruption left place field activity and post-experience SWR reactivation intact. These results provide a link between awake SWRs and hippocampal memory processes, which suggests that awake replay of memory-related information during SWRs supports learning and memory-guided decision-making.

Neural prosthetic interfaces use neural activity related to the planning and perimovement epochs of arm reaching to afford brain-directed control of external devices. Previous research has primarily centered on accurately decoding movement intention from either plan or perimovement activity, but has assumed that temporal boundaries between these epochs are known to the decoding system. In this work, we develop a technique to automatically differentiate between baseline, plan, and perimovement epochs of neural activity. Specifically, we use a generative model of neural activity to capture how neural activity varies between these three epochs. Our approach is based on a hidden Markov model (HMM), in which the latent variable (state) corresponds to the epoch of neural activity, coupled with a state-dependent Poisson firing model. Using an HMM, we demonstrate that the time of transition from baseline to plan epochs, a transition in neural activity that is not accompanied by any external behavior changes, can be detected using a threshold on the a posteriori HMM state probabilities. Following detection of the plan epoch, we show that the intended target of a center-out movement can be detected about as accurately as that by a maximum-likelihood estimator using a window of known plan activity. In addition, we demonstrate that our HMM can detect transitions in neural activity corresponding to targets not found in training data. Thus the HMM technique for automatically detecting transitions between epochs of neural activity enables prosthetic interfaces that can operate autonomously.

Probabilistic decoding techniques have been used successfully to infer time-evolving physical state, such as arm trajectory or the path of a foraging rat, from neural data. A vital element of such decoders is the trajectory model, expressing knowledge about the statistical regularities of the movements. Unfortunately, trajectory models that both 1) accurately describe the movement statistics and 2) admit decoders with relatively low computational demands can be hard to construct. Simple models are computationally inexpensive, but often inaccurate. More complex models may gain accuracy, but at the expense of higher computational cost, hindering their use for real-time decoding. Here, we present a new general approach to defining trajectory models that simultaneously meets both requirements. The core idea is to combine simple trajectory models, each accurate within a limited regime of movement, in a probabilistic mixture of trajectory models (MTM). We demonstrate the utility of the approach by using an MTM decoder to infer goal-directed reaching movements to multiple discrete goals from multi-electrode neural data recorded in monkey motor and premotor cortex. Compared with decoders using simpler trajectory models, the MTM decoder reduced the decoding error by 38 (48) percent in two monkeys using 98 (99) units, without a necessary increase in running time. When available, prior information about the identity of the upcoming reach goal can be incorporated in a principled way, further reducing the decoding error by 20 (11) percent. Taken together, these advances should allow prosthetic cursors or limbs to be moved more accurately toward intended reach goals.

Neural prostheses have received considerable attention due to their potential to dramatically improve the quality of life of severely disabled patients. Cortically-controlled prostheses are able to translate neural activity from cerebral cortex into control signals for guiding computer cursors or prosthetic limbs. Non-invasive and invasive electrode techniques can be used to measure neural activity, with the latter promising considerably higher levels of performance and therefore functionality to patients. We review here some of our recent experimental and computational work aimed at establishing a principled design methodology to increase electrode-based cortical prosthesis performance to near theoretical limits. Studies discussed include translating unprecedentedly brief periods of "plan" activity into high information rate (6.5 bits/s)control signals, improving decode algorithms and optimizing visual target locations for further performance increases, and recording from chronically implanted arrays in freely behaving monkeys to characterize neuron stability. Taken together, these results should substantially increase the clinical viability of cortical prostheses.

A new class of neural prosthetic systems aims to assist disabled patients by translating cortical neural activity into control signals for prosthetic devices. Based on the success of proof-of-concept systems in the laboratory, there is now considerable interest in increasing system performance and creating implantable electronics for use in clinical systems. A critical question that impacts system performance and the overall architecture of these systems is whether it is possible to identify the neural source of each action potential (spike sorting) in real-time and with low power. Low power is essential both for power supply considerations and heat dissipation in the brain. In this paper we report that state-of-the-art spike sorting algorithms are not only feasible using modern complementary metal oxide semiconductor very large scale integration processes, but may represent the best option for extracting large amounts of data in implantable neural prosthetic interfaces.

A new paradigm for decoding reaching movements from the signals of an ensemble of individual neurons is presented. This new method not only provides a novel theoretical basis for the task, but also results in a significant decrease in the error of reconstructed hand trajectories. By using a model of movement as a foundation for the decoding system, we show that the number of neurons required for reconstruction of the trajectories of point-to-point reaching movements in two dimensions can be halved. Additionally, using the presented framework, other forms of neural information, specifically neural "plan" activity, can be integrated into the trajectory decoding process. The decoding paradigm presented is tested in simulation using a database of experimentally gathered center-out reaches and corresponding neural data generated from synthetic models.

Model-based decoding of neural activity for neuroprosthetic systems has been shown, in simulation, to provide significant gain over traditional linear filter approaches. We tested the model-based decoding approach with real neural and behavioral data and found a 18% reduction in trajectory reconstruction error compared with a linear filter. This corresponds to a 40% reduction in the number of neurons required for equivalent performance. The model-based approach further permits the combination of target-tuned plan activity with movement activity. The addition of plan activity reduced reconstruction error by 23% relative to the linear filter, corresponding to 55% reduction in the number of neurons required. Taken together, these results indicate that a decoding algorithm employing a prior model of reaching kinematics can substantially improve trajectory estimates, thereby improving prosthetic system performance.