What is he best known for?
The fields of study he is best known for:
- Quantum mechanics
- Statistics
- Mathematical analysis
His primary areas of study are Neuroscience, Attractor, Lyapunov exponent, Chaotic and Neuron.
While the research belongs to areas of Neuroscience, Henry D. I. Abarbanel spends his time largely on the problem of Nonsynaptic plasticity, intersecting his research to questions surrounding Spike-timing-dependent plasticity.
His work carried out in the field of Attractor brings together such families of science as Phase space, Combinatorics, Embedding, k-nearest neighbors algorithm and Nonlinear system.
His Lyapunov exponent research is multidisciplinary, incorporating perspectives in Dynamical system, Dynamical systems theory, Statistical physics and Applied mathematics.
He combines subjects such as Optical chaos, Semiconductor laser theory and Synchronization of chaos, Synchronization with his study of Chaotic.
His Synchronization of chaos study incorporates themes from Control theory and Multitude.
His most cited work include:
- Determining embedding dimension for phase-space reconstruction using a geometrical construction (2666 citations)
- Analysis of Observed Chaotic Data (1874 citations)
- The analysis of observed chaotic data in physical systems (1442 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Neuroscience, Chaotic, Nonlinear system, Lyapunov exponent and Statistical physics.
His studies deal with areas such as Stomatogastric ganglion and Central pattern generator as well as Neuroscience.
Henry D. I. Abarbanel has researched Chaotic in several fields, including Control theory, Synchronization of chaos, Synchronization, Transmitter and Topology.
Henry D. I. Abarbanel has included themes like Attractor, Mathematical analysis and Applied mathematics in his Lyapunov exponent study.
The concepts of his Attractor study are interwoven with issues in Phase space and Predictability.
His Statistical physics research includes themes of Dynamical system, Monte Carlo method and Data assimilation.
He most often published in these fields:
- Neuroscience (18.48%)
- Chaotic (17.88%)
- Nonlinear system (13.33%)
What were the highlights of his more recent work (between 2011-2021)?
- Data assimilation (10.00%)
- Artificial intelligence (10.91%)
- Monte Carlo method (5.45%)
In recent papers he was focusing on the following fields of study:
Henry D. I. Abarbanel mainly investigates Data assimilation, Artificial intelligence, Monte Carlo method, State variable and Machine learning.
His study in Data assimilation is interdisciplinary in nature, drawing from both Complex system, Synchronization, Statistical physics, Nonlinear system and Algorithm.
His research in Complex system intersects with topics in Chaotic, Cognitive science and Dynamics.
His Nonlinear system study is concerned with the field of Control theory as a whole.
His Machine learning study combines topics from a wide range of disciplines, such as Noise, Equivalence, Phase space and Embedding.
As a member of one scientific family, Henry D. I. Abarbanel mostly works in the field of Projection, focusing on Dynamical systems theory and, on occasion, Central pattern generator.
Between 2011 and 2021, his most popular works were:
- Predicting the Future: Completing Models of Observed Complex Systems (48 citations)
- Estimating parameters and predicting membrane voltages with conductance-based neuron models (42 citations)
- Predicting the Future (37 citations)
In his most recent research, the most cited papers focused on:
- Quantum mechanics
- Statistics
- Mathematical analysis
His primary areas of investigation include State variable, Artificial intelligence, Neuron, Membrane potential and Nonlinear system.
His Neuron research is multidisciplinary, incorporating elements of Biological system and Zebra finch.
Henry D. I. Abarbanel interconnects Parameter space, Estimation theory, Neutrino, Complex system and Data assimilation in the investigation of issues within Nonlinear system.
The study incorporates disciplines such as Chaotic, Industrial engineering, Time series and Synchronization in addition to Complex system.
He focuses mostly in the field of Chip, narrowing it down to matters related to Gating and, in some cases, Dynamical systems theory.
His Network model study combines topics in areas such as Statistical inference and Neuroscience, Vocal learning.
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