Matching Items (13)
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- All Subjects: Learning
- Creators: Buneo, Christopher
- Creators: Glenberg, Arthur
- Status: Published
Description
Advances in implantable MEMS technology has made possible adaptive micro-robotic implants that can track and record from single neurons in the brain. Development of autonomous neural interfaces opens up exciting possibilities of micro-robots performing standard electrophysiological techniques that would previously take researchers several hundred hours to train and achieve the desired skill level. It would result in more reliable and adaptive neural interfaces that could record optimal neural activity 24/7 with high fidelity signals, high yield and increased throughput. The main contribution here is validating adaptive strategies to overcome challenges in autonomous navigation of microelectrodes inside the brain. The following issues pose significant challenges as brain tissue is both functionally and structurally dynamic: a) time varying mechanical properties of the brain tissue-microelectrode interface due to the hyperelastic, viscoelastic nature of brain tissue b) non-stationarities in the neural signal caused by mechanical and physiological events in the interface and c) the lack of visual feedback of microelectrode position in brain tissue. A closed loop control algorithm is proposed here for autonomous navigation of microelectrodes in brain tissue while optimizing the signal-to-noise ratio of multi-unit neural recordings. The algorithm incorporates a quantitative understanding of constitutive mechanical properties of soft viscoelastic tissue like the brain and is guided by models that predict stresses developed in brain tissue during movement of the microelectrode. An optimal movement strategy is developed that achieves precise positioning of microelectrodes in the brain by minimizing the stresses developed in the surrounding tissue during navigation and maximizing the speed of movement. Results of testing the closed-loop control paradigm in short-term rodent experiments validated that it was possible to achieve a consistently high quality SNR throughout the duration of the experiment. At the systems level, new generation of MEMS actuators for movable microelectrode array are characterized and the MEMS device operation parameters are optimized for improved performance and reliability. Further, recommendations for packaging to minimize the form factor of the implant; design of device mounting and implantation techniques of MEMS microelectrode array to enhance the longevity of the implant are also included in a top-down approach to achieve a reliable brain interface.
ContributorsAnand, Sindhu (Author) / Muthuswamy, Jitendran (Thesis advisor) / Tillery, Stephen H (Committee member) / Buneo, Christopher (Committee member) / Abbas, James (Committee member) / Tsakalis, Konstantinos (Committee member) / Arizona State University (Publisher)
Created2013
Description
Learning by trial-and-error requires retrospective information that whether a past action resulted in a rewarded outcome. Previous outcome in turn may provide information to guide future behavioral adjustment. But the specific contribution of this information to learning a task and the neural representations during the trial-and-error learning process is not well understood. In this dissertation, such learning is analyzed by means of single unit neural recordings in the rats' motor agranular medial (AGm) and agranular lateral (AGl) while the rats learned to perform a directional choice task. Multichannel chronic recordings using implanted microelectrodes in the rat's brain were essential to this study. Also for fundamental scientific investigations in general and for some applications such as brain machine interface, the recorded neural waveforms need to be analyzed first to identify neural action potentials as basic computing units. Prior to analyzing and modeling the recorded neural signals, this dissertation proposes an advanced spike sorting system, the M-Sorter, to extract the action potentials from raw neural waveforms. The M-Sorter shows better or comparable performance compared with two other popular spike sorters under automatic mode. With the sorted action potentials in place, neuronal activity in the AGm and AGl areas in rats during learning of a directional choice task is examined. Systematic analyses suggest that rat's neural activity in AGm and AGl was modulated by previous trial outcomes during learning. Single unit based neural dynamics during task learning are described in detail in the dissertation. Furthermore, the differences in neural modulation between fast and slow learning rats were compared. The results show that the level of neural modulation of previous trial outcome is different in fast and slow learning rats which may in turn suggest an important role of previous trial outcome encoding in learning.
