16 results on '"Goudar V"'
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2. Towards handoff tolerance in TCP: Link layer handoff detection for optimized data transport.
- Author
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Goudar, V., Cohen, M., Sanadidi, M.Y., Gerla, M., and Zampognaro, F.
- Published
- 2009
- Full Text
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3. Fault-Tolerant and Low-Power Sampling Schedules for Localized BASNs
- Author
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Goudar, V. and Potkonjak, M.
- Abstract
Recent advances in the scope of wearable devices and networks make body area sensor networks (BASNs) an extremely attractive tool to the fields of mobile and tele-health, owing to the range of medical applications they can serve and the diagnostic richness of patient data they can offer. However, for BASNs to achieve true ubiquity, they must be scalable in their support of automated patient data collection, making usability and reliability key considerations. Its designers must wrestle with the tradeoff between usability, hindered by device intrusiveness into the behaviors it measures, and lifetime, enhanced by large power supplies and expensive, sturdy components. Furthermore, the validity and reliability of the collected data are paramount. In this paper, we consider these issues in the context of localized multi-sensory wearable networks and present a method to generate low-power sampling schedules that are resilient to sensor faults while achieving high diagnostic fidelity. We jointly formulate this as a power-constrained sampling problem wherein the number of sensors sampled per epoch are limited, and, a fault tolerant scheduling problem wherein the sampling scheme offers enough redundancy to endure up to a predefined number of sensor faults while maintaining diagnostic accuracy. This formulation is based on, 1) the localized scope of BASNs that engenders strong spatio-temporal interactions in the samples, and, 2) the periodic nature of human behaviors measured. We present our algorithm in the context of gait diagnostics derived from a foot plantar pressure measurement platform and illustrate its performance based on real datasets collected by it.
- Published
- 2013
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4. Antenna actuation for radio telemetry in remote sensor networks
- Author
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Browne, D.W., primary, Goudar, V., additional, Borgstrom, H., additional, Fitz, M.P., additional, and Kaiser, W., additional
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5. Antenna actuation for radio telemetry in remote sensor networks.
- Author
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Browne, D.W., Goudar, V., Borgstrom, H., Fitz, M.P., and Kaiser, W.
- Published
- 2004
- Full Text
- View/download PDF
6. A Comparison of Rapid Rule-Learning Strategies in Humans and Monkeys.
- Author
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Goudar V, Kim JW, Liu Y, Dede AJO, Jutras MJ, Skelin I, Ruvalcaba M, Chang W, Ram B, Fairhall AL, Lin JJ, Knight RT, Buffalo EA, and Wang XJ
- Subjects
- Animals, Female, Male, Humans, Adult, Learning physiology, Young Adult, Species Specificity, Choice Behavior physiology, Reaction Time physiology, Macaca mulatta
- Abstract
Interspecies comparisons are key to deriving an understanding of the behavioral and neural correlates of human cognition from animal models. We perform a detailed comparison of the strategies of female macaque monkeys to male and female humans on a variant of the Wisconsin Card Sorting Test (WCST), a widely studied and applied task that provides a multiattribute measure of cognitive function and depends on the frontal lobe. WCST performance requires the inference of a rule change given ambiguous feedback. We found that well-trained monkeys infer new rules three times more slowly than minimally instructed humans. Input-dependent hidden Markov model-generalized linear models were fit to their choices, revealing hidden states akin to feature-based attention in both species. Decision processes resembled a win-stay, lose-shift strategy with interspecies similarities as well as key differences. Monkeys and humans both test multiple rule hypotheses over a series of rule-search trials and perform inference-like computations to exclude candidate choice options. We quantitatively show that perseveration, random exploration, and poor sensitivity to negative feedback account for the slower task-switching performance in monkeys., Competing Interests: The authors declare no competing financial interests., (Copyright © 2024 the authors.)
