What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Neuroscience
Dentate gyrus, Neuroscience, Hippocampal formation, Hippocampus and Dendritic spine are his primary areas of study.
His work on Dentate gyrus is being expanded to include thematically relevant topics such as Anatomy.
His Neuroscience research incorporates themes from Computational complexity theory and Sequence prediction.
His Hippocampal formation study incorporates themes from Working memory, Stimulus modality, Preprocessor and Combinatorial explosion.
His Hippocampus research incorporates elements of Synapse, Stimulation and Excitatory postsynaptic potential.
His work in the fields of Dendritic shaft overlaps with other areas such as High incidence and Electron microscopic.
His most cited work include:
- Temporal contiguity requirements for long-term associative potentiation/depression in the hippocampus (675 citations)
- Preferential localization of polyribosomes under the base of dendritic spines in granule cells of the dentate gyrus (588 citations)
- Synapses as associative memory elements in the hippocampal formation (506 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Neuroscience, Artificial intelligence, Hippocampal formation, Artificial neural network and Hippocampus.
His work on Neuroscience deals in particular with Dentate gyrus, Excitatory postsynaptic potential, Synapse and Synaptogenesis.
His Dentate gyrus research includes themes of Dendritic spine, Synaptic fatigue, Entorhinal cortex and Anatomy.
His Sequence learning study, which is part of a larger body of work in Artificial intelligence, is frequently linked to Context, bridging the gap between disciplines.
His work on Hippocampal function as part of general Hippocampal formation study is frequently linked to Classical conditioning, bridging the gap between disciplines.
His Stimulation research extends to Hippocampus, which is thematically connected.
He most often published in these fields:
- Neuroscience (37.24%)
- Artificial intelligence (32.41%)
- Hippocampal formation (30.34%)
What were the highlights of his more recent work (between 2011-2021)?
- Neuroscience (37.24%)
- Artificial neural network (27.59%)
- Synaptogenesis (11.03%)
In recent papers he was focusing on the following fields of study:
His main research concerns Neuroscience, Artificial neural network, Synaptogenesis, Topology and Neuron.
As part of his studies on Neuroscience, he frequently links adjacent subjects like Computational model.
His Artificial neural network research is multidisciplinary, incorporating elements of Hippocampal formation, Neurophysiology and Function.
William B. Levy interconnects Synapse, Parametric statistics and Network model, Artificial intelligence in the investigation of issues within Synaptogenesis.
His research integrates issues of Nerve net and Adaptive algorithm in his study of Artificial intelligence.
His research on Topology also deals with topics like
- Energy which is related to area like Computation and Information transfer,
- Biological neuron model that intertwine with fields like Hitting time, Upper and lower bounds, Theory of computation and Synaptic integration,
- Models of neural computation which is related to area like Excitatory postsynaptic potential, Linearization, Random variable, Probability distribution and Exponential family.
Between 2011 and 2021, his most popular works were:
- A consensus layer V pyramidal neuron can sustain interpulse-interval coding (18 citations)
- Neural Computation From First Principles: Using the Maximum Entropy Method to Obtain an Optimal Bits-Per-Joule Neuron (10 citations)
- Energy Efficient Neurons With Generalized Inverse Gaussian Conditional and Marginal Hitting Times (10 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Neuron
His scientific interests lie mostly in Mutual information, Generalized inverse, Artificial neural network, Conditional probability distribution and Marginal distribution.
His study in Mutual information is interdisciplinary in nature, drawing from both Exponential family and Monte Carlo method.
His Artificial neural network research is multidisciplinary, relying on both Function, Upper and lower bounds and Hitting time.
The concepts of his Conditional probability distribution study are interwoven with issues in Combinatorics, Multiplicative function, Poisson distribution, Distribution and Applied mathematics.
His Marginal distribution research includes elements of Algorithm, Sufficient statistic, Models of neural computation and Probability distribution.
His Biological neuron model research focuses on Topology and how it relates to Cortical neurons, Coding, Nonlinear system, Pyramidal Neuron and Neuron.
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