ContributorsYuan, Yu'an (Author) / Si, Jennie (Thesis advisor) / Buneo, Christopher (Committee member) / Santello, Marco (Committee member) / Chae, Junseok (Committee member) / Arizona State University (Publisher)
Created2014
Description
Animals learn to choose a proper action among alternatives according to the circumstance. Through trial-and-error, animals improve their odds by making correct association between their behavioral choices and external stimuli. While there has been an extensive literature on the theory of learning, it is still unclear how individual neurons and a neural network adapt as learning progresses. In this dissertation, single units in the medial and lateral agranular (AGm and AGl) cortices were recorded as rats learned a directional choice task. The task required the rat to make a left/right side lever press if a light cue appeared on the left/right side of the interface panel. Behavior analysis showed that rat's movement parameters during performance of directional choices became stereotyped very quickly (2-3 days) while learning to solve the directional choice problem took weeks to occur. The entire learning process was further broken down to 3 stages, each having similar number of recording sessions (days). Single unit based firing rate analysis revealed that 1) directional rate modulation was observed in both cortices; 2) the averaged mean rate between left and right trials in the neural ensemble each day did not change significantly among the three learning stages; 3) the rate difference between left and right trials of the ensemble did not change significantly either. Besides, for either left or right trials, the trial-to-trial firing variability of single neurons did not change significantly over the three stages. To explore the spatiotemporal neural pattern of the recorded ensemble, support vector machines (SVMs) were constructed each day to decode the direction of choice in single trials. Improved classification accuracy indicated enhanced discriminability between neural patterns of left and right choices as learning progressed. When using a restricted Boltzmann machine (RBM) model to extract features from neural activity patterns, results further supported the idea that neural firing patterns adapted during the three learning stages to facilitate the neural codes of directional choices. Put together, these findings suggest a spatiotemporal neural coding scheme in a rat AGl and AGm neural ensemble that may be responsible for and contributing to learning the directional choice task.
ContributorsMao, Hongwei (Author) / Si, Jennie (Thesis advisor) / Buneo, Christopher (Committee member) / Cao, Yu (Committee member) / Santello, Marco (Committee member) / Arizona State University (Publisher)
Created2014
Description
Categories are often defined by rules regarding their features. These rules may be intensely complex yet, despite the complexity of these rules, we are often able to learn them with sufficient practice. A possible explanation for how we arrive at consistent category judgments despite these difficulties would be that we may define these complex categories such as chairs, tables, or stairs by understanding the simpler rules defined by potential interactions with these objects. This concept, called grounding, allows for the learning and transfer of complex categorization rules if said rules are capable of being expressed in a more simple fashion by virtue of meaningful physical interactions. The present experiment tested this hypothesis by having participants engage in either a Rule Based (RB) or Information Integration (II) categorization task with instructions to engage with the stimuli in either a non-interactive or interactive fashion. If participants were capable of grounding the categories, which were defined in the II task with a complex visual rule, to a simpler interactive rule, then participants with interactive instructions should outperform participants with non-interactive instructions. Results indicated that physical interaction with stimuli had a marginally beneficial effect on category learning, but this effect seemed most prevalent in participants were engaged in an II task.
ContributorsCrawford, Thomas (Author) / Homa, Donald (Thesis advisor) / Glenberg, Arthur (Committee member) / McBeath, Michael (Committee member) / Brewer, Gene (Committee member) / Arizona State University (Publisher)
Created2014
Description
The purpose of this study was to investigate the effect of partial exemplar experience on category formation and use. Participants had either complete or limited access to the three dimensions that defined categories by dimensions within different modalities. The concept of "crucial dimension" was introduced and the role it plays in category definition was explained. It was hypothesized that the effects of partial experience are not explained by a shifting of attention between dimensions (Taylor & Ross, 2009) but rather by an increased reliance on prototypical values used to fill in missing information during incomplete experiences. Results indicated that participants (1) do not fill in missing information with prototypical values, (2) integrate information less efficiently between different modalities than within a single modality, and (3) have difficulty learning only when partial experience prevents access to diagnostic information.
ContributorsCrawford, Thomas (Author) / Homa, Donald (Thesis advisor) / Mcbeath, Micheal (Committee member) / Glenberg, Arthur (Committee member) / Arizona State University (Publisher)
Created2011
Description
Epilepsy is a group of disorders that cause seizures in approximately 2.2 million people in the United States. Over 30% of these patients have epilepsies that do not respond to treatment with anti-epileptic drugs. For this population, focal resection surgery could offer long-term seizure freedom. Surgery candidates undergo a myriad of tests and monitoring to determine where and when seizures occur. The “gold standard” method for focus identification involves the placement of electrocorticography (ECoG) grids in the sub-dural space, followed by continual monitoring and visual inspection of the patient’s cortical activity. This process, however, is highly subjective and uses dated technology. Multiple studies were performed to investigate how the evaluation process could benefit from an algorithmic adjust using current ECoG technology, and how the use of new microECoG technology could further improve the process.