- Published
- 2024
- Full Text
- View/download PDF
7. Training biologically plausible recurrent neural networks on cognitive tasks with long-term dependencies.
- Author
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Soo WWM, Goudar V, and Wang XJ
- Abstract
Training recurrent neural networks (RNNs) has become a go-to approach for generating and evaluating mechanistic neural hypotheses for cognition. The ease and efficiency of training RNNs with backpropagation through time and the availability of robustly supported deep learning libraries has made RNN modeling more approachable and accessible to neuroscience. Yet, a major technical hindrance remains. Cognitive processes such as working memory and decision making involve neural population dynamics over a long period of time within a behavioral trial and across trials. It is difficult to train RNNs to accomplish tasks where neural representations and dynamics have long temporal dependencies without gating mechanisms such as LSTMs or GRUs which currently lack experimental support and prohibit direct comparison between RNNs and biological neural circuits. We tackled this problem based on the idea of specialized skip-connections through time to support the emergence of task-relevant dynamics, and subsequently reinstitute biological plausibility by reverting to the original architecture. We show that this approach enables RNNs to successfully learn cognitive tasks that prove impractical if not impossible to learn using conventional methods. Over numerous tasks considered here, we achieve less training steps and shorter wall-clock times, particularly in tasks that require learning long-term dependencies via temporal integration over long timescales or maintaining a memory of past events in hidden-states. Our methods expand the range of experimental tasks that biologically plausible RNN models can learn, thereby supporting the development of theory for the emergent neural mechanisms of computations involving long-term dependencies.
- Published
- 2023
- Full Text
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8. Schema formation in a neural population subspace underlies learning-to-learn in flexible sensorimotor problem-solving.
- Author
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Goudar V, Peysakhovich B, Freedman DJ, Buffalo EA, and Wang XJ
- Subjects
- Brain, Neural Networks, Computer, Prefrontal Cortex, Artificial Intelligence, Learning
- Abstract
Learning-to-learn, a progressive speedup of learning while solving a series of similar problems, represents a core process of knowledge acquisition that draws attention in both neuroscience and artificial intelligence. To investigate its underlying brain mechanism, we trained a recurrent neural network model on arbitrary sensorimotor mappings known to depend on the prefrontal cortex. The network displayed an exponential time course of accelerated learning. The neural substrate of a schema emerges within a low-dimensional subspace of population activity; its reuse in new problems facilitates learning by limiting connection weight changes. Our work highlights the weight-driven modifications of the vector field, which determines the population trajectory of a recurrent network and behavior. Such plasticity is especially important for preserving and reusing the learned schema in spite of undesirable changes of the vector field due to the transition to learning a new problem; the accumulated changes across problems account for the learning-to-learn dynamics., (© 2023. The Author(s), under exclusive licence to Springer Nature America, Inc.)
- Published
- 2023
- Full Text
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9. Comparing rapid rule-learning strategies in humans and monkeys.
- Author
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Goudar V, Kim JW, Liu Y, Dede AJO, Jutras MJ, Skelin I, Ruvalcaba M, Chang W, Fairhall AL, Lin JJ, Knight RT, Buffalo EA, and Wang XJ
- Abstract
Inter-species comparisons are key to deriving an understanding of the behavioral and neural correlates of human cognition from animal models. We perform a detailed comparison of macaque monkey and human strategies on an analogue of the Wisconsin Card Sort Test, a widely studied and applied multi-attribute measure of cognitive function, wherein performance requires the inference of a changing rule given ambiguous feedback. We found that well-trained monkeys rapidly infer rules but are three times slower than humans. Model fits to their choices revealed hidden states akin to feature-based attention in both species, and decision processes that resembled a Win-stay lose-shift strategy with key differences. Monkeys and humans test multiple rule hypotheses over a series of rule-search trials and perform inference-like computations to exclude candidates. An attention-set based learning stage categorization revealed that perseveration, random exploration and poor sensitivity to negative feedback explain the under-performance in monkeys.
- Published
- 2023
- Full Text
- View/download PDF
10. Boron-enriched polyvinyl-alcohol/boric-acid nanoparticles for boron neutron capture therapy.
- Author
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Chan WJ, Cho HL, Goudar V, Bupphathong S, Shu CH, Kung C, and Tseng FG
- Subjects
- Boric Acids, Boron, Boron Compounds, Polyvinyl Alcohol, Polyvinyls, Boron Neutron Capture Therapy, Nanoparticles
- Abstract
Background: Due to the noninvasive nature of boron neutron capture therapy (BNCT), it is considered a promising cancer treatment method. Aim: To investigate whether polyvinyl alcohol/boric acid crosslinked nanoparticles (PVA/BA NPs) are an efficient delivery system for BNCT. Materials & methods: PVA/BA NPs were synthesized and cocultured with brain and oral cancers cells for BNCT. Results: PVA/BA NPs had a boron-loading capacity of 7.83 ± 1.75 w/w%. They accumulated in brain and oral cancers cells at least threefold more than in fibroblasts and macrophages. The IC
50 values of the brain and oral cancers cells were at least ninefold and sixfold lower than those of fibroblasts and macrophages, respectively. Conclusion: Theoretically, PVA/BA NPs target brain and oral cancers cells and could offer improved therapeutic outcomes of BNCT.- Published
- 2021
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11. A model of temporal scaling correctly predicts that motor timing improves with speed.
