What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Neuroscience
- Machine learning
Jeffrey L. Krichmar mostly deals with Neuroscience, Neuroanatomy, Spiking neural network, Artificial intelligence and Artificial neural network.
Jeffrey L. Krichmar undertakes interdisciplinary study in the fields of Neuroscience and Darwin through his works.
His biological study deals with issues like Pattern recognition, which deal with fields such as Data compression, Tree and Spatial reference system.
His work in Spiking neural network tackles topics such as CUDA which are related to areas like Synaptic weight.
He has researched Artificial intelligence in several fields, including Perception and Brain function.
His work deals with themes such as Categorization and Encoding, which intersect with Artificial neural network.
His most cited work include:
- 2009 Special Issue: A configurable simulation environment for the efficient simulation of large-scale spiking neural networks on graphics processors (135 citations)
- Effects of dendritic morphology on CA3 pyramidal cell electrophysiology: a simulation study (127 citations)
- A configurable simulation environment for the efficient simulation of large-scale spiking neural networks on graphics processors (116 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Artificial intelligence, Neuroscience, Robot, Neuromorphic engineering and Spiking neural network.
The concepts of his Artificial intelligence study are interwoven with issues in Neuroanatomy, Computer vision and Pattern recognition.
He interconnects Cognitive science, Human–computer interaction and Reinforcement learning in the investigation of issues within Robot.
His Neuromorphic engineering research includes elements of Computer architecture and Embedded system.
His research investigates the connection between Spiking neural network and topics such as CUDA that intersect with problems in Synaptic weight.
His studies deal with areas such as Computational neuroscience, Simulation, Encoding and Memory consolidation as well as Artificial neural network.
He most often published in these fields:
- Artificial intelligence (40.96%)
- Neuroscience (33.13%)
- Robot (18.07%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (40.96%)
- Neuromorphic engineering (17.47%)
- Spiking neural network (17.47%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Neuromorphic engineering, Spiking neural network, Robot and Reinforcement learning.
His Pattern recognition research extends to Artificial intelligence, which is thematically connected.
His Neuromorphic engineering research incorporates themes from CMOS and Embedded system.
His Spiking neural network research is multidisciplinary, incorporating perspectives in Synapse, Computer architecture and Parallel computing.
His study in Robot is interdisciplinary in nature, drawing from both Cognitive psychology, Surprise, Cognition and Human–computer interaction.
His work on Sensory system as part of general Neuroscience study is frequently linked to Associative property, therefore connecting diverse disciplines of science.
Between 2018 and 2021, his most popular works were:
- Mapping Spiking Neural Networks to Neuromorphic Hardware (28 citations)
- Neural correlates of sparse coding and dimensionality reduction. (22 citations)
- A Framework to Explore Workload-Specific Performance and Lifetime Trade-offs in Neuromorphic Computing (18 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Neuroscience
- Machine learning
His primary areas of study are Spiking neural network, Neuromorphic engineering, Synapse, Artificial intelligence and Energy consumption.
His Spiking neural network research includes themes of Computer architecture and Co-simulation.
He combines subjects such as CMOS and Embedded system with his study of Neuromorphic engineering.
His Synapse research overlaps with Function, Decomposition, Connection, Energy and Node.
His work on Artificial neural network, Robotics and Idiothetic as part of general Artificial intelligence study is frequently linked to Energy, bridging the gap between disciplines.
Energy consumption combines with fields such as Metaheuristic, Cluster analysis, Parallel computing, Technological change and Scalability in his investigation.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