Computational algorithms can quickly and objectively find signal characteristics that may not be detectable with visual inspection, but many assume the data are stationary and/or linear, which biological data are not. An empirical mode decomposition (EMD) based algorithm was developed to detect potential seizures and tested on data collected from eight patients undergoing monitoring for focal resection surgery. EMD does not require linearity or stationarity and is data driven. The results suggest that a biological data driven algorithm could serve as a useful tool to objectively identify changes in cortical activity associated with seizures.
Next, the use of microECoG technology was investigated. Though both ECoG and microECoG grids are composed of electrodes resting on the surface of the cortex, changing the diameter of the electrodes creates non-trivial changes in the physics of the electrode-tissue interface that need to be accounted for. Experimenting with different recording configurations showed that proper grounding, referencing, and amplification are critical to obtain high quality neural signals from microECoG grids.
Finally, the relationship between data collected from the cortical surface with micro and macro electrodes was studied. Simultaneous recordings of the two electrode types showed differences in power spectra that suggest the inclusion of activity, possibly from deep structures, by macroelectrodes that is not accessible by microelectrodes.
Computational algorithms can quickly and objectively find signal characteristics that may not be detectable with visual inspection, but many assume the data are stationary and/or linear, which biological data are not. An empirical mode decomposition (EMD) based algorithm was developed to detect potential seizures and tested on data collected from eight patients undergoing monitoring for focal resection surgery. EMD does not require linearity or stationarity and is data driven. The results suggest that a biological data driven algorithm could serve as a useful tool to objectively identify changes in cortical activity associated with seizures.
Next, the use of microECoG technology was investigated. Though both ECoG and microECoG grids are composed of electrodes resting on the surface of the cortex, changing the diameter of the electrodes creates non-trivial changes in the physics of the electrode-tissue interface that need to be accounted for. Experimenting with different recording configurations showed that proper grounding, referencing, and amplification are critical to obtain high quality neural signals from microECoG grids.
Finally, the relationship between data collected from the cortical surface with micro and macro electrodes was studied. Simultaneous recordings of the two electrode types showed differences in power spectra that suggest the inclusion of activity, possibly from deep structures, by macroelectrodes that is not accessible by microelectrodes.
ContributorsAshmont, Kari Rich (Author) / Greger, Bradley (Thesis advisor) / Helms Tillery, Stephen (Committee member) / Buneo, Christopher (Committee member) / Adelson, P David (Committee member) / Dudek, F Edward (Committee member) / Arizona State University (Publisher)
Created2015
Description
Motor behavior is prone to variable conditions and deviates further in disorders affecting the nervous system. A combination of environmental and neural factors impacts the amount of uncertainty. Although the influence of these factors on estimating endpoint positions have been examined, the role of limb configuration on endpoint variability has been mostly ignored. Characterizing the influence of arm configuration (i.e. intrinsic factors) would allow greater comprehension of sensorimotor integration and assist in interpreting exaggerated movement variability in patients. In this study, subjects were placed in a 3-D virtual reality environment and were asked to move from a starting position to one of three targets in the frontal plane with and without visual feedback of the moving limb. The alternating of visual feedback during trials increased uncertainty between the planning and execution phases. The starting limb configurations, adducted and abducted, were varied in separate blocks. Arm configurations were setup by rotating along the shoulder-hand axis to maintain endpoint position. The investigation hypothesized: 1) patterns of endpoint variability of movements would be dependent upon the starting arm configuration and 2) any differences observed would be more apparent in conditions that withheld visual feedback. The results indicated that there were differences in endpoint variability between arm configurations in both visual conditions, but differences in variability increased when visual feedback was withheld. Overall this suggests that in the presence of visual feedback, planning of movements in 3D space mostly uses coordinates that are arm configuration independent. On the other hand, without visual feedback, planning of movements in 3D space relies substantially on intrinsic coordinates.