- Author
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Hardy NF, Goudar V, Romero-Sosa JL, and Buonomano DV
- Subjects
- Adolescent, Humans, Neural Networks, Computer, Time Factors, Young Adult, Models, Biological, Motor Activity physiology
- Abstract
Timing is fundamental to complex motor behaviors: from tying a knot to playing the piano. A general feature of motor timing is temporal scaling: the ability to produce motor patterns at different speeds. One theory of temporal processing proposes that the brain encodes time in dynamic patterns of neural activity (population clocks), here we first examine whether recurrent neural network (RNN) models can account for temporal scaling. Appropriately trained RNNs exhibit temporal scaling over a range similar to that of humans and capture a signature of motor timing, Weber's law, but predict that temporal precision improves at faster speeds. Human psychophysics experiments confirm this prediction: the variability of responses in absolute time are lower at faster speeds. These results establish that RNNs can account for temporal scaling and suggest a novel psychophysical principle: the Weber-Speed effect.
- Published
- 2018
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12. Fluorescence-Based Nano-Oxygen Particles for Spatiometric Monitoring of Cell Physiological Conditions.
- Author
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Koduri MP, S Goudar V, Shao YW, Hunt JA, Henstock JR, Curran J, and Tseng FG
- Subjects
- Fluorescence, Humans, Hydrogels chemistry, Microscopy, Fluorescence, Pancreas, Artificial, Cell Physiological Phenomena, Cytological Techniques methods, Oxygen chemistry
- Abstract
Closed-loop artificial pancreas systems have recently been proposed as a solution for treating stage I diabetes by reproducing the function of the pancreas. However, there are many unresolved issues associated with their development, including monitoring and controlling oxygen, immune responses, and the optimization of glucose, all of which need to be monitored and controlled to produce an efficient and viable artificial organ that can become integrated in the patient and maintain homeostasis. This research focused on monitoring the oxygen concentration, specifically achieving this kinetically as the oxygen gradient in an artificial pancreas made of alginate spheres containing islet cells. Functional nanoparticles (NPs) for measuring the oxygen gradient in different hydrogel cellular environments using fluorescence-based (F) microscopy were developed and tested. By the ester bond, a linker Pluronic F127 was conjugated with a carboxylic acid-modified polystyrene NP (510 nm). A hydrophilic/hydrophobic interaction between the commercially available oxygen-sensitive fluorophore and F127 results in fluorescence-based nano-oxygen particles (FNOPs). The in-house synthesized FNOP was calibrated inside electrosprayed alginate-filled hydrogels and demonstrated a good broad dynamic range (2.73-22.23) mg/L as well as a resolution of -0.01 mg/L with an accuracy of ±4%. The calibrated FNOP was utilized for continuous measuring of the oxygen concentration gradient for cell lines RIN-m5F/HeLa for more than 5 days in alginate hydrogel spheres in vitro.
- Published
- 2018
- Full Text
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13. Encoding sensory and motor patterns as time-invariant trajectories in recurrent neural networks.
- Author
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Goudar V and Buonomano DV
- Subjects
- Computer Simulation, Humans, Models, Neurological, Time, Brain physiology, Cerebellar Cortex physiology, Nerve Net physiology, Neurons physiology
- Abstract
Much of the information the brain processes and stores is temporal in nature-a spoken word or a handwritten signature, for example, is defined by how it unfolds in time. However, it remains unclear how neural circuits encode complex time-varying patterns. We show that by tuning the weights of a recurrent neural network (RNN), it can recognize and then transcribe spoken digits. The model elucidates how neural dynamics in cortical networks may resolve three fundamental challenges: first, encode multiple time-varying sensory and motor patterns as stable neural trajectories; second, generalize across relevant spatial features; third, identify the same stimuli played at different speeds-we show that this temporal invariance emerges because the recurrent dynamics generate neural trajectories with appropriately modulated angular velocities. Together our results generate testable predictions as to how recurrent networks may use different mechanisms to generalize across the relevant spatial and temporal features of complex time-varying stimuli., Competing Interests: VG, DB No competing interests declared, (© 2018, Goudar et al.)