ContributorsRahman, Qasim (Author) / Buneo, Christopher (Thesis director) / Helms Tillery, Stephen (Committee member) / Barrett, The Honors College (Contributor) / Harrington Bioengineering Program (Contributor)
Created2014-05
Description
Motor behavior is prone to variable conditions and deviates further in disorders affecting the nervous system. A combination of environmental and neural factors impacts the amount of uncertainty. Although the influence of these factors on estimating endpoint positions have been examined, the role of limb configuration on endpoint variability has been mostly ignored. Characterizing the influence of arm configuration (i.e. intrinsic factors) would allow greater comprehension of sensorimotor integration and assist in interpreting exaggerated movement variability in patients. In this study, subjects were placed in a 3-D virtual reality environment and were asked to move from a starting position to one of three targets in the frontal plane with and without visual feedback of the moving limb. The alternating of visual feedback during trials increased uncertainty between the planning and execution phases. The starting limb configurations, adducted and abducted, were varied in separate blocks. Arm configurations were setup by rotating along the shoulder-hand axis to maintain endpoint position. The investigation hypothesized: 1) patterns of endpoint variability of movements would be dependent upon the starting arm configuration and 2) any differences observed would be more apparent in conditions that withheld visual feedback. The results indicated that there were differences in endpoint variability between arm configurations in both visual conditions, but differences in variability increased when visual feedback was withheld. Overall this suggests that in the presence of visual feedback, planning of movements in 3D space mostly uses coordinates that are arm configuration independent. On the other hand, without visual feedback, planning of movements in 3D space relies substantially on intrinsic coordinates.
ContributorsRahman, Qasim (Author) / Buneo, Christopher (Thesis director) / Helms Tillery, Stephen (Committee member) / Barrett, The Honors College (Contributor) / Harrington Bioengineering Program (Contributor)
Created2014-05
Description
Recent work in free-recall tasks suggest that human memory foraging may follow a Lévy flight distribution – a random walk procedure that is common in other activities of cognitive agents, such as animal and human food foraging. This study attempts to draw parallels between memory search and physical search, with the assumption that humans follow similar search patterns in both. To date, research merely equates the two processes (foraging in memory and the physical world) based on a similarity in statistical structure. This study starts with demonstrating a relationship between physical distance traveled and IRIs by having participants list countries. An IRI, inter-retrieval interval, is the time interval between items recalled. The next experiment uses multidimensional scaling (MDS) to derive a Euclidean perceptual space from similarity ratings of freely-recalled items and then maps the trajectory of human thought through this perceptual space. This trajectory can offer a much more compelling comparison to physical foraging behavior. Finally, a possible correlate of Lévy flight foraging is explored called critical slowing down. Statistically significant evidence was found in all three experiments. The discussion connects all three experiments and what their results mean for human memory foraging.
ContributorsGreer, Katharine Marie (Author) / Amazeen, Eric L. (Thesis director) / Glenberg, Arthur (Committee member) / Amazeen, Polemnia (Committee member) / Department of Psychology (Contributor) / School of Criminology and Criminal Justice (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
Estradiol (E2) and Levonorgestrel (Levo) are two hormones commonly used in hormone therapy (HT) to decrease symptoms associated with menopause. Both of these hormones have been shown to have beneficial effects on cognition when given alone in a rodent model of menopause. However, it is unknown whether these hormones, when taken in combination, are beneficial or harmful to cognition. This is a critically important question given that these hormones are most often given in combination versus separately. This thesis is composed of two studies examining the cognitive effects of E2 and Levo using a rat model of surgical menopause. Study 1 assessed how the dose of E2 treatment in rats impacted cognitive performance, and found that low dose E2 enhanced working memory performance. Next, based on the results from Study 1, Study 2 used low dose E2 in combination with different doses of Levo to examine the cognitive effects of several E2 to Levo ratio combinations. The results from Study 2 demonstrated that the combination of low dose E2 with a high dose of Levo at a 1:2 ratio impaired cognition, and that the ratio currently used in HT, 3:1, may also negatively impact cognition. Indeed, there was a dose response effect indicating that working and reference memory performance was incrementally impaired as Levo dose increased. The findings in this thesis suggest that the E2 plus Levo combination is likely not neutral for cognitive function, and prompts further evaluation in menopausal women, as well as drug discovery research to optimize HT using highly controlled preclinical models.
ContributorsBerns-Leone, Claire Elizabeth (Co-author) / Prakapenka, Alesia (Co-author) / Pena, Veronica (Co-author) / Northup-Smith, Steven (Co-author) / Melikian, Ryan (Co-author) / Ladwig, Ducileia (Co-author) / Patel, Shruti (Co-author) / Croft, Corissa (Co-author) / Bimonte-Nelson, Heather (Thesis director) / Glenberg, Arthur (Committee member) / Conrad, Cheryl (Committee member) / School of Life Sciences (Contributor) / Department of Psychology (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12