- Published
- 2018
- Full Text
- View/download PDF
14. Differential Encoding of Time by Prefrontal and Striatal Network Dynamics.
- Author
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Bakhurin KI, Goudar V, Shobe JL, Claar LD, Buonomano DV, and Masmanidis SC
- Subjects
- Animals, Conditioning, Psychological physiology, Male, Mice, Mice, Inbred C57BL, Anticipation, Psychological physiology, Corpus Striatum physiology, Nerve Net physiology, Prefrontal Cortex physiology, Time Perception physiology
- Abstract
Telling time is fundamental to many forms of learning and behavior, including the anticipation of rewarding events. Although the neural mechanisms underlying timing remain unknown, computational models have proposed that the brain represents time in the dynamics of neural networks. Consistent with this hypothesis, changing patterns of neural activity dynamically in a number of brain areas-including the striatum and cortex-has been shown to encode elapsed time. To date, however, no studies have explicitly quantified and contrasted how well different areas encode time by recording large numbers of units simultaneously from more than one area. Here, we performed large-scale extracellular recordings in the striatum and orbitofrontal cortex of mice that learned the temporal relationship between a stimulus and a reward and reported their response with anticipatory licking. We used a machine-learning algorithm to quantify how well populations of neurons encoded elapsed time from stimulus onset. Both the striatal and cortical networks encoded time, but the striatal network outperformed the orbitofrontal cortex, a finding replicated both in simultaneously and nonsimultaneously recorded corticostriatal datasets. The striatal network was also more reliable in predicting when the animals would lick up to ∼1 s before the actual lick occurred. Our results are consistent with the hypothesis that temporal information is encoded in a widely distributed manner throughout multiple brain areas, but that the striatum may have a privileged role in timing because it has a more accurate "clock" as it integrates information across multiple cortical areas., Significance Statement: The neural representation of time is thought to be distributed across multiple functionally specialized brain structures, including the striatum and cortex. However, until now, the neural code for time has not been compared quantitatively between these areas. Here, we performed large-scale recordings in the striatum and orbitofrontal cortex of mice trained on a stimulus-reward association task involving a delay period and used a machine-learning algorithm to quantify how well populations of simultaneously recorded neurons encoded elapsed time from stimulus onset. We found that, although both areas encoded time, the striatum consistently outperformed the orbitofrontal cortex. These results suggest that the striatum may refine the code for time by integrating information from multiple inputs., (Copyright © 2017 the authors 0270-6474/17/370854-17$15.00/0.)
- Published
- 2017
- Full Text
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15. A model of order-selectivity based on dynamic changes in the balance of excitation and inhibition produced by short-term synaptic plasticity.
- Author
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Goudar V and Buonomano DV
- Subjects
- Action Potentials physiology, Computer Simulation, Inhibitory Postsynaptic Potentials physiology, Neurons physiology, Nonlinear Dynamics, Synapses physiology, alpha-Amino-3-hydroxy-5-methyl-4-isoxazolepropionic Acid metabolism, gamma-Aminobutyric Acid metabolism, Auditory Cortex physiology, Auditory Perception physiology, Models, Neurological, Neural Inhibition physiology, Neuronal Plasticity physiology, Synaptic Transmission physiology
- Abstract
Determining the order of sensory events separated by a few hundred milliseconds is critical to many forms of sensory processing, including vocalization and speech discrimination. Although many experimental studies have recorded from auditory order-sensitive and order-selective neurons, the underlying mechanisms are poorly understood. Here we demonstrate that universal properties of cortical synapses-short-term synaptic plasticity of excitatory and inhibitory synapses-are well suited for the generation of order-selective neural responses. Using computational models of canonical disynaptic circuits, we show that the dynamic changes in the balance of excitation and inhibition imposed by short-term plasticity lead to the generation of order-selective responses. Parametric analyses predict that among the forms of short-term plasticity expressed at excitatory-to-excitatory, excitatory-to-inhibitory, and inhibitory-to-excitatory synapses, the single most important contributor to order-selectivity is the paired-pulse depression of inhibitory postsynaptic potentials (IPSPs). A topographic model of the auditory cortex that incorporates short-term plasticity accounts for both context-dependent suppression and enhancement in response to paired tones. Together these results provide a framework to account for an important computational problem based on ubiquitous synaptic properties that did not yet have a clearly established computational function. Additionally, these studies suggest that disynaptic circuits represent a fundamental computational unit that is capable of processing both spatial and temporal information., (Copyright © 2015 the American Physiological Society.)
- Published
- 2015
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16. Useful dynamic regimes emerge in recurrent networks.
- Author
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Goudar V and Buonomano DV
- Subjects
- Excitatory Postsynaptic Potentials physiology, Inhibitory Postsynaptic Potentials physiology, Neural Inhibition physiology, Neural Networks, Computer, Neurons physiology
- Published
- 2014
- Full Text
- View/download PDF
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